Dimosthenis Ioannidis

Angeliki Fotiadou

Stelios Krinidis

George Stavropoulos

Dimitrios Tzovaras

Spiridon Likothanassis

Big Data & Visual Analytics for Building Performance Comparison

Keywords: Big data analytics, Visual analytics, Building Performance

In this paper, big data and visual analytics techniques for comparing building performance under different scenarios and designs are presented. Large data consist of building information, energy consumption, environmental measurements and occupancy information, which are combined and correlated utilizing data analytics techniques, so as to extract useful semantic information about building performance. Also, visual analytics techniques are exploited to visualize them in a compact and comprehensive way taking into account properties of human cognition, perception and sense making. They analyze and visualize the performance of the buildings under comparison in the spatio-temporal domain assisting the analyst to compare them and detect patterns, templates and crucial points that are difficult to be detected otherwise. The performance comparison of different buildings or buildings of different designs or buildings with space usage rearrangement is an important factor in engineering that leads to building renovation and construction with low energy consumption and gas emissions in conjunction with comfort, utility and durability.

Introduction

Building behavior simulation is a fundamental task for energy management, building's spaces refinement, new building designing, etc. Building behavior simulation can lead to design buildings with higher energy efficiency and performance. It is a multi-parametric task, where each parameter affects different building's aspects and in different level. To this end, comparison tools able to correlate, combine and analyze all buildings information in conjunction with advanced visualization mechanisms are necessary to evaluate buildings' design performance and to compare them, so as to select and decide the optimal building's design. Building's behavior is comprised by a large number of heterogeneous information (e.g. geometric information, occupancy, energy consumption, etc.), which is very difficult to be handled, correlated and visualized. Statistical data analysis is useful if the relationship between variables is well defined. However, if the analyst does not know what to expect from the data then it often becomes necessary to visually explore the data in order to identify an appropriate statistical analysis technique. On the other hand, visual analytics techniques can correlate, analyze, extract semantic information and visualize the building information, assisting the analyst to make a decision about its performance.

There is a large number of visualization techniques in the literature, which can be divided into the following categories [START_REF] Keim | Visual data-mining techniques[END_REF][START_REF] Rzezniczak | Evaluation of multidimensional visualization techniques for medical patterns representation[END_REF][START_REF] Sun | A survey of visual analytics techniques and applications: State-of-the-art research and future challenges[END_REF]: a) 1D to 3D graphics: This category includes x-y-z plots, bar charts, line graphs, scatter plots, etc. [START_REF] Keim | Visual data-mining techniques[END_REF]. They are widely used to visualize an estimation of certainty about a state or hypothesis or the distribution of an attribute or even to visualize a data model [START_REF] Rzezniczak | Evaluation of multidimensional visualization techniques for medical patterns representation[END_REF]. b) Geometric techniques: Data with more than three dimensions are very difficult to be comprehensible by humans, so they are projected onto a 2D or 3D space providing an overview of their attributes. Matrix of Scatter Plots, Parallel Coordinates [START_REF] Wu | Opinionseer: Interactive visualization of hotel customer feedback[END_REF], BallotMaps [START_REF] Wood | Ballotmaps: Detecting name bias in alphabetically ordered ballot papers[END_REF], Prosection Views, Radial coordinates, Star coordinates, Kiviat diagrams, etc. [START_REF] Sun | A survey of visual analytics techniques and applications: State-of-the-art research and future challenges[END_REF], are some representative examples. Most of them represent data as a single 2D or 3D point cloud or object, and as a consequence, important information is missing. c) Pixel oriented techniques: They visually map each multivariate data item to a pixel or block with visual attributes such as color, size, position [START_REF] Oelke | Visual readability analysis: How to make your writings easier to read[END_REF]. Thus, the data are visualized as a set of pixels scattered around. d) Iconographic techniques: The attributes of the data are mapped as properties of an icon or glyph, which vary depending on the values of attributes (e.g. icons in format of faces of the Chernoff Faces, icons as stars of the Star Glyphs and icons in stick shape of the stick figures) [START_REF] Chan | A survey on multivariate data visualization[END_REF]. e) Graphs layout techniques: Graphs can be visually represented by matrix visualization [START_REF] Henry | Matrixexplorer: A dual-representation system to explore social networks[END_REF], node-link diagrams [START_REF] Cui | Geometry-based edge clustering for graph visualization[END_REF], hybrid views of node-link diagrams and matrix visualization [START_REF] Henry | Nodetrix: A hybrid visualization of social networks[END_REF], etc. f) Clutter reduction methods: Visual clutter is a commonly-found problem in information visualization [START_REF] Ellis | A taxonomy of clutter reduction for information visualisation[END_REF]. With ever increasing sizes of networks, reducing visual clutter has become even more important for visual analysis of large networks. Edge bundling [START_REF] Holten | Force-directed edge bundling for graph visualization[END_REF], geometry-based technique [START_REF] Cui | Geometry-based edge clustering for graph visualization[END_REF], skeletonbased technique [START_REF] Ersoy | Skeleton-based edge bundling for graph visualization[END_REF] and Parallel edge splatting [START_REF] Burch | Parallel edge splatting for scalable dynamic graph visualization[END_REF], are some representative techniques belonging to this category. g) Combinations: Mixed techniques that incorporate the advantages of each above mentioned visualization methodologies, providing visualizations and visual analytics techniques focused on the building performance [START_REF] Gehani | Data visualisation for building performance analysis[END_REF].

The main goal of visual analytics techniques is to assist humans, who posses different visual perceptual abilities and skills, such as memorizing visual infor-mation (visual memory) and matching identical forms (visual closure) [START_REF] Pylyshyn | Things and places how the mind connects with the world[END_REF]. Even for one individual, visual perception and visual illusion could be an issue [START_REF] Stacey | Visual Intelligence: Microsoft Tools and Techniques for Visualizing Data[END_REF].

The main motivation of the proposed work is the limited research in deploying visual analytic technologies in building performance simulation, and comparing analysis tools [START_REF] Hailemariam | Toward a unified representation system of performance-related data[END_REF]. Comparing alternative building designs before their implementation is a crucial issue in building construction industry. However, the output of the literature simulation tools comprised of complicated information, which could be very difficult assessed by the analyst. Also, the data mining results (coming from the analytics component) are often complex and unruly, so the visualization component allows for the optimal representation of different data structures assisting the user to efficiently and effectively perform data exploration and focus on the problematic or critical areas of interest.

In this paper, taking into account the limited research in deploying visual analytic technologies in building performance comparison tools [START_REF] Hailemariam | Toward a unified representation system of performance-related data[END_REF], new visual analytics schemes are proposed that offer new opportunities on evaluation and comparison of building information assisting to selection of the optimal building alternative before their implementation/construction. Also, the building alternatives comparison is enhanced by the extraction of meaningful semantic information for the building's operation and performance is proposed.

The rest of the paper is organized as follows. Section 2 presents the knowledge mining and visual analytics techniques utilized for the building alternatives comparison, while Section 3 presents some indicative use cases demonstrating the performance and the efficiency of the proposed comparison tool. Finally, conclusions are drawn in Section 4.

Data & Visual Analytics

Nowadays technology has enabled and has set as a priority the evaluation of the building performance towards energy savings and occupants' comfort, with the use of the necessary and corresponding software, during the design process. These Building Performance Software can simulate with high accuracy the energy consumption of every designed building, even more so if higher level of details are given for the materials, the construction itself, the occupants, the heating and cooling systems as well as the orientation and the place of location. Whether more detailed or less the description of the construction and therefore more realistic the simulation, the program generates a large amount of data regarding the temperature, humidity, energy consumption, CO 2 etc. for the whole measurement period set by the user, which need to be thoroughly analyzed in order to retrieve the correctness or not of the design choices. Handling however and analyzing this big amount of data can be a painful, time consuming process with uncertain results if some parameters are overlooked.

