Simeon Veloudis
email: sveloudis@seerc.org

Iraklis Paraskakis
email: iparaskakis@seerc.org

Andreas Friesen
email: andreas.friesen@sap.com

Yiannis Verginadis

Ioannis Patiniotakis

Alessandro Rossini
email: alessandro.rossini@sintef.no

Continuous Quality Assurance and Optimisation in Cloud-Based Virtual Enterprises

Keywords: virtual enterprises, cloud computing, cloud service brokerage, reference architecture, governance, quality control, failure prevention and recovery, optimisation

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The increasing adoption of cloud computing is altering the way in which IT resources have traditionally been managed and consumed, bringing about significant advantages for enterprises in terms of cost, flexibility and business agility [START_REF]Cloud: What an Enterprise Must Know[END_REF][START_REF] Vaquero | A break in the clouds: Towards a cloud definition[END_REF]. The cloud computing paradigm crucially involves the use of computing resources that are remotely delivered over the Internet as a service, and which are entrusted by users with data, software, and computation. Cloud computing has evolved out of Grid computing [START_REF] Vaquero | A break in the clouds: Towards a cloud definition[END_REF][START_REF] Foster | Cloud Computing and Grid Computing 360-Degree Compared[END_REF] as a result of a shift in focus from an infrastructure aiming to deliver storage and compute resources, to an economy-based computing paradigm offering a wide range of abstract resources and services [START_REF] Foster | Cloud Computing and Grid Computing 360-Degree Compared[END_REF]. Such a shift is anticipated to impact the manner in which businesses and organisations share skills and core competencies within a distributed collaborative network. More specifically, activities that take place within the confines of a dynamic multiinstitutional virtual enterprise (VE) may involve heterogeneous externally-sourced cloud services that span different clouds and capability levels (IaaS, PaaS, and SaaS [START_REF] Cretu | Cloud-based Virtual Organization Engineering[END_REF]). As an example, consider the following scenario (adapted from [START_REF] Foster | The Anatomy of the Grid: Enabling Scalable Virtual Organisations[END_REF]). An industrial consortium formed to develop a feasibility study for a next-generation supersonic aircraft undertakes a highly accurate multidisciplinary simulation of the entire aircraft. This simulation integrates software components offered as a service by different consortium participants (SaaS offerings). Each component may be operating on a participant's proprietary infrastructure or, alternatively, on infrastructure provisioned as a cloud service (IaaS offering). At the same time, the simulation requires the development of new specialised software components. To this end, the consortium is provisioned the necessary software platform for developing these applications as a cloud service (PaaS offering).

Evidently, the IT environment of such enterprises is progressively transformed into an ecosystem of intertwined infrastructure, platform, and application services, typically delivered by a multitude of diverse service providers. As the number of externallysourced services proliferates, it becomes increasingly difficult to keep track of when and how services evolve over time, either through intentional changes initiated by service providers, or through unintentional changes, such as variations in service performance and availability. Moreover, it becomes increasingly difficult to accurately predict the potential repercussions that such an evolution has with respect to a service's compliance to policies and regulations, and its conformance to service level agreements (SLAs). In addition, the proliferation of cloud services that offer similar functionality under comparable terms of provision, despite the obvious benefits, is adding to the overall complexity as the onus of discovering suitable alternatives inevitably falls on the service consumer.

In order to effectively deal with this complexity, future enterprises are anticipated to increasingly rely on cloud service brokerage (CSB) [6]. Reflecting frequently-cited views by analysts such as Gartner [START_REF] Plummer | Cloud Services Brokerage is Dominated by Three Primary Roles[END_REF], Forrester [START_REF]Cloud Brokers Will Reshape The Cloud -Getting Ready For The Future Cloud Business Models[END_REF], and NIST [6], CSB capabilities may be categorised along the following dimensions: (i) Service Discovery, (ii) Service Integration, (iii) Service Aggregation, (iv) Service Customisation, (v) Service Quality Assurance, and (vi) Service Optimisation.

This work reports on the conceptual architecture of a framework which provides solutions with respect to the latter two dimensions of brokerage capability. In particular, we envisage the development of a brokerage framework 1 which provides mechanisms that are organised around the following general themes: (i) governance and quality control; (ii) failure prevention and recovery, and (iii) optimisation. The 1 st theme is primarily concerned with checking the compliance of services with declaratively pre-specified policies concerning the technical, business, and legal aspects of service delivery. It is also concerned with testing services for conformance with their expected behaviour, and with continuously monitoring their operation for conformance to SLAs. The 2 nd theme is concerned with the reactive and proactive detection of cloud service failures, and the selection of suitable adaptation strategies to prevent, or recover, from failures. The 3 rd theme is concerned with continuously identifying opportunities to optimise service consumption with respect to such goals as cost, quality, and functionality.

The rest of this paper is structured as follows. Section 2 presents a set of requirements for the proposed framework, and Section 3 proposes a reference architecture that meets these requirements; Section 4 discusses the various mechanisms which materialise this reference architecture. Finally, Section 5 presents related work, and Section 6 presents conclusions and future work.

Framework Requirements

We argue that the incorporation of mechanisms organised around the general themes outlined in Section 1 will assist cloud service brokers in providing assurances with respect to the reliability and optimality of services. To motivate the need for such mechanisms, this section revisits the example of Section 1 and identifies a number of relevant functional capabilities that these mechanisms materialise. Functional capabilities are associated with tasks or activities that transform inputs into outputs. They are highlevel, possibly complex, services that are provided by a software system in order to fulfil a user need. In our work, functional capabilities express requirements upon the Broker@Cloud framework which pertain to the general themes of Section 1. Suppose that a cloud platform (call it CloudX) hosts various services that may be potentially used by the industrial consortium (hereafter referred to as the 'VE'). Let a service provider offer a new application service (call it fuelConsum_A) which supports collaborative creation of simulations for the aircraft's fuel consumption.

Capabilities of the Governance and Quality Control Mechanisms

Before fuelConsum_A can be offered to the VE for consumption, a number of onboarding criteria may need to be evaluated to ensure compliance with the VE's business policies (e.g. pricing, availability, response time, deployment infrastructure etc.). This implies a policy evaluation capability (hereafter referred to as C1) which must be supported by the platform's governance and quality control mechanisms. Moreover, other checks may verify -for example, through the provision of an automated functional testing capability (referred to as C2) -that the programmatic interfaces of fuelCon-sum_A adhere to the specifications of the CloudX platform. In addition, if fuelConsum_A is successfully onboarded and starts being consumed by the VE, its performance must be continuously evaluated with respect to the corresponding SLA. This clearly implies a monitoring capability (C3) that must be supported by the platform's governance and quality control mechanisms. The capabilities C1, C2, and C3 are summarised in Table 1.

Capabilities of the Optimisation Mechanism

Suppose now that, after onboarding fuelConsum_A, CloudX's optimisation mechanism identifies an optimisation opportunity with respect to a similar, albeit more expensive, service that the VE is currently consuming (call the latter fuelConsum_B). This clearly implies an optimisation analysis capability (C4 -see Table 1) which must be supported by the optimisation mechanism. An optimisation opportunity can only be identified relative to a set of consumer-expressed preferences regarding service consumption (e.g. service cost, reputation, etc.); it follows that the optimisation mechanism must also possess the capability to perform consumer preference analysis (hereafter referred to as C5). Upon identification of the optimisation opportunity, CloudX may recommend appropriate optimisation actions, e.g. the renegotiation of the terms of provision of fuel-Consum_B, or its replacement by fuelConsum_A. This clearly implies that the optimisation mechanism must also possess an optimisation recommendation capability (C6).

Capabilities of the Failure Prevention and Recovery Mechanism

Suppose now that during the consumption of fuelConsum_B by the VE, an SLA violation is detected. CloudX's failure recovery mechanism yields appropriate actions aiming at alleviating the effects of the violation, such as the substitution of fuelConsum_B with fuelConsum_A. This clearly implies a failure analysis capability (C7 -see Table 1). In addition, CloudX's failure prevention mechanism proactively generates suitable adaptation plans if certain key service attributes (such as, for instance, availability and response time) are deteriorating; these plans aim at averting an imminent failure. For this to be possible, the failure prevention mechanism must possess the capability of incorporating appropriate failure prevention rules (C8).

Platform-neutral Descriptions of Cloud Services

The aforementioned mechanisms must clearly be able to interact with each other, as well as with the underlying cloud service delivery platform. To this end, an additional general theme is discerned, namely platform-neutral description of cloud services, which is concerned with the development of platform-agnostic methods for the declarative description of the information required by these mechanisms. For reasons of space, the declarative capabilities3 associated with this theme are not considered here; the interested reader is referred to [START_REF]Broker@Cloud project Deliverable 20[END_REF] for a specification of such capabilities.

