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  1. Proceedings of the 17th International Workshop on Data Warehousing and OLAP (DOLAP '14)
  2. Recursive Query Evaluation in a Column DBMS to Analyze Large Graphs
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Querying Big, Dynamic, Distributed Data
GOLAM: A Framework for Analyzing Genomic Data
A Holistic Approach to OLAP Sessions Composition: The Falseto Experience
Recursive Query Evaluation in a Column DBMS to Analyze Large Graphs
An Advanced Data Warehouse for Integrating Large Sets of GPS Data
A Semantic Model for Movement Data Warehouses
fVSS: A New Secure and Cost-Efficient Scheme for Cloud Data Warehouses
Bijoux: Data Generator for Evaluating ETL Process Quality
SM4AM: A Semantic Metamodel for Analytical Metadata
What can Emerging Hardware do for your DBMS Buffer?
From Business Intelligence to Location Intelligence with the Lily Library
A Framework for User-Centered Declarative ETL
Optimization of Data-intensive Flows: Is it Needed? Is it Solved?
Big Graph Analytics: The State of the Art and Future Research Agenda

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Article

Recursive Query Evaluation in a Column DBMS to Analyze Large Graphs

Content Provider ACM Digital Library
Author Gurram, Achyuth Rai, Nirmala Ordonez, Carlos
Abstract Graphs represent a major challenge on big data analytics, for which there are many systems and prototypes, most of them not based on relational database management systems (DBMSs). Graph problems require substantially different algorithms compared to other analytical techniques (i.e., cubes, statistical models, machine learning) and they are especially important in the analysis of social networks and the Internet. On the other hand, recursive queries are a fundamental query mechanism to analyze graphs in a DBMS, but they can be slow with large graphs. Column DBMSs are a novel kind of faster database systems, but with significantly different storage and retrieval mechanisms compared to traditional row DBMSs. Thus we study the pros and cons of optimizing recursive queries on a column DBMS. Specifically, we study two inter-related graph problems: transitive closure and adjacency matrix multiplication, together with their respective optimization of queries combining recursive joins and recursive aggregations. An experimental evaluation with large graphs compares query optimization in a column DBMS and a row DBMS. We analyze performance tradeoffs with graphs having significantly different size, shape and connectivity. Our benchmark results prove column DBMSs are much faster than row DBMSs to analyze graphs, especially as graphs get larger and denser.
Starting Page 71
Ending Page 80
Page Count 10
File Format PDF
ISBN 9781450309998
DOI 10.1145/2666158.2666177
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-11-07
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Graph Matrix Query Column store Recursion
Content Type Text
Resource Type Article
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