Knowledge Mining

The big amount of data could provide very useful information about the building's operation and performance, but also their combination, correlation and analysis could provide enhanced semantic information as well. An example of the amount of data acquired and processed by the system during a three-month period of time is presented in Table 1, where a summary of different events is presented. Combining occupancy and building geometry information, one can derive useful information about space occupancy and information for the transitions among spaces, which can lead to meaningful information of the preferences of the occupants, the logical transition sequence among spaces in a building, etc. Furthermore, space occupancy and transitions, combined with equipment and space energy consumption, can lead to semantic information about the usage of each equipment, the needs for extras, or the redeployment specific equipment (e.g. the printer or the photocopier is better to moved to a more central space, so as to reduce the employees transitions, etc.).

All these information and data could also provide information about the comfort of the spaces, to determine overcrowded periods and spaces and analyze their circumstances (occupancy, time, etc.), visit frequency, mobility and transitions among spaces, and a number of other statistics related to the building. Based on these meaningful information, one can extract Key Performance Indicators (KPIs) related to the building's performance, so as to accurately determine its operational performance, even from the design phase. Figure 1 depicts an abstract block diagram of the knowledge mining process. In this section, an easy and more direct evaluation of the energy performance results is going to be presented. Taking into consideration all the aforementioned information, a user friendly tool has been developed which, through a set of visualization techniques, enables user to extract the useful information from a large data set. This data visualization tool uses a coarse-to-fine approach to visualize information and though its design it directs step by step the user from a more general view of the performance indicators to a more detailed one and in focus of a desired parameter (temperature, energy, etc.) selected by the user.

Visual Analytics

More specifically, the tool starts by exhibiting in a kiviat diagram all the main Key Performance Indicators (KPIs) referring to a specific building setup/ design that give the general description of the building performance. The KPIs are grouped into three categories based on the aspect of building performance that they address: a) energy performance, b) business performance and c) occupancy comfort which can be also colored identified (energy performance-green, business performance -red and occupancy comfort-blue). The green and blue colored Fig. 3. Energy consumption for a single day. On each space a clock map with the respected energy consumption for the different category devices per day is displayed. The radius of each category denotes the portion of the energy consumption due to the respective category for the specific slice, where the intensity of the color denotes the relation of the consumption of the specific category with respect to the rest of the same category slices.

KPIs have a positive meaning (the larger the better), while, on the contrary, red colored KPIs have a negative meaning. Furthermore, the density of the color of each KPI indicates whether it has a good or bad value; darker colors of a KPI illustrate better values, while light colors indicates bad values [Figure 2(a)]. From there on, by clicking in each one of the KPIs circles, new diagrams appear with more detailed information regarding the selected performance indicator.

Every indicator, e.g. energy consumption, is the sum of spatio-temporal data, stemming from the various energy consuming elements placed into different spaces or even from those elements that are not space-specific, like the centralized air-conditioning device, but whose consumption can also be calculated. With the help of the described tool and by visually analyzing the energy consumption per space, the user can easily identify the most energy consuming elements of a building and focus any consumption optimization efforts on those elements. The spatio-temporal data are graphically represented in every floor plan of building simulated and for every space of the floor, by displaying the cumulative energy consumption of three different energy consuming element categories: (1) lightning, (2) heating and cooling systems and (3) equipment devices (computerss, printers, white appliances etc.). Depending on the desired time resolution, the data is displayed per month, week or day. For the latter, an extension of the Clock Map proposed in [START_REF] Kintzel | Monitoring large IP spaces with ClockView[END_REF] was utilized, with the addition of a 3 rd dimension encoded in the radius using concentric cycles (Figure 3).

The data inside the clock map is organized starting from the top (black line) and moving in a clockwise direction, where each slice represents a time slot that is user configurable. The different elements are represented by different colors: blue color denotes lightning energy consumptions, green denotes HVAC consumption and red other equipment devices such as computers, kitchen equipment etc. Again the differentiation in color intensity depicts different values. More specifically the radius of each component in the clock map denotes the ratio of its energy consumption for the specific period of time (slice), where the intensity of the color compares the value of the ratio with the rest of the same component's values over the total visualized period. More detailed analysis over the selected KPI of a space can be shown in new graphic representations with all the parameters that affect the indicator, which can allow a targeted comparison such as e.g. the occupancy versus lighting. This option can prove to be very helpful in terms of design and energy control decisions, as it can stress out important resolutions, for example, if the lights were left open during the night or even when no occupant was present at the room.

Experimental Results

Therefore, it can be seen that beyond the graphically representation of all important KPIs, there is another feature that can probably be considered as the most important asset of the tool; the comparison that it allows in every level of its analysis. Indeed through the general representation of the kiviat diagram until the most detailed analysis of the parameters of a certain space, the user has the ability to use this software as helpful tool for the decision that he has to make, either in design stage or in real building energy performance control. Through the general view and the kiviat diagram, the user can compare the performance between two or more buildings or even between different time periods for the same building [Figure 2(b)]. By selecting one of the KPIS and enabling the option "Space View", the user can also have a very fast first impression of the energy consumption of every space and in comparison to the others; the spaces are colored in variations e.g. according to their energy consumption, in relation to the color scale described by the legend located at the left side of the floor graphic representations (Figure 4). Finally, in a more detailed analysis of the parameters affecting KPIs in every space, it is achieved a thorough, graphically represented comparison in four graphs: a) the space's energy consumption analyzed in every parameter, b) the element's consumption, c) consumption VS occupancy and d) consumption VS occupancy VS emissions (Figure 5).

In order to become more clear the procedure and profit of the comparison, an attempt will be made in this paper to compare two different designs of the same building. For demonstration reasons the primary design of the building consists of two spaces with all the necessary information regarding usage, materials, location, equipment etc. already introduced into the design and the simulation. By preserving all the aforementioned parameters, the proportions and dimensions of the building, we divide one of the spaces into two new equal ones, creating in this mater a building that consists of 3 spaces.

The kiviat diagram that arises from the comparison of the two new buildings can be seen in Figure 6. With the help of the diagram one can easily identify that the building with 3 spaces has higher Total Energy Consumption and correspondingly higher level of Emissions than the building with the 2 spaces. This change in consumptions leads to a lower Building Operational Performance and a higher predicted Dissatisfaction of the building users, a logical conclusion considering that after the division, the new space has new heating and cooling demands and therefore there is higher energy consumption which lowers the overall building performance. The percentage however of Work Efficiency seems to be higher in this case, possibly because the Overcrowding factor is quite lower than the 2 space building, meaning that the working environment can be more pleasant without the commotion that an overcrowded space has to face. Certainly this comparison doesn't conclude here as there are many other KPIs to be considered nor provides with a clear answer to which solution is better than the other; in every case selected there are always advantages and disadvantages that only the architect can decide which he/she can overlook or prioritize in order to achieve the best solution according to his/hers design scenario.

Conclusions

In this paper a building performance comparison application has been presented. It provides a large number of visualizations, allowing the user to evaluate the performance of a building from the energy consumption point of view using an intuitive graphical user interface. It has been designed in such a way that can deal with the large data volume produced by the simulation engines, providing to the user a set of tools that allow him/her to refine the analysis to a desired level of detail or focus on specific building elements.

Finally it must be pointed out that the proposed visual analytics techniques could provide comparative information about alternative building designs, before their construction, even at their design phase. The analyst/engineer could be able to determine the optimal solution regarding a number of well-defined building KPIs. All, these characteristics render the proposed application to a promising tool to the construction industry, as well as to the big data analytics domain.

Fig. 1 .

 1 Fig. 1. Data mining procedure for the building performance evaluation

Fig. 2 .

