Alberto Benegiamo
email: alberto.benegiamo@gmail.com

Patrick Loiseau
email: patrick.loiseau@eurecom.fr

Giovanni Neglia
email: giovanni.neglia@inria.fr

Dissecting Demand Response Mechanisms: the Role of Consumption Forecasts and Personalized Offers

Keywords: smart grid, demand-response, incentive mechanisms, energy network

Demand-Response (DR) programs, whereby users of an electricity network are encouraged by economic incentives to re-arrange their consumption in order to reduce production costs, are envisioned to be a key feature of the smart grid paradigm. Several recent works proposed DR mechanisms and used analytical models to derive optimal incentives. Most of these works, however, rely on a macroscopic description of the population that does not model individual choices of users. In this paper, we conduct a detailed analysis of those models and we argue that the macroscopic descriptions hide important assumptions that can jeopardize the mechanisms' implementation (such as the ability to make personalized offers and to perfectly estimate the demand that is moved from a timeslot to another). Then, we start from a microscopic description that explicitly models each user's decision. We introduce four DR mechanisms with various assumptions on the provider's capabilities. Contrarily to previous studies, we find that the optimization problems that result from our mechanisms are complex and can be solved numerically only through a heuristic. We present numerical simulations that compare the different mechanisms and their sensitivity to forecast errors. At a high level, our results show that the performance of DR mechanisms under reasonable assumptions on the provider's capabilities are significantly lower than those suggested by previous studies, but that the gap reduces when the population's flexibility increases.

Etude détaillée de mécanismes de "demand-response" : le rôle des prévisions de consommation et des offres personnalisées

Résumé :

Les programmes de "demand-response" (DR), qui consistent à donner aux utilisateurs d'un réseau électrique des incitations économiques pour les encourager à réarranger leur consommation de façon à réduire les coûts de production, sont pressentis comme étant un élément clef du paradigme de la "smart grid." Plusieurs travaux récents ont proposé des mécanismes de DR et utilisé des modèles analytiques pour calculer les incitations optimales. Cependant, la plupart de ces travaux s'appuient sur une description macroscopique de la population qui ne modélise pas les choix individuels des utilisateurs.

Dans cet article, nous menons une analyse détaillée de ces modèles et nous soutenons qu'une description macroscopique masque des hypothèses importantes (telles que la possibilité de faire des offres personnalisées et d'estimer parfaitement la demande déplacé d'un timeslot à un autre) qui peuvent compromettre l'implémentation des mécanismes. Nous partons donc d'une description microscopique qui modélise explicitement la décision de chaque utilisateur. Nous introduisons quatre mécanismes de DR avec diverses hypothèses sur les capacités du fournisseur. Contrairement à certaines études antérieures, nous observons que les problèmes d'optimisation résultant des mécanismes que nous proposons sont complexes et qu'ils ne peuvent être résolus qu'au moyen d'une heuristique. Nous présentons des simulations numériques comparant les différents mécanismes et leur sensibilité aux erreurs de prévisions de consommation. Nos résultats montrent que les performances des mécanismes de DR faisant des hypothèses raisonnables sur les capacités du fournisseurs sont significativement moins bonnes que celles suggérées par les précédentes études ; mais que la différence s'amenuise quand la flexibilité de la population grandit.

Mots-clés : smart grid, demand-response, mécanismes d'incitation, réseau d'énergie

Introduction

Demand Response (DR hereinafter) programs are envisioned to be a key feature of the Smart Grid paradigm [START_REF] Albadi | A summary of demand response in electricity markets[END_REF]. By means of economic incentives (discounts or penalties), DR schemes encourage users to rearrange their consumption in response to the network state, thus mitigating the grid overload and driving wholesale prices down.

Several analytical models are available in the literature, which describe and quantify the effects of DR mechanisms. Whatever their specifics are, these schemes need to model how users react to the incentives. Ideally the models should capture the most realistic features of a practical DR mechanism while maintaining tractability.

Among these contributions, the authors of [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF] study how an energy provider should select timedependent discounts to minimize its production costs. They assume that the percentage of users who shift their consumption from slot i to slot j is a decreasing function of the temporal distance between slots i and j and a concave and increasing function of the discount offered in slot j (R j ), independent from discounts in other slots. The paper claims that, under these assumptions along with the requirement of piecewise linearity of energy production costs, the problem of finding the set of discounts that minimize the provider's cost is convex and therefore simple to solve. Under similar modeling assumptions, however, we find that the optimization problem can be non-convex even in such a simple scenario (see Sec. 4). The same user's model as in [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF] is adopted also in [START_REF] Pan | Pricing Strategy for Device-Level Demand Response in a Microgrid[END_REF], where the optimization problem is extended in order to account for battery storages and distributed renewable sources available into a specific microgrid. Authors of [START_REF] Subramanian | Dynamic Price Optimization Models for Managing Time-of-Day Electricity Usage[END_REF] propose a day ahead pricing scheme which maximizes the provider's profitability and capacity utilization. Users are assumed to reschedule their consumption by comparing the utility v i they get by scheduling a task in each timeslot i; therefore they allocate their consumption proportionally to these utilities, i.e., they consume a fraction vi T j=1 vj of their total energy demand in timeslot i. The resulting optimization problem is non convex but some relaxation techniques are introduced, which allow one to calculate a solution within a reasonable amount of time. In [START_REF] Yang | Quantifying the benefits to consumers for demand response with a statistical elasticity model[END_REF], a more realistic model is proposed where each user first calculates the welfare (defined as utility minus time-dependent cost) she gets from consuming electricity in each of the possible timeslots, and then allocates all the consumption to the slot returning the largest welfare. As we show below (see Sec. 4.4) this model can lead to a much more complex optimization problem than the one presented in [START_REF] Yang | Quantifying the benefits to consumers for demand response with a statistical elasticity model[END_REF]. Finally, the authors of [START_REF] Song | Demand Side Management in Smart Grids Using a Repeated Game Framework[END_REF] propose a full-fledged game theoretical model, but their results hold only if users experience a large number of interactions without any change in the system.

We claim that these studies rely on too strong assumptions, which jeopardize their usability for practical purposes. Interestingly, we observe that the assumptions are sometimes hidden in the macroscopic models the papers start from. In particular in this paper we focus on [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF] and show that its model requires personalized offers and a very precise forecast of the baseline consumption of each user. The implementation of these features may require potentially significant costs in terms of communication, measurement and computation infrastructure. Besides highlighting these implicit requirements in the analytical framework in [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF] (and then also in [START_REF] Pan | Pricing Strategy for Device-Level Demand Response in a Microgrid[END_REF]), we explore their potentials considering four DR mechanisms with different levels of complexity:

1. the base mechanism corresponds to an optimization problem similar to the one considered in [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF], it requires personalized offers and individual consumption forecasts; the energy production cost is optimized over the discount values, each of which is offered to a given fraction of the population, 2. the optimized mechanism takes full advantage of personalized offers and consumption forecasts by minimizing the cost over both the discount values and the population fractions to which the discounts are offered, 3. the robust mechanism relies on personalized offers, but does not need individual consumption forecasts, 4. finally the broadcast mechanism (analogous to that in [START_REF] Yang | Quantifying the benefits to consumers for demand response with a statistical elasticity model[END_REF]) needs neither of the two features.

Interestingly, contrarily to prior studies, we find that the cost-minimization problems resulting from our DR mechanisms are not convex (even for the base mechanism). Nevertheless, simple heuristics can identify (potential) minima in a reasonable amount of time in realistic scenarios. Then, our numerical results show that the simpler robust and broadcast mechanisms achieve significantly lower cost reductions than the optimized mechanism, which is difficult to implement, but that the gap reduces when the population's flexibility increases.

The paper is organized as follows. In Sec. 2 we discuss how the macroscopic models considered in [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF][START_REF] Pan | Pricing Strategy for Device-Level Demand Response in a Microgrid[END_REF][START_REF] Subramanian | Dynamic Price Optimization Models for Managing Time-of-Day Electricity Usage[END_REF] hide some implicit assumptions about the user rationality or about the interactions between the provider and the user. We define our microscopic model in Sec. 3 and then describe different DR mechanisms and their corresponding optimization problems in Sec. 4. Finally, we evaluate their performance numerically in a realistic scenario in Sec. 5.

Pitfalls when Starting from Macroscopic Models

In this section, we describe in more detail the macroscopic models proposed in the literature for day-ahead price optimization. Consider a finite time horizon discretized in a set T of N timeslots and a large population S of users. The baseline aggregate energy consumption in slot j is denoted by E 0 j . The energy provider charges a flat rate B, but it can offer discount rates to incentivize the users to move some of their consumption so as to reduce the energy production cost. Due to consumption shifts, the actual aggregate consumption in time slot j is E 1 j . Observe that a usual assumption in the literature (including the papers mentioned above) is that the introduction of a DR scheme neither reduces nor increases users' demand; it merely rearranges users' consumption in a more cost effective way, so that

N j=1 E 0 j = N j=1 E 1 j . (1)
We denote the amount of consumption shifted from slot j to slot i = j as E j→i , and the amount of consumption the users refuse to shift away from j as E j→j . Then we have

E 1 i = E 0 i + N z=1 E z→i - N k=1 E i→k .
We now start to further detail the model considering some specific assumptions made in previous works. In [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF] and [START_REF] Pan | Pricing Strategy for Device-Level Demand Response in a Microgrid[END_REF], the electricity provider offers an energy price discount R i ≥ 0 in each slot i. The users are assumed to react to these incentives by shifting a fraction of their baseline consumption from slot j to slot i (|j -i| slots away) according to the following formula:

E j→i = E 0 j S j (R i , |j -i|). (2)
S j (R i , |j -i|) is called the aggregate sensitivity function and is increasing in the discount R i and decreasing in the temporal shift |j -i|, in order to take into account the user discomfort.

Inria

The provider selects the vector of discounts R in order to minimize its total cost, equal to the sum of the electricity generation costs and the loss of revenues due to the discounts. In particular the optimization problem considered in [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF] is the following:

min R i j =i R i E j→i + i c i E 1 i (3) s.t. 0 ≤ R i ≤ B ∀i = 1, . . . N, (4)
where c i (•) is the cost of electricity production in slot i. Eq. ( 4) guarantees that discounts R are non negative and smaller than the flat rate B, so that the money stream goes toward the provider.

