Maximos Kaliakatsos-Papakostas

Asterios Zacharakis

Costas Tsougras
email: tsougras@mus.auth.gr

Emilios Cambouropoulos
email: emilios@mus.auth.gr

Modelling cadence perception via musical parameter tuning to perceptual data

Conceptual blending when used as a creative tool combines the features of two input spaces, generating new blended spaces that share the common structure of the inputs, as well as different combinations of their non-common parts. In the case of music, conceptual blending has been employed creatively, among others, in generating new cadences (pairs of chords that conclude musical phrases). Given a specific set of input cadences together with their blends, this paper addresses the following question: are some musical features of cadences more salient than others in defining perceived relations between input and blended cadences? To this end, behavioural data from a pairwise dissimilarity listening test using input and blended cadences as stimuli were collected, thus allowing the construction of a 'ground-truth' human-based perceptual space of cadences. Afterwards, the salience of each cadence feature was adjusted through the Differential Evolution (DE) algorithm, providing a system-perceived space of cadences that optimally matched the ground-truth space. Results in a specific example of cadence blending indicated that some features were distinguishably more salient than others. This pilot study was a first step towards building self-aware blending systems and revealed that the salience of features in conceptual blending is an essential part for producing perceptually relevant blends.

Introduction

The cognitive theory of conceptual blending by [START_REF] Fauconnier | The Way We Think: Conceptual Blending And The Mind's Hidden Complexities[END_REF] has been extensively used in linguistics, music composition [START_REF] Zbikowski | Conceptualizing Music: Cognitive Structure, Theory, and Analysis[END_REF], music cognition [START_REF] Antovic | Musical Metaphors in Serbian and Romani Children: An Empirical Study[END_REF][START_REF] Antovic | Musical metaphor revisited: Primitives, universals and conceptual blending[END_REF] and other domains mainly as an analytical tool for explaining the cognitive processes that humans undergo when engaged in creative acts. In computational creativity, conceptual blending has been modelled by Goguen [START_REF] Goguen | Mathematical Models of Cognitive Space and Time[END_REF] as a generative mechanism, according to which two input spaces are blended to generate novel blended spaces, using tools of category theory. A computational framework that extends Goguen's approach has been developed in the context of the COncept INVENtion Theory1 (COINVENT) project [START_REF] Schorlemmer | Coinvent: Towards a computational concept invention theory[END_REF] based on the notion of amalgams [START_REF] Ontañón | Amalgams: A Formal Approach for Combining Multiple Case Solutions[END_REF][START_REF] Eppe | Asp, amalgamation, and the conceptual blending workflow[END_REF]. Following this framework, systems have been developed that blend features of two input musical cadences [START_REF] Eppe | Computational invention of cadences and chord progressions by conceptual chord-blending[END_REF][START_REF] Zacharakis | Conceptual blending in music cadences: A formal model and subjective evaluation[END_REF] (pair of ending chords) or chord transitions [START_REF] Kaliakatsos-Papakostas | An argument-based creative assistant for harmonic blending[END_REF], producing novel blended ones that incorporate meaningful characteristics of the input ones.

The paper at hand presents a pilot study for defining the salient features of cadences in the context of a cadence blending system, based on the data collected from perceptual experiments. The system-produced blended cadences incorporate combinations of features from manually-made input cadences, while the importance of each feature differs according to its perceptual salience. The salience of features is obtained by applying the Differential Evolution (DE) algorithm for optimally matching (in terms of pairwise dissimilarity) the system-perceived cadence relations with the perceptual space extracted by the experiment with humans. This study is a first step towards increasing self-awareness in a creative system that produces cadences through conceptual blending.

A formal description of cadences for generative conceptual blending

In this paper a cadence is considered as a special case of a transition (a chord following another), but with the second chord is fixed. Therefore, when blending two input cadences, the characteristics of the penultimate chords of the inputs are combined to produce new penultimate blended chords that are paired with the fixed final chord to constitute the blended cadence. For instance, the case of blending, e.g., the perfect with the Phrygian cadences is described by the transitions I 1 : G7 → Cm and I 2 : B m → Cm respectively, while a a blend of these inputs is the tritone substitution cadence, C 7 → Cm [START_REF] Eppe | Computational invention of cadences and chord progressions by conceptual chord-blending[END_REF][START_REF] Zacharakis | Conceptual blending in music cadences: A formal model and subjective evaluation[END_REF]. The perceptual characteristics of the penultimate chord that are considered for describing a cadence are the following: 8. DIC : The compete DIC vector of the chord transition. 9. asc: Existence of ascending semitone to the tonic. 10. desc: Existence of descending semitone to the tonic. 11. semi : Existence of semitone movement towards the tonic.

For computing the root and type in a consistent manner for all utilised chords, the General Chord Type (GCT) [START_REF] Cambouropoulos | An idiomindependent representation of chords for computational music analysis and generation[END_REF][START_REF] Kaliakatsos-Papakostas | Evaluating the General Chord Type representation in tonal music and organising GCT chord labels in functional chord categories[END_REF] has been employed, which allows the rearrangement of the notes of a harmonic simultaneity such that abstract types of chords along with their root may be derived. The GCT algorithm finds the maximal subset that forms the base upon which the chord type is built, while the lowest note of the base is the root of the chord; the potentially remaining notes are assembling the extension of the GCT, which is the set of notes that would not be a part of the maximally consonant subset. For example, the GCT representation of the first degree (I) chord in a major scale is [0, [0 4 7]], where 0 indicates the root note in relation to the scale (0 is the scale as first degree) and [0 4 7] is the chord's type (4 indicates a major third and 7 a perfect fifth). Accordingly, a V7 chord is denoted by [7, [0 4 7], [START_REF] Goguen | Mathematical Models of Cognitive Space and Time[END_REF]], where 10 is the extension (minor seventh), which cannot be included in the base considering that the tritone and minor seventh intervals are dissonant.

The aforementioned properties 1-2 describe the first chord of the cadence and the first two properties (chord root and type) are extracted from the GCT algorithm, considering that all the examined cadences are in the key of C minor. Property 4, the difference between the chord roots is an integer between -5 and 6, indicating the pitch class difference between the roots of the first and the second chords of the cadence. Property 5 captures the existence of a common note between the two chords, while properties 6 and 7 indicate the existence of a semitone movement (upward and downward respectively) in any pitch class of the cadence transition. These properties actually indicate if there is a 0, 1 or -1 in the Directional Interval Class (DIC) [START_REF] Cambouropoulos | Idiom-independent harmonic pattern recognition based on a novel chord transition representation[END_REF], flagging whether there are small pitch class voice leading movements (repeating notes or semitone movements) in the cadence. Property 8 incorporates the entire DIC vector of the transition/cadence. Properties 9 to 11 are used to highlight the importance of whether there is a semitone movement (property 11) to the tonic from the first to the second chord of the cadence as well as whether this movement is ascending (property 9) or descending (property 10); these properties reflect the importance of the leading note (upwards or, even, downwards).

Table 1 illustrates a blending example, where the tritone substitution cadence is created from the perfect and the phrygian cadences. This blend incorporates properties from both input spaces with a good balance, i.e. many properties that are common in both input spaces, while new properties have also been added through completion. Specifically, this blend includes five properties of input 1, four properties of input 2, three common properties and four new properties that were not present in any input space. The properties of the blended space come from either input space, or are completed by logical deduction through axioms describing cadences, as indicated in the parentheses next to each respective property.

Approximating the importance of properties according to the perceptual pairwise distances of cadences

As discussed in the introduction, one desirable property for a creative system is the ability to self-evaluate its products [START_REF] Jordanous | Evaluating computational creativity: a standardised procedure for evaluating creative systems and its application[END_REF]. In this respect, the cadence blending system should be able to make accurate predictions of how the blends are perceived in relation to the inputs. To this end, a vector containing differences on the utilised music properties, denoted by P C i . Since it is assumed that not all properties are of equal importance in deciding the distance between pairs of

0 0 DIC1 1 0 1 DIC-1 1 1 1 DIC [1, 2, 0, 2, 0, 1, 2, 1, 0, 1, 2, 0] [1, 0, 1, 1, 1, 0, 0, 3, 0, 0, 1, 1] [2, 1, 0, 1, 2, 0, 1, 2, 0, 2, 0, 1] asc 1 0 1 desc 0 1 1 semi 1 1 1
cadences, each property (P C i ) is assumed to have a weight of importance, denoted by w i . The overall distance between cadences can be then calculated by summing the weight values of the properties that are different in these cadences. Specifically, the distance between two cadences X and Y is calculated by:

D(X, Y ) = 11 i=1 w i f i , (1)
where f i is a function related to how distance is measured for each property, as analysed in Equations 2, 3 and 4.

Properties with indexes 1, 2, 4, 5, 6, 7, 9, 10, 11 have a binary f i function similar to the Kronecker delta function:

f i = 1, if P X i = P Y i , 0, if P X i = P Y i
, for i ∈ {1, 2, 4, 5, 6, 7, 9, 10, 11}.

