InfoNetOLAP: OLAP and Mining of Information Networks

Abstract

Databases and data warehouse systems have been evolving from handling normalized spreadsheets stored in relational databases to managing and analyzing diverse application-oriented data with complex interconnecting structures. Responding to this emerging trend, information networks have been growing rapidly and showing their critical importance in many applications, such as the analysis of XML, social networks, Web, biological data, multimedia data, and spatiotemporal data. Can we extend useful functions of databases and data warehouse systems to handle network structured data? In particular, OLAP (On-Line Analytical Processing) has been a popular tool for fast and user-friendly multi-dimensional analysis of data warehouses. Can we OLAP information networks and perform mining tasks on top of that? Unfortunately, to our best knowledge, there are no OLAP tools available that can interactively view and analyze network structured data from different perspectives and with multiple granularities. In this chapter, we argue that it is critically important to OLAP such information network data and propose a novel InfoNetOLAP framework. According to this framework, given an information network data set with its nodes and edges associated with respective attributes, a multi-dimensional model can be built to enable efficient on-line analytical processing so that any portions of the information networks can be generalized/specialized dynamically, offering multiple, versatile views of the data set. The contributions of this work are threefold. First, starting from basic definitions, i.e., what are dimensions and measures in the InfoNetOLAP scenario, we develop a conceptual framework for data cubes constructed on the information networks. We also look into different semantics of OLAP operations and classify the framework into two major subcases: informational OLAP and topological OLAP. Second, we show how an information network cube can be materialized by calculating a special kind of measure called aggregated graph and how to implement it efficiently. This includes both full materialization and partial materialization where constraints are enforced to obtain an iceberg cube. As we can see, due to the increased structural complexity of data, aggregated graphs that depend on the underlying “graph” properties of the information networks are much harder to compute than their traditional OLAP counterparts. Third, to provide more flexible, interesting, and insightful OLAP of information networks, we further propose a discovery-driven multi-dimensional analysis model to ensure that OLAP is performed in an intelligent manner, guided by expert rules and knowledge discovery processes. We outline such a framework and discuss some challenging research issues for discovery-driven InfoNetOLAP.