Database compression with data mining methods
Information organization and databases
The ODBC Solution: Open Database Connectivity in Distributed Environments
The ODBC Solution: Open Database Connectivity in Distributed Environments
Jdbc Database Access with Java: A Tutorial and Annotated Reference
Jdbc Database Access with Java: A Tutorial and Annotated Reference
Relational Database Compression Using Augmented Vector Quantization
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Compressing Relations and Indexes
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
An Algebraic Compression Framework for Query Results
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Integrating compression and execution in column-oriented database systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
How to wring a table dry: entropy compression of relations and querying of compressed relations
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
How to barter bits for chronons: compression and bandwidth trade offs for database scans
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Efficient index compression in DB2 LUW
Proceedings of the VLDB Endowment
Column-oriented database systems
Proceedings of the VLDB Endowment
An Online Algorithm for Lightweight Grammar-Based Compression
CCP '11 Proceedings of the 2011 First International Conference on Data Compression, Communications and Processing
Software-related energy footprint of a wireless broadband module
Proceedings of the 9th ACM international symposium on Mobility management and wireless access
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Client-server database query processing has become an important paradigm in many data processing applications today. In cloud-based data services, for example, queries over structured data are sent to cloud-based servers for processing and the results relayed back to the client devices. Network bandwidth between client devices and cloud-based servers is often a limited resource and the use of data compression to reduce the amount of query result data transmitted would not only conserve bandwidth but also help with battery lifetime in the case of mobile client devices. For query result compression, current data compression methods do not exploit redundancy information that can be inferred from the query structure itself for greater compression. In this paper we propose a novel query-aware compression method for compressing query results sent from database servers to client applications. Our method is based on two key ideas. We exploit redundancy information obtained from the query plan and possibly from the database schema to achieve better compression than standard non-query aware compressors. We use a collection of memory-limited dictionaries to encode attribute values in a lightweight and efficient manner. Each dictionary in the collection of dictionaries are also dynamically resized to adapt to changing temporal access characteristics. We evaluated our method empirically using the TPC-H benchmark show that this technique is effective especially when used in conjunction with standard compressors. Our results show that compression ratios of up to twice that of gzip are possible.