(

Equation 2 indicates that these properties need to be equal in both cadences in order not to be penalised by the respective w i values. The function for property 3 is computing the number of common over the number of total pitch classes in the first chords of two cadences. Specifically,

f 3 = N (∪(P X 3 , P Y 3 )) -N (∩(P X 3 , P Y 3 )) N (∪(P X 3 , P Y 3 )) , (3)
where N (∩(P X 3 , P Y 3 )) and N (∪(P X 3 , P Y 3 )) is the number of elements in the intersection and union of the pitch class sets. Equation 3 indicates that there is a proportional penalty to w i for pitch classes that are not common in the first chord of two cadences. Finally, DIC information (property 8) is measured according to the correlation of the DIC vectors of the cadences under examination. Weaker correlations are penalised proportionally with regards to w 8 , according to the following equation:

f 8 = (1 -corr(P X 8 , P Y 8 ))/2. ( 4
)
Correlation between DIC vectors conveys harmonic meaning at some extend, as indicated by the genre categorisation results based on DIC correlation reported in [START_REF] Cambouropoulos | A Directional Interval Class Representation of Chord Transitions[END_REF]. By calculating the distances between all pairs of the examined cadences according to Equation 1, a dissimilarity matrix that represents the pairwise differences among the nine cadences is constructed. This dissimilarity matrix is subsequently analysed through non-metric weighted MDS and results in a spatial configuration of the cadences that from now on will be called the 'algorithmic space'. Therefore, in order to define the contribution (i.e. weight value) of each parameter on deciding the overall distance, a differential evolution (DE) algorithm [START_REF] Storn | Differential evolution -a simple and efficient adaptive scheme for global optimization over continuous spaces[END_REF] was used to optimise the fit between pairwise distances in the perceptual space (used as ground truth) and the respective ones in the algorithmic space. An overview of the optimisation process is schematised in Figure 1.

The difference between the perceptual and algorithmic spaces is quantified through a fitness function that is estimated by taking the average of two similarity metrics, namely the m 2 statistic for Procrustes analysis [START_REF] Gower | Procrustes Problems[END_REF] and Tucker's congruence coefficient [START_REF] Borg | Modern Multidimensional Scaling: Theory and Applications[END_REF]. For a detailed discussion on the application of these metrics to comparison between timbre spaces please see [START_REF] Zacharakis | An interlanguage unification of musical timbre: bridging semantic, perceptual and acoustic dimensions[END_REF].

Perceptual Experiment

A pairwise dissimilarity listening test was deemed appropriate to act as a ground truth for modelling how a set of musical cadences is perceived by listeners, as the dissimilarity matrices it produces allow Multidimensional Scaling (MDS) analysis to generate geometric configurations that represent the relationships between percepts.

Twenty listeners (age range = 18-44, mean age 24.9, 10 male) participated to the listening experiment. Participants were students in the Department of Music Studies at the Aristotle University of Thessaloniki. All of them reported normal hearing and long term music practice (16.5 years on average, ranging from 5 to 35).

Participants were asked to compare all the pairs among 9 cadences using the free magnitude estimation method. Therefore, they rated the perceptual distances of 45 pairs (same pairs included) by freely typing in a number of their choice to represent dissimilarity of each pair (i.e., an unbounded scale) with 0 indicating a same pair. Each stimulus lasted around 4 seconds and interstimulus interval was set at 0.5 seconds. Listeners became familiar with the range of cadences under study during an initial presentation of the stimulus set (random order). This was followed by a brief training stage where listeners rated the distance between four selected pairs of cadences. For the main part of the experiment, participants were allowed to listen to each pair of cadences as many times as needed prior to submitting their dissimilarity rating. The pairs were presented in random order and participants were advised to retain a consistent rating strategy throughout the experiment. In total, the listening test sessions, including instructions and breaks, lasted around thirty minutes for most of the participants.

The stimulus set consisted of the two input cadences (the perfect and Phrygian) together with seven blended cadences. The selection of cadences was made manually after evaluating their blending elements so as to attain a theoretically valid, maximally diverse corpus. All cadences were assumed to be in C minor tonality/modality, consisted of two chords and the final chord was kept constant (C minor), thus variation between the stimuli resulted from altering the penultimate chords. The nine cadential pairs of chords are described from a music-theoretical perspective in the following list:

1. Perfect authentic cadence, featuring the full V7 dominant chord that resolves to the i tonic chord without 5th, in order to achieve correct voice leading. 2. Phrygian cadence, with the vii chord in first inversion resolving to the i tonic chord. 3. Tritone substitution progression, with the II7 chord (German-type augmented-6th chord) leading to the tonic. 4. Backdoor progression, with the VII7 chord in first inversion, in order to achieve maximum voice-leading uniformity. 5. Contrapuntal-type tonal cadence, with the viio6 resolving to the minor tonic. 6. Plagal-type cadence, with the iio6/5 progressing to the tonic. 7. Minor-dominant to minor-tonic progression, utilising chords from the natural minor scale (Aeolian mode). 8. Altered dominant-7th chord to minor-tonic progression, with the dominant in second inversion and with its 5th lowered (French-type augmented 6th chord). The optimised algorithmic cadence space that resulted from modelling the perceptual space through optimal weighting of the musical parameters.

9. Half-diminished 'dominant'-7th chord to minor-tonic progression.

Results

Before proceeding to the main body of the analysis for the dissimilarity data we examined the internal consistency of the dissimilarity ratings. Cronbach's alpha was .94 indicating high inter-participant reliability. In the main body of the analysis, the dissimilarity ratings within each linguistic group were analysed through non-metric (ordinal) MDS with dimension weighting (INDSCAL within SPSS PROXSCAL algorithm) [START_REF] Meulman | PASW Categories 18[END_REF]. A two-dimensional solution was deemed optimal for data representation as the improvement of measures-of-fit when adding a third dimension was minimal. Figure 2a and shows the configuration of the cadences within this 2-D space.

The optimisation process presented in Section 3 produced combinations of weights for each cadential feature that minimise the differences between the perceptual and the algorithmic spaces, providing an optimised modelling of cadence perception. The ideal combination should offer the highest possible fit (quantified by the similarity metrics) with the perceptual space.

Several optimisation simulations were run with different setups for the DE algorithm (concerning population members, number of generations etc.). Table 2 shows a property weight configuration that provided a satisfactory modelling of the perceptual space (population members = 50, number of iterations = 30). This configuration featured an m 2 = .027, a congruence coefficient = .994 and an RV modified coefficient = .966 indicating an excellent fit between the perceptual and algorithmic configurations. Had no optimisation taken place (i.e., all parameters assigned importance equal to 1), the similarity metrics between the two configurations would become: m 2 = .361, congruence coefficient = .942 and an RV modified coefficient = .523, representing a serious divergence of the algorithmic space in relation to the perceptual. Figure 2b shows the optimised 2-dimensional configuration of the algorithmic space. As expected, based on the similarity measures reported above, the two spaces (perceptual and algorithmic) are very closely related since the obtained algorithmic space maintains the majority of the perceptual relationships between cadences. Based on the above, it can be concluded that the penultimate chord types, their pitch classes, the information provided by the DIC vector and the presence or absence of a leading note account for the way listeners perceived the relationships of cadences within this particular set. It should be also noted that according to the DE simulations there are two additional properties of 'moderate' importance: those that examine whether there is at least one fixed (DIC0 ) or one descending by one semitone (DIC-1 ) pitch class.

Conclusions

This paper presents a first pilot study towards increasing the self-evaluation ability of a creative cadence blending algorithm, by utilising data from perceptual experiments. The listening experiment incorporated two cadences (the perfect and the phrygian) as a starting point along with seven system-produced blends. The blending algorithm combines characteristics of the input spaces, generating several blends that include different combinations of characteristics from the input cadences. Aim of this paper is to identify whether any of the cadence characteristics are perceptually more salient in defining pairwise cadence similarities. The Differential Evolution (DE) algorithm was employed in order to fine-tune the salience weight of every cadence property, so that the relative placement of cadences obtained with system-based metrics optimally matches the user-obtained perceptual space.

The dissimilarity rating experiment revealed a categorical perception of cadences reflected by positioning on the 1st MDS dimension and clearly dictated by the existence of an upward semitone movement to the tonic (leading note) in the left-hand cadences in comparison to the lack of a leading note in the right-hand cadences. This fact is also evident by the high weight of the asc value shown in Table 2. Two major clusters of cadences were formed based on this differentiation together with one outlier (the plagal cadence) that featured neither and upward semitone nor an upward tone to the tonic but a duplication of the tonic. It is also shown that both the intra and inter-cluster relations could be adequately modelled mainly through four salient musical properties, namely the penultimate chord type, its pitch classes, the DIC vector of the cadence and the existence of the leading note.

This being a pilot study, the generalisation of these findings for a wider range of cadences as well as a detailed mapping of musical properties to perceptual dimensions is a necessary step that is left for future work. For instance, initial findings indicate that the differentiation of cadences along the 2nd dimension could be explained by the inherent dissonance of the penultimate chords (as expressed by the MIR Toolbox roughness calculation) together with their distances from the final chord in Lerdahl's Tonal Pitch Space. Identification of such complementary measures could help towards increasing self-awareness of a cadence blending system, according to various diverse aspects of its creative products.

Fig. 1 :

 1 Fig. 1: The optimisation through the Differential Evolution.

Fig. 2 :

 2 Fig. 2: (a) The 2 dimensional perceptual space of the nine cadences. The perfect and the Phrygian cadences (No. 1 & 2) are positioned far away from each other on the 1st dimension. b)The optimised algorithmic cadence space that resulted from modelling the perceptual space through optimal weighting of the musical parameters.

Table 1 :

 1 Example of the tritone substitution cadence invention, by blending the perfect and the phrygian cadences.

	Property name Input 1 (Perfect)	Input 2 (Phrygian)	Possible blend
	fcRoot	7	10	1
	fcType	[0 4 7 10]	[0 3 7]	[0 4 7 10]
	fcPCs	{7, 11, 2, 5}	{10, 1, 5}	{11, 1, 5, 8}
	rDiff	5	2	1
	DIC0	1

Table 2 :

 2 Optimal weights of the musical properties for modelling cadence relationships. A combination of four prominent parameters (in bold) and two weaker ones (in italics) achieved an excellent model of the perceptual space.