As it often happens, the devil is hidden in the details, and in this case in Eqs. ( 2) and (3). Our first remark is that the cost of lost revenues i j =i R i E j→i in Eq. ( 3) implicitly assumes the possibility to reward only the consumption actually shifted from j to i, i.e., E j→i , but this quantity cannot be directly measured. The actual consumption E 1 i can be measured, and then E j→i can be quantified provided that we have good estimates of the sensitivity function S j (R i , |j -i|) and of the baseline consumption E 0 i . Let us assume for a moment that S j (R i , |j -i|) is known from historical data and that the aggregate baseline consumption may be predicted with a reasonably high level of accuracy on a large set of users. Then it seems possible to solve the macroscopic problem in Eqs. ( 3) and ( 4), but we need to consider also what should happen at the microscopic scale. While the estimates for the aggregate baseline consumption can be adequately precise, finally the billing is done at the user's granularity and each user expects to receive the price discount corresponding to the energy consumption she actually moved. If the energy bill's reduction does not correspond to her forecast, the user is likely to opt out of the program (in particular if she has experienced underpayments) or to reduce her efforts and milk occasional discounts. It appears then that Eq. ( 3) implicitly requires very precise predictions of individual consumptions.

We now observe that the form of the sensitivity function S j (R i , |j -i|) in Eq. ( 2) indicates that the amount of energy shifted from j to i depends on the discount R i but not on the other discounts. We can then ask ourselves which individual decisions may lead to this aggregate behavior, an issue ignored both in [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF] and [START_REF] Pan | Pricing Strategy for Device-Level Demand Response in a Microgrid[END_REF]. As long as a rational individual is offered two different discounts R i and R k , it seems natural that her decision to move some consumption from j to i or from j to k or to keep it in j will take into account both the discounts. To stress the point, consider a case when both S j (R i , |j -i|) and S j (R k , |j -k|) are positive, but moving the consumption from i to k is both less inconvenient (i.e., |j -k| < |j -i|) and more rewarding (i.e., R k > R i ). There is then no reason why the user would move consumption to i. The conclusion is the same for all the users and then we should have E j→i = 0 at the aggregate level, in contradiction with Eq. ( 2). We can then conclude that the expression of the sensitivity function in Eq. ( 2) is not suited to model the situation when a user is offered two or more rewards, but it can capture the case when the user decides between moving from j to i in exchange of a discount R i or staying in j. If every user is offered a single discount to move to a given slot, but different users can receive different offers, then Eq. ( 2) can reasonably describe the macroscopic effect of such personalized offers. The details are described in Sec. 4.1, here we only highlight that Eq. ( 2) requires then that the electricity provider i) calculates an offer for each user, ii) communicates individually to the user, iii) considers the individual offer when billing the user. This is clearly more demanding than simply advertising to the whole population the same set of discounts.

We observe that the equivalent sensitivity function considered in [START_REF] Subramanian | Dynamic Price Optimization Models for Managing Time-of-Day Electricity Usage[END_REF] poses similar problems. Using our notation, we have

E j→i = vi k∈T v k E 0 j
, where v i is the net utility a user gets by consuming electricity in slot i and can be a function of the timeslot itself and of the discount R i .

RR n°8881

This formula tries to capture the effect of the whole set of discounts, but it is not clear again what is the underlying user's model: if slot i has a larger utility than slot k (v i > v k ), why should the user consume in k?

Starting from a Microscopic Model

In the previous section we made the point that, while aggregate population models may be convenient, it is necessary to explicitly consider the microscopic level: how the user takes the decisions and how the provider and the user are supposed to interact. In this paper we follow the opposite path in comparison to the existing works mentioned: we move from the microscopic level to the macroscopic one. In particular, in this section, we start from a clear model of rationality for the single user and then move to describe how aggregate quantities can be derived.

Each user u has a baseline energy consumption

{e 0u j } j=1,...N , (5)
that leads to the aggregate baseline consumption E 0 j = u∈S e 0u j for j = 1, . . . N . We assume in what follows that users are homogeneous, i.e., ∀ u, e 0u j = e 0

j j = 1, . . . N. (6)
In section 5.1 we show how the DR mechanisms perform when this assumption does not hold. User u is characterized by a private type D u = {d u j→i } j,i=1,...N where d u j→i indicates the discomfort due to shifting one unit of consumption from timeslot j to timeslot i. We assume that discomforts are expressed in monetary units; and that, ∀u ∈ S,

d u j→j = 0 and d u j→i > 0, ∀j, i = j, (7)
i.e., there is a strictly positive discomfort if and only if consumption is shifted from its original timeslot. The provider does not know the private type D u of each user u: from its point of view, each discomfort d u j→i is drawn from a known, continuous distribution F j→i on [0, α j,i ] (where possibly α j,i = +∞). Discomforts of distinct users are independent but note that we do not assume that, for a given user, the discomforts {d u j→i } j,i=1,...N are mutually independent.

Rational Users

We assume that a user simply chooses the option that maximizes her utility. In particular let T u j be a set of timeslots the user could move the baseline consumption e 0u j to in exchange for different discounts R u j = {R u j→k ≥ 0, k ∈ T u j }. The set pair (T u j , R u j ) defines the offer user u receives for timeslot j. The set of options includes the possibility to keep the consumption in j, i.e., j ∈ T u j . A rational user maximizes her utility by scheduling her consumption e 0u j to a timeslot

i * ∈ argmax k∈T u j R u j→k -d u j→k . (8)
We assume that if two or more timeslots are equally palatable, the whole consumption is shifted to only one of them, picked at random with equal probability.

Inria

Aggregation

We observe that the quantities d u j→k in Eq. ( 8) are random variables, then two different users could take different decisions while confronted with the same offers. The aggregate consumption E 1 i , for i ∈ T , would then be a random variable. Here we assume (as it is implicit in the other works) that we always work with large sets of the population so that the variability can be neglected by approximating actual random quantities with their expected values. In particular, if a subset Q containing a fraction q of the population receives an offer (T j , R j ), the corresponding consumption shifted from j to a time slot i, denoted as E Q j→i will be

E Q j→i = qE 0 j Prob i ∈ argmax k∈Tj R u j→k -d u j→k , (9)
if the probability that a user has two or more equally palatable timeslots is zero. When discomforts are continuous random variables (as we consider in this paper), this is always the case if each user receives only one offer (the first three mechanisms introduced below) or if the discomforts {d u j→i } j,i=1,...N are mutually independent. In Sec. 4.4, we discuss how Eq. ( 9) should be modified if this probability is not zero. We denote Prob i ∈ argmax k∈Tj R j→k -d u j→k simply as P j→i (R u j , T j ).

DR mechanisms

Under different assumptions on the provider's capabilities, we introduce different demand response mechanisms based on the microscopic model above, which are therefore practically implementable. We introduce and study the corresponding optimization problems. We start by the base mechanism that leads to the same aggregate optimization problem considered in [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF][START_REF] Pan | Pricing Strategy for Device-Level Demand Response in a Microgrid[END_REF].

Base mechanism

This mechanism requires that the energy provider can manage personalized offers to its customers and moreover that it has perfect knowledge (or very precise estimates) of the baseline consumption of each user.

The population is segmented into N 2 disjoint subsets Q j→i , for j, i ∈ T , respectively including a fixed fraction q j→i of the population. Each user in Q j→i is simply offered to move her baseline consumption in slot j (e 0 j ) to slot i in exchange for a price discount R i . The total consumption that is shifted from j to i is then

E j→i = q j→i E 0 j Prob R i -d u j→i > 0
as it can be obtained from Eq. ( 9), taking into account that in this case T j = {j, i} and R jd u j→j = 0. We observe that the probability appearing on the right-hand side only depends on the reward R i and on the random variable d u j→i . If the discomfort is only a function of the temporal distance |j -i|, then the sensitivity function (the ratio of people who move from j to i) has the same properties than in [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF], in particular:

S j (R i , |i -j|) = q j→i P j→i (R i ),
where for P j→i (•) we have made explicit the only variable it depends from.

RR n°8881

As we discussed in Sec. 2, because the provider knows exactly the consumption shifted from each user, it can formulate the optimization problem [START_REF] Pan | Pricing Strategy for Device-Level Demand Response in a Microgrid[END_REF][START_REF] Subramanian | Dynamic Price Optimization Models for Managing Time-of-Day Electricity Usage[END_REF]. In [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF] it is stated that the problem is convex if i) the productions costs c j (•) are continuous piecewise linear and increasing and ii) the discomfort distributions F j→i (•) are continuous and concave. We show in Appendix A that this is not the case by providing a counterexample. Stronger hypotheses are required for the problem to be concave, as for example the linearity of the discomfort functions.

In particular in [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF] the numerical evaluation considers

S j (R i , |i -j|) = 1 N k=1 1 (|k-j|+1) R i B • (|i -j| + 1)
, that leads us to consider

q j→i = 1 |i-j|+1 N k=0 1 |k-j|+1 , P j→i (R i ) = R i B . (10)
This particular expression for P j→i can be obtained if d u j→i is a uniform random variable with support in [0, B]. The numerical results for the base mechanism in Sec. 5 are obtained considering the same expression for the fractions q j→i . Due to the non-convexity of the optimization problem (3-4) we cannot use one of the classic algorithms for convex optimization. For the results shown in section 5 we have adopted instead a multi start approach: we have generated random starting points uniformly distributed in the problem domain and we have run per each point a descendent algorithm which converged on a local minimum; the optimal offers are therefore those returning the smallest cost among these minimizers. This approach does not guarantee convergence to the global optimum but its reliability can be improved by increasing the number of starting points.

