Towards Social Network Extraction Using a Graph Database

Soussi, Rania; Aufaure, Marie-Aude; Baazaoui, Hajer

doi:10.1109/dbkda.2010.19

Cited by 17 publications

(9 citation statements)

References 16 publications

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“…In response, hierarchical data models were proposed. In a similar way, new requirements also triggered the need for general graph support in large-scale databases; main drivers here have been ontologies requiring comparatively small, heterogeneous graphs [49] and social networks with their large, homogeneous graphs [105]. A further relevant data structure is comprised by multi-dimensional arrays.…”

Section: Introductionmentioning

confidence: 99%

Array databases: concepts, standards, implementations

et al. 2021

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Multi-dimensional arrays (also known as raster data or gridded data) play a key role in many, if not all science and engineering domains where they typically represent spatio-temporal sensor, image, simulation output, or statistics “datacubes”. As classic database technology does not support arrays adequately, such data today are maintained mostly in silo solutions, with architectures that tend to erode and not keep up with the increasing requirements on performance and service quality. Array Database systems attempt to close this gap by providing declarative query support for flexible ad-hoc analytics on large n-D arrays, similar to what SQL offers on set-oriented data, XQuery on hierarchical data, and SPARQL and CIPHER on graph data. Today, Petascale Array Database installations exist, employing massive parallelism and distributed processing. Hence, questions arise about technology and standards available, usability, and overall maturity. Several papers have compared models and formalisms, and benchmarks have been undertaken as well, typically comparing two systems against each other. While each of these represent valuable research to the best of our knowledge there is no comprehensive survey combining model, query language, architecture, and practical usability, and performance aspects. The size of this comparison differentiates our study as well with 19 systems compared, four benchmarked to an extent and depth clearly exceeding previous papers in the field; for example, subsetting tests were designed in a way that systems cannot be tuned to specifically these queries. It is hoped that this gives a representative overview to all who want to immerse into the field as well as a clear guidance to those who need to choose the best suited datacube tool for their application. This article presents results of the Research Data Alliance (RDA) Array Database Assessment Working Group (ADA:WG), a subgroup of the Big Data Interest Group. It has elicited the state of the art in Array Databases, technically supported by IEEE GRSS and CODATA Germany, to answer the question: how can data scientists and engineers benefit from Array Database technology? As it turns out, Array Databases can offer significant advantages in terms of flexibility, functionality, extensibility, as well as performance and scalability—in total, the database approach of offering “datacubes” analysis-ready heralds a new level of service quality. Investigation shows that there is a lively ecosystem of technology with increasing uptake, and proven array analytics standards are in place. Consequently, such approaches have to be considered a serious option for datacube services in science, engineering and beyond. Tools, though, vary greatly in functionality and performance as it turns out.

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Section: Introductionmentioning

confidence: 99%

Array databases: concepts, standards, implementations

et al. 2021

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“…For example, in power-inspection route planning [1,2], all task points and equipment can be regarded as nodes in the graph, and path length between each task point or equipment can be regarded as an edge; thereby, a road graph on power inspection is formed. In another example, an entire social network [3][4][5] is a graph in which the user is the vertex of the graph, and the relationship between the user and another user is regarded as the edge. As can be seen from the above examples, as a storage or presentation form in a specific application, how to mine the knowledge contained in a graph by quickly traversing the vertices in the graph is a research hotspot in the field of graphics computing.…”

Section: Introductionmentioning

confidence: 99%

Traveling-Salesman-Problem Algorithm Based on Simulated Annealing and Gene-Expression Programming

Zhou

Zhu

et al. 2018

Information

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The traveling-salesman problem can be regarded as an NP-hard problem. To better solve the best solution, many heuristic algorithms, such as simulated annealing, ant-colony optimization, tabu search, and genetic algorithm, were used. However, these algorithms either are easy to fall into local optimization or have low or poor convergence performance. This paper proposes a new algorithm based on simulated annealing and gene-expression programming to better solve the problem. In the algorithm, we use simulated annealing to increase the diversity of the Gene Expression Programming (GEP) population and improve the ability of global search. The comparative experiments results, using six benchmark instances, show that the proposed algorithm outperforms other well-known heuristic algorithms in terms of the best solution, the worst solution, the running time of the algorithm, the rate of difference between the best solution and the known optimal solution, and the convergent speed of algorithms.

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“…The main advantages of this transformation are (1) to discover underlying graphs of objects from relational databases, taking into account the implicit relations expressed by the means of primary and foreign keys and (2) to model data in a more flexible way (objects can easily be added or removed in a graph). The reader interested by details about this approach can refer to [25]. The resulting hypernode database schema -h denotes the name of H, -N h denotes a set of nodes N h := {n h |n h := n, t } where n is the node name, t is the type.…”

Section: Graph Extractionmentioning

confidence: 99%

“…In our process to transform the relational database into a hypernode database [25], we have defined four types of relations: IS-A, Part-of, dependency with known name (using the initial relational tables containing only foreign keys), dependency with unknown name.…”

Section: Graph Extractionmentioning

confidence: 99%

DB2SNA: An All-in-One Tool for Extraction and Aggregation of Underlying Social Networks from Relational Databases

Soussi

Cuvelier

Aufaure

et al. 2012

Lecture Notes in Social Networks

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Abstract. In the enterprise context, People need to visualize different types of interactions between heterogeneous objects (e.g. product and site, customers and product, people interaction (social network)...). The existing approaches focus on social networks extraction using web document. However a considerable amount of information is stored in relational databases. Therefore, relational databases can be seen as rich sources for extracting a social network. The extracted network has in general a huge size which makes it difficult to analyze and visualize. An aggregation step is needed in order to have more understandable graphs. In this chapter, we propose a heterogeneous object graph extraction approach from a relational database and we present its application to extract social network. This step is followed by an aggregation step in order to improve the visualisation and the analyse of the extracted social network. Then, we aggregate the resulting network using the k-SNAP algorithm which produces a summarized graph.

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Towards Social Network Extraction Using a Graph Database

Cited by 17 publications

References 16 publications

Array databases: concepts, standards, implementations

Array databases: concepts, standards, implementations

Traveling-Salesman-Problem Algorithm Based on Simulated Annealing and Gene-Expression Programming

DB2SNA: An All-in-One Tool for Extraction and Aggregation of Underlying Social Networks from Relational Databases

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