		Weights (wi values)
	fcRoot fcType fcPCs rDiff DIC0 DIC1 DIC-1 DIC	asc	desc semi
	.020 .790 .751 .000	.311	.014	.270 .760 .731 .001	.003

http://www.coinvent-project.eu

Acknowledgments

This work is funded by the Concept Invention Theory (COINVENT) project. The project COINVENT acknowledges the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under FET-Open grant number: 611553.


Michalis Vafopoulos

Gerasimos Razis
email: razis@dib.uth.gr

Ioannis Anagnostopoulos

Georgios Vafeiadis
email: vafeiadis.giorgos@gmail.com

Dimitrios Negkas

Eleftherios Galanos

Aggelos Tzani

Ilias Skaros

Konstantinos Glykos

Κ Glykos

The eLOD Ontology: Modeling Economic Open Data

Keywords: Linked Data, Semantic Web, Knowledge Representation, Economy

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

Introduction -Related works

In this paper, we introduce the eLOD Ontological schema as the "heart" and basic data modelling and handling infrastructure of LinkedEconomy1 portal. LinkedEconomy aims at providing a universal access to Greek and international economy data, as well as at promoting the benefits of linking heterogeneous sources under the concept of open and reusable data in this critical domain [START_REF] Vafopoulos | The Web economy: goods, users, models and policies[END_REF]. In addition, it targets to offer a unique platform of rich linked economy data for enhancing the citizens' awareness in respect to economic issues in Greece and worldwide, as well as to provide curated and semantified data to the Linked Open Data research community. It is the ancestor of the Public Spending (PSNET) initiative 2 , which was the first attempt to harvest, align, interlink, analyze and distribute as Linked Open Data massive amounts of public spending data from Greece and from six other (local and national) governments [START_REF] Vafopoulos | Public spending as LOD: The case of Greece[END_REF]. Related worldwide initiatives are driven from similar Open Government Data projects. Below, we classify them in four major fields according to their applications in the economy domain.

In the field of open budgets, the International Budget Partnership advocates for public access to accountable budget systems. One of its projects, the Open Budget Survey Tracker3 allows citizens to monitor whether central governments are releasing the requisite information on how the government is managing public finances. Starting from 2015, EU funds the Open Budgets project 4 to provide a scalable platform for public administrations to publish open budget data that is easy-to-use, flexible, and attractive for all. In addition, the Open Spending Initiative5 offers an easy system to upload, explore and share public finance data (e.g. budgets or expenditure databases). In 2013, more than 16 million transactions from 363 different datasets around the world have been uploaded. A similar project from the Sunlight Foundation6 analyzes the spending data uploaded in the official portal of US government. OpenTED 7 is an initiative that provides data dumps of tenders from the joint European procurement system more easily accessible to journalists and researchers.

In the field of data standards, the Fiscal Data Package8 is developed as a simple, open technical specification for government budget and spending data. Public Contracts Ontology has been introduced by the LOD2 project [START_REF] Jakub; Klimek | LOD2 -Creating Knowledge out of Interlinked Data Framework for Creating Linked Data in the Domain of Public Sector Contracts[END_REF] to provide an ontological basis for representing key concepts in tenders and expressing structured data about public contracts. The Core vocabularies have been developed by European Commission's Interoperability Solutions for European Public Administrations (ISA) programme as simplified, generic, re-usable and extensible data standards that model the characteristics of basic entities in a context-neutral and syntax-neutral fashion. Most of them have been incorporated in our modelling, as it will be described below. Open Contracting Partnership9 is a consultation process to create a set of global principles that can serve as a guide to advance open contracting around the world. In this context, the Open Contracting Data Standard (OCDS) sets out key documents and data that should be published at each stage of a contracting process.

In the field of company data, Open Corporates 10 aggregates company information from different countries and jurisdictions. The specific team is working on creating Linked Data representations out of their databases, by mapping company metadata to certified ontologies such as the Core Business Vocabulary and linking them to other data hubs, such as DBpedia.org and Geonames. Financial statements and accounting reports that are published by companies contain important data.

In the area of product data, Open Product Data -POD11 hosted by OKF as a community project, stands as a public database of product data connected to barcodes, in order to empower consumers with useful and machine-readable product information (e.g. prices).

Other related research approaches have a two-fold orientation. In the first fold, we have efforts that analyse the benefits of using open data in economy, trying in parallel to provide a unified framework and modelling strategy [START_REF] Seán O'riain | XBRL and open data for global financial ecosystems: A linked data approach[END_REF], [START_REF] Hodess | Open Budgets: The Political Economy of Transparency, Participation, and Accountability[END_REF], [START_REF] Tygel | How much?" Is Not Enough-An Analysis of Open Budget Initiatives[END_REF], while, in the second fold, we have the contributions that integrate the benefits of openness, thus creating a flourishing linked ecosystem of economy data [START_REF] Petrou | Towards a methodology for publishing Linked Open Statistical Data[END_REF], [START_REF] Höffner | LinkedSpending: OpenSpending becomes Linked Open Data[END_REF], [START_REF] Alvarez-Rodríguez | Enabling policy making processes by unifying and reconciling corporate names in public procurement data. The CORFU technique[END_REF], [START_REF] Vafopoulos | Linked Open Economy: Take Full Advantage of Economic Data[END_REF].

eLOD ontology: modelling economic data under semantics

In this section, we describe some well-known and established structured vocabularies, which are used in the economy domain, as well as the model that incorporates interconnections between public finances and market processes in Greece and consists of the main classes and properties of our ontology.

2.1

Description of sources and vocabularies used

Table 1 depicts the data sources used as well as some information regarding them (API support, URI). During the ontology design phase, our basic aim was to reuse other well-known and established vocabularies and ontologies that cover our needs. When existing models were inadequate we introduced and defined new concepts. . FOAF, which is an acronym of "Friend of a Friend", is a vocabulary for describing people, their activities and relationships with other people and objects. This concept can be generalized in order to describe all types of entities, named "agents", who are responsible for specific actions. FOAF aims to describe the world by using simple ideas inspired by the Web. In our economic context, this vocabulary is used in order to define and describe the agents who are responsible for specific actions. Two categories of agents are used, namely "Persons" and "Organizations".

GoodRelations is an ontology, which aims at defining a data structure for products related to electronic commerce, the prices, the stores and the data of the companies. Its use allows the expression of the commercial and operational details of scenarios for e-commerce. The main entities in this domain are the involved agents, in terms of persons or companies, the objects of the commercial activities, the items for sale, lease or repair, as well as the locations where such offers are available. In our economic context, this ontology is used in order to identify and describe the "Business Entities" which are involved in a commercial activity (their legal names and their Vat Ids), the type of their services, and the financial details of the contract or of the payment (i.e. the wholesale or retail price, whether tax is included in the price and the expressed currency of the price). Moreover, it is also used to express a point or area of interest from which a particular product or service is available.

Public Contracts is an ontology that aims at describing the contracts in the public sector. It is based on the "GoodRelations" ontology for the modeling of business entities and price specifications.

In our economic context, this ontology is used in order to define and describe the following:

12 http://www.foaf-project.org 13 http://purl.org/goodrelations 14 https://code.google.com/p/public-contracts-ontology 15 http://www.w3.org/TR/vocab-org 16 http://www.w3.org/TR/vocab-regorg 17 http://dublincore.org 18 http://www.w3.org/2004/02/skos 19 http://www.w3.org/TR/vcard-rdf 20 http://wiki.dbpedia.org/services-resources/ontology -the public contracts during all stages of their existence (tender, contract, payment), -the procedures specifying how the details of a contract are published and how a supplier is chosen, -the basic focus of the contract (i.e. works, supplies or services), -the price of the contract, depending on its phase (before or after the offer), -the award criteria that define the conditions under which the best offer will be selected and awarded along with their weights, and -the main and supplementary products or services purchased by the contract (determined by the CPV codes). The Organization ontology aims at describing the organizational structures in order to support the disclosure interconnected data of organizational information in various sectors. It is designed as to allow extensions in specific areas for classifying the organizations and roles as well as for extensions in order to support relevant information, such as organizational activities. Its design allows the publication of information on organizations and their organizational structures, including governmental ones. In our economic context, this ontology is used in order to define and describe the organizations and their organizational units. Their structure is also represented using the properties of this ontology. Additional properties are also provided in order to illustrate the members and their structures within an organization, as well as the roles, positions and relationships between people and organizations.

The Registered Organization vocabulary aims at describing the entities, which have obtained the status of a legal entity through a formal registration process, usually at the national or regional registry. It includes a minimum number of classes and properties that are designed to depict the typical information recorded by the business' registries, thus facilitating the exchange of information between them, despite having considerable variation between the recorded and the published data. In our economic context, this vocabulary is used in order to identify and describe the business entities and their properties, including their type, status and activity.

Dublin Core and Dublin Core Terms is a small vocabulary for the description of general metadata of the Web and of natural resources. In our economic context, this vocabulary is used in order to define the entities that are responsible for publishing a contract, the subject of the contract, and the date of its formal issue.

SKOS, which is the acronym of "Simple Knowledge Organization System", is a vocabulary, which is designed in order to represent thesaurus, classification schemes, classifications, lists or any other type of structured controlled vocabulary. Its main aim is to allow the easy publication and use of these vocabularies as interconnected data. In our economic context, this dictionary is used in order to define numerous controlled vocabularies and to represent code lists (e.g. Currencies).

The specification of vCard is generally used for describing people and organizations. Usually, vCard objects are encoded based on their own syntax or in their XML format. The vCard ontology can be used for the semantic representation of any vCard data and is also focused on describing people and organizations, including location information and groups of such entities, as "FOAF" and "Organization" do. In our economic context this ontology is used in order to represent geographical information regarding agents and products, such as delivery addresses.