Optimized Mechanism

We have now understood which DR mechanism can lead to the optimization problem [START_REF] Pan | Pricing Strategy for Device-Level Demand Response in a Microgrid[END_REF], but now that we look at its implementation at the microscopic level and the need for personalized offers, some specifics of the base mechanism look arbitrary and unjustified. For example, given that discounts are not broadcast but each user receives an individual offer, why should the discounts offered to the two disjoint sets of users Q j→i and Q k→i be equal to the same value R i ? It is clear that the energy provider can further reduce the cost if it can independently choose R j→i and R k→i . Moreover, there is no reason to think that the size of the sets {Q j→i } should be fixed, the fractions {q j→i } can also be optimization variables.

We allow the provider to take advantage of these additional degrees of freedom that-we repeat-do not impose any additional requirement to the system. We call this new DR mechanism optimized. The load E j→i (R j→i , q j→i ) rescheduled from j to i is now

E j→i (R j→i , q j→i ) = q j→i P j→i (R j→i )E 0 j (11)
and the cost minimization problem becomes:

min R,q cost opt. (R, q) = i z =i R z→i E z→i + i c i E 1 i (12) s.t. 0 ≤ R z→i ≤ B ∀z, i = 1, . . . N (13) 0 ≤ q z→i ≤ 1, z, i = 1, . . . N (14) i q z→i ≤ 1, ∀z = 1 . . . N. ( 15
)
Inria Eq. ( 13) guarantees that discounts R are non negative and smaller than the flat rate B, Eq. ( 15) is a consequence of the fact that each user receives at most one offer for its baseline consumption in a given slot.

The optimization problem (12-15) can be solved with the same heuristic proposed for problem (3-4).

Robust Mechanism

The optimization problems (3-4) and (12-15) assume that the provider has perfect knowledge of each user's baseline consumption, so that it can correctly identify the consumption shifted and reduce accordingly the energy bill. This assumption is probably unrealistic. If the provider does not have such capability, then it can offer the user a discount for all the consumption in a given timeslot i and not just for the consumption moved to i. The population is then divided into N subsets Q i , each containing a fraction q i of the users. All users in Q i receive one and only one offer: they are encouraged to shift their consumption from any timeslot in the time horizon to timeslot i and they get the discount R i for all the electricity consumed in i, including the one originally in i.

We call this scheme robust, because it does not rely on estimates of individual consumption. It is clearly simpler than the previous two, because the provider needs only to measure the amount of consumption in i for the users who got the offer and to bill them accordingly.

The load E j→i (R i , q i ) shifted from j to i is E j→i (R i , q i ) = q i P j→i (R i )E 0 j . Note that users in Q i have no interest to move their baseline consumption away from i, then E i→i = q i E 0 i . The robust mechanisms leads to the following optimization problem:

min R,q cost rob. (R, q) = i,z R i E z→i + i c i E 1 i (16) s.t. 0 ≤ R i ≤ B i = 1, . . . , N (17)
0 ≤ q i ≤ 1, i = 1, . . . , N (18)
N i=1 q i ≤ 1. (19)
Note that in Eq. ( 16) the first sum includes also E i→i because all the final consumption in i from the users in Q i is paid at a discounted price. The term does not appear in Eq. (3) and Eq. ( 12).

The optimization problem (16-19) can be solved with the same heuristic proposed for problem (3-4).

Broadcast Mechanism

In the three mechanisms introduced above, the provider makes personalized offers to users in selected fractions of the population. This may not always be possible (due to the complexity it introduces for instance in billing) or desirable (for perceived fairness issues). Our last mechanism, which is the simplest (in its definition), does not assume personalized offers. The provider selects a single vector R of discounts for every time slot and broadcasts these discounts to all users (hence the name broadcast mechanism). Users then re-arrange their demand and pay the discounted price for their demand in each slot (hence this mechanism also does not rely on the need to estimate shifted demand).

As explained in Sec. 3.1, each individual user moves her demand from slot j to a slot (potentially j itself) that maximizes her net utility (discount minus discomfort). Recall that if several slots give equal net utility, the user chooses one of them randomly.

Until now, we have not made any assumption on the possible correlation of a given user's discomforts. This is because, in the previous three mechanisms, each user was receiving only one offer.In the broadcast mechanism, each user has several offers to compare to decide on his new demand schedule, we therefore need to describe the discomfort correlations.

Let us consider now the particular case when two slots, say h and k may appear equally attractive to a user, i.e. R h -d j→h = R k -d j→k . If we assumed that, for each user, the discomforts {d j→i } {i,j=1,••• ,N } were mutually independent, this event would have probability zero according to our assumption on F i→j , and therefore it would not appear at the aggregate level. As a result, the aggregate demand moved from j to i would be

E j→i (R) = P j→i (R)E 0 j , (20)
where

P j→i (R) = Pr R i -d j→i ≥ max k =i {R k -d j→k } . (21)
However, rather than making the above independence assumption, we prefer to assume that the discomforts have the form d j→i = β j |i -j| tj , where t j is a constant independent of the user and β j is a random variable with concave Cumulative Distribution Function (CDF) F j (•) This model describes a symmetric delay sensitivity of users (users are indifferent between moving two hours earlier or two hours later) while keeping the flexibility of users having a different flexibility of demand of different times (since β and t are indexed by the origin timeslot j); but it also introduces correlations between the discomforts of a user. As a result, the fraction of demand shifting from j to i is

P j→i (R) = Pr (R i -d j→i ≥ max k =i {R k -d j→k }) 1 + 1 R2j-i=Ri , (22)
rather than (21). The denominator in (22) accounts for cases when a slot other than i (which has to be 2j -i) gives equal net utility for all users. The broadcast mechanism then leads to the following optimization problem:

min R cost brd. (R) = z i R i E z→i + i c i E 1 i (23) s.t. 0 ≤ R i ≤ B ∀i = 1, . . . N. ( 24
)
Unfortunately, due to indicator function in Eq. ( 22), the cost function (23) of the broadcast mechanism is not continuous, even in very simple scenarios with continuous production costs. See Appendix B for details. Discontinuity arises also in the macroscopic model in [START_REF] Yang | Quantifying the benefits to consumers for demand response with a statistical elasticity model[END_REF], but it seems to have been ignored.

In practice, we solve problem (23-24) using the same heuristic proposed for the previous problems, but we work on a continuous and smooth approximation of the cost function.

Ranking DR mechanisms

Under certain assumptions, the DR mechanisms introduced above can be partially ranked in terms of the cost savings they generate. Proposition 4.1. Assume that the discomforts have the form d j→i = β j |i -j| tj , where t j is a constant independent of the user and β j is a random variable with concave CDF F j (•);and assume that the production costs c j (•) are piecewise linear, continuous and increasing. Then, for a given initial demand E 0 , the final cost generated by the optimized scheme is smaller than the cost of the robust and base mechanism. See appendix C for the proof. As we show in Appendix C, the ranking cannot be extended further. In particular, the broadcast and robust mechanisms cannot be compared (one or the other is more efficient depending on the scenario).

Numerical Results

In this section we evaluate the performance of the different DR mechanisms in the realistic scenario considered in [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF] and based on energy data about the Ontario province in Canada. We extracted from [START_REF] Joe-Wong | Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility[END_REF] the baseline consumption E 0 , reported in Fig. 1, the flat rate B = 110$/MWh and the timeslot-independent cost function c(•) is piecewise linear with derivative:

c (E) =    $10 E ≤ C 1 , $72.46 E ∈ (C 1 , C 2 ), $91 E ≥ C 2 , (25)
where

C 1 = 16.3 GWh, C 2 = 17.9 GWh, (26)
represent respectively the base to intermediate load capacity and intermediate to peak load capacity. Baseline consumption E 0 and the cost functions are estimated from the IESO energy portfolio [9], consisting of nuclear plans, hydro gas powered stations and renewable and from typical costs associated to these energy sources. We assume that discomforts take the form:

d j→i = β j |i -j|, (27)
where β j is an exponential random variable with CDF

F j (β) = 1 -e -β µ , ( 28
)
where µ is a parameter representing the population's flexibility. The larger it is, the smaller (in a stochastic order sense) the discomfort of the users to shift their consumption. In Figs. 2 and Fig. 3 we respectively show final load distributions E 1 and optimal discounts and segment sizes of the four DR mechanisms, for the flexibility parameter µ = 1 10 . Since production costs c(•) are constant across the horizon, provider's costs are cut by flattening the load; this is mostly clear in the optimized scheme where the peak of 19 GWh in E 0 is reduced to 17.9 GWh which is exactly the intermediate to peak capacity C 2 , as can be seen in Fig. 2. In Fig. 4, we report the cost savings of the DR schemes, normalized to the initial cost, for four different values of the µ parameters: 1 10 , 1 6 , 1 3 , 1. The dashed line represents the saving which could be achieved if users' demand could be rearranged at the provider's will without providing any discount (we indicate it as the dictatorial solution). Consistently with Proposition 4.1, the optimized mechanism returns larger savings than the robust and the base ones. Interestingly, the robust mechanism performs consistently better than the base one despite the fact that it does not require the ability to estimate the demand shifted and it therefore "wastes" some discount by giving it to demand that was already scheduled in a given timeslot in the baseline demand. Moreover, as the population flexibility increases, the savings gap between the optimized scheme and the robust mechanism reduces, the latter being effectively close to exploiting all the population's flexibility.

In Fig. 5, we focus on the case µ = 1 3 and analyze the components of the cost for each DR mechanism. Fig. 5 confirms that the optimized scheme provides the largest savings as it can minimize the production cost while paying the smallest amount of discounts. We indicate with wasted discounts the amount of discounts paid to consumption that would in any case have been scheduled in that timeslot. The base and optimized mechanisms do not waste any discount, while the robust mechanism and the broadcast scheme do, as they provide the discount R i to all the electricity consumed in i, including the part of E 0 i that remains in i.

The effect of noise

In this section we evaluate how sensitive the DR mechanisms are to the following two different sources of randomness: 1) the number of users who accept an offer is a random variable and 2) baseline consumption forecasts can be more or less accurate.