 2 Fig. 2. (a) Kiviat Diagram: Visualization of the building's KPIs, (b) Building performance comparison using Kiviat diagrams

Fig. 4 .

 4 Fig. 4. Spaces energy consumption visual comparison.

Fig. 5 .

 5 Fig. 5. Space energy consumption. Top left: Bar chart displaying the energy consumption per category over time. Top right: Energy consumption versus occupancy. Bottom left: Individual energy consuming per element. Bottom right: Total energy consumption versus CO2 emissions and occupancy.

Fig. 6 .

 6 Fig. 6. Building performance comparison between alternative designs.

Table 1 .

 1 Events produced for a three-month period of time.

	Type of event	Number of events/data
	Environmental/Energy consumption	52.852
	Space occupancy events	47.823
	Occupancy trajectories	188.325
	Occupancy trajectory points	747.059.925
	Total	747.348.925

This work was supported by the Greek funded research project ECOBUILDING (GSRT-national)


Oksana Kalita

Aristidis Ilias

George Pavlidis

Enhancing Distance Learning Platforms with Social Media Analytics

Keywords: intelligent agent, distance learning enhancement, social media and services analytics, formal and informal events, russian verbs of motion

The current paper emphasizes on how enhancement of human interconnection improves distance learning processes through the use of social media analytics. Until today, educators, learners, parents and everyone else interested in a distance learning activity were able to customize and personalize their educational trajectory through representative structures, which actually promoted very general composites of the individual opinions. Over time, distance learning platforms, combined with social media and services, have to evolve and focus in strengthening the influence of the voices of their users, as well as promote the active participation of them in the educational activities. In accordance to this need, the purpose of this paper is to present an intelligent extension of existing distance learning platforms. We discuss the details of a system that utilizes big amount of structured and unstructured, historical and real-time data in order to inform, create new contacts and strengthen the dialogue and cooperation between the participants in an educational procedure. We cite an example from the field of learning Russian language from Greek students.

Introduction

Students' conversations on social media highlight their experiential knowledge, feelings, and concerns in all levels of the learning process. The course-learning experiences as well as their sense of classroom community help to understand and account for how social media and services can be used as a supplementary tool to enhance an academic community. It has been shown that the educational value of social networking for learning to higher education contexts is very important. Social media open up the opportunity for class members to interact beyond the walls of the classroom, and such extended interaction can also lead to additional learning opportunities or enhance participation as in the face-to-face classroom [START_REF] Yuen | Social Networks in Education, World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education[END_REF][START_REF] Borgman | Fostering learning in the networked world: The cyberlearning opportunity and challenge[END_REF].

The students' positive learning experiences with the use of social media were highly related to the information-sharing and the interactional features. Social media's defining feature of information-sharing and its implications for learning is underresearched, which points to a fertile area that deserves more research attention [START_REF] Yuen | Social Networks in Education, World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education[END_REF]. The generated data is distributed to the social media or/and service. The availability of these rich data on such a large scale makes it difficult -but very interesting-to analyze the actions, behavior and needs of users and to associate the actions of interconnected users.

The relation data of a user without addressing the correlation were studied in specific cases [START_REF] Backstrom | Group formation in large social networks: Membership, growth, and evolution[END_REF][START_REF] Marlow | Ht06, tagging paper, taxonomy, Flickr, academic article[END_REF]. Elsewhere, again in specific social media, has studied the relationship of behavior based on a schedule of users' actions [START_REF] Anagnostopoulos | Influence and Correlation in Social Networks[END_REF].

Ad-hoc data from such environments can provide valuable inferences and knowledge about enriched learning process [START_REF] Vorvoreanu | Teaching and Learning in a Social Media Ecosystem: A Case Study[END_REF]. Using data for making decisions is a widespread process in business environments. The process based on complex computations on available data for artificial intelligence or analytics. Artificial intelligence techniques can discern historical patterns and trends from data and can create models that predict future trends and patterns. Analytics, broadly defined, comprises applied techniques from computer science, mathematics, and statistics for extracting usable information from very large datasets.

A typical example is exploring online behavior of Web analytics that is still used to understand and improve how student use the Web services. Analyzing events requires new techniques to work with unstructured text and image data, data from multiple sources, and vast amounts of data ("big data"). Big data does not have a fixed size; any number assigned to define it would change as computing technology advances to handle more data [START_REF] Manyika | Big Data: The Next Frontier for Innovation, Competition, and Productivity[END_REF].

Big data captured from users' behaviors can infer the users' knowledge, intentions, and interests and to create models for predicting future behavior and interest via appropriate methods. Educational data mining looks for new patterns in data and develops new algorithms and/or new models, while learning analytics applies known predictive models in instructional systems.

Several models supports discovery of relationships between student behaviors, characteristics, variety of interests and valuable contexts, effective topics, actions associated with more learning, satisfaction, features of the learning environment that lead to better success etc. [START_REF] Bienkowski | Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief[END_REF][START_REF] Chen | Mining Social Media Data for Understanding Students' Learning Experiences[END_REF]. Learning analytics emphasizes measurement and data collection as activities that institutions need to undertake and understand, and focuses on the analysis and reporting of the data. Technical methods used in learning analytics are varied and draw from those used in educational data mining. Additionally, learning analytics may employ:  social media analysis, e.g. analysis of student-to-student and student-to-tutor relationships and interactions to identify disconnected students, influencers, etc. and  social or "attention" metadata to determine what a user is engaged with [START_REF] Bienkowski | Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief[END_REF].

The acquisition of knowledge and skills constitutes a complex educational, learning or/and training process that includes many intermediate paths, information and advanced instructions. In parallel, this process imposes the use of specific organizational and management systems that configures specialized Distance Learning (DL) platforms, like moodle, efront, chamilo etc [12]. Each of these platforms has its own advantages and disadvantages, morphological and essential features directly related to the object of learning they serve.

Furthermore, in the case of learning foreign language a new DL platform could be developed, specialized for this purpose. Foreign language distance learning is an interactive process not only among members of academy, but among civilizations, sciences, societies, religions, economies etc. Although, from a different point of view, all DL platforms should have some common morphological and essential features, such as "mobility" as a morphological one, or "testing" and "assessment" as two of the essentials.

Social media analytics is concerned with developing and evaluating informatics tools and frameworks to collect, monitor, analyze, summarize, and visualize social media data, usually driven by specific requirements from a target application [START_REF] Zeng | Social Media Analytics and Intelligence[END_REF].

Opinion mining (or sentiment analysis) is an attempt to take advantage (offline and online) of the vast amounts of user-generated content. One of the primary characteristics of such content is its textual disorder and high diversity. Natural language processing, computational linguistics and text analytics are deployed to identify and extract subjective information from source content. The general aim is to determine the attitude of a writer (student) with respect to some topic or the overall contextual polarity of a document. Automated sentiment analysis of digital texts uses elements from machine learning such as latent semantic analysis, support vector machines, bag-of-words model and semantic orientation. These techniques come from computational statistics, machine learning and complexity sciences and are deployed in data mining and simulation modeling [START_REF] Batrinca | Social media analytics: a survey of techniques, tools and platforms[END_REF].

This work proposes one direction in which an existing or a new DL platform, which specializes into foreign language learning, can be extended. The combination of social media analytics with the results of the educational process and the management of the generated information allow the study of the correlation of the data of the training process and the needs of the end-users: tutors, students, administrators, pedagogues etc.