DBpedia is an ontology, which covers different domains and is created manually based on the most frequently used information from infoboxes of Wikipedia. For the purposes of our case, properties of this ontology are used in order to define different type of information about businesses (e.g. subsidiaries of a company).

2.2

The eLOD ontological schema

The ontology of each data source along with its description and example queries can be found on Github 21 . In this repository, we describe in an analytical way the ontological schema created for the modeling of each data source described in Section 2. Apart from the ontology description, SPARQL queries are provided along with a sample of their responses. The model of our ontological schema is depicted in Figure 1. As it shows, government forms and publishes budgets, parts of which include projects and works that are assigned through calls for tenders. After contracts have being signed and projects are fulfilled, funds are transferred. Spending data are often used to assess the completion of public budgets. However, another type of added-value fiscal information that has been started to provided publicly is subsidies and aid data. Subsidies include government payments to firms and households based on a development plan (e.g. in Greece we have the Greek National Strategic Reference Framework (anaptyxi.gov.gr/), while in EU the farmsubsidy.openspending.org). Procurements, subsidies and aid awarding processes are involving the exchange of information among authorities (e.g. tax offices, business registries and various public agencies) and the official publication of relevant information (e.g. call for tenders, payments).

Fig. 1. Ontological model that incorporates interconnections between public finances and market processes

In addition to the above, we have also modeled the Market domain. Information about the market process (e.g. price, value of sales and quality) is an important source of business value and is mostly closed into corporate environments. Market process information is partially shared with government authorities (e.g. tax office), suppliers and consumers. Although, there is a small but crucial part of business information that should shared publicly as open data to ensure that quality and competition are best served. This set of publicly available information should include the vector of prices (at least for basic goods and services), aggregated quantities sold in wholesale and retail markets and all the relevant input to assess quality of provisioned products and services. In Greece, a representative sampling of retail prices for thousands basic consumer goods and fuels can be retrieved in regular basis. In some cases, (e.g. Central Market of Thessaloniki) wholesale prices and quantities are provided for fruit, vegetables and meat. In order to make clearer to the reader the reuse and incorporation of the aforementioned ontologies and vocabularies to the eLOD Ontology, two informative tables are provided. Table 2

Approach and reuse

The eLOD ontology is based more on a theory-driven than in data-or statisticsdriven approach. It has been designed to better balance the trade-off of being as generic as to be scalable to future open data categories and as specific as to be compatible with existing initiatives. It could be an opportunity for many of the diverse communities, which are working on transparency, global standards and economic data, to join forces in addressing useful and not-yet-answered questions. For example, we can't yet answer, if public spending is expensive and comparable across countries? or can we compare financial ratios in public budgets? Can we comparatively analyze wholesale and retail prices? Due to its theory-driven approach eLOD is used by two major European projects related to open economic data. Your Data Stories (YDS, yourdatastories.eu) is an EU funded project (Grant Agreement No.645886) that aims to convert publically available economic open data into re-usable and interoperable building blocks that can be used to construct applications. YDS will allow any actor to design and implement personalised public services. Based on the eLOD model feeds from trusted sources will be interconnected with the new and re-purposed data feeds provided by users of the social web in order to form a meaningful, searchable, customizable, re-usable and open hyper-market of data, feeds, and services. The second project is Big Data Europe (http://www.big-data-europe.eu/) which is funded by EU (grant agreement n. 644564) for enabling European companies to build innovative multilingual products and services based on semantically interoperable, large-scale, multi-lingual data assets and knowledge, available under a variety of licenses and business models. LOE model has been selected to support the implementation of one of the seven societal challenges that Big Data Europe focuses. In particular, the social sciences challenge refers to statistical and research data linking and integration and will focus on citizens budgeting and control. Asking the data: a case study

Diavgeia

The aim of LinkedEconomy.org is not only to transform and semantically enrich the input data into RDF graphs, but also to apply a unified ontological model in order to treat and query distinct datasets as one.

As already mentioned, a public endpoint22 allows the search of the collected semantic data and their combination and enrichment other sources. SPARQL Query 1 displays the advantages of having a unified ontological model, as it combines such data from three different sources, namely Diavgeia, e-Procurement and NSRF.

The query returns the number of decisions and subsidies referring to payments and their total amount in Euros for the case of the Hellenic Telecommunications Organization S.A (member of the Deutsche Telekom Group). For these decisions this organization appears as "seller", meaning that it receives payments for offering a service or from selling a product. One of the main characteristics of the semantic data is that other publicly available datasets from the LOD cloud can at the same time be queried and combined with relevant information. Such an example is the federated SPARQL Query 2, which combines the economical data of Diavgeia, as stored in our graph, along with DBpedia information. Table 2 depicts the results we receive when we "ask" our data combined with DBpedia for the case of the Hellenic Telecommunications Organization S.A. Apart from the information from Diavgeia, the user receives information in respect to the organization's founding year, net income and number of employees.

Discussion -Future work

This paper presents i) the economic open data sources used, ii) the ontological model that orchestrate linked data flows, iii) the common controlled vocabularies that are incorporated in eLOD ontology in order to place it the global economic Linked Data cloud. All consist of the "data engine" of LinkedEconomy, a project that lead to publishing data of high added value not only for exploration purposes, but also for exploiting the benefits of transparency and openness to the citizens, the research community, and the government itself. Through this research initiative, our team tackled the challenge of building a common terminology for the basic financial and economy activities, which will -in turn-facilitate the research over new linked data and sources. All described components form a system capable of linking economy-related data at large scale, creating in parallel a framework for collect, validate, clean and publish linked data streams. Our efforts produced a CKAN repository, which publishes datasets from sources that are being updated regularly and contain valuable information in respect to social and economic research 23 . Citizens and economy stakeholders (government, local authorities, etc.) can exploit 27 datasets from 14 classified data sources in machine-readable format (xlsx, csv, rdf). Examples include economy data, such as public procurements, budgets, prices, expenditures, data from the insurance domain and employment, as well as financial and macro-economic data. As we envisage the use of Web 3.0 technologies and we acknowledge the benefits of openness, we offer a publicly available SPARQL endpoint that consists of more than 210M triplets in total, and we share all ontological schemas and related specifications in Github.

Finally, as publicly available open data are growing rapidly worldwide, we currently work on modeling many foreign economic datasets according the already semantic knowledge we developed. We expect to support really soon the provision of more than 15 different economic datasets from Europe, UK, Australia, USA and Canada, while we plan to further extend this economy-linked data cloud in the near future.

Table 2 .

 2 presents the percentage of the classes and properties belonging to each vocabulary. Classes / Properties percentage use of established vocabularies in eLOD Ontology

	Vocabulary	Distribution of Classes	Distribution of Proper-
		in eLOD Ontology (%)	ties in eLOD ontology
	{own semantics}	50%	67.7%
	Good Relations	15.9%	8.7%
	Public Contracts	9.1%	7.7%
	Organizations	9.1%	3.1%
	SKOS	4.5%	3.1%
	Registered Organization	4.5%	2.6%
	FOAF	4.5%	1.0%
	vCard	2.3%	2.6%
	Dublin Core	-	3.1%
	DBpedia	-	0.5%

Table 3 .

 3 Query 1 results [Hellenic Telecommunications Organization S.A.]

		Number of Payments	11,288
		Total Amount (in Euros)	44,550,925.34
		Number of Payments	289
	e-Procurement	Total Amount (in Euros)	30,570,036.85
		Number of Subsidies	5
	NSRF	Total Amount (in Euros)	208,311
	Expense Approval Counter	7,738
	Expense Approval Amount	35,746,746.58
	Founding Year	1949
	Net Income	676,400,000
	Number of Employees	29,330

Table 4 .

 4 Federated Query 2 results [Hellenic Telecommunications Organization S.A.]

http://linkedeconomy.org/en/

http://publicspending.net

http://obstracker.org

http://openbudgets.eu

http://openspending.org

http:// clearspending.org

http://ted.openspending.org/

http:// fiscal.dataprotocols.org

http://www.open-contracting.org/

http://opencorporates.com

http://product.okfn.org

https://github.com/LinkedEcon/LinkedEconomyOntology-ELOD

http://elod7.linkedeconomy.org:8890/sparql

http://ckan.linkedeconomy.org

Acknowledgements

This work has been supported by a national co-funded project (LinkedEconomy.org) under the 4 th National Strategic Reference Framework (NSRF) period between 2007 and 2013. It was implemented by the Ministry of Education and Religious Affairs and the University of Piraeus Research Centre. The project aimed at providing a universal access to Greek and International economy data, as well as at promoting the benefits of linking heterogeneous sources under the concept of open and reusable data in this critical domain.

DATA SOURCES USED


Marios Koniaris

Ioannis Anagnostopoulos

Yannis Vassiliou

Diversifying the Legal Order

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

Introduction

Nowadays, as a consequence of many open data initiatives, more and more publicly available portals and datasets provide legal resources to citizens, researchers and legislation stakeholders. Thus, legal data that was previously available only on a specialized audience and in "closed" format is now freely available on the internet. Portals as the EUR-Lex4 , the European Union's database of regulations, the on-line version of the United States Code5 , United Kingdom 6 ,and the Australian7 , just to mention a few, serve as an endpoint to access millions of regulations, legislation, judicial cases, or administrative decisions. Such portals allow for multiple search facilities, as to assist users to find the information they need. For instance the user can perform simple search operations or utilize predefined classificatory criteria (e.g. year, legal basis, subject matter) to find relevant to his/ her information needs legal documents.