Let U be the total number of users. Under the base mechanism, if q j→i U users (or better q j→i U ) are offered the discount R i to move their consumption from i to j, Eq. (9) assumes that q j→i U P j→i (R i ) of them accept. In reality the number of those who accept is a Binomial random variable U a i→j Bin( q j→i U , P j→i (R i )). Similar considerations hold for all the mechanisms. About the second aspect, we consider that the energy utility solves any of the specific optimization problems introduced above, starting from the forecast E 0 i of the aggregate baseline consumption for i = 1, • • • N . Let e 0 i = E 0 i /U be the forecast of the individual baseline consumption. We assume that the actual baseline consumptions (ẽ 0 i ) are i.i.d. random variables with expected value E[ẽ u i ] = e 0 i (i.e. the energy utility forecasts are unbiased) and coefficient of variation δ. The actual aggregate baseline consumption is then a random variable Ẽ0 i = U u=1 ẽi i with expected value the forecast E 0 i and coefficient of variation δ/ √ U . This coefficient captures the uncertainty of aggregate consumption forecasts.

We have then evaluated how the performance metrics change if these two sources of randomness are present, but are ignored by the energy utility in the optimization phase. For the base and optimized mechanisms we maintain that the energy utility can estimate the exact amount of energy shifted between two slots, even if this value is different from what predicted the day ahead.

We considered U = 13.6 millions users (equal to the current estimate of Ontario's population). Simulating the behaviour of each user would have been very time-demanding because we would have had to generate N U random consumptions and roughly N U random choices for the offers. Instead we have approximated by matching the first two moments: i) the Binomial random variables U j→i as gaussian ones and ii) the aggregate consumption of a group of U users as a lognormal random variable as suggested in [8]. We need then to generate only O(N 2 ) random variables.

We report in Figs. 6 and 7 respectively the final total costs and the cost savings achieved by the DR mechanisms for µ = 1/3 and different values of the aggregate relative forecast uncertainty δ √ U . Usually the consumption forecast errors over large populations are evaluated to be of a few percents. Results are averaged over 10 5 realizations of the set of random variables. Fig. 6 indicates that costs increase with uncertainty for both the DR mechanism and the idealized Inria

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schemes. This feature is explained by the Jensen's inequality as the cost functions ( 16), ( 3), ( 12) and ( 23) are convex in the aggregated forecast E 0 . The figures confirm the relative ranking of the DR mechanisms shown in Fig. 4, but for the base mechanism that appears to be the most sensitive to the effect of noise and becomes worse than the broadcast mechanism already for values of δ/ √ U as small as 0.2%.

Conclusions

In this paper, we have shown that macroscopic descriptions of DR mechanisms can hide important assumptions that can jeopardize the mechanisms' implementation. For this reason, our proposal moved from a microscopic description that explicitly models each user's decision. We have then introduced four DR mechanisms with various assumptions on the provider's capabilities. Interestingly, contrarily to previous studies, we find that the optimization problems that result from our mechanisms are complex and can be solved numerically only through a heuristic. Moreover, our results show that the performance of DR mechanisms under reasonable assumptions on the provider's capabilities are significantly lower than those suggested by previous studies, but that the gap reduces when the population's flexibility increases.

[7] J. Gorski, F. Pfeuffer, and K. Klamroth. "Biconvex sets and optimization with biconvex functions: a survey and extensions." Math Meth Oper Res, vol. 66, no. 3, pp. 373-407, 2007.

[8] H. Mouri, "Log-normal distribution from a process that is not multiplicative but is additive," in Phys. Rev. E vol 88 issue 4, pp. 042124

[9] IESO Ontario, "Market data," Sep. 2011, http://www.ieso.ca/imoweb/marketdata/ marketData.asp

A Problem (3-4) is not convex

We prove the problem is not convex by providing the following counterexample:

• N = 3 slots.

• Initial consumption in each slot:

E 0 1 = 1, E 0 2 = 1, E 0 3 = 2.
• Sensitivity functions to rewards:

S 2 (R i , |2-i|) = 1-e -R i /α 2 |2-i| , S 3 (R i , t) = 1-e -R i /α 3 |3-i|
, concave in R i and decreasing in |j -i|. Consider α 2 = 1, α 3 = 1/10.

• Cost functions: c i (x) = β i x. Consider β 1 = 1, β 2 = 11, β 3 = 1/2. The derivative versus R 1 of the cost in R = 0 is ∂cost base ∂R 1 R=0 =R 1 E 0 2 S 2 (R 1 , t) + E 0 2 S 2 (R 1 , t) + R 1 E 0 3 S 3 (R 1 , t) + E 0 2 S 3 (R 1 , t) + β 1 (E 0 2 S 2 (R 1 , t) + E 0 3 S 3 (R 1 , t)) -β 2 E 0 2 S 2 (R 1 , t) -β 3 E 0 3 S 3 (R 1 , t) = = β 1 E 0 2 e -R1/α2 α 2 |2 -1| + β 1 E 0 3 e -R1/α3 α 3 |3 -1| -β 2 E 0 2 e -R1/α2 α 2 |2 -1| -β 3 E 0 3 e -R1/α3 α 3 |3 -1| = = β 1 α 2 + β 1 α 3 - β 2 α 2 - β 3 α 3 = -5 < 0,
then it is convenient for the energy provide to increase the reward R 1 .

The second derivative (in R = 0) is

∂ 2 cost base ∂R 2 1 R=0 =E 0 2 S 2 (R 1 , t) + R 1 E 0 2 S 2 (R 1 , t) + E 0 2 S 2 (R 1 , t) + E 0 3 S 3 (R 1 , t) + R 1 E 0 3 S 3 (R 1 , t) + E 0 3 S 3 (R 1 , t) + β 1 E 0 2 S 2 (R 1 , t) + β 1 E 0 3 S 3 (R 1 , t) -β 2 E 0 2 S 2 (R 1 , t) -β 3 E 0 3 S 3 (R 1 , t) = = 2 e -R1/α2 α 2 + 2 e -R1/α3 α 2 -β 1 e -R1/α2 α 2 2 -β 1 e -R1/α3 α 2 3 + β 2 e -R1/α2 α 2 2 + β 3 e -R1/α3 α 2 3 = = 2 α 2 + 2 α 3 - β 1 α 2 2 - β 1 α 2 3 + β 2 α 2 2 + β 3 α 2 3 = -18 < 0,
then the function is not convex. Moreover, this non-convexity is at a point where the derivative of the total cost is negative.

B Proof of discontinuity of Broadcast mechanism cost function

Suppose the provider broadcasts discount rates R such that

R z = R > 0 if z = j -k, j + k 0 otherwise. ( 29
)
The consumption shifted out of j will then be equally divided between the slots j + k and j -k.

In formulas, from (22) follows:

P j→(j-k) (R) = P j→(j+k) (R) = Pr (R -β j k ≥ 0) = 1 2 F j R |k| tj (30)
Consider now another discount R * such that

R z =    R + if z = j + k R if z = j -k 0 otherwise. ( 31
)
where is a positive. Then all the consumption shifted out of j will be moved to j + k. From (22) follows

P j→(j+k) (R) = F j R |k| t j P j→(j-k) (R) = 0. (32)
A positive arbitrary small generate a finite change in probabilties P j→i . Hence P j→i (R) are discontinuous in R which is why the final cost (23) is discontinuous.

C Mechanisms ranking

We start proving proposition 4.1, i.e. that the optimized mechanism achieves a lower cost than the robust and the base mechanisms. We first observe that the base mechanism solves the same problem solved by the optimized mechanism, but with the additional constraint that fractions q j→i have to be constant as indicated in Eq. (10). The feasibility set of the base mechanism's optimization problem is then a subset of the feasibility set of the optimized mechanism's optimization problem. It follows that the optimized mechanism achieves a lower cost.

We now consider the robust mechanism, and assume that the pair (R rob. , q rob. ) is an optimizer of (( 16)-( 19)). Choose the pair (R opt , q opt ) such that:

∀(j, i) s.t. j = i R j→i = R i q j→i = q i which implies that ∀(j, i) s.t. j = i, P j→i (R i )q i = P j→i (R j→i )q j→i
and set (R j→j , q j→j ) = (0, 0) ∀ j = 1, . . . , N

The two configurations (R rob. , q rob. ) and (R opt , q opt ) generate the same final distribution E 1 . However while the robust mechanism awards with a discount R i a fraction q i of the consumption E 0 i originally scheduled in i, the optimized scheme does not. So the optimized scheme can achieve the same distribution provided by the robust mechanism paying less rewards. It follows that the total cost achieved by the optimized mechanism is never larger than that of the robust mechanism. This last point completes the proof.

Inria

The ranking cannot be extended further as we show through the following examples.

Example C.1. We show first a case where the broadcast scheme (23-24) provides larger savings than the optimized one (12-15). Consider a three timeslots scenario with initial consumption

E 0 = 10 j = 1 0 j = 2, 3 timeslot-dependent costs c j (•) c j (E) =    100E j = 1 10E j = 2 1E j = 3
and a maximum discount rate B = 20; finally assume discomforts are formulated as in (27) with

P r(β j ≤ R) = 1 -e -R 6 (33)
Electricity provider can reduce the cost it carries by shifting consumption in timeslots 2 and 3 where there are zero demands and lower marginal production costs. We report in figures 8 and 9 respectively the rearranged consumptions and the minimizers of the optimized and broadcast schemes. We observe that the optimized mechanism offer to all the users to move in to slot 2, while the broadcast mechanism is able to have some of them moving to the more convenient slot 3. The final cost of the optimized scheme (12-15) is 311 and is larger than the final cost of the broadcast one (23-24), 286.

The broadcast scheme is able to outperform the optimized scheme, because users can choose between two different offers and then the more flexible ones move to slot 3 and the less flexible ones to slot 2. On the contrary, in order to move some consumption to slot 3, the optimized mechanism should offer to a fraction q 1→3 to move, but those who do not accept the offer would stay in 1 with corresponding elevated costs. Observe that this example has been built in such a way that the broadcast mechanism does not "waste" rewards paying for consumption that does not move.

