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

Abstract

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.

BIIIG: Enabling business intelligence with integrated instance graphs

“…Several graph-based frameworks focus on the transformation of source data into problem-specific [14] [7] or more general [15] graph-based data warehouse models but do not aim at preserving all relationships from the source systems that might become of interest in unexpected ways. A notable exception is DB2SNA [23] that extracts social networks from relational databases and analyzes them, albeit it is not concerned with business analytics. The automated extraction of interrelated data objects from ERP systems is discussed in [17], but without using a graph model and for the single analytical goal of process mining.…”

“…Second, all relationships are added as edges to the IIG within a loop over all associations (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29). Analogous to nodes, the association-specific mapping µ is used to query relationships, represented as sets of attribute-value pairs (13).…”

“…At the end the (20). In the case of an undirected relationship we construct a second edge with same relationship type and properties (22) but switched start and end nodes (23,24) and add it to the IIG (25).…”