Graph OLAP: a multi-dimensional framework for graph data analysis

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, graphs 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 graph 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 graphs? Unfortunately, to our best knowledge, there are no OLAP tools available that can interactively view and analyze graph data from different perspectives and with multiple granularities. In this paper, we argue that it is critically important to OLAP graph structured data and propose a novel Graph OLAP framework. According to this framework, given a graph dataset with its nodes and edges associated with respective attributes, a multi-dimensional model can be built to enable efficient online analytical processing so that any portions of the graphs can be generalized/specialized dynamically, offering multiple, versatile views of the data. The contributions of this work are three-fold. First, starting from basic definitions, i.e., what are dimensions and measures in the Graph OLAP scenario, we develop a conceptual framework for data cubes on graphs. 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 a graph 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 "network" properties of the graph dataset are much harder to compute than their traditional OLAP counterparts. Third, to provide more flexible, interesting and informative OLAP of graphs, 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 Graph OLAP.

A Framework for Building OLAP Cubes on Graphs

“…Multiple classification for graph measures were proposed in the literature, such as the classification by the aggregation type (i.e., distributive, algebraic and holistic) [3]. Here we propose a new classification of graph measures, based on the type and the computation algorithm.…”

“…The second possible classification makes the distinction between (1) local measures, which are computed separately for graph nodes or edges (e.g., the centrality of an actor), and (2) global measures which are computed for the whole graph (e.g., the diameter or number of cycles of the graph). -The Graph as a Measure: As discussed by Chen et al in [3], the graph itself could be considered as a measure examined from different perspectives and at different aggregation levels.…”

“…OLAP on Graphs GraphOLAP is a conceptual framework for OLAP analysis of a collection of homogeneous graphs [3]. Attributes of the snapshots are considered as the dimensions.…”

“…Many approaches were proposed in the literature to extend current decision support systems with graphs [3][4][5]. They suggested the first foundations for building OLAP cubes on graphs.…”

“…They suggested the first foundations for building OLAP cubes on graphs. However, their techniques focused mostly on homogeneous graphs (i.e., graphs where all nodes are of the same type, and all edges are the same), and the OLAP analysis focus mainly on the graph topology as the measure of interest [3][4][5]. In such cases, all the attributes of the graph elements are considered as the dimensions and are used for aggregating the graph and performing its multi-perspective analysis.…”