Note that, due to Proposition 4.1, in this scenario the broadcast scheme allows larger savings than the robust mechanism and the base scheme. We report in figures 10 and 11 respectively the rearranged consumptions and the minimizers of the robust and broadcast schemes. The final cost of the robust mechanism (16-19), 580.75 is smaller than the final cost of the broadcast scheme (23-24), 594. The broadcast scheme assigns only a reward R 1 to the first timeslot. Flexible enough consumption will be moved from timeslots 2 and 3. Notably, the fraction of consumption shifted from timeslot 2 is twice larger than the fraction of consumption shifted from 3 because the discomfort is linear in the distance (see Eq. ( 27)). The broadcast scheme cannot alter this proportion, though it could be beneficial as in timeslot 3 there is a larger demand. It then achieves an unbalanced consumption in slot 1 and 2

On the contrary the robust mechanism can equalize consumption in slots 1 and 2 by offering to a small fraction of the population to move to slot 2.

Observe that, because of Proposition 4.1, in this scenario also the optimized mechanism returns larger savings than the broadcast one.

Example C.3. In this final example, we show that the base scheme can perform better than the robust mechanism and broadcast scheme. Consider a two timeslots scenario with initial consumption In this scenario the robust mechanism and the broadcast one achieve a cost equal to 154.75, larger than the cost of the base mechanism, 152.92. We report in figures 12 and 13 respectively the rearranged consumptions and the minimizers of the three mechanisms.

In this scenario all the schemes the robust and broadcast mechanisms are equivalent: they both shift the same amount of consumption to slot 2 and pay the same amount of rewards. The base scheme performs better than the other two since it does not waste any discount, i.e. it does not award discount to the demand E 0 2 originally scheduled in timeslot 2. The base and broadcast schemes do waste some discount and therefore offer smaller discounts than the base scheme, rearrange smaller amount of demand and return a larger cost.

Inria

Figure 1 :

 1 Figure 1: Initial consumption E 0 extracted from [2].The red lines represent the capacities C 1 and C 2 .

Figure 2 :

 2 Figure 2: Final consumption in GWh for the four DR mechanisms (µ = 1 10 ). The black lines represents respectively C 1 and C 2 .

Figure 3 :

 3 Figure 3: Minimizers for the four DR mechanisms (µ = 1 10 ).

Inria

Figure 4 :

 4 Figure 4: Cost savings normalized to the initial cost, for various flexibility parameters µ.

Figure 5 :

 5 Figure 5: Analysis of the components of the cost savings. All the quantities are normalized to the initial cost.

Figure 6 :

 6 Figure 6: Global cost versus relative uncertainty δ √ U (%).

Figure 7 :

 7 Figure 7: Savings versus relative uncertainty δ √ U (%). Savings are normalized by the initial cost.

Figure 8 :

 8 Figure 8: Initial and final consumptions in example C.1.

Figure 9 :

 9 Figure 9: Optimized and broadcast schemes minimizers in example C.1.

Figure 10 :

 10 Figure 10: Initial and final consumptions in example C.2.

Inria

Figure 11 :

 11 Figure 11: Robust mechanism and broadcast scheme minimizers in example C.2.

2 a

 2 timeslot independent cost c(•), such that c(E) = 10E E ≤ 7 15E -35 E ≥ 7a maximum discount rate B = 10 and discomforts formulated as in (27) with P r(β j ≤ R) = R B

Figure 12 :

 12 Figure 12: Initial and final consumptions in example C.3.

Figure 13 :

 13 Figure 13: Robust mechanism, optimized and broadcast schemes minimizers in example C.3.

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Acknowledgements

This work was partly funded by the French Government (National Research Agency, ANR) through the "Investments for the Future" Program reference #ANR-11-LABX-0031-01.


2/ some major challenges exemplified through the French ARA program

With regard to this general challenge we call the semiotic processing of digital media objects, we would like to introduce and present here briefly the French programme Audiovisual Research Archives (ARA) 1 we have started in 2001 in Paris. This archive actually recovers more than 6000 hours of online streamed videos, accessible for everybody, covering a large diversity of disciplines in human and social sciences. The records composing the ARA corpus contain individual interviews with researchers, conferences, lectures, research seminars, workshops, small reportages about the daily life in research labs, scientific expositions, documentaries, travelogs and road movies, ethnographical films and field work recordings. The offer is composed of almost 270 research seminars, symposia and workshops as well as of more than 380 interviews with researchers and experts in social and human sciences. More than 2800 researchers, scholars and professionals from 80 countries have contributed to the cultural and scientific heritage stored and diffused through the ARA portal. Even if half of all videos are in French language, there also exist large corpora of videos in English, Italian, Spanish, German, Chinese or Russian. The average growth per month of this audio-visual archive is of about 30 hours on line, this means of about 60 hours of video taking and digitizing.

The semiotic turn in digital archives and libraries

1/ the meaning of data

To-day, we witness the massive digitization of any kind of media objects, the storing and diffusion of it in form of digital (multimedia) libraries, archives or any other form of digital "(multimedia) information spaces". The general policy is to make available the enormous quantities of media data in form of relevant (critical) knowledge or knowledge resources for a target public -a person, a social group, an institution (this policy underlies the shift from an "information society to a knowledge society" as proclaimed, for instance, in the Lisbon 2001 Declaration or again in the actual Europe 2020 program). However if we want to progress in this historically and culturally certainly highly exciting and innovative direction, a series of serious -technical, social and scientific -problems has to be solved.

One of the most complex ones is without any doubt the question of how to process the symbolic or the meaning of (digital) media data with respect to a given -analogically speakingmarket place of meaning production, sharing and consumption (potential users of meaning, cultural expectations of meaning, needs and desires of meaning, uses and exploitations of meaning, added value of specific forms of meaning …). Indeed: … a (digital) media data (a still image, a video, a sound record, an oral record, a printed document, …) is not in itself already a genuine cognitive resource for a given "reader" or "community of readers", that will say a relevant "means" for an agent to solve a problem, to answer a question or again to satisfy a (personal or collective) goal or a need.

A digital media data is, in other terms, only a potential cognitive resource. It has to "undergo" more or less significant qualitative transformations in order to become a user or a user community relevant one. These qualitative transformations are performed through series of concrete operations such as the constitution and classification of relevant corpora of digital records, the description and indexing of records, the processing of digital data (segmentation, tagging, linking, montage, …), the (cultural, linguistic) versioning (commenting, translating, …) of given source records or again the (re-)publishing of digital records.

These and other operations constitute what we call the semiotic processing of (digital) media objects, corpora of (digital) media objects or again entire archives and libraries. They demonstrate practically and theoretically the well-known "from data to meta-data" or the "from (simple) information to (relevant) knowledge" problem -problem that obviously determines the effective use and also the future of digital knowledge archives.

In short, the central question here is that of the semiotic structure of (digital) media data, i.e. the structural organization of (digital) media data, their status and function(s) for a given user or community of users and of how to deal with them, how to work concretely with them in order to achieve not only theoretical but also practical -educational, economical or other -objectives.

The principal user communities of the ARA cultural and scientific heritage are the (French speaking and international) research community itself, the educational community and specialized professional communities (science journalists, stakeholders especially in non-profit organization, governmental agencies …). The proposed content composes indeed a highly specific "market niche" in the digital (audio/video) content production and communication. All available indicators show that this archive has a potentially high impact for teachers in formal contexts but also for educators and learners in informal settings (lifelong learning, etc.), for professionals working, for instance, in the sector of specialized and highly specialized information media or in organizations where an expertise based on knowledge produced by the social and human sciences is indispensable (NGOs, community and territorial structures, political actors, …).

But such as, this whole heritage has however to undergo a complex process of digital repurposing, of digital re-writing of streamed videos in order to fit more precisely with specific user profiles and user contexts.

The reason for this is that the specific auctorial profile, the "auctorial identity" of the digital media objects composing the fonds of the ARA program does not necessarily fit with the expectations, needs or desires of an individual user or a target user community. Another reason is that this auctorial profile simply isn't perceived by a target user community. It is, so to speak, hidden either from a strictly cognitive point of view (the content remains not understandable for a target community) or from a physical point of view (the content is not attainable). In a nutshell, there are (among other) the following three serious challenges that have to be faced in a digital repurposing (re-writing) process of digital media objects or corpora of objects:

1/ the language limitation: the diffusion of digital content has to be "improved" in opening and making it available to an intrinsically multilingual knowledge market by the means of an extensive use of hints and aids for an at least basic linguistic understanding of a content performed in a given source language; 2/ the hidden information: the quality of existing digital content has to be improved in eliciting, systematizing and classifying the hidden information in large audio-visual databases by the means of context-sensitive description and indexing of digital media corpora using the same analytical (meta-linguistic or, more broadly speaking, metasemiotic) resources (verbal, iconic or other kinds of thesauri, ontologies, description models…) or again analytical resources which are interoperable (which, in some way, are able to communicate between them); 3/ the adaptation to specific purposes: the quality of existing digital content has to be improved for specific contexts of use (especially: formal and informal education) and an intrinsically multicultural market (characterized by diverging knowledge and value references, by a diversity of expectations and beliefs, interests and needs, …) with the help of context-sensitive re-authoring and re-publishing models and tools.

The scientific, technical and practical work on these three limitations has constituted (and still constitute) the principal motivation of a series of European and French R&D projects which we have coordinated or in which we have participated as a consortium partner since 19892 .

In the following chapters we would like to present globally the general "philosophy", the general assumptions that underlie our research agenda since the last twenty years and which we summarize under the label of the semiotic turn in digital archives.

3/ digital media object repurposing

One of the major challenges (if not the major challenge) for the constitution of a genuine knowledge community within an intrinsic multilingual and multicultural world, is the context-sensitive exploitation of existing digital resources such as videos, sound tracks, electronic versions of printed texts, etc. produced by the concerned users themselves or other authoring instances and stored in central or distributed digital libraries. This challenge is identified under the heading of (re-)purposing of (digital) media data.