At the same time, however, the amount of Open Legal Data makes it difficult, both for legal professionals or the citizens to find relevant and useful legal resources. For example, it is extremely difficult to search for a relevant case law, by using boolean queries or the references contained in the judgment. Consider, for example, a patent lawyer who want to find patents as reference case and submits a user query to retrieve information. A diverse result, i.e. a result containing several claims, heterogeneous statutory requirements and conventions -varying in the numbers of inventors and other characteristics-is intuitively more informative than a set of homogeneous results that contain only patents with similar features. In this paper, we propose a novel way to efficiently and effectively handle similar challenges when seeking information in the legal domain.

Diversification is a method of improving user satisfaction by increasing the variety of information shown to user. As a consequence, the number of redundant items in a search result list should decrease, while the likelihood that a user will be satisfied with any of the displayed results should increase. There has been extensive work on query results diversification (see Section 2), where the key idea is to select a small set of results that are sufficiently dissimilar, according to an appropriate similarity metric.

Diversification techniques in legal information systems can be helpful not only for citizens but also for law issuers and other legal stakeholders in companies and large organizations. Having a big picture of diversified results, issuers can choose or properly adapt the legal regime that better fits their firms and capital needs, thus helping them operate more efficiently. In addition, such techniques can also help lawmakers, since deep understanding of legal diversification promotes evolution to better and fairer legal regulations for the society [START_REF] Alces | Legal Diversification[END_REF].

The objective of this paper is to define and evaluate the potential of results diversification in the field of legal information retrieval. To this end, we adopt various methods from the literature that are introduced for search result diversification [MMR [START_REF] Carbonell | The use of mmr, diversity-based reranking for reordering documents and producing summaries[END_REF], Max-Sum [START_REF] Gollapudi | An axiomatic approach for result diversification[END_REF], Max-Min [START_REF] Gollapudi | An axiomatic approach for result diversification[END_REF] and MonoObjective [START_REF] Gollapudi | An axiomatic approach for result diversification[END_REF]]. We evaluate the performance of the above methods on a legal corpus subjectively annotated with relevance judgments, using metrics employed in TREC Diversity Tasks. To the best of our knowledge none of these methods were employed in the context of diversification in legal information retrieval and evaluated using diversity-aware evaluation metrics.

Our findings reveal that, diversification methods, employed in the context of legal IR, demonstrate notable improvements in terms of enriching search results with otherwise hidden aspects of the legal query space. Furthermore our qualitative analysis can provide helpful insights for legal IR systems, wishing to balance between reinforcing relevant documents, result set similarity, or sampling the information space around the query, result set diversity.

The remainder of this paper is organized as follows: Section 2 reviews previous work in query result diversification and in the field of legal text retrieval. Section 3 introduces the concepts of search diversification and presents diversification algorithms, while section 4 describes our experimental results and discuss their significance. Finally, we draw our conclusions and future work aspects in Section 5.

Related Work

In this section, we firstly present related work on query result diversification and then we focus on same issues in legal text retrieval techniques.

In order to satisfy a wide range of users, query results diversification has attracted a lot of attention in the field of text mining. The published literature on search result diversification is reviewed in [START_REF] Drosou | Search diversification[END_REF]. The maximal marginal relevance criterion (MMR), presented in [START_REF] Carbonell | The use of mmr, diversity-based reranking for reordering documents and producing summaries[END_REF], is one of the earliest works on diversification and aims at maximizing relevance while minimizing similarity to higher ranked documents. Search results are re-ranked as the combination of two metrics, one measuring the similarity among documents and the other the similarity between documents and the query. In [START_REF] Gollapudi | An axiomatic approach for result diversification[END_REF] a set of diversification axioms is introduced and it is proven that it is not possible for a diversification algorithm to satisfy all of them. Additionally, since there is no single objective function that is suitable for every application domain, the authors propose three diversification objectives, which we adopt in our work. These objectives differ in the level where the diversity is calculated, e.g. whether it is calculated per separate document or on the average of the currently selected documents.

In another approach, researchers utilized explicit knowledge as to diversify search results. [START_REF] Santos | Exploiting query reformulations for web search result diversification[END_REF] proposed a diversification framework, where the different aspects of a given query are represented in terms of sub-queries and documents are ranked based on their relevance to each sub-query. [START_REF] Agrawal | Diversifying search results[END_REF] propose a diversification objective that tries to maximize the likelihood of finding a relevant document in the top-k positions given the categorical information of the queries and documents. [START_REF] Hu | Search result diversification based on hierarchical intents[END_REF] organizes user intents in a hierarchical structure and proposes a diversification framework to explicitly leverage the hierarchical intent. The key difference between these works and the ones utilized in this paper is that we do not rely on external knowledge e.g. taxonomy, query logs to generate diverse results. Queries are rarely known in advance, thus probabilistic methods to compute external information are not only expensive to compute, but also have a specialized domain of applicability. Instead, we evaluate methods that rely only on implicit knowledge of the legal corpus utilized and on computed values, using similarity (relevance) and diversity functions (e.g., tf-idf cosine similarity) in the data domain.

In respect to legal text retrieval that traditionally relies on external knowledge sources, such as thesauri and classification schemes, various techniques are presented in [START_REF] Moens | Innovative techniques for legal text retrieval[END_REF]. Several supervised learning methods that have been proposed to classify sources of law according to legal concepts can be found in [START_REF] Biagioli | Automatic semantics extraction in law documents[END_REF], [START_REF] Mencia | Efficient pairwise multilabel classification for largescale problems in the legal domain[END_REF], [START_REF] Grabmair | Introducing luima[END_REF]. Legal document summarization techniques that scope to make the content of the legal documents, notably cases, more easily accessible are described in [START_REF] Farzindar | Legal text summarization by exploration of the thematic structures and argumentative roles[END_REF], [START_REF] Farzindar | Letsum, an automatic legal text summarizing system. Legal knowledge and information systems[END_REF], [START_REF] Moens | Summarizing court decisions[END_REF].

Finally, a similar approach with our work is described in [START_REF] Aktolga | Detecting outlier sections in us congressional legislation[END_REF], where the authors utilize information retrieval approaches to determine which sections within a bill tend to be outliers. However, our work differs in a sense that we maximize the diversify of the result set, rather than detect section outliers within a specific bill.

Legal Document ranking using diversification

Here, we firstly provide an overview of general diversification processes focusing in the problem we address. Then, we define the ranking features and describe the diversification algorithms employed in this work.

Diversification Overview

Initially, the user submits his/ her query as a way to express an information need and receives relevant documents. Diversification aims at finding a subset of those documents that maximize an objective function that quantifies the diversity of documents in S. More specifically, the problem is formalized as follows:

Definition 1 (Legal document diversification) Let q be a user query and N a set of documents relevant to the user query. Find a subset S ⊆ N of documents that maximize an objective function f that quantifies the diversity of documents in S. S = argmax

|S|=k S⊆N f (N ) (1)
Typicaly, diversification techniques measure diversity in terms of content, where textual similarity between items is used in order to quantify information similarity. In the Vector Space model, each document u can be represented as a term vector U = (is w1u , is w2u , ..., is wmu ) T , where w 1 , w 2 , ..., w m are all the available terms, and is can be any popular indexing schema e.g. tf, tf -idf, logtf -idf . Queries are represented in the same manner as documents.

-Document Similarity. Various well-known functions from the literature (e.g. Jaccard, cosine similarity etc.) can be employed at computing the similarity of legal documents. In this work, we choose cosine similarity as a similarity measure, thus the similarity between documents u and v, with term vectors U and V is:

sim(u, v) = cos(u, v) = U • V U V (2)
-Document Distance. The distance of two documents is

d(u, v) = 1 -sim(u, v) (3)
-Query Document Similarity. The relevance of a query q to a given document u can be assigned as the initial ranking score obtained from the IR system, or calculated using the similarity measure e.g. cosine similarity on the corresponding term vectors r(q, u) = cos(q, u)

Diversification Heuristics

Diversification methods usually retrieve a set of documents based on their relevance scores, and then re-rank the documents so that the top-ranked documents are diversified to cover more query subtopics. Since the problem of finding an optimum set of diversified documents is NP-hard, a greedy algorithm is often used to iteratively select the diversified set S. Let N the document set, u, v ∈ N , r(q, u) the relevance of u to the query q, d(u, v) the distance of u and v, S ⊆ N with |S| = k the number of documents to be collected and λ ∈ [0..1] a parameter used for setting trade-off between relevance and similarity. In this paper, we focus on the following representative diversification methods:

-MMR: Maximal Marginal Relevance [START_REF] Carbonell | The use of mmr, diversity-based reranking for reordering documents and producing summaries[END_REF], a greedy method to combine query relevance and information novelty, iteratively constructs the result set S by selecting documents that maximizes the following objective function

f M M R (u, q) = (1 -λ) r(u, q) + λ v∈S d(u, v) (5)
MMR incrementally computes the standard relevance-ranked list when the parameter λ = 0, and computes a maximal diversity ranking among the documents in N when λ = 1. For intermediate values of λ ∈ [0..1], a linear combination of both criteria is optimized. The set S is usually initialized with the document that has the highest relevance to the query. Since the selection of the first element has a high impact on the quality of the result, MMR often fails to achieve optimum results. -MaxSum: The Max-sum diversification objective function [START_REF] Gollapudi | An axiomatic approach for result diversification[END_REF] aims at maximizing the sum of the relevance and diversity in the final result set. This is achieved by a greedy approximation algorithm that selects a pair of documents that maximizes Eq.6 in each iteration.

f M AXSU M (u, v, q) = (1 -λ) (r(u, q) + r(v, q)) + 2λ d(u, v) (6)
where (u, v) is a pair of documents, since this objective considers document pairs for insertion. When |S| is odd, in the final phase of the algorithm an arbitrary element in N is chosen to be inserted in the result set S. -MaxMin: The Max-Min diversification objective function [START_REF] Gollapudi | An axiomatic approach for result diversification[END_REF] aims at maximizing the minimum relevance and dissimilarity of the selected set. This is achieved by a greedy approximation algorithm that select a document that maximizes Eq. 7 in each iteration.

f M AXM IN (u, q) = (1 -λ) r(u, q) + λ min v∈S d(u, v) (7)
where min v∈S d(u, v) is the minimum distance of u to the already selected documents in S. -MonoObjective: MonoObjective [START_REF] Gollapudi | An axiomatic approach for result diversification[END_REF] combines the relevance and the similarity values into a single value for each document. It is defined as:

f M ON O (u, q) = r(u, q) + λ |N | -1 v N d(u, v) (8)

Experimental Setup

In this section, we describe the legal corpus we use, the set of query topics and the respective methodology for subjectively annotating with relevance judgments for each query, as well as the metrics employed for the evaluation assessment. Finally, we provide the results along with a short discussion.