Framework Reference Architecture

Our aim is to develop a framework which will be adoptable by cloud service intermediaries in order to equip their platforms with the functional capabilities of Table 1. This section presents a reference architecture for such a framework. The architecture is kept generic and minimal in order to increase its adoptability. The reference processes that comprise this architecture may be refined (adapted, extended, modified, etc.) by the adopting cloud service intermediaries in order to align them with the concrete architectural requirements of their platforms, providing that: (i) the capabilities that these processes entail are treated as black boxes; (ii) any interdependencies between these capabilities are dealt with by the adopting platform. These reference processes are categorised below relative to the main phases of a service's lifecycle, namely Service Onboarding, Service Operation, and Service Evolution. This allows a clear association between the capabilities of Table 1 and the particular phases of a service's lifecycle in which these capabilities are offered.

Service Onboarding Reference Processes

A service enters the Service Onboarding phase either when it is initially submitted, or when it is upgraded to a fresh version. The primary focus within this phase is the Service Assessment process. This process certifies that a new service, or an existing upgraded one, complies with the relevant onboarding business policies. It comprises two processes, namely Policy Compliance Evaluation and Testing (see Fig. 1 4 ).

The former process checks that the service description is compliant with the relevant CSB business policies; it is thus associated with capability C1 of Table 1. If a service fails to comply, it is deemed unsuitable for deployment and the boundary escalation event Compliance Failed occurs. This event captures an inner escalation event in the Policy Compliance Evaluation process (see Fig. 5); it triggers, in turn, the Certification Failed event which signifies the termination of the onboarding process. The latter process functionally tests the service in order to ensure that its description constitutes an accurate reflection of its behaviour; it is associated with capability C2. The event Testing Failed is analogous to the Compliance Failed event. checks whether a set of metrics that characterise the service's behaviour adheres to the SLA; which exact metrics are monitored is determined by the relevant policy in the CSB Policies store (see Fig. 2); monitored values are stored in the Monitoring Data store. The signal event Service Deprovisioned signifies that the service is no longer consumable and suspends monitoring. The Monitoring process is associated with capability C3 of Table 1. The latter process is associated with capability C7. It analyses the monitoring data in order to identify potential behavioural patterns indicative of a service failure. In such a case, the boundary event AE (stands for "Analysis Event") occurs which captures a corresponding inner escalation event. This triggers the Service Alert event which invokes the Failure Prevention and Recovery Mechanism (see Section 3.3).

Service Evolution Processes

The Service Evolution phase spans throughout the entire service lifecycle. Two main processes are discerned here: Optimisation Management and Failure Prevention and Recovery Management.

Optimisation Management. This process (see Fig. 3) offers optimisation recommendations to service consumers; Table 2 outlines its constituent processes.

Analyse

Optim. Trigger

Strives to identify optimisation opportunities and thus it is directly associated with capability C4 of Table 1. It is initiated periodically (i.e. by the scheduler event of Fig. 3) or by a set of triggering events which are assumed to occur when a service is onboarded, deprecated, or when its contract changes; it may also be initiated when a consumer's preferences (regarding the consumption of a service) change.

Manage Criteria

Allows service consumers to express their preferences regarding service consumption with respect to a predetermined set of relevant service attributes or criteria. Preferences are expressed either as precise crisp values (for quantitative criteria, e.g. service response time), or as imprecise fuzzy values and linguistic expressions (for inherently qualitative criteria, e.g. service reputation). This process is directly associated with capability C5.

Manage Services

For each consumed service, collects consumer opinions relative to the imprecise criteria that characterise the service 5 . It then assigns an appropriate characterisation to each such criterion based on the majority of opinions. This ensures that per-service imprecise characterisations are consistent with current consumer opinions. This process is associated with capability C4.

Service Ranking

Discerns services that could be recommended to a consumer for replacing the currently consumed service. To this end, services within the same functional category6 as the one being consumed are ranked according to the current precise and imprecise criteria characterisations, and the consumer preferences. This process is associated with capabilities C4 and C6.

Failure Prevention and Recovery Management. This process recommends appropriate actions for recovering from a service failure, or for averting an impending one; it comprises the processes outlined in Table 3 and depicted in Fig. 4.

Manage Services

Classifies services under appropriate functional categories (e.g. fuelCon-sum_A and fuelConsum_B are classified under the category fuelConsumSimulations). These categories form the basis of recommending appropriate alternative services in case a service fails or is about to fail. This process is initiated by the same triggering events as the ones that initiate the Analyse Optim. Trigger process of Table 2; it is associated with capability C7. Manage Adapt.

Discerns alternative services that could be recommended to a consumer if, for a particular service, the Service Alert event occurs (see Section 3.2). It thus ranks services according to their current precise and imprecise criteria characterisations, and the expressed consumer preferences. This process is too associated with capability C7.

Framework Mechanisms

This section elaborates on the mechanisms implementing the reference processes of Section 3. Table 4 outlines a number of requirements on these mechanisms. For reasons of space, we confine ourselves to the Policy Compliance Evaluation process (see Fig. 5); mechanisms for the rest of the reference processes can be found in [START_REF]Broker@Cloud project Deliverable 30[END_REF].

Availability

For each identified capability, there must be at least one mechanism specification and an associated mechanism implementation.

Dependency Handling

A mechanism assumed to operate as part of a collection of mechanisms must ensure interoperability with the other mechanisms in the collection.

Flexibility

A mechanism may be replaceable not only by another mechanism but also by an ecosystem of mechanisms which implement the same capabilities as the mechanism under replacement. Replaceability Methods must be provided for developing new mechanisms, or new implementations of existing mechanisms. The mechanism implementing the Policy Compliance Evaluation process determines whether a service description (SD) complies with the relevant CSB policy. To this end, it entails a number of processes which determine the compliance of key service-level attributes. These processes are intended to implement a general framework of policy-compliance checks, one which is potentially applicable to a wide range of services. Fig. 6 depicts an internal view of such a policy-checking process; Table 5 provides brief descriptions of the activities entailed by such a process. Get Relevant Policy Determines the applicable policy to the SD.

Get Relevant Data

Extracts from the SD those artefacts that are to be checked.

Check Compliance

Checks compliance of the SD artefacts with the policy; it outputs the outcome in the Report data object.

Related Work

To the best of our knowledge, there are no works addressing the quality assurance and optimisation dimensions of CSB in the context of VEs. [START_REF] Cretu | Cloud-based Virtual Organization Engineering[END_REF] recognises the need for frameworks that guide the creation, execution, and management of services in cloudbased VEs; it does not, however, address the quality assurance and optimisation aspects of such frameworks. The rest of this section outlines work related to the three general themes of our conceptual architecture, namely cloud service governance and quality control, failure prevention and recovery, and optimisation. Cloud service governance refers to policy-based management of cloud services with emphasis on quality assurance [START_REF] Kourtesis | Policy-driven governance in cloud application platforms: an ontology-based approach[END_REF]. Current practice [START_REF] Marks | Service-Oriented Architecture Governance for the Services Driven Enterprise[END_REF][START_REF] Zhang | CCOA: cloud computing open architecture[END_REF] focuses on the use of registry and repository systems combined with purpose-built software to check the conformance of services with relevant policies [START_REF] Kourtesis | Policy-driven governance in cloud application platforms: an ontology-based approach[END_REF][START_REF] Marks | Service-Oriented Architecture Governance for the Services Driven Enterprise[END_REF][START_REF] Kourtesis | A registry and repository system supporting cloud application platform governance[END_REF]. A major weakness in these works is failure to achieve appropriate separation of concerns between defining governance policies and evaluating data against these policies. This has a number of negative repercussions such as lack of portability and lack of explicit representation of policy interrelations. Turning to service certification, functional testing methods are largely interface-based [START_REF] Bai | WSDL-based automatic test case generation for web services testing[END_REF][START_REF] Chakrabarti | Test-the-REST: an approach to testing RESTful web services[END_REF]. Some attempts to specify complete behaviour have been suggested using (i) graph transformation rules [START_REF] Heckel | Automatic conformance testing of web services[END_REF], (ii) WSDL augmented with UML state machines [START_REF] Bertolino | Audition of web services for testing conformance to open specified protocols[END_REF], and (iii) SAWSDL augmented with pre-and post-conditions [START_REF] Tsai | Extending WSDL to facilitate web services testing[END_REF]. An extensive survey of the state-of-the-art in cloud service governance and quality control can be found in [START_REF]Broker@Cloud project Deliverable 20[END_REF].

The field of adaptive service-based systems (SBSs) [START_REF]Service research challenges and solutions for the future internet: S-cube -towards engineering, managing and adapting servicebased systems[END_REF] investigates techniques for monitoring and adapting SBSs. SBSs share similar characteristics with cloud service ecosystems, hence techniques from the former domain can be readily adopted in our work. An extensive survey of the state-of-the-art in adaptive SBSs is provided in [START_REF]Broker@Cloud project Deliverable 20[END_REF]. This survey identifies challenges relevant to cloud service failure prevention and recovery that are not addressed in the literature, including metrics for identifying failures, and scalable prediction techniques for identifying impending failures.