(Re-) purposing of digital media objects is a more or less complex (individual or collective) process by the means of which a digital media object or a corpus of digital media objects (such as a corpus of video clips, a corpus of printed or handwritten texts, a corpus of still images, a corpus of sound recordings, etc.) are adapted, attuned to a specific context of use and, this, following a sort of authoring or publishing scenario. There exist many different related notions that capture the one or the other specific aspect of the process of repurposing of digital media objects -notions such as:

 the segmenting or re-segmenting of video or audio-files,  the delimitation or re-delimitation of 2-D regions in still images,  the classification or re-classification of media objects (or parts of a media object),  the description or re-description of the content of media objects (or parts of a media object),  the description or re-description of the audiovisual expression, of the formal and the the physical organization of a media object (or a part of a media object),  the "subjective", theory-bounded, … interpretation and annotation of media objects (or parts of it),  the (thematic, rhetorical, …) positioning or re-positioning of a media object (or a part of it) within a field (a context) of related media objects,  the processing/re-processing of specific features of a media object (for instance, a video or a still image) itself: the blurring of a person's face, …,  the publishing/republishing of (virtual) parts of one or more audiovisual media objects (in form of, for instance, mash-ups, web documentaries, thematic, pedagogical or bilingual folders, etc.),  the collaborative and personalized archive building and diffusion for a given domain of discourse;  the channelization of digital media assets with respect to a user's interests or preferences,  etc.

In considering more precisely the (re-)processing process (figure 1) itself, it has to be explained with respect to: 5. The process of (re-)purposing itself: the phases and tasks composing a (re-)authoring chain (such as "publishing genre selection and preparation"; "corpus constitution"; "description and indexing"; "translation"; etc.).

Figure 1: Central components specifying the (re-)authoring process

The (re-)authoring process, can be understood as a -in a broad sensecultural translation process. Digital (media) objects are meaningful entities with respect to context(s) of uses, destines, social practices, etc. for which they have been designed. In other words, they belong and refer to a culture embodied in the life-world of social actors, viz. user communities.

For instance, there exists a corpus of audio-visual files in the already quoted audiovisual research archive for human and social sciences (i.e. the ARA program3 ) which deals extensively with the topic of linguistic diversity from a socio-linguistic point of view and of which the main target public are linguists, experts in this field. However, this corpus can be (re-)purposed (re-authored) for a high diversity of potentially relevant contexts of use and potentially interested destines. It can, for instance, be repurposed as a semantically restricted audio-visual library dedicated exclusively to this domain of knowledge [START_REF]Conservation model[END_REF] ; it also can be re-authored by the means of different publishing formats: as an introductory seminar in philological studies, as a complementary course in anthropology or sociology, as a mashup for a wider interested public, as a set of info flashes for people only with local interests in this domain, as a discovery game for children.

4/ digital media repurposing examples from the ARA program

In order to make more concrete our explanations, we would like to quote here briefly four concrete examples taken from the ARA program. Indeed, thanks to the already quoted European and French R&D projects, we have had the possibility to constitute a research team aiming at the design, the implementation and the practical experimentation of different repurposing (or republishing) approaches in form of:

1) the instrumentation of typical repurposing tasks and activities (= working environment and methodologies); 2) a library of dynamic publishing templates.

We will come back again to the definition and general architecture of a working environment for digital media repurposing activities (cf. next chapter). Concerning the second one -the definition and implementation of a library of dynamic publishing templates -, we have experimented mainly with following genres: 1) specialized web portals for diffusing and sharing thematically or otherwise circumscribed events and videos; 2) semantic video libraries for accessing videos or segments (sequences) of videos with respect to their topics, their rhetorical specificity, their audiovisual specificity, etc.; 3) thematic video folders dedicated to the editing of a selection of video segments that are relevant for a given topic; 4) bilingual folders dedicated to the opening of monolingual source videos to a multilingual public; 5) dynamic video corpora constitution "through the time" with respect to a specific theme, a period, a place, a personality, etc.; 6) dynamic video-books based on the re-editing of a given source video in taking as a cultural reference the traditional "book"-genre.

Figure 2: Repurposing of a source video in different publishing formats

Figure 2 shows us the republishing of a given source video with the help of different publishing or republishing formats. The source video is a documentary about the small Chilean commune Alto Bio Bio (8 th Region) and the Pehuenches population threaten by the construction of a giant dam in the 90ies. The documentary has been produced by a group of anthropologists under the direction of José Bengoa5 of the Universidad Academia de Humanismo Cristiano in Santiago de Chile. A first republication of this documentary has been undertaken on the ARA web portal in form of a digitized, streamed video6 . This first republication is a kind of a "simple" digital copy of the original source documentary. The goal of this republication has been three-folded: first to diffuse more broadly the content of this exceptional document showing the disintegration of traditional social structures opposed to important industrial and financial interests; second the long-term preservation of this document; third the possibility to share and to reuse this document for research, educational and also (broadly speaking) political aims. A second and third republication of this documentary has been in form of a selection of (virtually segmented) sequences of the whole documentary in form of "chapters" and the commenting, annotating of each chapter in order to produce a sort of a hypermedia, interactive web book.

Figure 3: Repurposing of a digital video corpus in form of a domain restricted web portal

Figure 3 shows a second example of republishing digital audiovisual media objects in form of a semantically restricted video-library and a dedicated web portal. In this concrete case, a corpus of more than 350 hours of videos, originally diffused through the ARA web portal, have been selected, partially re-segmented ("de-linearized"), re-categorized, redescribed and re-indexed by the means of a domain-specific ontology and library of description models and republished in form of a web portal specifically dedicated to Latin America's history and culture [START_REF]Physical) support component[END_REF] . One obvious objective of this semantically restricted video-library and dedicated web portal is to provide (pedagogically…) relevant material about this world region. A second -important and -following our point of view -innovative objective is to build up a common digital (local, regional or global) meaning production, sharing and exploitation place where individual and institutional actors can cooperate as knowledge producers and/or users.

It's the upper menu bar which suggests us the principal features of the repurposed video corpus: on the one hand there are so-called "automatic" publications of this corpus based on a previous description and indexing process and on the other hand there are reauthored versions of this corpus (parts of this corpus) in form of collections of videofolders. These collections of video folders group together sequences of different videos which refer to a common knowledge objet. There are, for instance, a series of video-folders dedicated to Amerindian languages and civilizations, other video-folders are dedicated to the environmental and ecological questions in Latin America, a third series is dedicated to the historical and cultural presentation of Latin-American countries; and so on. Each video folder is thoroughly analyzed, explained, enriched with relevant resources belonging to exterior web sites.

The organization of the virtual video-library dedicated to the history and culture of Latin America, recovers, as shown by figure 3 and4, a series of headings offering a diversity of accesses to and exploration paths of the whole repurposed video corpus: one access takes into account the main topics of a video or of a part of a video; another access uses a domain specific thesaurus; a third access is based on the global narrative or rhetorical structure of a video distinguishing, for instance, between scientific exposés, historical presentations, documentaries, and so on.

Figure 4: The "Subject-area" of the semantic virtual video-library of the AMSUR portal

As figurer 4 shows us, the access "Topics" is organized in several main collections of topics related to circumscribed, more or less well identified knowledge domains such as "Countries, regions and localities", "Cultural references", "History", etc. Each one of these collections is composed of one or a list of more specialized subjects. For instance, the topic referring to the broad knowledge domain "Cultural diversity" can be actually explored through three more specific subjects referring to: "Religious culture and popular believes", Figures of veneration", "Religious practices".

If somebody is interested in specific topics related to the domain "Figures of veneration", he/she has the possibility to access a small dynamic corpus composed of "whole videos" or segments of whole videos dealing with a series of such specialized topics as the Blessed Virgin of Petorquita or La Tirana (two small localities in Chile), the Fiesta of San Pedro in Quilama in Chile, the figure of the jaguar in the Kuna culture of Panama, and so on. This small corpus is open in the sense that it can be enriched with other video resources documenting the domain "Figures of veneration". Actually, all the videos are stored in and diffused originally via the ARA web portal but, in principal, the corpus can be composed of video resources located elsewhere (relevant -academic -content providers in France are, for instance, Canal U8 , UOH9 or HAL Video10 ).

A last example of repurposing digital media objects is shown by figure 5: an interview in French with the researcher Sabine Trebinjac from the CNRS on the muqam genre in the Chinese Turkestan. This interview has been re-versioned in several target languages (such as English, Chinese, Russian, Turkish or Spanish). The obvious objective of this repurposing activity is to open this interview to a non-French speaking public.

Figure 5: Re-versioning of an original video in different target languages

Each one of the five above discussed examples shows (re-)purposing as a process ofso to speakcultural adaptation of the content, form and function of a given source media object with the aim to bring it into line with the (supposed) interests, needs but also abilities of the culture of target publics.

The (re-)purposing process of a digital media object can definitively be understood as the opening of the cultural specificity of a concrete digital media object with respect to a given diversity of target cultures and the attempt to encourage the circulation of a digital media object or artefact within an intrinsically multi-cultural (knowledge) space. Linguistic translation, in this sense, is only a very specific case of cultural translation.

5/ the Studio ASA

The re-purposing is the central activity of the intentional manipulation of the source profile, the source identity of digital media objects or of corpora of digital media objects in order to attune them to the expected profile of a user community and/or contexts of uses. For example, the use of available digital objects such as videos or images for educational purposes, presuppose in general a whole series of such repurposing tasks which may more or less drastically change the content of these objects, their "look", their purposes and goals.

Thanks to the already quoted French and European R&D projects -and especially thanks to the ANR founded ASA-SHS project (2009 -2012) 11 -we have designed and implemented with our research team in Paris12 an environment called -in French -Studio ASA (Atelier de Sémiotique Audiovisuelle, i.e. Audio-visual Semiotics Workplace) with the help of which we realize our different audio-visual archive projects as well as all of our video corpus repurposing activities. Figure 6 shows us in a nutshell the principal components of the Studio ASA. Each one of these workshops have been extensively discussed and presented in [STO 11a] and [STO 11b]):

The video processing workshop provides the analyst (i.e. the person or group of persons working with digital media objects) with the technical means he/she needs namely for extracting from a given source video the relevant segments (or sequences) which he/she wants to analyze further and to publish/republish.

The video description workshop provides the analyst with a whole library of dynamic interactive formularies he/she needs in order to describe, index, enrich, and interlink a video or a part (a segment) of a video.

The video publishing workshop enables the user to publish or republish a video, parts of a video or a corpus of several videos previously indexed.