Legal Corpus

Our corpus contains 3.890 Australian legal cases from the Federal Court of Australia 8 . The cases were originally downloaded from AustLII9 and were used in [START_REF] Galgani | Combining different summarization techniques for legal text[END_REF] to experiment with automatic summarization and citation analysis. The legal corpus contains all cases from the Federal Court of Australia spanning from 2006 up to 2009. From the cases, we extracted all needed text for our diversification framework. Our index was built using standard stop word removal and porter stemming, with log based tf -idf indexing technique, resulting in a total of 3.890 documents, 9.782.911 terms and 53.791 unique terms. Table 1 summarizes testing parameters and their corresponding ranges. To obtain the candidate set N , for each query sample we keep the top -n elements using cosine similarity and a log based tf -idf indexing schema. Our experimental studies are performed in a two-fold strategy: i) qualitative analysis in terms of diversification and precision of each employed method with respect to the optimal result set and ii) scalability analysis of diversification methods when increasing the query parameters.

Evaluation Metrics

We evaluate diversification methods using metrics employed in TREC Diversity Tasks 10 . In particular we report:

-a-nDCG: a-Normalized Discounted Cumulative Gain [START_REF] Clarke | Novelty and diversity in information retrieval evaluation[END_REF] metric quantifies the amount of unique aspects of the query q that are covered by the top -k ranked documents. We use a = 0.5, as typical in TREC evaluation.

-ERR-IA: Expected Reciprocal Rank -Intent Aware [START_REF] Chapelle | Expected reciprocal rank for graded relevance[END_REF] is based on interdependent ranking. The contribution of each document is based on the relevance of documents ranked above it. The discount function is therefore not just dependent on the rank but also on the relevance of previously ranked documents.

-S-Recall: Subtopic-Recall [START_REF] Zhai | Beyond independent relevance[END_REF] quantifies the amount of unique aspects of the query q that are covered by the top -k ranked documents

Relevance Judjements

As mentioned above, the evaluation of diversification requires a data corpus, a set of query topics and a set of relevance judgments, preferably made by human assessors for each query. In the absence of a standard dataset and since it was not feasible to involve legal experts in this study, we have employed an subjective way to annotate our corpus with relevance judgments for each query. To this end, we employed the following method: User Profiles/ Queries We used the West Law Digest Topics11 as candidate user queries. In other words, each topic was issued as candidate query to our retrieval system. Outlier queries, whether too specific/rare or too general, where removed using the interquartile range, below or above values Q1 and Q3, sequentially in terms of number of hits in the result set and score distribution for the hits, demanding in parallel a minimum cover of min|N | results. In total, we kept 289 queries. Table 2 provides a sample of the topics we further consider as user queries. Query assessments and ground-truth. For each topic/ query we kept the top -n results. An LDA topic model, using an open source implementation 12 , was trained on the top -n results for each query. Based on the resulting topic distribution and with an acceptance threshold of 20%, we can infer whether a document is relevant for an aspect. We have made available our complete dataset, ground-truth data, queries and relevance assessments in standard qrel format, as to enhance collaboration and contribution in respect to diversification issues in legal IR 13 .

Results

As a baseline to compare diversification methods, we consider the simple ranking produced by cosine similarity and log based tf-idf indexing schema. The interpolation parameter λ ∈ [0..1] is tuned in 0.1 steps separately for each method. Results are presented with fixed parameter n = |N |. Note that each of the diversification variations, is applied in combination with each of the diversification algorithms and for each user query. Fig. 1: alpha-nDCG at various levels @5, @10, @20 for baseline, MMR, MAX-SUM, MAXMIN and MONO methods [Best viewed in color]

Figure 1 shows the a-nDCG of each method for different values of λ. Interestingly, all methods (MMR, MaxSum, MaxMin and Mono) outperformed the baseline ranking, while as λ increases, preference to diversity also increases for all methods. The trending behavior of MMR, MaxMin, and MaxSum is very similar especially at levels @10, and @20, while at level @5 MaxMin and Max-Sum presented nearly identical a-nDCG values in many λ values (e.g. 0.1, 0.2, 0.4, 0.6, 0.7). Finally, MMR constantly achieves better results in respect to the rest methods, while MaxMin and MaxSum follow. MONO despite the fact that performs better than the baseline in all λ values, still always presents the lower performance when compared to MMR, MaxMin, and MaxSum. (c) S-Recall@20 Fig. 3: SubTopic Recall at various levels @5, @10, @20 for baseline, MMR, MAX-SUM, MAXMIN and MONO methods. [Best viewed in color]

Figure 2 depicts the ERR-IA plots for each method in respect to different values of λ, while similarly Figure 3 shows the Subtopic-Recall plots. It is clear that all of the approaches (MMR, MaxSum, MaxMin and Mono) tend to perform better than the selected baseline ranking method. Moreover, as λ increases, preference to diversity as well as Subtopic-Recall accuracy increases for all tested methods. We noticed a Similar trending behavior with the one discussed for Figure 1. We also observed that MaxMin tends to perform better than MaxSum. There were few cases where both methods presented nearly similar performance especially in lower recall levels (e.g. for nERR-IA@5 when λ equals to 0.1, 0.4, 0.6, 0.7, and for S-Recall@5 when λ equals to 0.1, 0.2, 0.6, 0.7, 0.8). Once again, MONO presents the lower performance when compared to MMR, MaxMin, and MaxSum for both nERR-IA and S-Recall metric for all λ values applied.

In summary, among all the results, we note that the trends in the graphs look very similar. Clearly enough, the utilized diversification methods statistically significantly 14 outperform the baseline method, offering legislation stakeholders broader insights in respect to their information needs. Furthermore trends across the evaluation metric graphs, highlight balance boundaries for legal IR systems between reinforcing relevant documents or sampling the information space around the legal query.

Conclusions

In this paper, we studied the novel problem of diversifying legal search results. We adopted and compared the performance of several state of the art methods from the web search domain as to deal with the challenges in this paradigm. We performed an exhaustive evaluation of all the methods, by using a real data set from the Common Law domain that we subjectively annotated with relevance judgments. Our findings i) reveal that diversification methods offer notable improvements and enrich search results around the query space and ii) offer balance boundaries between reinforcing relevant documents or sampling the information space around the legal query.

A challenge we faced in this work was the lack of ground-truth. We hope on an increase of the size of truth-labeled data set in the future, which would enable us to draw further conclusions about the diversification techniques. We also plan to incorporate additional features in our legal search result diversification framework, specifically tailored across the legislation domain. Finally, we aim at investigating the performance of heuristics provided for other domains, e.g. for text summarization and graph diversification.

Table 1 :

 1 Parameters Tested in the Experiments

	Parameter	Range
	tradeoff l values	0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9
	candidate set size n = |N |	100
	result set size k = |S|	5, 10, 20
	# of sample queries	298

Table 2 :

 2 West Law Digest Topics as user queries

	1: Abandoned and Lost Property	3: Abortion and Birth Control
	24: Aliens Immigration and Citizenship	88: Compromise and Settlement
	291: Privileged Communications and Confidentiality 363: Threats Stalking and Harassment

http://www.worldlii.org/worldlii/declaration/

http://eur-lex.europa.eu/

http://uscode.house.gov/

http://www.legislation.gov.uk/

https://www.comlaw.gov.au/

http://www.fedcourt.gov.au

http://www.austlii.edu.au

http://trec.nist.gov/data/web10.html

The West American Digest System is a taxonomy of identifying points of law from reported cases and organizing them by topic and key number. It is used to organize the entire body of American law

http://mallet.cs.umass.edu/

http://www.dbnet.ntua.gr/ mkoniari/LegalDiv

Statistical significance with the paired two-sided t-test (p -value < 0.05)


Ioannis P Chochliouros
email: ichochliouros@oteresearch.gr

Ioannis Giannoulakis

Tassos Kourtis
email: kourtis@iit.demokritos.gr

Maria Belesioti
email: mbelesioti@oteresearch.gr

Evangelos Sfakianakis

Anastasia S Spiliopoulou
email: aspiliopoul@ote.gr

Nikolaos Bompetsis
email: nbompetsis@oteresearch.gr

Emmanouil Kafetzakis

Leonardo Goratti
email: leonardo.goratti@create-net.org

Athanassios Dardamanis
email: adardamanis@smartnet.gr

A Model for an Innovative 5G-Oriented Architecture, Based on Small Cells Coordination for Multi-tenancy and Edge Services

Keywords: Fifth Generation Mobile Technology (5G), Mobile Edge Computing (MEC), Multi-tenancy, Network Functions Virtualization (NFV), Software Defined Networking (SDN), Small Cell (SC), Virtualised Network Function (VNF)

