Research and Implement about Embedded Database System Base-on NAND Flash Memory

Article Preview

Abstract:

Currently, the embedded database technology has become a very active research field, and attracted more and more attention. This paper research how to improve the storage performance of embedded database indexing mechanism using read and write characteristics of NAND flash memory. Based on the reviewed and compared of existing design of NAND flash memory, this article expressed the design and implementation of dynamic index mechanism base on B + Tree. The prototype system combines the advantages of operation of the adapted read operation disk model and write adapted write operation log model, optimal matching and converting for storage model of each node in B + tree model while running which make a variety of devices have better read and write performance.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1892-1896

Citation:

Online since:

February 2013

Authors:

Keywords:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Hwang, C. -G.: Nanotechnology Enables a New Memory Growth Model. Proceedings of the IEEE 91(11), 1765–1771 (2003).

Google Scholar

[2] Lee, S. -W., Moon, B.: Design of Flash-Based DBMS: An In-Page Logging Approach. In: Proceedings of the ACM SIGMOD, Beijing, China, p.55–66 (June 2007).

DOI: 10.1145/1247480.1247488

Google Scholar

[3] Cirrus Logic. EP9315 Data Sheet - Enhanced Universal Platform System-on-Chip Processor (March 2005) DS638PP4.

Google Scholar

[4] Hal Berenson, Philip A. Bernstein, Jim Gray, Jim Melton, Elizabeth J. O'Neil, and Patrick E. O'Neil. A Critique of ANSI SQL Isolation Levels. In Proceedings of the ACM SIGMOD, pages 1–10, San Jose, CA, May (1995).

DOI: 10.1145/568271.223785

Google Scholar

[5] Andrew Birrell, Michael Isard, Chuck Thacker, and Ted Wobbe. A Design for High-Performance Flash Disks. Technical Report MSR-TR-2005-176, Microsoft Research, December (2005).

Google Scholar

[6] Transaction Processing Performance Council. TPC Benchmark. http: /www. tpc. org.

Google Scholar

[7] David J. DeWitt, Randy H. Katz, Frank Olken, Leonard D. Shapiro, Michael Stonebraker, and David A. Wood. Implementation Techniques for Main Memory Database Systems. In Proceedings of the ACM SIGMOD, pages 1–8, Boston, MA, June (1984).

DOI: 10.1145/971697.602261

Google Scholar

[8] Eran Gal and Sivan Toledo. Mapping Structures for Flash Memories: Techniques and Open Problems. In International Conference on Software - Science, Technology & Engineering (SwSTE'05), Herzelia, Israel, February (2005).

DOI: 10.1109/swste.2005.14

Google Scholar

[9] Goetz Graefe. Sort-Merge-Join: An Idea Whose Time Has(h) Passed? In Proceedings of ICDE, pages 406–417, Houston, TX, February (1994).

DOI: 10.1109/icde.1994.283062

Google Scholar

[10] Goetz Graefe. The Five-minute Rule Twenty Years Later, and How Flash Memory Changes the Rules. In Third International Workshop on Data Management on New Hardware (DAMON2007), Beijing, China, June (2007).

DOI: 10.1145/1363189.1363198

Google Scholar

[11] Goetz Graefe, Ann Linville, and Leonard D. Shapiro. Sort versus Hash Revisited. IEEE Transactions on Knowledge and Data Engineering, 6(6): 934–944, December (1994).

DOI: 10.1109/69.334883

Google Scholar

[12] Jim Gray. Rules of Thumb in Data Engineering. In Proceedings of ICDE, pages 3–12, San Diego, CA, March (2000).

Google Scholar

[13] Jim Gray and Bob Fitzgerald. Flash Disk Opportunity for Server-Applications. http: /www. research. microsoft. com/˜gray, January (2007).

Google Scholar