(1 + ε)-approximate sparse recovery
Eric Price, David P. Woodruff
FOCS 2011
In database systems, the cost of data storage and retrieval are important components of the total cost and response time of the system. A popular mechanism to reduce the storage footprint is by compressing the data residing in tables and indexes. Compressing indexes efficiently, while maintaining response time requirements, is known to be challenging. This is especially true when designing for a workload spectrum covering both data warehousing and transaction processing environments. DB2 Linux, UNIX, Windows (LUW) recently introduced index compression for use in both environments. This uses techniques that are able to compress index data efficiently while incurring virtually no performance penalty for query processing. On the contrary, for certain operations, the performance is actually better. In this paper, we detail the design of index compression in DB2 LUW and discuss the challenges that were encountered in meeting the design goals. We also demonstrate its effectiveness by showing performance results on typical customer scenarios. © 2009 VLDB Endowment.
Eric Price, David P. Woodruff
FOCS 2011
Minkyong Kim, Zhen Liu, et al.
INFOCOM 2008
Maurice Hanan, Peter K. Wolff, et al.
DAC 1976
Donald Samuels, Ian Stobert
SPIE Photomask Technology + EUV Lithography 2007