The proposed extension

The high increase in the number of social network users along with the intrusion of mobile smart devices in everyday life, defines a new social and technical framework for distance learning processes. In order to follow this development and meet the new requirements, the enhancement of existing DL platforms through the integration of an Intelligent Agent (IA) is proposed [START_REF] Kalita | Dimensions of an Etho-Oriented Distance Learning Methodology: Greek Students who Learn Russian Language[END_REF]. As shown in Fig. 1, with the addition of the specialized Knowledge and Indexes Base that serves the IA's functionality, a new sub-system is formed that completes the DL platform both quantitatively and qualitatively. Two sources of information are used as input for the Intelligent agent. First, there is the information residing in the DL platform itself, including personal data of the students and tutors as well as comments, proposed ideas, requirements and notes produced during the educational process. This information can be found in the relevant profiles forums, chats, emails, reviews, blogs and other appropriate sources.

It should be noted that except from the students and tutors presented in Fig. 1, there are many other groups of people participating in the educational process. Specifically, groups directly participating in the educational process include the DL platform administrators, the technical support group, the pedagogues defining the complexity and the amount of the transmitted portions of knowledge, the teaching assistants taking care of the frequently asked questions etc.

The second source is located on the cloud and particularly in the social network accounts of the above mentioned participants. The system utilizes the social network addresses of the DL platform users and monitors, with their consent, the comments and notes as well as the comments and notes they make of their followers regarding e.g. the quality, drawbacks and problems of the educational process.

Groups that indirectly relate to the process include friends and family members of the students, colleagues of the tutors, state institutions and private agencies that relate to the specific educational process.

The proposed subsystem aims at analyzing the comments and opinions of the persons that belong to the aforementioned groups, in order to suggest improvements in the direction of the acquired sentiment using machine learning techniques, statistics etc. Specifically, the analysis results have to lead to innovative ideas, qualitative suggestions or new requirements regarding: a. the content of the teaching material, the structure and complexity of the educational process, the mechanisms of testing and assessment, b. the tools integrated in the DL platform supporting the internal communication process, meaning the communication between members of the particular educational community as well as the communications concerning the external image of the particular educational process in social media environments, c. the interoperability of the DL platform, referring to the tools provided for interconnection and exchange of new experience, knowledge etc. between equivalent systems, including between two different DL platforms. Apparently, the system discussed is an innovative and flexible asset in the hands of the tutor and his assistants, contributing to the optimization of the educational process. By monitoring and coordinating the teaching activities according to the sentiment extracted from the social media, the tutor: a. gets to know his students and collaborators better, b. can constantly enrich the learning content according to the students' abilities and interests, c. provides different learning paths in order to meet the specific needs and preferences of each student. As a result, the teaching process becomes more professional, more flexible, more effective and efficient.

From the student's perspective, the system brings the following undeniable benefits, since through the connection with the social media and services: a. they become members of a wide community of people sharing similar thoughts, interests, skills and goals. This raises the students' motivation and creativity leading to more active and responsible participation in the educational process b. they have a better understanding of the teaching subject and especially its practical usage by utilizing tools of bidirectional communication c. they are provided with the most fitting learning material and a customized learning path according to their profile and sentiment towards the learning process. The incentive of the described approach is to bring the student in the center of the educational process. The student, while being the target of the analysis, acts at the same time as an equal, creative member of the educational community.

Moreover, the subsystem essentially contributes in the constructive opinion exchange between the tutors and the platform's technical support group, which is important for the optimal utilization of the variety of tools provided by modern technology, related to: a. the discovery, interconnection and integration of different data sources, b. the qualitative improvement of the design in educational processes or material, i.e. of a multimedia presentation: cartoon, animation, video or interactive tests, c. the implementation of specific techniques that are used in the teaching of different subjects, etc.

Development Challenges

The rapid evolution in the field of DL platforms, social networks and mobile technology has a large impact on the educational sector. As the number of connected people increases, the classic notion of education fades. The proposed system is called upon to deal with the described evolution by using tools and techniques from the analysis of big data, with the data having diverse forms and originating from different sources. The resulting system shows innovative ways and creates novel opportunities, which have to be properly exploited in order to assist the tutors' leading role and place the students in the center of the educational process.

As shown in Fig. 2, for the needs of this essay we use the term "formal communication events" to refer to events corresponding to data residing in the first data source (the DL platform), since they are created during a local discussions with a well-defined topics. Respectively, we use the term "informal communication events" to refer to the events that can reside in the second data source (the social media), as they are products of more general discussions.

The construction of metadata from a big amount of messages with different level of completeness is a serious problem. The IA, as a complex subsystem, has to implement strong heuristic, tools and artificial techniques that will refer to increasingly complex syntactic and semantic interconnections. For example, questions such as "which is the central topic", "who talks to whom about it?" and "what is the problem?" can be answered through network graph analysis. The question of importance may not be as straightforward as it seems. There are several dimensions along which one may be considered important. Measures of importance in social media are called "centrality measures". "Which topic gets the most retweets" can be answered by Degree Centrality, "Which topic is the most popular" by Eigenvector Centrality and "Who controls the flow of information" by Betweenness Centrality [START_REF] Kumar | Twitter Data Analytics[END_REF].

The case of Russian verbs of motion

In Russian language there is a small group of the verbs designating various ways of movement in space. They differ from other verbs in the special correlation -paired opposition. For Greeks who learn Russian language, this subject presents difficulty as the group of verbs of motion in Russian language has lexical, grammatical and wordformation features, a ratio of prefixes and verbs, and also major and figurative sense. In the Greek language the verbs expressing the movement do not have semantic characteristics like Russian motion verbs.

The visualization of the learning content, plus the basic semantic structures of the educational material in a multidimensional way, as in the case of Russian verbs of motion with or without prefixes is quite complex task (Fig. 3). Some motion verbs differ either partially or entirely, and there are cases of motion verbs which do not exist in one of the two languages. The verbs of motion without prefixes: (i) are always in imperfect form, (ii) appear as pairs, i.e. the first one illustrates movement toward one direction and the other one movement towards multiple directions, and (iii) consist of two groups of motion verbs: intransitive and transitive. The intransitive verbs concern the movement of the subject, while the transitive ones the motion of the subject together with the object. In the case of the Russian motion verbs with prefix, the "way to move" is represented in the root of the verb, while the more abstract concept "direction" is usually expressed through the prefix ( with or without opposites), and the union of the two components completes semantically the verb.

Consequently, according to the prefix used by the student, the meaning of the expression is defined. All derivative verbs with prefixes are conjugated like verbs without prefixes. The addition of a prefix to a multidirectional verb results in a verb in imperfective form. The addition of a prefix to a unidirectional verb results in a verb in perfective form. There is a distinction between multidirectional and unidirectional verbs of motion without prefixes and verbs of motion with prefixes. These verbs express different meanings and aspects. This kind of situations increases the complexity of the educational process [START_REF] Kalita | Supporting and Consulting Infrastructure for Educators during Distance Learning Process: The Case of Russian Verbs of Motion[END_REF][START_REF] Kalita | Dimensions of an Etho-Oriented Distance Learning Methodology: Greek Students who Learn Russian Language[END_REF]. Form this point of view, in addition to network analysis is key word's frequency analysis and sentiment analysis. The more someone "tweets" about a specific subject that contains the desired "hotkeys", e.g. verbs of motion, prefixes, perfective/imperfective form, cases etc. the more trustworthy as a Central topic it becomes. To determine about the sentiment of the tweet, a lexicon to explain must exist, a dictionary of words with their positive and negative scores have to be supported [START_REF] Pang | Opinion Mining and Sentiment Analysis[END_REF].

Conclusions

The basic concept in which is based the necessity of the development of DL platforms is that the student must have: a. the provided functionality to learn in multiple ways (plurimedia modalities), b. access to rich informational and educational resources (open universities, elibraries etc.), c. equal opportunities for learning and training, regardless of local or time limitations, d. options on how, when and what to learn, as also to be the "focal point" of the educational process. At the same time, the tutor has the ability to enrich the educational content, use innovative technologies and information sources which make the lesson more interesting and offer more possibilities. The proposed subsystem is directly related to these two dimensions of DL. With the extension, the platform becomes more efficient, effective, constructive and objective.