Finally, the archive discourse modeling workshop provides the competent user (the knowledge designer, the semiotician, …) with the technical and conceptual resources for designing and developing an archive specific domain ontology (a conceptual vocabulary), a domain specific thesaurus of predefined values or descriptors, as well as a library of description models which are indispensable for the elicitation of the universe of discourse of the corpus of videos composing the fonds of the archive.

The (software) tools composing these four workshops are provided with dynamic formularies based on a common meta-language called the ASA meta-language. This ASA meta-language is composed of:

1) an ontology (a vocabulary of concepts), 2) a thesaurus (predefined values for a subset of concepts) 3) and a library of description models (i.e. interrelated concepts or conceptual maps). Some of the concepts (of the ASA ontology), values (of the ASA thesaurus) and description models are shared by all archive or archive projects designed and realized with the Studio ASA. These elements compose the generic dimension of the ASA metalanguage. For instance, all audiovisual archive projects share a set of models for the description of the audiovisual expression of content in videos; all audiovisual archive projects share a set of models for specifying copyrights and other rights and duties with respect to the use/reuse of videos; and so on.

However, the content strictly speaking (the subjects or themes) vary from one archive to another. Hence, each archive project possesses also its own, specific conceptual vocabulary, thesaurus and library of description models. These elements form the domain (or archive) specific dimension of the ASA meta-language.

6/ a text-or discourse based vision of digital archives

The Studio ASA has been designed and developed with respect of what we call a textor discourse based vision in digital archive production, management and exploitation. The upper case in figure 7 shows the typical activities that we summarize under the general label text/discourse-based processing (of digital media data, corpora of media data or "whole" archives). Text/discourse-based processing covers all interactions between a (human or artificial) actor and a digital media data, in our case, a digital audiovisual source data in order to transform it in something which possesses a relevancy (an interest, an added value, an "attractiveness") for the concerned actor. 1. The conceptual modelling activity: its principal objective is to elaborate media processing, domain-appropriate description and indexing as well as (re-)publishing models.

Semiotic theory of text (as a 1/ compositional entity, 2/ a layered feature entity and 3/ a processing/processed entity) 4. The processing activity covers on the one hand the "enriching" of a given digital source record and on the other hand the qualitative modification of the textual structure itself of a source record (for instance, through a virtual montage adding a sound track, new filmic elements, … to an existing record). Together with the description/indexing activities, they contribute to the versioning of a digital record, i.e. to the re-purposing, the adaptation of existing media data with respect to the cognitive, cultural and linguistic particularities of a user or a community of users.

5. The editing and publishing activities, finally, intend to make accessible ((re-)described, (re-)commented, (re-)processed…) digital media data for a target public, via specific publishing genres (such as virtual narrative paths through collections of segments, video-dictionaries, bilingual folders …).

Figure 7 also shows us that no activity can be performed without the recourse to a specific type of intellectual or cognitive resources we summarize under the general label of meta-linguistic (or more generally speaking meta-semiotic) resource, this means of a language -a meta-language -we use for dealing with the object text broadly speaking. Such meta-linguistic (meta-semiotic) resources are for instance ontologies and (verbal, iconic, acoustic …) thesauri.

The lower case in figure 7 represents schematically the dependencies between semiotic processing activities of digital records and a library of functionally diversified metalinguistic resources such as content description and indexing models, versioning models, publishing models, etc.

7/ some major traditions and trends in dealing with (digital) media data

In order to show the specific intellectual and scientific context in which we place our research activities on digital (audiovisual) archives in the sense of an integrated set of textor discourse based activities, let us discuss very briefly some major traditions in the field of (digital) media object storing, processing, indexing and publishing.

There have been, in the last (three) decades, many valuable contributions for organizing and explaining the semantic structure of (digital or non-digital) media data mainly in form of controlled vocabularies, thesauri, classifications, ontologies, folksonomies, conceptual schemas, conceptual graphs or again other topic or cognitive maps that are supposed to represent the cognitive structure of a knowledge domain. We may distinguish, roughly, between at least five main trends in this domain of research and development:

1. A first important tradition is "materialized" in a wide range of "meta-linguistic resources" such as (mono-or multilingual, domain specific…) thesauri, controlled vocabularies, terminologies, etc. which are largely used in the context of digital libraries and archives. All these resources represent kinds of lexicalized visions of knowledge domains. Well-known examples are, among many others, the WebDewey forming the "heart" of the WordCat -the worldwide network of libraries of the Online Computer Library Centre of Ohio, the UNESCO's thesaurus 15 , Getty's AAT -Art & Architecture Thesaurus, the RAMEAU indexing language of the Bibliothèque Nationale de France 16 , the Library of Congress Subject Headings (LCSH) 17 , the Social Science Thesaurus of the Leibniz-Institut für Sozialwissenschaften GESIS 18 , the Humanities and Social Science Electronic Thesaurus (HASSET) 19 of the UK Data Archive. But the structural and cognitive organization of a (digital) media data doesn't play any particular role in these and similar meta-linguistic resources.

2. A second trend has to do with the accelerated production of norms and standards in ICT -from basic ones such as the Dublin Core Metadata Initiative 20 or MARC standards 21 to specialized ones aiming at the "management" of high level cognitive activities in the work with and exploitation of digital data. Three important examples here are the PAIMAS and OAIS standards for archive projects [START_REF] Cf | the explanations of Kari R. Smith on the blog[END_REF] , the e-learning standard LOM (for France, more particularly, LOMFR 23 ) for dealing with pedagogically relevant data, and the TEI (Text Encoding Initiative) 24 dedicated to the representation (composition, formal organization, …) of electronic texts.

3. A third trend of research is dedicated to the automation processes of cognitive "high level activities". Important issues here are that of the automatic segmentation of audiovisual records (in smaller segments or "scenes"), the automatic recognition of visual or acoustic forms and figures in (audiovisual) scenes, the (for audiovisual archives) extremely important speech-to-text conversion (i.e. the conversion of spoken discourse into text), the automatic semantic or conceptual indexing based on statistical and/or linguistic procedures with the aim to map textual features ("words") to concepts or conceptual graphs representing the meaning of a given domain, the automatic or semi-automatic production of domain specific terminologies or ontologies based on textual corpora, and the automatic translation between pairs of languages.

4.

A fourth and for our own research activities important trend covers (cognitive, linguistic, formal and applied) researches in knowledge description and representation. These researches have started in the 70ies and 80ies (cf. for instance the researches on semantic networks, topic maps or again conceptual graphs based on J. Sowa's25 groundbreaking work) and are "materialized" to-day in the development of generic and domain-specific ontologies, tools and environments (cf., for instance, the Univ. of Stanford's Protégé environment).

5. The fifth trend of researches is next to our own theoretical assumptions and research objectives. It covers all those activities in the last 30 years that have aimed at a systematic and operational understanding of the semiotic structure of texts broadly speaking (i.e. the organization of content and its expression in form of mono-media, multimedia or again in form of cross-and trans-media objects). We think here 1) on structural and functional semiotics (i.e. mainly on the work of A.J.

8/ the structural organization of a data

Let us have now a closer look on the semiotic structure of a digital (media) data. As already stressed, the conception of the ASA Studio (figure 6) is based on structural (text and discourse) semiotics and other related approaches such as the rhetorical structure theory, discourse analysis, thematic and cognitive analysis of texts, knowledge representation … The development of the Studio ASA has followed, as precisely as possible the "instructions" formulated from the side of human sciences (in other words, we have tried to adopt the holistic vision called "humanistic computing" which stands for a human/social science based design of technical or technological systems28 ).

In order to understand and to deal adequately with the semiotic structure of a (digital) media data stored in an archive or as an archive (cf. figure 7), it is obviously not enough to deal only with the cognitive structure of a given knowledge domain: we have to take into account the structure, the organization of the data in itself given the self-evident fact that the knowledge of a domain is necessarily mediatized by the data that "contain" a knowledge of a domain, that produce it, that communicate it, that conserve it. And these data are (written or oral) data, photos, drawings, maps, films, and so on. And, as obvious too, all these textual "artefacts" (called simply text in the specialized literature) possess a highly specific and constraining structure.

Therefore, if we want to progress in our (theoretical as well as operational) understanding of the qualitative transformation of (digital) media data as the result or consequence of user-centric activities of adapting or repurposing "original" (or better given "source") data in order to transform them into genuine added value cognitive (or more generally, epistemic) resources for a target user community, the central problem is obviously the appropriate understanding of the structural organisation of the digital data itself.

From a structural or semiotic point of view, a digital media object (a digital text, film, image) can be characterised, described with respect to a set of central features such as the themes or topics encapsulated in a media data, the (linear or non-linear) development of a topic provided by a media data, the forms of (verbal and/or non-verbal) expression of a topic, and so on.

Like the grammar of a natural language, these features form together the specific identity or profile of a media data or of a corpus of media data. Such a profile or identity is not arbitrary but belongs to a tradition represented by genres or cultural models of production and sharing of meaning to which people refer and which people use in their own activities of writing, reading, exchanging information and knowledge. 1. The text as a compositional entity: in principal, a text always can be decomposed in smaller parts (sequences, scenes, statements…) and it belongs always to inter-textual fields -archives, libraries, collections, etc. -forming more or less institutionalized or un-formal and ephemeral textscapes or textspheres.

2. The text as a layered feature entity: a text possesses a number of layers assuring the specific information-to-meaning processing: a topical layer ("what is the content of a text?"), a syntagmatic layer ("how information is merged in a text?"), a media expression layer ("what are the medias expressing an information?"), etc.

The text as a processed entity":

A text is an historical entity (it refers to traditions in form of genres), a genetic 29 entity (it evolves, for instance, from a simple draft or plan to a definitive product) and a functional entity (a text belongs to specific social practices and entities).

29 "genetic" in the sense of the -philologically inspired -"genetic text analysis" (cf. for instance the works of the French research laboratory ITEM: http://www.item.ens.fr/ Media object:

The text/ textscape as a structural whole

Compositional model

The structural organization of a text becomes manifest through its compositional nature (a text as a stand-alone entity, as a whole composed of parts entity or as a part of a whole entity), its nature as a layered feature entity (corresponding to a more precise formulation of the traditional Saussurian or Hjelmslevian distinction between content and expression of content) and as a processed/processing entity (as a historical entity, as a text-genetic entity evolving from a first sketch to some steady state version, as a functional entity integrated in a social practice).