Cloud service optimisation has been primarily investigated from a cloud provider's perspective [START_REF] Moon | SLA-aware profit optimization in cloud services via resource scheduling[END_REF][START_REF] Li | CloudOpt: multi-goal optimization of application deployments across a cloud[END_REF], considering consumer satisfaction as a constraint rather than as an optimisation goal. From a cloud consumer perspective, Han et al. [START_REF] Han | Efficient service recommendation system for cloud computing market[END_REF] propose a service recommender framework for assisting optimisation decisions based solely on IaaS considerations; they fail to consider the dynamically changing conditions that typically characterise cloud service ecosystems. In [START_REF] Lawrence | Using service level agreements for optimising cloud infrastructure services. Towards a Service-Based Internet[END_REF], a service optimizer is proposed to manage dynamic SLAs at the IaaS layer. These works focus exclusively on quantitative metrics which may fail to accurately reflect the relative ranking among services for optimisation purposes [START_REF] Doyle | Background to qualitative decision theory[END_REF].

and Future Work

Unencumbered by the physical constraints of data centres and hardware platforms, cloud computing is anticipated to give rise to VEs that tie together services from vast networks of providers, enabling the creation of a wide range of new, more competitive, and more agile products and solutions [START_REF]Cloud computing issues and impacts[END_REF]. For this shift to be sustainable however, enterprises are expected to increasingly rely on cloud service brokerage (CSB). This work has presented a conceptual architecture for a framework which provides solutions with respect to the quality assurance and optimisation dimensions of CSB in the context of VEs. Such a framework revolves around the general themes outlined in Section 1, whilst it offers a range of reference processes which are refineable to the level of functional capabilities. The framework is open to the specification of new capabilities, and to the specification of new reference processes. Moreover, it allows adopters to only select individual capabilities providing that these are independently consumable.

In the future, we shall further elaborate and concretise the mechanisms of the proposed CSB framework. We are currently in the process of: (i) developing RDFS-based declarative models in Linked USDL [29] that will enable the interoperable exchange of service and consumer-preference descriptions, and the evaluation of these descriptions against pertinent CSB policies; (ii) developing APIs and prototypes for the CSB mechanisms of Section 3.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Service Assessment reference process3.2 Service Operation Processes

Fig. 3 .

 3 Fig. 3. Optimisation Management reference process

Fig. 4 .

 4 Fig. 4. Failure Prevention and Recovery Management reference process

Fig. 5 .

 5 Fig. 5. Internal view of the Policy Compliance Evaluation process

Fig. 6 . 5 .

 65 Fig. 6. Internal view of a policy-checking processTable 5. Policy-checking process Name Description

Table 1 .

 1 Functional Capabilities2

	Id Name	Description
	C1 Policy Evaluation	Assesses a service's conformance with Broker policies.
	C2 Functional testing	Assesses a service's conformance with its specification.
	C3 Monitoring	Collects and aggregates service performance data.
	C4 Optimisation analysis Analyses optimisation opportunities.
	C5 Preference analysis	Handles and exploits consumer preferences expressed as precise
		or imprecise criteria.
	C6 Optimisation recom-	Reasons about alternative optimisation actions and recommends
	mendation	the best alternative.
	C7 Failure analysis	Generates appropriate failure recovery or prevention actions.
	C8 Failure prevention	Expresses rules for proactively identifying service failures.

Table 2 .

 2 Optimisation Management processes

	Process	Description

Table 3 .

 3 Failure Prevention and Recovery Management processes

	Process	Description

Table 4 .

 4 Architectural requirements on Broker@Cloud mechanisms

This framework is being developed as part of the EU-funded project 'Broker@Cloud'.

This is not the full set of functional capabilities identified for the Broker@Cloud framework, but a suitable subset discerned for the purposes of this paper; the full set can be found in[START_REF]Broker@Cloud project Deliverable 30[END_REF].

Declarative capabilities are specifications of data models or structures.

We use BPMN diagrams to illustrate reference processes and their corresponding mechanisms (see Section 4). BPMN has been chosen because it is intuitive, whilst its modelling power is, according to[START_REF] Peixoto | A Comparison of BPMN and UML 2.0 Activity Diagrams[END_REF], analogous to that of other comparable modelling languages such as UML 2.0 Activity Diagrams. For reasons of space, and to enhance clarity, the BPMN diagrams have been simplified; their full versions can be accessed in[START_REF]Broker@Cloud project Deliverable 30[END_REF]; a BPMN tutorial can be found in[START_REF]Business Process Model and Notation (BPMN[END_REF].

Precise criteria can be measured objectively and are not amenable to subjective opinions.

According to their function, services may be categorised into functional categories -e.g. 'map services', 'calendar services', 'fuel consumption simulation services', etc. A functional category thus characterises the kind of a service.

Acknowledgements. This research is funded by the EU 7 th Framework Programme under the Broker@Cloud project (www.broker-cloud.eu), grant agreement n°328392.


Victor Zakharov

Alexander Krylatov
email: aykrylatov@yandex.ru

Equilibrium Assignments in Competitive and Cooperative Traffic Flow Routing

Keywords: competitive routing, cooperative routing, traffic flow assignment, Wardrop equilibrium, Nash equilibrium, Pareto optimality

The goal of the paper is to demonstrate possibilities of collaborative transportation network to minimize total travel time of the network users. Cooperative and competitive traffic flow assignment systems in case of m ≥ 2 navigation providers (Navigators) are compared. Each Navigator provides travel guidance for its customers (users) on the non-general topology network of parallel links. In both cases the main goals of Navigators are to minimize travel time of their users but the behavioral strategies are different. In competitive case the behavioral strategy of each Navigator is to minimize travel time of traffic flow of its navigation service users while in cooperative case -to minimize travel time of overall traffic flow. Competitive routing is formalized mathematically as a non-zero sum game and cooperative routing is formulated as an optimization problem. It is demonstrated that Nash equilibrium in the navigation game appears to be not Pareto optimal. Eventually it is shown that cooperative routing systems in smart transportation networked environments could give users less value of travel time than competitive one.

Introduction

One of the important trends during last decades is essential increasing the amount of navigation providers on the roads. The influence of competitive navigation services on a traffic flow assignment on transportation networks is not as good as expected. The problem is that competitive routing proposed by navigation systems to minimize travel time of their customers in case of huge amount of cars driving through the network could leads to decreasing the total travel time of network users. The basic reason for that is the conflict of interests between different navigation providers. From game theory point of view it means that Nash equilibrium traffic assignment leads to less value of total travel time compare to Pareto optimal solution.

Collaboration of navigation provider can be mathematically formalized as centralized traffic assignment which guaranties user equilibrium or system optimum on transportation network. The main principles for collaborative traffic flow assignments were formulated in 1952 by Wardrop who stated two principle of equilibrium traffic flow assignment in transportation networks [START_REF] Wardrop | Some Theoretical Aspects of Road Traffic Research[END_REF]. In 1956 Beckmann proposed mathematical formalization of Wardrop's principles that is today considered as classical [START_REF] Beckmann | Studies in the Economics of Transportation[END_REF][START_REF] Sheffi | Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods[END_REF]. One of the most disadvantages of this model is computational complexity for using in large networks. For details one can read articles [START_REF] Frank | An Algorithm for Quadratic Programming[END_REF][START_REF] Shvetsov | Algorithms for Distributing Traffic Flows[END_REF]. In the present paper we offer a method for finding in Wardrop's model the traffic flow assignment strategies explicitly in case of linear BPR-delay function.

Considering competitive routing we use Nash equilibrium optimality principle. We guess that comparison of cooperative and competitive routing efficiency is highly important to make a dicision either collaborative or competitive principle should be taken as a basis for smart transportation networked environments. Thus relation between Wardrop equilibrium and Nash equilibrium has to be explored. For the first time this issue was raised in [START_REF] Haurie | On the Relationship between Nash-Cournot and Wardrop Equilibria[END_REF] where origin-destination areas had been considered as players. Unfortunately such problem formulation as available in [START_REF] Haurie | On the Relationship between Nash-Cournot and Wardrop Equilibria[END_REF] is not connected with competitive routing. Another works on this topic mostly have not any analytical representation, for example [START_REF] Altman | Equilibrium, Games, and Pricing in Transportation and Telecommunication Networks[END_REF]. In this work we compare Wardrop equilibrium and Nash equilibrium expressed explicitly and show that cooperative routing systems in smart transportation networked environment could give users less value of travel time than competitive one.

Thereby the present work is devoted to the following three interrelated problems: 1) receiving the explicit form of traffic flow assignment strategies; 2) finding relationship between Wardrop and Nash equilibria; 3) comparison of collaborative and competitive smart transportation networked environments.

Mathematical Models of Competitive and Cooperative Traffic Flow Routing

In this section we formulate competitive and cooperative traffic flow routing problems mathematically and find corresponding equilibrium strategies of assignments.

First of all, it should be noted that analyzing an arbitrary transportation network we rely on the idea according to which: any transportation network could be decompose to the set of subnets consisting of one origin-destination pair and certain amount of parallel routes [START_REF] Korilis | Architecting Noncooperative Networks[END_REF][START_REF] Korilis | Avoiding the Braess Paradox in Non-Cooperative Networks[END_REF][START_REF] Daganzo | The Cell Transmission Model: A Dynamic Representation of Highway Traffic Consistent with the Hydrodynamic Theory[END_REF]. On the one hand, such an idea is reasonable due to the fact that narrowing of the road (using the same link in different routes) leads to congestion [START_REF] Daganzo | The Cell Transmission Model: A Dynamic Representation of Highway Traffic Consistent with the Hydrodynamic Theory[END_REF]. On the other hand, parallel structure of transportation network contributes to avoiding the Braess's paradox [START_REF] Korilis | Architecting Noncooperative Networks[END_REF][START_REF] Korilis | Avoiding the Braess Paradox in Non-Cooperative Networks[END_REF].