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

Introduction

Internet and electronic communication networks are fundamental "enablers" of our modern economy as they promote, support and disperse an extended variety of digital applications and enhanced facilities, satisfying both residential and corporate customers' various needs. In particular, the immense penetration and the continuous growth of wireless data services driven by mobile Internet and smart devices has further promoted the investigation effort of the fifth generation mobile technology (5G), which is expected to "fulfill" the demands and business contexts of the year 2020 and beyond. 5G is expected to assist an entirely mobile and fully connected and converged society as well as to endow a diversity of socio-economic transformations in immeasurable ways, many of which are unimagined today, including those for productivity, sustainability and well-being [START_REF] El Hattachi | Next Generation Mobile Networks (NGMN) Alliance: 5G White Paper[END_REF]. The claims of a modern society where both mobility and connectivity are among the "core" features are characterized by the incredible growth in connectivity and density/volume of traffic, the required multilayer densification in enabling this, and the broad range of use cases and business models expected to assist any relevant process [2]. Therefore, in 5G there is a need to "push" the envelope of performance to provide, where necessary, much greater throughput, much lower latency, ultra-high reliability, much higher connectivity density, and higher mobility range. This enhanced way of performance is expected to be provided along with the capability to "control" a highly heterogeneous environment with the aim, among others, to ensure security and trust, identity and privacy [START_REF] Zakrzewska | Towards Converged 5G Mobile Networks -Challenges and Current Trends[END_REF]. 5G is to become the fundamental means to realize the full potential of the global "networked society". The new capabilities of 5G span several dimensions, including tremendous flexibility, lower energy requirements and improved energy efficiency, greater capacity, bandwidth, security, reliability and data rates, as well as enhanced indoor coverage lower latency and device costs [START_REF]5G Public Private Partnership (5G-PPP): 5G Vision: The 5G-PPP Infrastructure Private Public Partnership: The Next Generation of Communication Network and Services[END_REF].

Technology advancements of the recent years (e.g., SDN [START_REF] Nadeau | SDN: Software Defined Networks, 1st Edition[END_REF], NFV ([6], [START_REF] Patouni | Network Virtualisation Trends: Virtually Anything is Possible by Connecting the Unconnected[END_REF]), big data, All-IP) can drastically modify the way networks are being constructed and managed. These changes "enable" the development of a highly flexible infrastructure that allows cost-efficient development of networks and of associated services. The 5G architecture should comprise modular network functions that could be further expanded and scaled on demand, to accommodate several use cases in an agile and cost-efficient manner [START_REF] Thompson | 5G Wireless Communication Systems: Prospects and Challenges[END_REF]. As a consequence, future networks are becoming highly dynamic and distributed, typically consisting of various multi-operator heterogeneous networks and a huge number of network elements and users' devices/equipment.

In order to efficiently deal with automated network management solutions able to "address" resource utilization, one current trend is to "move functionality at the network edge", thus reducing complexity at the network core, leveraging on the increasing computing and processing power of the end-user devices [START_REF] Manzalini | Clouds of Virtual Machines in Edge Networks[END_REF]. Network virtualization and Software Defined Networking (SDN) compose a much promising solution for orchestrating the allocation of physical and virtual resources that can be instrumental in this direction. Network Functions Virtualization (NVF) distinguishes logical services from physical resources, also allowing for moving resources to other network locations [START_REF]Network Functions Virtualisation -Introductory White Paper[END_REF]. This concept is different from the notion of SDN networks which proposes the full decoupling of the network control and data planes, moving the control of the network behavior to third party software running in external dedicated or distributed servers [START_REF] Manzalini | Clouds of Virtual Machines in Edge Networks[END_REF]. Delivering services at the edge of the modern network enables service providers to offer services and a user experience that cannot be surpassed in terms of responsiveness and performance.

Future networks will also have to be expanded much more densely than today's networks and will become significantly more heterogeneous than today, especially in terms of: transmit power, antenna configuration, supported frequency bands, transmission bandwidths, directional blindness, multi-hop architecture and duplex arrangements. The radio-network architectures of the nodes are expected to vary from stand-alone base stations (BSs) to systems with different degrees of centralized processing, depending on the sort of the available backhaul technology [START_REF] El Hattachi | Next Generation Mobile Networks (NGMN) Alliance: 5G White Paper[END_REF]. One major venue in future 5G networks is dense deployment of Small Cells (SCs) coexisting with micro-and macro-cells as well as other systems (such as WiFi, 4G and 3G), thus comprising a Heterogeneous Network (HetNet) [START_REF] Andrews | Seven Ways that HetNets are a Cellular Paradigm Shift[END_REF]. As the number of mobile devices and data traffic increases following a clear exponential growth, the installation of SCs appears to be a "suitable and efficient way" so that to achieve enhanced performance and capacity to both indoor and outdoor places/locations, with significant impact upon all any of the corresponding market-related scenarios. Small Cells are radio transmitters [START_REF]European Commission: 5G: Challenges, Research Priorities, and Recommendations -Joint White Paper[END_REF] whose complexity range from just antennas and radio circuits (i.e. remote radio head) up to a full functioning evolved Node B (eNodeB) base station. Small cells are increasingly recognised by the global telecom operators as "playing an essential role" in future broadband networks. They address -quite satisfactorily- [START_REF]An Assessment of the Value of Small Cell Services to Operators (based on Virgin Media Trials) -Version 3[END_REF] many of the key challenges faced by market actors such as: (i) increasing capacity (which actually appears to be the most critical challenge); (ii) improving depth of coverage, especially inside buildings; (iii) improving user experience, especially the typical available data rates, and; (iv) delivering value added services, especially those enabled by high-precision location information. Future 5G networks will be "denser" to realize the capacity increase offered by the deployment of SCs. In that sort of considered scenario, reducing the overall costs becomes an issue of major importance. One option of realizing this is by enabling an effective "sharing" of the network infrastructure. To this aim, Radio Access Networks (RAN) virtualization techniques are ultimately to provide logical isolated pieces ("slices") of the access infrastructure to individual tenants, so they can operate them as "if each virtual slice were a single real physical infrastructure". A well-coordinated sharing of the access infrastructure yields a higher throughput per area.

The target of the SESAME EU-funded H2020 5G-PPP project (Grant Agreement No.671596) [START_REF] Sesame H | 5G-PPP Project[END_REF] is to design and develop a novel 5G platform based on small cells, featuring multi-tenancy between network operators and also attach to them edge cloud capabilities to be offered to both the network operators and the mobile users. Thus, the key innovations proposed by SESAME focus on the novel concepts of a multioperator (multi-tenancy) enabling framework and also on providing an edge-based, virtualised execution environment. SESAME aims at providing a fresh 5G mobile network architecture so as to support the ambitious goal of small cell virtualization, multitenancy and edge cloud services. The present work discusses a model for architecture able to fulfil multi-tenancy purposes within a small cell-based concept as developed by the actual SESAME effort, still being in dynamic progress. In Section 2 we identify several essential definitions, as well as the fundamental SESAME components and their roles. In Section 3 we focus upon the proposed architectural approach and we discuss the corresponding "key features" affecting the entire scope. Finally, in Section 4 we summarize and we also propose some potential extensions, in parallel with options for future work in more enhanced environments.

Fig. 1. Scope of SESAME components (physical view)

In order to achieve the previously mentioned essential aims, some further considerations need to be made on "how to separate -or combine-the network and the computing resources", and also, "which small cell functions should be physical network functions" and "which ones should be virtual". The SESAME approach to this challenge is performed by enhancing the Small Cells with Micro-Servers that are able to deliver virtualized computing and networking resources and by being able to "form" clusters, thus creating a kind of Light Data Centre (DC) at the edge. This Light DC is further complemented by supplementary components that reside either close to the edge or to the backbone, such as the VIM or the CESCM, in order to provide the proper reference points -or the scope-for the whole network (as depicted in Fig. 1). In order to "translate" the concept of that figure, we consider the following definitions that are also fundamental for the wider SESAME-based scope:  Execution infrastructure, micro-server (μS): Specific hardware that is placed inside the Small Cell and provides processing power (also can potentially include some memory and storage capabilities).  Small Cell Network Operator (SCNO): A legal entity that provides the physical connection to Virtual Small Cells and CESCs.  Virtual Small Cell Network Operator (VSCNO): This implicates companies/legal entities that do not possess the equipment but lease it (through appropriate Service Level Agreements (SLAs) from another company), so as to provide wireless communications services and deliver services to end users.  Cloud Enabled Small Cell (CESC): The Small Cell device which includes a micro-server in hardware form.  Cluster of CESCs: A group of CESCs that are collocated, exchange information and are properly coordinated. As a trivial case, one CESC can be called CESC cluster.  Light Data Centre (Light DC): The hardware entity composed by the microservers of the CESCs forming a cluster (see Fig. 3).  CESC Manager (CESCM): The architectural component in charge of managing and orchestrating the cloud environment of the Light DC, as well as management of small cell functions. It can manage, at the same time, multiple clusters, a cluster or a single CESC.  Virtualised Infrastructure Management (VIM): Manager of the HW and networking resources (i.e., lifecycle, provision, placement and operation) constituting of a cluster of micro-servers, namely the Light DC, and the networking nodes and links (i.e., both virtual and physical).  Backhaul Provider (BP): A legal entity/company that provides the backhaul connection (either wired or wireless) of the Small Cells and Macro Cells. This could be an Internet Service Provider (ISP) or the traditional Mobile Network Operator;

 "NMS" stands for the respective Network Management System and "EPC" for the Evolved Packet Core (also known as the System Architecture Evolution (SAE) Core) which is amongst the main components of the SAE architecture and will serve as the "equivalent" of GPRS networks1 .