9/ semiotics and digital archives

In order to conclude our article, we want to briefly systematize the role (or, more precisely, the potential role of semiotics in the field of digital archives.

Very generally speaking, semiotics can be defined as a theoretical framework and methodology for describing and analyzing ("expertizing"):

1. the production, exchange, sharing, conservation, reuse, … of messages 2. by the means of one or more medias and in form of texts (broadly speaking) 3. in using a language (the "semiosphere" peculiar to the culture of a social actor) 4. attuned to the specific the context of use ("social structure"). 2) Basic corpus processing and recording.

3) Web site (portal) editing. 4) Right management, longterm conservation…

III) (Re-)processing and (re-)analyzing digital media

1) Media data processing.

2) Media data description and indexing.

3) Media data translation/adaptation. 4) Media data interlinking.

IV) (Re-)publishing of digital media data

1) "Automatic" production of semantic video-libraries.

2) Authored versions of digital media objects.

3) "User" community based exploitations of media objects. 4) Cross-/trans-media exploitations of media objects Greimas, R. Barthes; a second one is represented by the functional text-semiotic approach of M.A.K. Halliday; a third tradition is represented by the cultural semiotics approach of J. Lotman and the Tartu school in Estonia; a fourth one is represented by the pragmatic and sign-theoretic approach of Ch. S. Peirce. It is beyond the scope of this small article to investigate and compare these several traditions and to assess their interest for the field of digital (audiovisual) archives. However, personally, I believe that in general semiotics can be used in several ways and for several objectives.

Figure 9 summarizes our vision of the central role of semiotic in the field of digital (audiovisual) archives and libraries that we have tried to implement partially in one of our previous R&D projects -the already quoted French ANR funded ASA-SHS project 30 -and that we continue to exploit in the actually ongoing Campus AAR project31 .

As suggested in figure 9, one important application of semiotics in this domain is its use as a methodology for a digital archive project: the definition of the scope of an archive project (= its domain and its context of use); the definition of the universe of discourse of the intended archive (archive-specific topics, rhetorical genres, auctorial visions, …); the specification of the appropriate meta-language (a domain-specific ontology, a domainspecific thesaurus, a domain-specific library of description models and, eventually, archivespecific publishing/republishing templates).

A second application domain for semiotics is its role as a methodological tool during the process of the constitution, the production of a digital archive: definition and production of an appropriate corpus of media data; the definition of basic recording procedures and templates of the media objects composing an archive; the design and editorial follow-up of the web portal used for diffusing the media data of an archive.

A third application of semiotics in the field of digital archives consists in its use as a methodology and guide for the appropriate use of the domain specific meta-language (library of description models) during the processing, description, indexing, commenting, versioning/translating, interlinking, … of media data composing the fonds of an archive. This possible role of semiotics includes, for instance, the writing of guides and "best practices" for analysts, the design of courses for future analysts, etc.

A fourth and important field of application for semiotics in the field of digital archives consists, finally, in its possible role as a methodological reference for the authoring (reauthoring) of digital media data with the help of (archive-specific) publishing/republishing templates. The role of semiotics here is to provide the users (publishers) with publishing guides and -once more again -"best practices" as well as with pedagogical resources for people intending to become publishers.

  Prof. Dr. Peter Stockinger Institut National des Langues et Civilisations Orientales (INALCO) Filière Communication Interculturelle (CFI) Equipe Sémiotique Cognitive et Nouveaux Médias (ESCoM) Programme « Archives Audiovisuelles de la Recherche » (AAR)

(

Figure 6 :

 6 Figure 6: The Studio ASA The Studio ASA is composed of four more specialized "workshops" (i.e. software tools together with user guides and examples of best practices): 1. a video processing workshop, 2. a video description, indexing and annotation workshop, 3. a video publishing workshop 4. and an archive discourse modeling workshop.

Figure 7

 7 Figure 7 summarizes this vision that constitutes the central investigation domain of the actually ongoing ANR funded Campus AAR 13 project (2014 _ 2017) of which a central aim is to provide any actor actively implied in the production and exploitation of digital audiovisual archives with a new and enhanced version of the Studio ASA.

Figure 7 :

 7 Figure 7: The domain of investigation of the Campus AAR project) Indeed, we distinguish six main types of activities composing the realm of text/discourse-based processing of digital (audiovisual) records:

Figure 8 :

 8 Figure 8: The general structural model of a digital resource in a semiotic perspective Figure 8 shows the general structural picture of the (digital) media data understood as a meaning loaded text or network of texts ([STO 99], [STO 01], [STO 12]) which becomes manifest, basically, through three complementary aspects:

Figure 9 : 4 )II) Archive production 1 )

 941 Figure 9: 4 major application domains of semiotics in the field of digital archives As well known, there are different traditions in semiotic research. One important tradition is represented by the structuralism in social and human sciences and structural linguistics going back to de Saussure, R. Jakobson, L. Hjelmslev, C. Lévi-Strauss, A.J.

  The corpus and/or archive management activity is concerned with the creation/production and management either of corpora of digital media resources or of (more or less important) archives or libraries 14 .3. The description of the media content is concerned with the context-sensitive interpretation and indexing of digital media data or corpora of digital media data. The notion "media content" is a complex one and covers, among other features, a topic feature (a digital record "speaks" about what?) ; a discursive function feature (what is the place of the topic in an argumentation, a narration, a didactic exposé, etc.); a rhetorical devise feature (the topic, is it defined, exemplified, literally/metaphorically developed, …?); a media expression feature (what are the -visual, acoustic, …medias used for the expression of the topic and what are the used expression methods ?); a formal and physical organization feature (where do you find a topic in a digital record?); etc. All these features determine the specific meaning structure of a digital media record.

	2.
		Archives (libraries, collections, corpora …)
		of audiovisual digital records
		Text/discourse-based (context-sensitive)
		processing of audiovisual digital records
	Concetual	Management	Description	Processing	Editing
	modelling	of digital	and	(enriching,	and
	of	records and	Indexing	mounting)	Publishing
	knowledge	archives	of digital	of digital	of digital
	domain	(corpora)	records	records	records
	Domain	Digital library/	Content	Digital data	Editing &
	specific	archive	description	versioning	(re-)
	ontologies	management	& indexing	models and	publishing
	and models	models & tools	models and	tools	schemas
	& tools		tools
	Text/discourse-based (context-sensitive) library of meta-linguistic resources
		for the processing of digital records (corpora, archives)
		Semiotic description language and ontology

  Greimas, R. Barthes and M.A.K. Halliday); 2) on linguistic, pragmatic and cognitive text and discourse approaches (among many different examples, we would like to mention here the researches in text linguistics of W. Dressler or T.A. van Dijk, B. Mann's and S. Thompson's persuasive RST (Rhetorical Structure Theory) 26 and 3) on applied researches in text and discourse comprehension and generation (cf., for instance, K. Mc Keown's seminal work on text generation based on rhetorical devises 27 ).

URL of web ARA web portal : http://www.archivesaudiovisuelles.fr/EN/

The full list of the R&D projects in which we have been implied since 1989 can be found here: http://semioweb.msh-paris.fr/escom/ressources_enligne/p_stockinger/2010/CVAnglais_2010.pdf. We would like to stress here more particularly the importance of the ANR founded French project ASA-SHS (Audio-visual semiotic workshop for processing and describing video corpora in social and human sciences) which has offered us the possibility to design and to develop an environment for researchers working in the humanities and aiming at the production of "personal" audio-visual research archives. This environment is composed of a whole meta-language (ontology, thesaurus and description models) adapted to the specificities of research in the humanities as well as of the necessary tools for adapting the generic version of the meat-language to domain specific requirements, for segmenting and tagging video files, for describing and indexing video files and, finally, for republishing video-files online. Information about the ASA-SHS project which has started in 2009 and finished in 2012 can be found here: http://asashs.hypotheses.org/. The recently started ANR founded project -Campus AAR -continues to develop this environment. The main objectives are the following ones: 1) integration, in the existing environment, of the processing of other audiovisual media objects: still images, sound records and printed material; 2) interoperability of the used meta-language with major standards and thesauri;

3) multilingual version of the used metalanguage; 4) user-friendly and user-adapted versioning of the existing environment. More information of the Campus AAR project which has started in January 2014 for three years can be found here: http://campusaar.hypotheses.org/.

http://www.archivesaudiovisuelles.fr/EN/

Thanks to a 7th FP European founded R&D project(LOGOS, 2006(LOGOS, -2009)), we have had the possibility to repurpose a small corpus of interviews with (socio-)linguists and to republish (parts of) them in form of a semantically restricted virtual video-library called « video-lexicon ». This specific video-lexicon is dedicated to the diglossic situations all over the world. The URL of this small semantically defined video-library is: http://www.archivesaudiovisuelles.fr/FR/_Encyclo_Situation_dlc.html.

http://antropologia.academia.cl/iciis-investigadores/jose-bengoa-2

http://www.archivesaudiovisuelles.fr/EN/Event.asp?id=1129&url=/1129/presentation.asp

http://www.amsur.msh-paris.fr/

http://www.canal-u.tv/

http://www.uoh.fr/front

http://www.ccsd.cnrs.fr/

http://asashs.hypotheses.org/

Cf. the web site of the ESCoM-AAR research team belonging to the French Fondation Maison des Sciences de l'Homme (FMSH) : http://www.semionet.fr

http://campusaar.hypotheses.org/

Cf. our explanations in[STO 11a] as well as my online paper Digital audio-visual archives, semiotics and digital humanities : http://www.academia.edu/5877963/Digital_audiovisual_archives_semiotics_and_digital_humanities

http://www.jfsowa.com/

cf. http://www.sfu.ca/rst/

http://www1.cs.columbia.edu/~kathy/

Cf. for instance the programme of the International Journal of Social and Humanistic Computing : http://www.inderscience.com/jhome.php?jcode=ijshc

http://asashs.hypotheses.org/

http://campusaar.hypotheses.org/