Efficient Relational Techniques for Processing Graph Queries

Abstract

Graphs are widely used for modeling complicated data such as social networks, chemical compounds, protein interactions and semantic web. To effectively understand and utilize any collection of graphs, a graph database that efficiently supports elementary querying mechanisms is crucially required. For example, Subgraph and Supergraph queries are important types of graph queries which have many applications in practice. A primary challenge in computing the answers of graph queries is that pair-wise comparisons of graphs are usually hard problems. Relational database management systems (RDBMSs) have repeatedly been shown to be able to efficiently host different types of data such as complex objects and XML data. RDBMSs derive much of their performance from sophisticated optimizer components which make use of physical properties that are specific to the relational model such as sortedness, proper join ordering and powerful indexing mechanisms. In this article, we study the problem of indexing and querying graph databases using the relational infrastructure. We present a purely relational framework for processing graph queries. This framework relies on building a layer of graph features knowledge which capture metadata and summary features of the underlying graph database. We describe different querying mechanisms which make use of the layer of graph features knowledge to achieve scalable performance for processing graph queries. Finally, we conduct an extensive set of experiments on real and synthetic datasets to demonstrate the efficiency and the scalability of our techniques.

Large scale graph processing systems: survey and an experimental evaluation

“…In addition, the study has considered the memory usage and total network usage metrics for its benchmarcking. The results of this study show that Giraph and GraphLab's synchronous mode have good all-around performance while 33 GPS excels at memory efficiency. The results also show that the synchronous mode of Giraph, GPS, and GraphLab outperforms that of Mizan in all experiments.…”

“…In practice, graph analytics is an important and effective big data discovery tool [36]. For example, it enables identifying influential persons in a social network, inspecting fraud operations in a complex interaction network and recognizing product affinities by analyzing community buying patterns [33].…”

A distributed query execution engine of big attributed graphs

“…Therefore, several techniques and systems have been utilizing the efficiency of the relational model and RDBMSs for storing and querying various more sophisticated data models including XML (Gou and Chirkova 2007 ; Grust et al. 2004 ), RDF (Sakr and Al-Naymat 2009 ) and graphs (Sakr 2009 ; Sakr and Al-Naymat 2010 ). On the other hand, relational databases have shown to be inefficient for querying operations that involves recursive access or looping for significant numbers of rows via performing various expensive join queries that may lead to considerably huge intermediate results.…”