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Systems for Graph Extraction from Tabular Data

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Anzum_Nafisa.pdf (2.11 MB)

Date

2020-09-28

Authors

Anzum, Nafisa

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University of Waterloo

Abstract

Connections amongst real-world entities provide significant insights for numerous real-life applications in social networks, semantic web, road maps, finance, among others. Graphs are perhaps the most natural way to model such connections in application data. However, in many enterprises, an application data is still primarily stored in an RDBMS in a tabular format and users extract graphs out of an RDBMS and store them in specialized graph processing systems. As a result, many users face two major challenges before conducting any graph analysis. First, extracting graphs from an RDBMS requires building an ETL pipeline, which can require a significant amount of time. Second, keeping the extracted graph in the graph processing system, such as a graph database management system (GDBMS), in sync with the original data in the RDBMS requires developing additional non-trivial synchronization code. In this thesis, we study and address these two challenges and present two software systems, GraphWrangler and R2GSync, that we have developed to solve these challenges. GraphWrangler is an interactive system that streamlines the ETL pipeline. Users connect to an RDBMS using GraphWrangler and with several simple interactions, such as dragging and dropping of rows and columns and drawing edges on the screen, they describe table-to-graph mappings. This way, users can describe the graphs they would like to extract without writing any custom scripts. In addition, GraphWrangler allows user to immediately visualize their tables in the form of a graph. Our second system, R2GSync, uses the mappings of an extracted graph and maintains a consistent, i.e., in sync, copy of this graph in a GDBMS as updates happen to the original RDBMS from which the graph was extracted. Querying the extracted graph inside the GDBMS requires a new querying functionality inside the GDBMS that we call edge views. We describe our implementation of edge views and several optimizations to make queries that contain edge views more efficient.

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Keywords

graph database, graph transformation, tabular data, relational database management systems, edge views, data transformation

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http://hdl.handle.net/10012/16380

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Computer Science