Goebel, Randy Tanaka, Yuzura Wahlster, Wolfgang Siekmann, Joerg
Lecture Notes in Artificial Intelligence vol:7250
Bisociative Knowledge Discovery pages:147-165
One of the key steps in data analysis is the exploration of data. For traditional relational data, this process is facilitated by rela- tional database management systems and the aggregates and rankings they can compute. However, for the exploration of graph data, relational databases may not be most practical and scalable. Many tasks related to exploration of information networks involve computation and analy- sis of connections (e.g. paths) between concepts. Traditional relational databases offer no specific support for performing such tasks. For in- stance, a statistic such as the shortest path between two given nodes cannot be computed by a relational database. Surprisingly, tools for querying graph and network databases are much less well developed than for relational data, and only recently an increasing number of studies are devoted to graph or network databases. Our position is that the devel- opment of such graph databases is important both to make basic graph mining easier and to prepare data for more complex types of analysis.
In this chapter, we present the BiQL data model for representing and manipulating information networks. The BiQL data model consists of two parts: a data model describing objects, link, domains and networks, and a query language describing basic network manipulations. The main focus here lies on data preparation and data analysis, and less on data mining or knowledge discovery tasks directly.