The system collects and redistributes useful messages and educational content, while also selected papers are stored (published) into its "Knowledge and Indexes Base" in order to inform, encourage and support the educational process.

Moreover, it allows the planning and organizing of an open interaction between interested person, so as to deepen human relations, create discussions, collaborative spirit and mutual learning.

Finally, from an overall perspective, the proposed subsystem intends to promote a new learning culture.

Fig. 1 .

 1 Fig. 1. The architecture of the proposed subsystem as an extension of a distance learning platform.

Fig. 2 .

 2 Fig. 2. Process flow and interactions in an educational activity.

Fig. 3 .

 3 Fig. 3. Part of Russian verbs of motion semantic structure.


Charalampos Goulas
email: goulasch@ceid.upatras.gr

Dimitrios Chondrogiannis
email: dchondrogiannis@ceid.upatras.gr

Theodoros Xenakis
email: xenakis@ceid.upatras.gr

Alexandros Xenakis
email: xenaki@ceid.upatras.gr

Photis Nanopoulos

HCuRMD: Hierarchical Clustering Using Relative Minimal Distances

Keywords: Artificial Intelligence, Machine Learning, Hierarchical Clustering, Relative Minimal Distances

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

Introduction

On the rise of the 21 st century, towards a new and emerging information society, the convergence of computers and telecommunication has led to a continuously, increasing production and storage of huge amounts of data for almost any field of human engagement. It has been estimated that Google handles over four million different queries in every minute [START_REF]What Happens in an Internet Minute?[END_REF], while almost seventeen terabytes of data is produced every day only by the users of Facebook and Twitter [START_REF] Zikopoulos | Understanding Big Data[END_REF] and CERN produces even more than that in every hour [START_REF]CERN: Computing[END_REF]. Hence, if data is the recorded facts of human evolvement, information is the rules that govern them and while society is depending on and looking earnestly in finding them, artificial intelligence is the answer in discovering them. Under these circumstances, clustering, as an integral part of machine learning and artificial intelligence, has become one of the most widely used exploratory tools, with applications ranging from statistics, computer science and biology to education, social sciences and psychology.

In general, clustering is called the process of grouping a set of physical or abstract objects , based on their similarity, into groups , so as to and . However, a vital question remains: "how can researchers quantify the concept of similarity?" According to [START_REF] Estivill-Castro | Why so many clustering algorithms: A Position Paper[END_REF] similarity concepts cannot be easily determined accurately. And this is happening because the whole process of clustering is not just a particular algorithm, but more a general problem of dispersions that needs a solution. Thus, researchers may use different models, in each of which can be proposed significantly different algorithms using alternate concepts of what constitutes a cluster and how this can be discovered effectively.

In this sense, there are partitioning methods, the main representative of which is the very well-known K-Means algorithm [START_REF] Lloyd | Telephone Laboratories: Least square quantization in PCM[END_REF], that are trying to divide a search space into K sub regions while minimizing the inter-cluster distance, density-based algorithms such as DBSCAN [START_REF] Ester | A density-based algorithm for discovering clusters in large spatial databases with noise[END_REF] and OPTICS [START_REF] Ankerst | OPTICS: Ordering Points to Identify the Clustering Structure[END_REF], that are searching for dense areas of a search space in order to create clusters, hierarchical or else connectivity-based clustering algorithms, that are using linkage criteria in order to bring together and merge different points or clusters, as well as more advanced techniques that are able to handle large spatial databases, as shown in [START_REF] Nanopoulos | C2P: clustering based on closest pairs[END_REF] and [START_REF] Corral | Algorithms for processing K-closest-pair queries in spatial databases[END_REF].

Hierarchical Clustering

The clustering algorithms that belong to this corresponding category are seeking to build hierarchical, tree structures of nested clusters. Depending on the strategy they follow in order to achieve the desired result, these algorithms are divided into the following two sub-categories [START_REF] Rokach | Clustering methods. Data mining and knowledge discovery handbook[END_REF]:

 Agglomerative or else Bottom-Up.  Divisive of else Top-Down.

On the one hand, the first ones start their operation considering each of the distinct objects as a separate cluster (bottom) followed by a gradual at each step merging of one pair of them that meets some certain criteria, till there is only one cluster (up). On the contrary, the second ones start from one unique cluster that contains all given objects (top) and gradually divide it into smaller and smaller clusters till each object corresponds to a separate cluster (down). However, despite this core strategic difference, in both cases, such algorithms are always using a linkage criterion in order either to decide which clusters will be combined or at which point an existing cluster will be divided.

Linkage Criteria

A linkage criterion determines the (dis)similarity between different clusters as a function of the distances of all pairs of objects within them. The most commonly used criteria are the Single-Linkage and the Complete-Linkage that can be described as follows.

Single-Linkage Criterion:

The dissimilarity (distance) between two clusters and , with and the corresponding objects that belong to them, is equal to:

, where is a distance metric, such as Euclidean or Manhattan.

Note. The corresponding algorithm that uses this criterion merges at each step this pair of clusters that has the minimum value among all possible pairs (Fig. 1).

Complete Linkage Criterion:

The dissimilarity between two clusters and is calculated as the maximum distance among all possible pairs of objects (one per cluster):

Note. The corresponding algorithm merges at each step this pair of clusters that has the minimum value in the above criterion among all possible pairs (Fig. 1).

Fig. 1. Single-Linkage Criterion (left) vs. Complete Linkage Criterion (right)

Other Linkage-Criteria: Some other known, but not as frequently used linkage criteria are the followings [START_REF]The CLUSTER Procedure: Clustering Methods[END_REF]:

 Mean or Average Linkage Criterion:

 Minimum Energy Criterion:

Time Complexity

On the one hand, the time complexity of most agglomerative clustering algorithms is , where n is the total number of the problem's objects, which makes them too slow in analyzing large datasets. On the other hand the time complexity of most divisive algorithms is , which in fact is even worse than the previous one. In general it has been shown that a hierarchical clustering algorithm can have time complexity, independently of the clustering distance function [START_REF] Nanopoulos | C2P: clustering based on closest pairs[END_REF][START_REF] Eppstein | Fast Hierarchical Clustering and Other Applications of Dynamic Closest Pairs[END_REF]. However, there have been found agglomerative algorithms, such as the single-linkage SLINK algorithm [START_REF] Sibson | SLINK: an optimally efficient algorithm for the single-link cluster method[END_REF] and the complete-linkage CLINK [START_REF] Defays | An efficient algorithm for a complete link method[END_REF] that are able to achieve an optimal time complexity of for some special cases of problems.

Hierarchical Clustering using Relative Minimal Distances

Given a set of points defined in with Euclidian norm, where and is the number of the problem's variables, the goal of the HCuRMD algorithm is to create clusters so as to , and , while improving the overall time complexity of the provided solution.

In order to achieve the desired outcome, the algorithm firstly takes into account the following basic consideration:

 Each cluster
is represented by its center , where is the mean of all observations of objects for the variable j:

Subsequently and according to the above, the algorithm is described in the sections that follow.

3.1

Rescaling the Data.

Each variable represents a different feature of the given problem. The undesirable effect of different variables' measurement units can be eliminated by rescaling the data and essentially compressing the observations of all variables in the range [0, 1]:

, where corresponds to the column with all observations of the variable j.

3.2

Calculating the Dissimilarity Matrix.

At each iteration a symmetric matrix with dimensions is created, where is the sum of the number of points that has not been used by the algorithm till this exact iteration plus the number of clusters that has been created, each of one is represented only by one point, its corresponding mean (Eq. 1). Therefore, each cell stores the square Euclidean distance between each possible pair of points :

Finding Nearest Neighbors.

