An index rewriting scheme using compression for flash memory database systems

Abstract

Flash memories are one of the best media to support portable computers' storage areas in mobile database environments. Their features include non-volatility, low power consumption, and fast access time for read operations, which are sufficient to present flash memories as major database storage components for portable computers. However, we need to improve traditional index management schemes based on B-Tree due to the relatively slow characteristics of flash operations, as compared to RAM memory. In order to achieve this goal, we propose a new index rewriting scheme based on a compressed index called F-Tree. FTree-based index management improves index operation performance by compressing pointers and keys in tree nodes and rewriting the nodes without a slow erase operation in node insert/delete processes. Based on the results of the performance evaluation, we conclude that the F-Tree-based scheme outperforms the traditional schemes.

Survey of indexing schemes for information retrieval on flash memory based wireless sensor networks

“…In order to improve the performance of index operations in a flash memory database system, the work in [11] proposed an index management structure named F-Tree (Flash memory based Tree) based on the notion of compressed index rewriting to handle slow write/erase operations. This scheme resides between BFTL and FTL in order to compress and rewrite the packed index unit of BFTL.…”

“…Thus, B + -Tree is able to search more quickly with less I/O than B-Tree. F-Tree is based on B + -Tree and is able to handle the characteristics of flash memories efficiently [11]. Nevertheless, B-Tree, FTree and B + -Tree are not best suited for spatial and multimedia indexing applications.…”

“…A comprehensive evaluation of the active and sleep-mode energy consumption of flash memory based storage for sensor networks nodes was done in [9]. In portable devices which operate in small batteries, the flash memory write operation consumes nine times more energy than the read operation [11]. Extracting relevant information with minimum cost of the WSNs limited resources such as energy of battery-powered nodes is one of the principal constraints in the design and operation of WSNs.…”

Spatial Access Method for Flash Memory Embedded Systems

“…Flash memory is commonly used as a secondary storage device for storing large capacity data in mobile terminals. However, it is relatively slow in processing write and delete operations compared to read operation, is unable to overwrite, and can only perform a limited number of deletes per page [2,5,6]. Accordingly, if a conventional spatial index is applied to flash memory as it is, then the number of write and delete operations of flash memory will be increased due to frequent node updates, which, in turn, increases operation time and lowers index performance.…”