Hereby we consider transportation subnet presented by digraph consisted of one origin-destination pair and n parallel links. On this subset m Navigators act. Let us introduce the following notation: N = {1,…,n} -set of numbers of routes; M = {1,…,m} -set of numbers of Navigators; i -number of the route, i∈N; j,q -numbers of Navigators, j,q∈M; 0 > ( )

m i i i f f f , , 1 K = -

vector of traffic flow values of all

Navigators assigned through route i, wherein ( )

m i j i j i i j i f f f f f , , , , , 1 1 1 K K + - -= ; ∑ = = m j j i i f F 1 -traffic flow value through route i; 0 0 > i t -free travel time through route i; 0 > i c -capacity of i-th route; ( ) i i F d -delay of i F traffic flow value on route i. Vector of strategies of Navigator j is ( ) T j n j j f f f , , 1 K = and
( )

m f f f , , 1 K =
. Now we are ready to formulate competitive and cooperative traffic flow routing problems mathematically. According to [START_REF] Sheffi | Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods[END_REF] cooperative case is expressed by the following optimization problem:

( ) ∑ = = n i i i f cpr f F d z 1 min min i F , (1)
with constraints

∑ = = n i i F F 1 , (2) 0
≥ j i f ∀ i∈ N, j∈ M, (3)
while competitive case can be expressed by the following non-zero sum game between Navigators:

( )

∑ = = n i i i f cmp j f F d z j j 1 min min j i f ∀ j∈ M, (4)
with constraints

∑ = = n i j j i F f 1 ∀ j∈ M, ( 5
) 0 ≥ j i f ∀ i∈ N, j∈ M, (6)
Hereby we can see that in cooperative case Navigators try to achieve system optimum of Wardrop for whole traffic flow [START_REF] Sheffi | Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods[END_REF], whereas in competitive case -each Navigator tries to achieve system optimum only for its own customers. However in competitive case any Navigator j is affected by other Navigators so that mutual influence of Navigators' strategies addresses us to find Nash equilibrium. Further we will use explicit BPR-delay function:

( )         + = i i i i i c F t F d 1
0 [START_REF]Traffic Assignment Manual[END_REF]. Without loss of generality to find Nash equilibrium we assume

0 0 2 0 1 n t t t < < < K . ( 7
)
Theorem 1. Subject to [START_REF] Altman | Equilibrium, Games, and Pricing in Transportation and Telecommunication Networks[END_REF] Nash equilibrium in the game of Navigators is achieved by the following strategies , 1

1 1 * ∑ = + - = m q q i j i j i b m b f (8)
where

, 1 0 1 1 0 i n r r r n r r m s s i i j i c t c c F F t с b - + + = ∑ ∑ ∑ = = = (9) ∀ i∈ N, when , 1 1 1 1 0 0 ∑ =         - + > n i i n i j t t c m F (10)
∀ j∈ M.

Theorem 1 provides explicit optimal strategies for Navigators in the case of competitive traffic flow routing. To find optimal strategies in case of cooperative routing it is sufficiently to equate m to 1 in formulas ( 8)- [START_REF] Daganzo | The Cell Transmission Model: A Dynamic Representation of Highway Traffic Consistent with the Hydrodynamic Theory[END_REF]. Indeed [START_REF] Wardrop | Some Theoretical Aspects of Road Traffic Research[END_REF] shows that in cooperative case collaboration of Navigators is reduced to finding system optimal assignments on all routes for whole traffic flow (unlike optimal assignments on all routes for each Navigator in competitive routing). In other words in cooperative case Navigators try to find * i F and it is clear that in such a case * f is not unique and limited only by condition

∑ = = m j j i i f F 1 * ∀ i∈ N.
Theorem 2. Total travel time of whole traffic flow in case of cooperative routing is strictly less than in competitive case (Nash equilibrium is not Pareto optimal). Theorem 2 states that Wardrop equilibrium leads to less travel time than Nash equilibrium. Moreover it indicates that applying cooperative or centralized traffic flow navigation systems in smart networked environments is more preferable than competitive systems in terms of travel time value.

Numerical Experiments

In previous section it was shown that employing of cooperative or centralized traffic navigation system in smart transportation networked environments leads to less travel time value than competitive systems. To illustrate this result we have investigated transportation network of one of the central districts in Saint-Petersburg -Vasileostrovsky district.

Consider Fig. 1. Denote two areas: origin area (red bold circle) and destination area (green bold circle). Moreover there are 4 potential routes from origin to destination: red line, orange line, blue line and green line. Investigated district has the following parameters: 0 1 t = 7,5 and 1 c = 300 (orange line), 0 2 t = 9 and 2 c = 400 (blue line), 0 3 t = 12 and 3 c = 500 (green line), 0 4 t = 13,5 and 4 c = 600 (red line). Then we assume that the flow F = 1 000 has to be assigned and we compare final travel time of whole traffic flow when there is 1 (cooperative or centralized system), 2, 4 and 10 competing Navigators acting on the network. It is to be noted that in case of m > 1 Navigators we devide whole flow F between Navigators equally. Firstly we propose that there are only 3 routes (orange, blue and green lines). Results are shown in Table 1. Thereby, when there is a little network with only three possible routes the more competing Navigators give assignment with the worse travel time of whole traffic flow F but it is not so crucial. Let us increase amount of routes adding just one new route. In such a case results are shown in table 2. We can see that the bigger transportation network the worse results given by more competing Navigators. It has to be mentioned that classical algorithms such as Frank-Wolfe algorithm would demand much more operations for getting the same results. Indeed it needs information about all links in the network and, consequently, dimension of the problem grows extremely when the network becomes larger. Interested one can see [START_REF] Shvetsov | Algorithms for Distributing Traffic Flows[END_REF].

Proposed simple example of only one district of Saint-Petersburg shows that application of collaborative systems in smart transportation networked environments in large cities is more reasonable than cooperative systems.

Conclusion

In this work we conducted a comparative analysis of two smart transportation networked environments: one based on cooperative routing and another based on competitive routing. Mathematical expression of cooperative and competitive cases led to the conclusion that cooperative navigation systems could provide aggregate traffic flow of all transportation network users with less total travel time than competitive one. It is clear that government of any large city is interested in improving the transportation situation uniformly on the entire network. Thereby in terms of governance collaborative traffic flow navigation systems based on explicit assignment strategies in smart transportation networked environments are highly accurate and operative equipment for decision making in transportation area.

In our further works we are going to generalize obtained results for the large transportation networks. For this purpose we are investigating whole transportation network of Saint-Petersburg city using developed technique.

Appendix

Proof of Theorem 1. Using Theorem 1 from [START_REF] Zakharov | Equilibrium Traffic Flow Assignment in Case of Two Navigation Providers[END_REF] we can get the following expression 11) by i and expressing j F we eventually obtain the condition (10) subject to [START_REF] Altman | Equilibrium, Games, and Pricing in Transportation and Telecommunication Networks[END_REF].

Fig. 1 .

 1 Fig. 1. Transportation network of Vasileostrovsky district of Saint-Petersburg.

  Summing such expression by i and j we get

Proof of Theorem 2 ..

 2 Union of domains of functions Moreover equality is possible if and only if m = 1. Consequently,

Table 1 .

 1 Dependence of goal-function value on amount of competing Navigators (3 routes).

	Amount of Navigators, m	m ∑	z	cmp j	( ) f *
		j	= 1

Table 2 .

 2 Dependence of goal-function value on amount of competing Navigators (4 routes).

	Amount of Navigators, m	m ∑	z	cmp j	( ) f *
		j	= 1
	1 (cooperation)		16178,624
	2		16205,103
	4		16264,417
	10		16338,157


António Lucas Soares
email: asoares@inescporto.pt

Maria Almeida
email: mi.almeida88@gmail.com

Filipa Ramalho
email: filiparamalho@gmail.com

A Retrospective Socio-Semantic Analysis of the PRO-VE Conferences

Keywords: socio-semantic network, social-networks analysis, PRO-VE conference

The IFIP WG 5.5 Working Conferences on Virtual Enterprises (PRO-VE) has created, in 14 editions, a remarkable scientific and professional community intersecting several disciplines and resulting in a new one: collaborative networks. In this paper we present the results of a retrospective study of the conceptual system evolution of PRO-VE providing an instrument to reflect about the field's past and future. Our approach was socio-semantic trying to devise the influence between the researchers social network and the evolution of the conceptual system. Firstly, we made a terminological analysis of every PRO-VE proceeding resulting in a picture of the main concepts used in each edition and their relative importance. Then, we used social network analysis techniques to conclude about the influence of the researchers on the conceptual system evolution. The results suggest a relatively stable set of concepts influenced by a network of core researchers. However, some marked evolution in the relative importance of the concepts can be identified.