For each distinct point or cluster, the nearest neighbor is determined, regarding either the nearest point or the nearest cluster's center. More specifically, for each column of the table , the algorithm finds the cell with the smallest value greater than zero and stores the result in an auxiliary vector , each cell of which holds the index of the corresponding cell that is the nearest neighbor of the distinct point or cluster :

3.4

Pair Selection for Merging.

Once the above discovery of the nearest neighbors of all existing points or clusters is completed, the next step is the selection of the most "appropriate" of them in order to be merged. However, what does the term "appropriate" mean? A pair of points is considered to be suitable for merging if the distance between its components is the "relatively" minimal one. But, what exactly does the term "relatively" mean? At this point, the algorithm proposes and uses a more relative perspective in defining minimal distances among different objects, called "Relative Minimal Distance" that can be described as follows.

Relative Distance:

The distance between two objects (points or clusters), defined in any p-dimensional space, is not exclusively determined only by the distance metric that is used, but in fact it primarily depends on the surrounding environment in which the corresponding objects are located as well as by the nature of the observers themselves. For example, the distance between two objects could be considered as a small one in a sparse region of a search space, but at the same time this exact distance could in fact be too large in a dense region of the same search space. On the other hand, the distance between the Earth and the Moon can be held as a large one by a casual ob-server, but in fact it is considered to be a very small one by an astrophysicist, who is looking for another "Earth" in the chaotic area of space.

Relative Minimal Distance:

In addition to the above description, the distance between two objects in a search space is considered to be minimal, if and only if each of these objects is nearest to the other one (1-1 nearest Neighbors), regardless of the actual distance between them that is measured by any of the existing distance metrics (Fig. 2).

Fig. 2. Pairs of Nearest Neighbors according to "Relative Minimal Distances" distance metric

Therefore, two points or clusters' centers are selected for merging if and only if each of them in Table is carrying the other one as its nearest neighbor:

Repetition and Termination

The above process (from the step 2) is repeated until a termination criterion is achieved. A termination criterion could be the depth of the constructed tree that the algorithm would stop or the number of the clusters that the user would like to be created.

Discussion

Therefore and based on the above process a naturally extracted conclusion could be that using Relative Minimal Distances does not provide any advantage over existing techniques, as the ultimate shortest distances that are obtained in each iteration using some of the previously discussed linkage criteria are already included in the groups of the relative shortest distances that are obtained by the proposed methodology. However, this observation, as shown clearly in Fig. 2, does not have absolute power. Even if the final result could in certain cases be the same, the automatic determination of multiple starting points, by the use of relative minimum distances, it provides certain advantages in this process of a hierarchical clustering, which can be summarized as follows:

1. Indirect parallelization of the process of clustering. Subsequently and according to the above parallelization process, the proposed technique completes the operation in only 6 iterations, which is way faster of the corresponding process by using a single-linkage hierarchical clustering algorithm that needs 17 repetitions. In particular, the single-linkage clustering algorithm merges only the pair of clusters with the shortest distance between all possible pairs each time, a process that requires at least n-1 iterations, where n is the initial number of objects till the completion.

On the contrary, the proposed clustering algorithm by using the concept of "relative minimum distances" needs in best case scenario repetitions, if all pairs merge into each repetition and repetitions in a single worst case scenario, where the distances between the corresponding objects show a gradual , incremental voltage (Fig. 4)

Conclusion and Evaluation

This paper presents a smart method for hierarchical clustering, named HCuRMD, which, unlike classical hierarchical clustering algorithms, does not take into consideration the actual Euclidean distances between all the pairs of objects, but instead considers only the 1-1 neighborhood graph. In particular, HCuRMD takes into account the mutual kNN graph for k=1 and drops the actual distances between the different objects. This approach drops unnecessary complexity and makes the algorithm terminate in fewer iterations (

) in comparison to iterations that classical hierarchical clustering methods need.

2 .

 2 Better exploration and "reading" of space exploration and the structure of the problem. 3. Reduction of the time complexity in solving a given problem. 4. Ability to identify and address the endpoints. 5. Improving the management ability and analysis of sparse or multidimensional search spaces.

Fig. 3 .

 3 Fig. 3. Parallelization Process of Hierarchical Clustering using the "Relative Minimal Distance"

Fig. 4 .

 4 Fig. 4. Example of Worst Case Scenario in using "Relative Minimal Distances"


R Bouhali
email: armbouhali@gmail.com

A Laurent
email: laurent@lirmm.fr

Exploiting RDF Open Data Using NoSQL Graph Databases

Keywords: RDF, Open Data, NoSQL Graph Databases

RDF, or Resource Description Framework, is a well-established format for representing data. Based on the so-called RDF triples, it allows to represent data and properties over the data through the subjectpredicate-object framework. By linking objects defined by unique identifiers, it is widely used for Open Data and Linked Open Data. On the other hand, relationships between objects are more and more managed through NoSQL graph databases which use the property graph model to structure information. Such databases provide indeed very efficient tools for handling huge volumes of complex data. They are especially powerful for retrieving relationships between objects (as for instance the famous friend of friend query). While many works have dealt with the transformation from various data formats to RDF, no work has addressed, as far as we know, the transformation from RDF to NoSQL graph databases. We thus consider this topic in this paper by proposing transformation rules and by testing our approach on real data.

Introduction

The Semantic Web is now widely recognised as a key for promoting data exchanges over the Web of data. Data exchanges rely on the RDF format, especially considered in the Open Data and Linked Open Data frameworks as a key component. These frameworks are studied and used by many researchers and practitioners. For this reason, works have been proposed to translate resources either into RDF format so as to ease the publication of the data, or from RDF to perform on them some application-specific processing. Linked open data gathers a huge number of datasets. It is often represented by pictures such as Fig. 1 taken from the EU research project PlanetData (http://planet-data.eu/).

Besides, NoSQL graph databases have become highly demanded to achieve the data management needed by some tasks mainly focusing on relationships. For example, social networks, and variety of scientific and business application tend to be easy to manage if data were stored and queried as graphs. NoSQL graph database engines are recognised as being scalable [START_REF] Barmpis | Evaluation of contemporary graph databases for efficient persistence of large-scale models[END_REF] and efficient for some complex data management operations.

We thus propose to discuss how RDF data can be managed in NoSQL graph databases. Our work relies on two main motivations explained below.

First, we consider the extension of proprietary data available in a graph database. RDF is a standard for data publishing on the Web. Therefore, there is a wide variety of public data sets available for applications, having their own permissions and restrictions. Such external data are a rich source of information and can be used as Background Knowledge by applications either to enhance the quality of service or to provide users with extra features.

Second, we consider the improvement of performance of data processing applications. RDF serialization formats, such as RDF/XML, Turtle, and JSON-LD, are stored as text documents and cannot be queried in an efficient manner unless loaded into a SPARQL engine. Although studies have driven to some improvements in SPARQL efficiency, there are still some limits with complex or large-scale queries, where some of the current Graph Database solutions generally show better performance and are open to [START_REF] Barmpis | Evaluation of contemporary graph databases for efficient persistence of large-scale models[END_REF] The paper is organized as follows. Section 2 recalls the basics of NoSQL graph databases and RDF data and presents some of the existing work for data transformation in this framework. Section 3 introduces our proposition. Implementation issues and experiments are reported in Section 4 while Section 5 concludes the paper and discusses further works.

Related Work

RDF

RDF stands for Resource Description Framework. A resource may be any type of object, for instance a person, a picture, a Web page, etc. Resources are identified by Uniform Resource Identifiers (URI) which are unique sequences of characters. URLs are a type of URIs. Some examples can be found in [START_REF] Sauermann | Cool uris for the semantic web[END_REF].

