Concepts for graph-oriented object manipulation

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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“…Most of these models (GOOD [14]), GMOD [15], etc.) represent both schema and instance database as a labeled directed graph.…”

Section: Graph Database Modelsmentioning

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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“…Most of these models (GOOD (Gyssens et al 1990), GMOD (Andries et al 1992), etc.) represent both schema and instance database as a labeled directed graph.…”

Section: Models Based On Simple Nodementioning

confidence: 99%

“…Fig.1.7 illustrates two examples of GOOD query: First query to find student and their supervisor the secand one to find student working on ontology topic. This language was followed by the proposals GMOD (Andries et al 1992), PaMaL (Gemis and Paredaens 1993) and GOAL (Hidders and Paredaens 1993). These languages use GOOD principal's features and add some new functionality.…”

Section: Fig 17 Good Queriesmentioning

confidence: 99%

Graph Database for Collaborative Communities

Soussi

Aufaure²,

Baazaoui³

2011

Community-Built Databases

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Data manipulated in an enterprise context are structured data as well as unstructured data such as emails, documents, social networks, etc. Graphs are a natural way of representing and modeling such data in a unified manner (Structured, semi-structured and unstructured ones). The main advantage of such a structure relies in the dynamic aspect and the capability to represent relations, even multiple ones, between objects. Recent database research work shows a growing interest in the definition of graph models and languages to allow a natural way of handling data appearing. In this chapter, we give a survey of the main graph database models and the associated graph query languages. We then present an application using a graph database to extract social networks.

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“…This takes O(S(n) 3 log S(n)T (n) 2 ). So, in total, the simulation of the effect of the MIs takes O(S(n) 5 log S(n)T (n) 2 ) steps.…”

Section: Proposition 33 a Ggm With Time Complexity T (N) And Space mentioning

confidence: 99%

“…However, later, the focus shifted to the computation of graph functions, in the new context of object-oriented databases. Indeed, various manipulations of object databases can be modeled as graph manipulations [1], [5].…”

Section: Introductionmentioning

confidence: 99%

Expressiveness and Complexity of Generic Graph Machines

Gemis¹,

Paredaens²,

Peelman³

et al. 1998

Theory of Computing Systems

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Abstract. The Generic Graph Machine (GGM) model is a Turing machine-like model for expressing generic computations working directly on graph structures. In this paper we present a number of observations concerning the expressiveness and complexity of GGMs. Our results comprise the following: (i) an intrinsic characterization of the pairs of graphs that are an input-output pair of some GGM; (ii) a comparison between GGM complexity and TM complexity; and (iii) a detailed discussion on the connections between the GGM model and other generic computation models considered in the literature, in particular the generic complexity classes of Abiteboul and Vianu, and the Database Method Schemes of Denninghoff and Vianu.

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Concepts for graph-oriented object manipulation

Cited by 23 publications

References 17 publications

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

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

Graph Database for Collaborative Communities

Expressiveness and Complexity of Generic Graph Machines

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