Introduction

Scientific conferences series are somehow archetypes of researchers communities creation, evolution and death. If we envisage conference research communities as strong knowledge communities [START_REF] Roth | Social and Semantic Coevolution in Knowledge Networks[END_REF], we assist at knowledge creation and dissemination in varying degrees according to its (time-based) relevance. If we look at them as collaborative networks [START_REF] Camarinha-Matos | Collaborative Networks: a New Scientific Discipline[END_REF] we assist at social capital creation and dismissal and trust building and destruction influencing directly and indirectly single or collective research activities. It seems then clear that the dyad knowledge-collaboration is not separable in the studies of conference research communities even if the majority of the published research on the subject focus on one dimension or the other. The PRO-VE conference series reaches this year its 15th edition. This round number was the spark that motivated us to study the PRO-VE community evolution in two intertwined dimensions: the conceptual system and the social system. This is not "just another bibliometric or scientometric study" of conference proceedings. Although the later seem to be much less frequent than the studies based on scientific journals, our goal was to characterise qualitatively and in detail the evolution of the conceptual system of PRO-VE. Yet, the conceptual system evolution cannot be studied detached from the community of researchers, as the relationships among them and the patterns of use of the concepts are fundamental for the understanding of such evolution. Our stance is thus socio-semantic, and the object of study is the socio-semantic network. The approach followed by us is original in several ways. The study of knowledge networks as socio-semantic networks is not new although not widespread [START_REF] Roth | Social and Semantic Coevolution in Knowledge Networks[END_REF]. The study of the conceptual system within these studies is normally restricted to keyword identification or extraction. Our approach relied in a comprehensive terminological and conceptual semi-automated analysis that enabled the re-construction of the de facto conceptual system of each conference edition. The study of the social network usually rely on the identification of coauthorship and citation analysis. Our approach relies on 2-mode (researchers-concepts) social networks analysis (SNA), enabling the study of the socio-semantic network. The rest of the paper is organised as follows: in the next section we briefly review some important related work, then we describe in detail the research design followed by the presentation of the results. Finally, the results are discussed, the limitations of the study pointed out and the paper is concluded. It is also important refer the authors motivation for this study. One of the co-authors is an active member of the PRO-VE community having participated in all but three conference editions. The other co-authors are young information science professionals, interested in understanding better the study of scientific communities.

Related Work

There has been recently a growing body of work on the analysis of communities and their temporal evolution in dynamic networks [START_REF] Greene | Tracking the Evolution of Communities in Dynamic Social Networks[END_REF]. In the literature we can find some research studying scientific conferences, namely bibliometric and scientometric studies. [START_REF] Hofer | Conference Proceedings as a Matter of Bibliometric Studies: the Academy of International Business 2006-2008[END_REF] analysed semantically the proceedings of a conference in the area of international business. The study was done with a corpus that joined three years of proceedings. The main objective was to represent the current academic interest in the area, grouping keywords and analysing the most studied areas in the articles. Another bibliometric study is decribed in [START_REF] Cash | A Bibliometric Analysis of the Design 2012 Conference[END_REF] analysing one year of a conference (DESIGN 2012). This study used the citations in the papers to describe citation trends by field, type of work and its distribution.

Within the PRO-VE conferences two papers addressed the social network and the conceptual evolution of the PRO-VE community. In [START_REF] Volpentesta | Eigenvector centrality based on shared research topics in a scientific community[END_REF] the authors exploited the concept of eigenvector centrality starting from the papers proceedings (2005)(2006)(2007)(2008)(2009) and propose a weighted multi-hypergraph model to study the (eigenvector) centrally of PRO-VE authors and research topics. The model is roughly equivalent to our 2-mode network being the concepts taken from reference models and some text processing made to identify concepts through the papers "keywords". As we will see bellow, our research go beyond this model by extracting terms referring to domain concepts in the form of multi-word phrases that constitute a substantial majority of all technical vocabulary. We also propose to reuse a unified socio-semantic model borrowed from [START_REF] Roth | Social and Semantic Coevolution in Knowledge Networks[END_REF]. The very same approach to the domain concepts identification from the proceedings corpus, distinguishes our research from [START_REF] Picard | Extracting the Dynamic Popularity of Concepts from a Corpus of Short-Sentence Documents[END_REF] whose main limitation is to consider only single-word terms.

The empirical and computational study reported in [START_REF] Roth | Social and Semantic Coevolution in Knowledge Networks[END_REF] suggests that the dynamics of a community, i.e. communities of scientists, software developers, wiki contributors and others, can be amply described as the coevolution of a social and a socio-semantic network. The authors presents a theoretical framework based on a social network and a socio-semantic network. In this study we can see a description of the dynamics of a community comprising the social structure and socio-semantic structure. It is exactly in the work of Roth that we found our theoretical reference. A social network is denoted by G=(S,R S ) where S is the agent set and R S =R⊂ S×S×N denotes the set of dated links: a link l=(s,s′,t)∈R S means that s is related to s′ at t. Considering now C a set of semantic objects which we call "concepts" and which correspond here to terms or noun phrases considered as atomic units, a socio-semantic network is thus defined formally as a network G C , made of actors of S, concepts of C and links between these elements: R C thus denotes the use of concepts by actors: an actor is linked to concepts he/she mentioned (e.g., in a paper from a proceeding). Thus, R C

⊂ S×C×N, and a link l C =(s,c,t)∈R C means that s used c at t. Although Roth studies computationally the coevolution of social and socio-semantic networks, in this paper we use only the sociosemantic network to study the evolution of the PRO-VE conceptual system.

Research Design

Recalling the research relevance and goals introduced in the beginning of this paper, our aim was to know more on how the PRO-VE community evolved regarding the technical and scientific topics (concepts) addressed in 14 editions of existence of the conference. Furthermore, we wanted to understand if there were traces of influence from the structures of collaboration of the researchers in the number and intensity of the addressed concepts. The specific research questions were: RQ1. What were the main technical and scientific concepts addressed de facto in each of the PRO-VE conference editions? Are there any patterns of evolution? RQ2. Is it possible to identify sub-sets of researchers having in common the use of similar sets of concepts (socio-semantic structures)? How did these structures evolved?

This research was fundamentally exploratory, and being so there were not strong hypothesis to advance. Nevertheless, a couple of them were advanced as preliminary answers to the research questions: H1.1. There is a core set of concepts used in all the editions of the conference; H1.2. The variability of the first ranked concepts after the core is high; H2.1. A core set of concepts is shared by the majority of the researchers; H2.2. There are groups of researchers that are likely to influence the adoption of new concepts.

The research strategy to answer the above questions recurred to two perspectives on the PRO-VE conferences: a conceptual (semantic) perspective and a social network perspective. The idea was to firstly identify and analyse the concepts that the researchers de facto used in the conference papers using terminology and natural language processing tools and then to identify and analyse the socio-semantic structures (sets of researchers and concepts they used) emerging from the conceptual analysis. This approach has several dimensions. Regarding the epistemology, our study is essentially interpretative, although strongly based on fact discovering through quantitative methods. Regarding the methods, the approach is multi-disciplinary as it was informed by and tools were used from terminology and natural language processing, along with social networks analysis techniques. The data sources used were the 14 PRO-VE proceedings in electronic (pdf) format. From these data sources, three basic data-sets (per year) were extracted: authors, papers/authors, conferencetitle/table-of-contents/editorial. The overall research design is depicted in the figure 1.

Terminological and Conceptual Analysis

The goal of this phase was to identify and characterise the conceptual system suggested by each of the fourteen PRO-VE conference proceedings and to identify some patterns of evolution. For that, a textual corpus of the conference proceedings was created and organised by year (for the sake of economy of space we will not describe the data-sets preparation methods). A twofold approach was followed: first, a NLP tool was used to extract the candidate terms 1 from each proceeding; second, a terminological and conceptual analysis of the candidate terms was performed to result in a ranked list of terms (concepts) for each conference year. The term identification was made with the help of an automatic term recognition (ATR) service: the TerMine service provided by NaCTeM 2 . TerMine is based on a domain-independent method (C-Value) for the automatic extraction of multi-word terms, from machine-readable special language corpora. As described in [START_REF] Frantzi | Automatic recognition of multi-word terms[END_REF], C-value combines linguistic and statistical analyses with an emphasis placed on the statistical part. The linguistic analysis enumerates all candidate terms in a given text by applying part-of-speech tagging, extracting word sequences of adjectives/nouns based, and stop-list. The statistical analysis assigns a termhood to a candidate term by using the following four characteristics: (i) the occurrence frequency of the candidate term, (ii) the frequency of the candidate term as part of other longer candidate terms, (iii) the number of these longer candidate terms, and (iv) the length of the candidate term.