RDF describes information through the (Subject, P redicate, Object) framework. The predicate is a relationship linking the subject to the object. RDF can thus be seen as a labeled oriented graph, as well as NoSQL graph databases described below. They are queried through SPARQL queries [START_REF] Prud'hommeaux | Sparql query language for rdf[END_REF].

NoSQL Graph Databases

NoSQL has recently emerged as a new framework for databases [START_REF] Cattell | Scalable sql and nosql data stores[END_REF]. The main advantage of NoSQL databases is often viewed as the capacity to deal with Big Data. This does not only mean that such NoSQL databases are scalable and can thus handle huge volumes of data, but also means that it can manage more complex data. That's for instance the case with document-oriented NoSQL databases. That's also the case with NoSQL graph databases [START_REF] Castelltort | Eighth International Conference on Digital Information Management[END_REF] that are especially designed for managing data that are intrinsically graph-based, such as social networks, biological data, etc.

NoSQL graph databases implement data management systems based on oriented graphs. These graphs are labeled with a complex structure that contains information as a set of (key : value) properties. For this reason, we consider such data as a Property Graph (hereafter PG).

Fig. 2 depicts an example where people and accommodations are managed. Every object, should it be a node or a relationship, can be associated with properties. For instance, the REN T S relationship has some properties such as the fees and the dates of renting.

Serveral graph database engines exist, such as Neo4j, OrientDB, FlockDB, InfiniteGraph, etc.. In our work, we consider Neo4j (http://neo4j.com/) for experiments.

Those database management systems are not only data repositories but allow to deal with some constraints and provide querying tools. Queries can be run from a program or can rely on a declarative language. For instance, Neo4j provides a declarative query language called Cypher.

In this paper, we focus on how to build a bridge between the RDF format and the concept of property graphs, allowing then to manage these data by a chosen NoSQL Graph DBMS.

Graph Transformations

Graphs are usual data structures. Graph transformations have thus been studied for many years in graph theory and in both the fields of artificial intelligence and databases. Some other works have focused on the transformation of several data formats to RDF in order to publish information on the Web [START_REF] Braatz | Graph transformations for the resource description framework[END_REF]. One of the main used languages is R2RML, which provides a markup language to describe how to transform a given relational database into an RDF triple set [START_REF] Das | R2rml: Rdb to rdf mapping language (w3c working draft[END_REF]. Mappings are defined between relational data and graph patterns so as to allow the automatic exportation of table rows to RDF. Examples are provided by Figures 3,4,5, taken from http://www.w3.org/TR/r2rml/. Regarding the transformation of RDF to other formats, little works exist. SPARQL queries return information in either XML or JSON, or Notation3 (N3) or textual/HTML formats, which allows to export RDF data into several other formats provided the fact that some transformation tools are available. CONSTRUCT queries return RDF/XML format. However, direct exportation has been less addressed in the literature. [START_REF] Baget | RDF to Conceptual Graphs Translations[END_REF] addresses the topic of transforming RDF to conceptual graphs.

From RDF Data to NoSQL Graph Databases

The PG model is very general. It extends the basic graph model and adds some concepts from the class model where objects and attributes are equivalent to nodes properties. This model has almost no restrictions except the fact that a relationship can have one and only type. Indeed, if a relationship is multi-typed, its types are either equivalent and only one should be kept, or different and in this case a new relationship should be created for every distinct type.

RDF is a very basic model allowing to represent information as triples (Subject, Predicate, Object). It can be extended to something more complex by adding a semantic layer.

Since RDF is nothing but a sub-model of PG, a straightforward transition is possible from RDF to PG. In other words, any set of RDF triples can be translated into a property graph without any loss in terms of structure.

However, the opposite transition, namely from PG to RDF, is generally structure-lossy, because some concepts found in property graphs, such as relationship properties and collection properties, cannot be directly represented using the (Subject, Predicate, Object) paradigm without a decomposition into atomic triples.

In both directions, the description of the final usage of the data is what really determines the appropriate transformations and the semantics of structures, rather than the trivial correspondences between the two models.

We propose describing in this work a solution to convert RDF data to fit the Property Graph model. Our solution comprises two approaches: the first is to establish direct correspondences between RDF triples and the primitives of property graphs; the second approach offers more flexibility by integrating a detailed mapping for each category of RDF triples.

Approach 1: Direct Correspondences

This approach processes RDF triples in the most intuitive manner and uses correspondences that are generally correct for the majority of known datasets. Its objective is to simply afford a conversion that meets the nature of the data, and thus allow to start processing them as soon as possible using a graph database.

The conversion system is illustrated by Fig. 6 Fig. 6. Description of RDF to PG Conversion System -Approach 1 First, the system takes an input triple (S, P, O) and injects it into the Classifier. The Classifier then annotates the triple according to its structure (i.e. the nature of the object O and the predicate P's name). The annotated triple is then passed to the Query Builder which integrates the three parts of the triple in an insertion query, taking in account the previously added meta-data. The consequent query is finally sent to the output of the system where the graph database engine is awaiting for it to be executed.

Below, we concisely define the chosen matching between RDF and Property Graph entities, and explain the rules used to convert any given set of triples into insertion queries intended to be run on a NoSQL graph database.

RDF Entities

Property The rules are :

-Rule #1: Every RDF resource becomes a PG node. Since the subject in RDF is always a resource, there is at least one PG node created or merged into an existing node. -Rule #2: If the object is a literal, the predicate and object become respectively a propertys name and value. The property is added to the created or existing node corresponding to the resource. -Rule #3: If the object is a resource, then the subject and object are each transformed into a node. A relationship between them is created and it holds the predicate's name as a relationship type. -Rule #4: If the predicate is rdf:type, the subject becomes a node having a label set to the objects name (which is actually the resource type).

This rule set is minimal. It guarantees a coverage of the three different classes defined DF triples. A concise example of the application of these rules is described in Fig. 7 The main advantage of this method is that all the rules are triple-intrinsic. in other words, the conversion system processes only one triple at a time and doesn't take in account many triples. This makes it possible to handle the input as a serial or continuous stream of RDF triples and achieve conversions on-thefly. This system can fit well with continuously evolving data such as data series, dynamic databases and transactional databases in general.

The main drawback of this approach is the fact it doesn't make full use of the Property Graph model. In fact, the conversion system cannot produce any properties over relationships. Relationships are provided only by applying Rule #3 defined above. But since all the data are present, it is possible to extend this conversion with refactoring queries executed by the graph database engine: their role is to match specific graph patterns and apply on them some modifications to create new structures.

Approach 2: Correspondences by Mapping

This approach is an extension of the previous system. The Query Builder is enhanced with an input mapping that associates to each RDF entity a chosen correspondence among many possibilities.

The conversion system for this approach is described by Fig. The mapping is divided into 4 sub-mappings, each of them is stored in a (key:value)-style vector. We define them as follows:

1. Literal Datatype Mapping: RDF data types are not always rigorously attributed to literals. We thus propose this mapping as a correction measure

(p1) -[: EM AILED{$1}] → (p2)
where $1 is used to capture all the properties of nodes labeled EMAIL and adds them as properties to the new relationship EMAILED. This mapping reduces a node with two relationships into a relationship holding the node's properties.

The use of a mapping is common in most data conversion solutions. However, it assumes the user has a good knowledge, not only about his input dataset, but also the final graph processing application, and this is generally not the case.