Key Features of the SESAME-based Architecture

The key innovations proposed in the SESAME architecture do emphasize on the novel concepts of virtualising Small Cell networks by leveraging the paradigms of a multi-operator (multi-tenancy) enabling framework coupled with an edge-based, virtualised execution environment. SESAME falls in the scope of these two principles and "promotes" the adoption of Small Cell multitenancy -i.e., multiple network operators will be able to use the SESAME platform, each one using his own network "slice". Moreover, the principal idea is to "endorse" the deployment of Small Cells with some virtualized functions, with each Small Cell containing also a micro-server through appropriate fronthaul technology. A micro-server is based on a non-x86 architecture2 using 64-bit ARMv8 technology 3 . Together with the SC, they form the Cloud-Enabled Small Cell (CESC), while a number of CESCs compose the "CESC cluster" capable to provide access to a geographical area with one or more operators. At this point, we illustrate a brief description of the two main technological fields that constitute the core innovative fields of the SESAME framework. This targeted way of "decomposition" has been the "starting point" for building, in the continuity, an accurate framework for the intended SESAME architecture. To that end, the NFV technology is going to be used as a fundamental enabler that will offer a virtualisation platform and "meet" the SESAME requirements, namely NFV-driven small cell functions and NFV-based network services. The left-hand side of Fig. 2 presents the Management and Orchestration (MANO) framework for the NFV part.

Fig. 2. Scope of SESAME components (physical view)

On the other hand, a Small Cell network capable to support more than one network operator is also envisaged (Fig. 2 -right-hand side). Relevant 3GPP specifications have already added some support for Radio Access Network (RAN) sharing [START_REF]Generation Partnership Project (3GPP)[END_REF]. Although two main architectures are identified, namely Multi-Operator Core Network (MOCN), where the shared RAN is directly connected to each of the multiple operator's core networks, and Gateway Core Network (GWCN), where a shared core network is deployed so that the interconnection of the multiple operator's core networks is done at core network level, the MOCN case has been identified as the exclusive enabler for multitenancy features in SESAME platform. The related infrastructure consists of a number of Small Cells and the corresponding SC network functions such as gateways and management systems. The adopted architecture is based on the current 3GPP framework for network management in RAN sharing scenarios ([16], [17]). Assuming LTE (Long Term Evolution) technology as the contextual framework of basis, the interconnection of the SCs of the SCaaS provider to the Evolved Packet Core of the tenant is done through the S1 interface, delivering both data (e.g., transfer of end-users traffic) and control (e.g., activation of radio bearers) plane functions. Based on the required functionalities as well as to the architectural principles that have been mentioned above, it is possible to derive an overall, high-level view of the SESAME system, as the one proposed by Fig. 3, above. To that end, the CESC offers computing, storage and radio resources. Through virtualization, the CESC cluster can be seen -or assessed-as a cloud of resources which can be "sliced" to enable multitenancy. Therefore, the CESC cluster becomes a neutral host for mobile Small Cell Network Operators (SCNO) or Virtual SCNO (VSCNO) who desire to share IT-and network-resources at the edge of the mobile network. In addition, cloud-based computation resources can be provided through a virtualised execution platform. This execution platform is used to support the required Virtualized Network Functions (VNFs) that implement the different features/capabilities of the SCs (and eventually of the core network), the cognitive management and "self-x" operations [START_REF] Sesame H | 5G-PPP Project: Deliverable D2.4 -Specification of the Infrastructure Virtualisation, Orchestration and Management -First Iteration[END_REF] (e.g. self-planning, self-optimising and self-healing) 4 , as well as the computing support for the mobile edge applications of the involved end-users.

The CESC clustering enables the achievement of a micro-scale virtualised execution infrastructure in the form of a distributed data centre (i.e., the Light Data DC), enhancing the virtualisation capabilities and process power at the network edge. Network Services (NS) are supported by VNFs hosted in the Light DC -constituted by one or more CESC-, leveraging on SDN and NFV functionalities that allow achieving a satisfactory level of flexibility and scalability at the cloud infrastructure edge. More specifically, VNFs are executed as Virtual Machines (VMs) inside the Light DC, which is provided with a hypervisor (based on the concept of a Kernel-based Virtual Machine (KVM)) specifically extended to support carrier grade computing and networking performance.

Over the provided virtualised execution environment (i.e., the Light DC), it is possible to chain different VNFs to meet/fulfil a requested NS by a tenant (i.e., a mobile network operator). Note that, in the context of SESAME, a NS is conceived as a "collection" of VNFs that jointly supports data transmission between User Equipment (UE) and operators' Evolved Packet Core, with the possibility to involve one or several service VNFs in the data path. Therefore, each NS is deployed as a chain of SC VNFs and Service VNFs.

Finally, the CESC Manager (CESCM) is the central service management and orchestration component in the overall architecture figure. Generally speaking, it integrates all the necessary network management elements, traditionally suggested in 3GPP, and the novel recommended functional blocks of NFV MANO [START_REF]ETSI): NFV Management and Orchestration -An Overview[END_REF]. A single instance of CESCM is able to operate over several CESC clusters, each constituting a Light DC, through the use of a dedicated Virtual Infrastructure Managers per cluster. With regard to interfaces, it must be noted that Fig. 3 mostly depicts reference pointswhich may contain one or more actual interfacesbetween architectural layers. Each reference point label starts with "S-" to differentiate it from interfaces defined in ETSI 4 Self-Organizing Networks (SON), also referred to as "self-x" features, include several techniques for automating the operation of the network, by automatically tuning different network settings; these can be implemented either as physical or as virtual network functions instantiated and executed in the micro server. Typical examples of self-x features include: Inter-Cell Interference Coordination (ICIC) to configure the power; time and frequency resources to minimize inter-cell interference; Coverage and Capacity Optimization (CCO) to adjust RF parameters; Automatic Neighbour Relationships (ANR) to manage neighbour lists; Mobility Load Balancing (MLB) to manage traffic loads between cells, and; Mobility Robustness Optimisation (MRO) to optimize the operation of handover procedures.

NFV ISG documents 5 (and in specific Vi-Vnfm, Or-Vi, Ve-Vnfm, Nf-Vi) -although in several cases the functionality of the reference point will be almost "aligned" to the actual ETSI definitions.

Concluding Remarks and Future Aspects

In order to suitably "address" the needs and requirements of a vigorous and agile network management in the forthcoming -purely innovative-5G era, and though a proper building effort upon the pillars of NFV, mobile edge computing and cognitive management, SESAME's main goal is both the development/expansion and the demonstration of an innovative architectural model, capable of providing Small Cell coverage to multiple operators "as a Service", so that to strongly support multitenancy. To this fundamental aim which implicates an enormous business potential, SESAME envisages not only to "virtualise" but also to "partition" Small Cell capacity, while simultaneously aims to support enhanced edge cloud services by enriching Small Cells with dedicated micro-servers. This work has thus presented a "first conceptual approach" to the high-level overall architecture of the broader SESAME system. The SESAME architecture discussed in the present paper can be further extended based on several current trends towards 5G, in particular those already identified by the ETSI MEC standardization group. Recent virtualization technologies permit bringing mobile core functions "close" to the mobile edge, hence enabling the deployment of the respective service platform alongside the components of the EPC while still being in proximity to the involved users. There are several benefits coming from this way of deployment, since platform(s) can leverage on many tasks performed by legacy core components (e.g., the PDN Gateway), thus without the requirement of implementing them. Such tasks are related to, inter-alia, gating, GTP (GPRS Tunneling Protocol) encapsulation, QoS enforcement, charging, lawful interception and mobility support. The direct influence upon the current SESAME architecture would be to install/deploy the virtual EPC (vEPC) [START_REF] Basta | A Virtual SDN-Enabled LTE EPC Architecture: A Case Study for S-/P-Gateways Functions[END_REF], alongside with the virtual small cell and allow the NFV components executing their tasks at the egress point of the core (i.e., the SGi interface 6 ), instead of the ingress point (i.e., the S1 interface). In this manner, as aforementioned, NFVs can take actions directly on IP packet flows without the need for implementing GTP encapsulation/decapsulation mechanisms, as currently required in the SESAME architecture.

Fig. 3 .

 3 Fig. 3. SESAME Overall Architecture

The SESAME-based Conceptual ApproachIn reference to the fundamental SESAME approach, the Small Cell concept is evolved so that not only be able to provide multi-operator radio access capacity with virtualised Small Cells that can be integrated within the operator (tenant) infrastructures, but also to be capable of providing a virtualised implementation environment for delivering Cloud services at the network's edge. In order to achieve this, however, the Small Cell needs to offer mobile-edge computing (MEC) capabilities which, sequentially, will allow the virtual or mobile operators to increase the capacity of their own 4G/5G RAN infrastructures or to spread the range of their provided services, while preserving the essential agility to be able to offer these extensions, on demand.

More information about the detailed specific concept of the EPC can be found at: http://www.3gpp.org/technologies/keywords-acronyms/100-the-evolved-packet-core. (Additional supportive information is given in the references therein).

The x86 is a family of backward compatible instruction set architectures based on the Intel 8086 CPU and its Intel 8088 variant. More related -and indicative-information for this case can be found, for example, at: https://en.wikipedia.org/wiki/X86.

More relevant information about the corresponding technology can be found, for example, at: http://www.arm.com/products/processors/armv8-architecture.php.

More relevant information about the respective ETSI documentation can be found at: http://www.etsi.org/technologies-clusters/ technologies/689-network-functions-virtualisation

More informative data about this interface can be found at: http://lteworld.org/ltefaq/whatare-lte-interfaces.

Acknowledgments. The present work has been performed in the scope of the SESAME ("Small cEllS CoordinAtion for Multi-tenancy and Edge services") European Research Project and has been supported by the Commission of the European Communities (5G-PPP/H2020, Grant Agreement No.671596).