Figure 1 -Overall research design

From the several ATR tools we tested, TerMine showed the best performance by maximizing precision and recall, while minimizing noise. Most of the other tools available are optimized for recognizing single word terms, which is not what was 1 In terminology, a (technical/scientific) term is a possible designation for a concept considered to be part of a given domain. A term must have the explicit or tacit agreement of the technical/scientific community dealing with the concept. A candidate term is a term that was identified in some source and that needs the validation of a domain specialist to be considered a domain term. 2 The National Centre for Text Mining (NaCTeM) -http://www.nactem.ac.uk/ required for analysing the PRO-VE proceedings. In fact, technical terms consist mostly of noun phrases containing adjectives, nouns, and occasionally prepositions. The discourse properties of the PRO-VE community are patterns of repetition that distinguish noun phrases that are technical terms, especially multi-word phrases that constitute a substantial majority of all technical vocabulary, from other types of noun phrase [START_REF] Justeson | Technical terminology: some linguistic properties and an algorithm for identification in text[END_REF]. The TerMine service only provided a ranked list of candidate terms. The next and crucial step was to analyse the lists by specialists. Here, specialists are PRO-VE participants. We chose three specialists, actively working in the PRO-VE topics, with different degrees of participation in the fourteen editions (12, 8, and 3 participations). The conceptual analysis involved the following tasks performed by a specialist in the PRO-VE domains: (i) to go through the first 100 candidate terms returned by TerMine for each year, to clean non-terms and discard non-relevant terms; (ii) to make two rankings for each year, one of the first 10 and other of the first 20 terms using the C-Value termhood returned by TerMine; (iii) to fine-tune the rankings for each year by looking over the proceedings chapters and editorials 3 . This phase finished with the validation of each ranking by the above mentioned specialists.

Social network analysis

The goal of the social network analysis phase in our approach was to provide a description of the socio-semantic structure of the PRO-VE scientific community. SNA techniques were used to (i) to identify the network of relationships between authors and the concepts used in each conference proceeding, (ii) to identify meaningful substructures of concept-author relationships and (iii) to relate the co-authorship network with the socio-semantic network. For this we used mostly two-mode (2-mode) network analysis techniques. Due to the effort required to prepare the data-sets and to perform 2-mode network analysis, we selected a sample of 4 PRO-VE proceedings: 1999, 2005, 2009 and 2013. For each of the selected years, a rectangular data matrix of authors (rows) by concepts (columns) was built 4 , according to the following protocol: (i) the set of concepts to be used is the top 10 ranking for the year in consideration; (ii) for each of the concepts, the full text of the proceedings was searched to count how many times the concept appears in each paper; (iii) this number was cumulatively added to intersection of the paper authors (row) with the concept (column). For each year, the resulting authors-by-concepts matrix is calculated as:

, = , , , ∈
where vi,j is the i,j cell value, pk,i is a paper co-authored by author ai and included in the set of papers P of the proceedings, cj is the concept being look up and f a function that returns the frequency of presence of cj in paper pk. Exclusion conditions were defined so as to discard papers were a term appears in a non relevant way. A min , , = 3 was established. Also, if the concept appeared only in the title or abstract and not in the paper body, it would be excluded i.e, , , = 0.

Usually, 2-mode data in SNA can be analysed either as 1-mode or 2-mode. We used both, but focusing on algorithms and representations that highlight "sub-structures" instead of the characteristics of nodes (authors or concepts) within the network [START_REF] Hanneman | Introduction to social network methods[END_REF]. For our purposes, we selected the 1-mode networks to be run by the k-core analysis and the core-periphery analysis. The 2-mode network was analysed in terms of degree centrality. A k-core is a maximal group of actors, all of whom are connected to some number (k) of other members of the group. It allows actors to be included in the group if they are connected to other k members, regardless of how many other members they may not be connected to. By varying the value of k (that is, how many members of the group do you have to be connected to), different pictures can emerge [START_REF] Hanneman | Introduction to social network methods[END_REF]. In the case of our 2-mode network of authors-by-concepts, the k-core algorithm finds the maximal group of authors that use k concepts and concepts that are used by k authors. The coreperiphery structure is an ideal typical pattern that divides both the rows and the columns into two classes. One of the blocks on the main diagonal (the core) is a high-density block; the other block on the main diagonal (the periphery) is a low-density block. In our case, when applying the core-periphery model to the author-by-author data, the model seeks to identify a set of authors who have high density of ties among themselves (the core) by sharing many concepts in common, and another set of authors who have very low density of ties among themselves (the periphery) by having few concepts in common. We chose degree centrality in terms of socio-semantic relations because, as suggested by [START_REF] Roth | Social and Semantic Coevolution in Knowledge Networks[END_REF] it may be loosely interpreted as semantic capital: in other words, the number of concepts an actor has used is likely to render the variety of topics s/he has dealt with in the epistemic network.

The data sets were analysed by two software packages: NetMiner 3 from Cyram Co. Ltd. and UCINET 6 from Analytical Technologies.

Results

The PRO-VE Conceptual System Evolution

From the terminological and conceptual analysis described in the previous section resulted the top-10 and top-20 rankings of concepts for each of the PRO-VE proceedings. An extract of them is shown in figure 2. In the top-10 ranking, 43 distinct concepts were identified. Analysing the rankings for the whole existence of PRO-VE, it is possible to extract a conceptual core i.e., the foundational concepts of PRO-VE defined as the maximal sets of concepts that span a maximum number of years. The results are the following: The foundational concepts represent approximately 16% of the total distinct top 10 concepts. We call contingent concepts to the concepts that were included in the top-10 ranking for at most 2 consecutive years and then disappeared. These concepts are likely to appear and disappear due to combinations of factors such as the "fashionable" research topics and technologies, major on-going projects or even the suggested topics from the "Call for Papers". The contingent category represents 43% of the total. A third class of concepts are those who are neither foundational nor contingent. We call these regular concepts. These are:

Creg={knowledge management, information system, business opportunity, virtual team, virtual community, web service, VBE member, VO creation, social capital, social network, CN member, VBE member, VE member, collaborative process, supply network, service design} Within the foundational concepts, it is interesting to analyse the case of collaborative network. This concept is mentioned for the first time in 2000 by Devine and Filos and more consequently used in 2002 by de Joode. In 2003 papers referring to this concept continued to grow and finally in 2004 collaborative network was definitely consolidated (top-10). From 2005 on, the collaborative network concept acquired a status of the most important concept within the PRO-VE community by being proposed to name a new scientific discipline [START_REF] Camarinha-Matos | Collaborative Networks: a New Scientific Discipline[END_REF]. Another interesting result comes from looking at the average of the C-values for each of the core concepts (see figure 3). Virtual Breeding Environment has the highest C-value average which is indicative of the importance of this concept within PRO-VE. Figure 3

represent the PRO-VE conceptual

The results (see table 3) suggest that in PRO-VE 2005 there was a relatively big group of authors addressing 4 concepts and also a relatively big group of concepts addressed by 4 authors. The other years, regardless the value of k, this group is quite small. Table gives an idea of the connectedness of the 5-core and 3-core groups for 2013. A categorical core-periphery model enables to subdivide the authors-by-authors 1-mode network in two groups of authors: one exhibiting a very strong resemblance in the concepts and number of concepts addressed (the "core"), and other with very few communalities (the "periphery").

Table 4 shows the evolution of the "cores" for the four years in analysis. Each "core" of authors has an high density of ties among themselves because they addressed many concepts in common. We can observe a small "core" in 1999 and "cores" with almost the double of members in 2009 and 2013.

Discussion

We will discuss now the results presented in the previous section, using for that the hypothesis formulated in section 3.

H1.1. There is a core set of concepts used in all the editions of the conference

This hypothesis is confirmed as we identified, through the longitudinal terminological and conceptual analysis, a common set of concepts present in the top-10 ranking. These are the foundational concepts of the PRO-VE research community. The degree centrality measure of the concepts and the concepts returned by the k-core analysis seems to further confirm the hypothesis.

H1.2. The variability of the concepts not in the core is high;

Approximately 53% of the concepts in the top 10 are contingent, meaning that they are addressed in at most two consecutive editions of the conference. This suggests an high variability of the non foundational concepts. However, if we take the top 20, the variability ceases to be high as very few concepts entered anew for the top 20 each year.

H2.1. A core set of concepts is shared by the majority of the researchers

The results from the k-core analysis seem to suggest the contrary. Neither the foundational concepts all appear in the k-core nor the group of authors in the core is the majority. Even if we consider a lower k this is nor confirmed. For example, in 2009, the 2-core -the set of authors using two concepts -includes only 57% of that year's proceedings. This way, we cannot talk about epistemic communities, as defined by [START_REF] Roth | Social and Semantic Coevolution in Knowledge Networks[END_REF], within the PRO-VE community. To be considered an epistemic community, a group of authors should all share all the concepts in a given set. H2.2. There are groups of researchers that are likely to influence the adoption of new concepts.

Although this is perceived empirically, it doesn't follow from the results of our study. From the degree centrality measures, the authors Camarinha-Matos and Afsarmanesh are likely to be the main influencers, given their centrality in the network. However, this is not confirmed neither the k-cores nor in the core-periphery analysis.

The conceptual system of PRO-VE evolved, but not as much as we expected. There is a core set of seven concepts in the top 10, which makes the concepts used in the majority of the papers very stable: the last foundational concepts appearing by the first time in the top 10 backs to 2004. On the contrary, the groups of authors sharing more concepts is not very stable throughout the years, as can be observed in the results of the k-cores and core-periphery analysis. On the other side, the more central actors are also more stable. The limitations of this study lie mainly in using a derived social network. In fact, it is formed by linking the authors that co-use of the same concepts in their papers. This prevents an orthogonal analysis between the social network and the semantic network (conceptual system).