Discussion

NoSQL Graph Database Engines are characterized by multi-level cache management policies. In other words, data primitives (node, relationship, properties ...) are not always stored in the same cache or accessed in same manner. This leads to a contrast in the time necessary to retrieve a specific information. For example, Neo4j defines the following cache priority order over its different primitives (1 being the highest priority) :

1. Nodes + Labels, Relationships + Types 2. Indexed Properties 3. Unindexed properties:

(a) Static size properties (int, float, etc.) (b) Dynamic size properties (Collections, Strings)

This access policy and caching should be taken into account to make the good mapping decisions that will likely improve query performance. Indeed, any frequently accessed information should be put forward as a node or a relationship if possible. or as an indexed priority. The same goes for irrelevant information that should be kept away from being cached by putting them in Dynamic size properties, or even by removing them if necessary.

As mentioned at the end of section 3 as a drawback for the second approach, when the user is inexperienced, it is preferable to employ user-friendly solutions with a higher abstraction level to help him understand the needs of the system, and assist him to make the good decisions. Thus we propose two ideas worth working on :

1. One keypoint is to deal with the structure of the data. Some works in the literature have dealt with schema extraction in the context of semi-structured graph data, i.e., XML data. 2. Another part of the issue is how to know more about the application. In this case, instead of taking a mapping as an input, we can generate a mapping from a given application profile. The profile is supposed to provide information about Graph Database queries, their extent, frequency, and their estimated running time. If such information is not available, it is still possible to ask the user in a more user-friendly way about the application through a set of artificially generated questions. The user's answers on those questions will allow to define specific constraints on the nature of the queries and make, according to that, the necessary refactoring and optimizations to improve the overall performance.

Implementation Issues

We have implemented the conversion system detailed by the Approach 2 (see Fig. 8). The solution is coded in Java. It uses Apache Jena packages to parse and process RDF triples, and imports data into a Neo4j Graph database by generating and running Cypher queries directly using the Neo4j API. It is also possible to provide the queries in an output file instead of injecting them into the database engine directly.

The experiments are led on the DBLP database dump provided in RDF format by RKBExplorer (http://dblp.rkbexplorer.com/).

Evaluation Measures

In order to check the reliability of our method and provide the user with a relevant feedback to make some changes on his input mapping, we define three quality measures. Given a Graph Database G = (N, R) where an N and R are respectively the node set and the relationship set of G:

1. Conciseness: this measure simply calculates the sum of the number of nodes and the number of relationship instances in the Graph database. The lower this number, the better the performance.

Conciseness(G) = |N | + |R|

Can be used can be used to calculate the variation of the graph size when the user has to make a choice among different mappings. 2. Connectivity: it is defined as the the number of relationships among the total number of nodes.

Connectivity(G) = |R| |N |

A connected graph has a connectivity around 1 and a graph having at least Connectivity(G) > 1.5 is considered good for processing. Higher values mean the graph database contains strong connections between its nodes. This measure can be seen as the average rank of a vertex in graph theory. 3. Key Connectivity: instead of calculating the connectivity of the whole graph database, we focus here only on a set of predefined key relationship types. We note R as the set of key relationship instances, and N the set of distinct Nodes related by the elements of R . R and N are by definition subsets of R and N respectively.

KeyConnectivity(G) = Connectivity((N , R )) = |R | |N |
The smallest value of KeyConnectivity(G) is 0.5 and is obtained when we choose a single relationship type, or many unrelated relationship types. Any value larger than this reference means that elements of N are related by more than one relationship. This measure can be interpreted in several ways and depends of the elements of R . The principle is similar to independence calculations of statistical variables. We applied these measures on an imported chunk of the DBLP dataset in Neo4j and we obtained the results shown in Table 3 Connectivity is around 3 which means our graph is rich in terms of relationships. it can thus be used and explored in different ways since its nodes have many connections.

Key connectivity equals 0.5. As seen in definition, considering has author as the only key relationship type, this value means there are no double relationship instances between nodes related by has author. It would be interesting if the measure is applied on more relationship types, but since the database is small, other relationship type are under-represented.

Conclusion

In this paper, we discuss how to transform RDF data to Property Graph (PG) in the NoSQL graph database context. Transformation rules are proposed. They have been implemented and tested on real data. There is still room for improvements in theory as well as practice. For example, we can define explicitly what kind characteristic information should reflected by the application's and the dataset's profiles. And then how to use those profiles to achieve conversion in a more automatic way with a minimum user intervention.

Further work will also include the study of transformations in the opposite direction, i.e. from property graphs to RDF. A transition from the property graph model to RDF is mainly motivated by:

-The need to publish data: RDF is a standard of the Semantic Web. It is not only a data representation convention but also a set of ready-to-use tools which have been studied and developed in way that guarantees interoperability with different applications. -The need to link the published dataset with other existing open data sources on the Web (for instance, the LOD project) to make it more useful. This can be done by enriching datasets with a semantic RDFS/OWL layer named ontology.

Our main research is about using NoSQL graph databases as support for summarizing Open Data, and this paper has details one preliminary phase in this process and provides a way to import data from RDF triplestores to Graph Databases built upon the Property Graph paradigm.

Fig. 1 .

 1 Fig. 1. Linked Open Data

Fig. 2 .

 2 Fig. 2. Example of a Property Graph illustrating all the characteristics of the model

Fig. 3 .

 3 Fig. 3. Example of a relational database to be transformed with R2RML (http://www.w3.org/TR/r2rml)

Fig. 4 .Fig. 5 .

 45 Fig. 4. Example of the resulting RDF data from an R2RML mapping (http://www.w3.org/TR/r2rml)

Fig. 7 .

 7 Fig. 7. Description of RDF to PG Conversion System -Approach 1

8

 8

Fig. 8 .

 8 Fig. 8. Description of RDF to PG Conversion System -Approach 2

4 .

 4 Structural Mapping: The three previous mappings are all triple-intrinsic and are not designed to handle multi-triple based conversions. The role of Structural mapping is to achieve any missing transformations. It concerns the post-import phase and works directly on the graph database. It defines a set of match-replace rules to build the necessary refactoring queries. . Fig. 10 takes the output of the previous email data sample conversion shown in Fig. 7, and runs a refactoring query on it by replacing every match of the pattern: (p1) ← [: F ROM ] -(e : EM AIL{$1}) -[: T O] → (p2) by the pattern:

Fig. 9 .

 9 Fig. 9. Mapping RDF literals to PG structures. A is the original RDF sample. B and C are PG conversions of A with different mapping parameters.

Fig. 10 .

 10 Fig. 10. Example of structural mapping applied on an e-mail graph database

Table 1 .

 1 Direct correspondences between RDF and PG model entities

	Graph Primitives

Table 2 .

 2 Table of Mappingsin order to store literal values in the most efficient manner. The output type is by default String and it can be set to an Integer or F loat value. 2. Literal Mapping: A literal found in a triple always causes a property to be created. The goal of this mapping is to define the correct structure of this property. Fig.9shows an example with a bibliographic dataset. The choice of the mapping is established according to the relevance of the information to the user: If authors are auxiliary they can be grouped in a Collection Property as in (C), and if they are important, they can be represented with Nodes as in (B). 3. Resource Type Mapping: rdf:type objects are by default mapped to labels over the nodes. But when many rdf:type statements are defined for the same resource, this may pollute the label space and affect performance. This mapping offers the possibility to store insignificant rdf:type objects within a Collection Property instead of a Label.

	Mapping	Key	Possible values
	Literal Datatype Mapping Predicate Name Primitive data types : Integer, Float, String
	Literal Mapping	Predicate Name Property, Node with Property, Collection Property
	Resource Type Mapping rdf:type Object Label, Label Collection Property
	Structural Mapping	Graph Pattern Graph Pattern replacement

Table 3 .

 3 Import results and evaluation measures

	Total Nodes	3406
	Total Relationships	9862
	Total Relationships for has author 2364
	Total Nodes related by has author 4728
	Conciseness	13268
	Connectivity	2.895
	Key Connectivity for has author	0.5