Graphs for Business Intelligence

Marie-Aude Aufaure, Chair in Business Intelligence - Laboratoire MAS - École Centrale
16 juin 2011 à 11h : salle 25-26/101
Abstract

Business Intelligence aims at supporting better business decision-making, by providing tools and methods for collecting, modeling and interacting with data. Users have to deal with big data from structured databases and unstructured content (emails, documents, social networks, etc). Moreover, these data are often distributed and highly dynamic. Social Media and mobile technologies have changed our way to access information, facilitating communication and data exchange/sharing.  All these evolutions refer to Business Intelligence 2.0.

An adapted modeling and visualization technique of links and interactions between several objects (e.g. products and sites, customers and products, social network...) is a precious mean to permit a good understanding of a lot of situations in the enterprise context. In this latter context, most of the time, these objects and their relations are stored in relational databases. But extracting and modeling such heterogeneous graphs, with heterogeneous objects and relations, are outside of the classical graph models capabilities, moreover when each node contains a set of values. On the other hand, graph models can be a natural way to present these interactions and to facilitate their querying. In this way, we propose a graph model named SPIDER-Graph which is adapted to represent interactions between complex heterogeneous objects extracted from relational databases, used for heterogeneous objects graph extraction from a relational database. One of the steps involved in this approach consists in identifying automatically the enterprise objects. Since the enterprise ontology has been used for describing enterprise objects and processes, we propose to integrate it in the object identification process (identify objects to be able to transform a graph of heterogeneous objects according to the user choice). Finally, we introduce the main principles of an aggregation algorithm used for community detection and graph visualization.


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