6

It was a long way for the PRO-VE community since the dominance of the concept virtual enterprise in 1999 to the relevance and centrality of collaborative network in 2013. It was also a long way from 1999 where Camarinha-Matos and Afsarmanesh had a degree centrality more that the double of any other to 2013 where the centrality is more distributed between the more central researchers.

In spite of this evolution, it is our opinion that the evolution of the PRO-VE sociosemantic network is not enough to produce a more visible evolution in the knowledge communicated in the PRO-VE conferences. Apparently, the newcomers largely reproduce the "official" conceptual system (foundational concepts).

This study can be greatly improved, opening new possibilities for research. The more important improvement is to develop the social network model in order to include a temporal dimension. Besides building the usual co-authoring network for each year, the dynamics of this network could be captured by creating a cumulative function in the form N O (! ) = (N O (! R )), where N O (!) is a co-authorship network in the year t. This would provide a more detailed ways to analyse the evolution of the PRO-VE sociosemantic-network.

Figure 2 -

 2 Figure 2 -Extract of the rankings of concepts for each PRO-VE proceeding

  $ & '#( )#! &(, !"#$ *(!* +*, ,"+ (*++ & *++, +" $-ℎ# (/ (span: 14 years) 0 = &$$#,& #! * (*!1& 2/ (span: 11 years) 3 = !"#$ , **4 (' *( &(5*(!, ,"+ (*++ 5&4*$/ (span: 10 years)

Table 3

 3 -k-core results for the maximum k in each year (concepts in bold) (NetMiner)

	1999	2005	2009	2013

Table 4 -

 4 Categorial "cores" resulting from the core-periphery analysis of the authors-by-authors network

	1999	2005	2009	2013

For the conceptual analysis phase it would be expectable to use the keywords assigned by the authors to their papers. In PRO-VE, only from 2009 on keywords where used to describe the paper subject. For the sake of consistency, we decided not to use the keywords in the conceptual analysis.

In our case the 2-mode network is represented by a "author-by-concept" (rectangular) matrix. This 2-mode network can be transformed in two 1-mode networks: a "author-by-author" and a "concept-by-concept". These are represented by square matrices.

Acknowledgements

This research was co-financed by the North Portugal Regional Operational Programme (ON.2 -O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF).

core together with a set of relationships between concepts. These relationships were established by the three specialists after debate. Of course this is just a possible set. Other specialists could come out with a different set. It would be not odd to look at this concept map as an upper-level ontology of PRO-VE.

The PRO-VE Socio-Semantic Network Evolution

As mentioned in the previous section a sample of four years was selected to analyse the PRO-VE socio-semantic network: 1999, 2005, 2009 and 2013. For each of these years the k-core and the core-periphery analysis were run respectively on the actors-byconcepts and actors-by-actors data sets. Degree centrality was calculated for the 1mode networks (authors-by-authors and concepts-by-concepts) generated from the 2mode one. Figure 4 shows a spring-like representation of the socio-semantic network. We can qualitatively observe which concepts are more used and, conversely, which authors use more concepts. The representation of the network follows an algorithm that group spatially the nodes having more connections. This can be also observed by looking at the size of the nodes which is proportional to the degree centrality of the actor or concept.

An extract of the degree centrality values for the first five authors and concepts is shown in table 2. This representation enables a qualitative appreciation of the sociosemantic network. Using simultaneously the scores of degree centrality both for the authors and the concepts, a more detailed assessment can be made. The centrality of some authors and some concepts is immediately evident. Although not shown in the network representation, each edge linking an author to a concept is weighted. This explains for example the relatively high degree of the concept near the top left corner (actually senior professional) even if it has fewer authors talking about it than the concepts more centrally located. This can for example indicate a couple of papers intensively referring the concept. As defined above, a k-core is a maximal group of actors, all of whom are connected to some number (k) of other members of the group. The k-core algorithm was applied successively to the 1-mode bipartite matrix of each year. We selected the highest k from each year resulting in the groups represented in the next table. The k-core algorithm tries to find the maximum k for which there is a maximal group of authors connected to k concepts and vice-versa.


Rolando Vargas Vallejos

Jefferson De

Oliveira Gomes
email: gomes@ita.br

Celson Lima
email: celson.lima@ufopa.edu.br

Jefferson De Oliveira Gomes

Priscila Nesello
email: pnesello@ucs.br

Combining Collaborative Networks and Knowledge Management: The SENAI Case

Keywords: Collaborative networks, Knowledge Management, Cultural barriers

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The global context of economic crisis brings opportunities and concerns for the Brazilian industry, sensitive to the business cycle and international competition. In order to stand out in this scenario of changes, the industry needs a more efficient and effective production, where technological innovation is a permanent asset required in all fronts.

One of the strategies that organizations are adopting to face these challenges is to change their relationship with customers, suppliers and even competitors, to work collaboratively on networks [START_REF] Porter | On Competition. The Harvard Business Review Book Series[END_REF]. The survival of an organization has become a competition for participation in the emerging opportunities and not for market share [START_REF] Prahalad | The Core Competence of the corporation[END_REF]. One way to prepare a competitive organization is through the development of competences differentiated from others. In collaborative network, an organization must have a differential that enables the complementation with other organizations in the work development.

The Brazilian National Service for Industrial Apprenticeship (SENAI) was created to serve the industry. In 2011, was developed the SENAI Support program for industrial competitiveness, to promote professional and technological education, innovation and technology transfer to make the Brazilian industries more competitive. SENAI has a physical structure in all Brazilian States, those structures were created over 70 years. In each state, SENAI has autonomy to meet the demands of local industry. The present work starts changing the culture of collaborative work, understanding that SENAI developed competences that can be leveraged working on technical networks. Therefore to enable the SENAI Support program for industrial competitiveness the new collaborative network culture becomes strategic.

For historical reasons, after 70 years essentially targeting "professional education" in an autonomous way (i.e., SENAI representations at each state work in an isolated way), a deeply rooted culture on this matter was built. Nowadays, Brazilian industry is also asking from SENAI the right support to jump into the innovation arena through a huge innovation program covering the whole country, involving more than a hundred of institutes providing innovation and technological services to the national industry. In this context, many challenges were naturally raised and this work points out some issues and possible solutions to overcome them. Two well-known institutions are SENAI partners in this quest: Fraunhofer (through the Institut für Produktionsanlagen und Konstruktionstechnik -IPK and other institutes) and Massachusetts Institute of Technology -MIT (through the Industrial Performance Center -IPC).

This paper describes the design and implementation of collaborative work in SENAI technical networks, introducing the framework developed and its deployment strategy, especially focusing on the cultural-related matters. Additionally, the work carried out in the innovation program, where Knowledge Management plays a role, is also discussed here, again focusing on cultural matters. Fraunhofer and IPC roles are also presented and future of the innovation program is shortly discussed.

About SENAI

By the end of 2012, Brazil was ranked as the sixth top world economy. In a country where 80% of the population lives in urban areas, the industry is absolutely the strategic vector of development. Brazilian industry answers for 27% of total salaries and employs one out of each four Brazilians registered in the employment booklet. It represents about 70% of the exports and 22% of the Gross Domestic Product.

Under that perspective, SENAI targets professional education for the industrial sector, also helping to reduce both economic and social differences. In an increasingly more competitive and demanding market, programs for management improvement, productivity increase and creation of social-environmental responsibility culture become essential to best qualify workers and companies.

SENAI is one of the five largest professional education organization in the world, the largest in Latin America. Since its foundation, in 1942, it has already graduated more than 52 million students. Last year, SENAI received nearly 2.7 million people.

All across the country, its courses train professionals required for the development of 28 areas of the Brazilian industry, including young apprentices, qualified technicians, technologists and specialized professionals.

SENAI has over 800 education centers throughout Brazil, including vocational education centers, training centers, technology centers and mobile training units. Along its over 70 years of history, SENAI has provided attendance to thousands of Brazilian enterprises.

The SENAI Support program for industrial competitiveness was conceived based on two main pillars, namely education and support in technical and technological areas. Whilst the former aims to surpass four million enrollments in 2014, the latter aims to create 25 SENAI Institutes of Innovation and 62 SENAI Institutes of Technology. However, in order to meet those challenging goals is mandatory to develop a new culture of collaborative work among the whole SENAI universe.

Framework Supporting Instantiation of SENAI Technical Networks

In order to start leveraging a culture of collaborative work SENAI developed a framework based on the AmbianCE model proposed by Vallejos et al. [START_REF] Vallejos | A framework to create a virtual organisation breeding environment for small and medium enterprises[END_REF][START_REF] Vallejos | Towards the development of a framework to create a virtual organisation breeding environment in the mould and die sector[END_REF]. This framework is based on three steps (Figure 1), namely: • Step 1: Prepare the creation of SENAI environment networks. This step is divided in three following phases: analysis of previous experiences on

Strategy

Step 3

Step 2

Act (TN lifecycle)

Knowledge Management

Phase 2.4

Develop product portfolio

Step Figure 2 presents a scheme where the SENAI units interact to attend a business opportunity through technical networks. The figure represents a network attendance 1 (NA1) being coordinated by the Ceará Regional Department (Ceará RD) and having as collaborators RDs of other states such as Rio de Janeiro, Santa Catarina, and Minas Gerais. At the same time a network attendance 2 is settled (NA2) being coordinated by Minas Gerais RD with the collaboration of other RDs.

Fig. 2. SENAI technical networks attendance schema

The technical network has to be very dynamic and flexible, based on two "ifs": if responsibilities of all members are clearly defined, and if rules and procedures are established with the agreement of all participants. This necessarily involves the narrowing of the relationship and the development of trust among the members of the network. Developing trust is necessary to recognize and develop the competences of those involved in the network. Soon, it may be stated that in order to form a network it is necessary to change the culture of labor relations referring to the notions of competences, trust, and collaboration.

The change of basic concepts like culture, competences, trust and collaboration, also represents an opportunity for the institution to reorganize a powerful set of procedures and indicators, in order to encourage networking work, thus enabling a more agile performance.

Creating, Deploying, and Operating SENAI Technical Networks

The creation and deployment of SENAI technical networks is based on the following phases:

• Creating a network. In order to create a technical network, SENAI ND considers: a) the demand of an industrial sector; b) establishment through National strategic planning; c) RDs proposition; and d) National Network Market proposition. Any technical network must involve six to nine RDs, promoting the new culture of collaborative work and conducting pilot projects (business focused). • Organize a network: once the network starts producing good results, it can be extended to include six to nine DRs who already have maturity in the technical area. This is the consolidation phase of the network and its development should brings the guarantee that there is a new job culture in the whole system SENAI being put in place. • Expansion of a network: the initiative to participate in the expansion of the network will be up to DRs who show interest, following the requirements and skills necessary for election. It is desirable to open it to all DRs in the latter stage, because we firmly believe that there is a potential market in attendance through network all over the country. Once a technical network was created and organized it is necessary to internalize the operational procedures. The success of a technical network is based on the ability to create and establish temporary cooperation among RDs to answer business opportunities.

Technical networks target the search for supplement of internal competences offered by SENAI in education, technology, and innovation, in order to answer to industries needs in effective form. The technical network attendance life cycle covers the following:

• Creation: is the phase in which the technical network attendance is configured to better attend that industry necessity. In this phase the business opportunity is detected and the interlocutors are responsible for defining which RD will be Technical Coordinator and which RD or RDs will be Operators. • Operation and Evolution: phase in which the technical network is performed based on the work plan agreed between the stakeholders, with systematic monitoring, ownership of results and records of occurrences, based on the rules and practices of project management. When problems occurs during the operation phase and is necessary to restructure the team or process planed, the Evolution phase is executed. In the Evolution phase is possible to incorporate other RD or stakeholder during the Operation phase. • Dissolution: phase in which, after product delivery and acceptance by the customer, gains and losses generated by the technical network are distributed. The gains may be tangible (e.g. financial, equipment, laboratories) or intangible (e.g. information, knowledge, patents, methods).

Validation of Framework for SENAI Technical Networks

In order to validate the framework, governance, policies and procedures of SENAI technical networks, the strategy was to create a new technical network: the textiles network. This sector is priority because is suffering a fierce competition and is one of the main financial contributors to SENAI. Six RDs were selected considering: expertise and experience in work development within local companies in the textile area, and the local market in that RD.

Each RD pointed a technical interlocutor and a market relationship person. After that, meetings and activities to develop the new culture of work were performed, considering: the culture of competence (all RDs recognized the competences of each RD involved), the culture of trust (better knowing and understanding each representative/RD involved) and the culture of collaboration were set (developing network attendances NAs). These meetings and activities were held in different RDs.

Together with this group, rules and procedures for technical networks were developed. These rules and procedures were evaluated in some pilots by the textiles network.

These rules and procedures were assessed by the following people before they were accepted for the whole SENAI system: a) SENAI ND networks coordinators; b) regional directors from 6 RDs involved in textiles network; and c) regional directors of all the whole SENAI system.

Knowledge Management Facilitators & Policies into the SENAI System

In parallel to the work conducted over the technical networks, SENAI has launched a huge initiative in order to build (in all senses of the word) a network of innovation institutes across the country (25 of them have been planned so far, including embedded systems, mineral technology, electrochemistry, etc.) knowing in advance that SENAI institutes must be free to collaborate among themselves according to the business opportunities. As part of this work and in perfect harmony with the technical networks (now these networks involve not only RDs but also innovation institutes), an ongoing work focused on the use of Knowledge Management (KM) concepts and practices was started, in order to help creating a KM-oriented culture, a KM way of working, a KM environment. It is worth noticing that such a work was not targeting the development of "yet another KM tool"; rather, the idea was to put technology as the last bit of the work and to consider it only (and if only) deemed really necessary. Fig. 3. KM facilitators at SENAI system KM group, responsible for the KM angle in the SENAI system, is working supported in two major vectors: facilitators and policies. Facilitators (figure 3) represent the elements that play a relevant role as part of the creation and dissemination of a KMoriented culture.

Therefore, figure 3 must be read bottom-up, as follows:

• Organization is the basic foundation supporting the whole work. In this case, organization represents the SENAI high administration level, which in this case, is firmly committed to the whole project. what are the knowledge sources to be shared, how they are shared,); b) incentives to collaboration (why institutes must collaborate, from all perspectives); c) management of knowledge sources (responsibilities about the knowledge sources, their availability, quality, etc.); and d) interoperation among institutes at a national level, knowing that for the first time in SENAI history one entity from a given RD will be able to conduct business with clients from other RDs. These 4 subjects were chosen due to their strong influence on knowledge management practices. The competences (from institutions and people) are the target in collaborative projects. The exchange of knowledge relies on collaboration channels, which by SENAI very nature, must be strongly promoted. Knowledge sources are the currency supporting successful collaborative (and sometimes innovative) processes. Finally, historical paradigms of SENAI behavior must be broken in order to give rise to a new culture of work, which must be collaborative and based on knowledge exchange, essentially. The success of SENAI networks will strongly rely on them.

Conclusions

The global stage competitive imposes organizations to change the way of work with their customers, suppliers, and even competitors. One strategy that is being implemented by these organizations is to work collaboratively or in networks. SENAI was created to attend Brazilian industry and in this context developed, in 2011, the SENAI Support program for industrial competitiveness. In this program there are challenges for the vocational and technological education areas as in the innovation and transfer technology to make industries more competitive. One strategy to achieve this program is to develop a new collaborative work culture in the SENAI system, leveraging the competences that exist in the 27 different RDs.

At the same time, launching the national network of innovation institutes set the challenge to a more complex level. Collaboration is a must to provide the power to SENAI networks to effectively attend industry demands, on one hand and, on the other hand, knowledge-based culture must be put in place in order to create the assets to fully provide an innovation-oriented environment for the Brazilian industry. After all, industry required a very clear help: a network of innovation institutes capable to push our potential forward and really gives birth to a new (and innovative) industry. We believe that this work is one of the most important for the maintenance of the SENAI system providing effectively and efficiently the Brazilian industry.

Fig. 1 .

 1 Fig. 1. Framework for the SENAI Networking

  creation of rules and procedures for network attendance, and validation those rules and procedures. • Step 2: Organize SENAI networks. Covers: the identification of the strategic sectors to form SENAI networks, selection of Regional Departments (Brazilian States) that will take part of each network, development of the roots to promote the collaborative work culture, and development of portfolio of products for each network considering its competences. • Step 3: Act (technical network lifecycle). Organized into four phases [5, 6], namely: technical network (TN) creation, TN operation, TN development, and TN dissolution. A strategy to deploy this framework in SENAI networks will use Knowledge Management supported by a system of benchmarking, developed by Vallejos et al. [4].

•

  Culture represents the required change towards a KM culture. Actors involved in all SENAI networks must be educated to work collaboratively in an environment where knowledge exchange is vital. This means they must get the appropriate help to: give value to the knowledge created, provide a permanent knowledge memory, share, transfer, and reuse knowledge. • Rules & Guidelines can be defined by the organization in order to help creating and promoting the KM environment. Few examples of rules & guidelines are the following: promoting collaborative work, pushing the creation and transfer of knowledge, providing access to the knowledge sources spread across the country. • Operation represents the modus operandi of every single member of SENAI networks. Here is the stage where knowledge really takes form and action, during the activities performed by all members of SENAI system. How knowledge is created, stored, shared, and reused is effectively considered as part of the operational sphere of SENAI networks. • Technology, as previously stated, may play a role but never the central one. KM platforms, if required, are tools intended to facilitate the technical procedures guiding the daily work of SENAI members, targeting the creation and dissemination of a KM environment. KM tools may help acquiring, storing, sharing, and reusing of knowledge as part of the collaborative work conducted by SENAI technical networks. It is important to say that technology is 100% dependent of the other levels and not the contrary. Regarding KM policies, they are needed to guide the following subjects: a) knowledge sharing among SENAI institutes (

Acknowledgments. Authors would like to express their gratitude to SENAI ND that supported the development of the whole work.