| Project/Area Number |
11558030
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (B).
|
| Allocation Type | Single-year Grants |
|---|
| Section | 展開研究 |
|---|
| Research Field |
計算機科学
|
|---|
| Research Institution | University of Tokyo |
|---|
Principal Investigator |
KITSUREGAWA Masaru University of Tokyo, Institute of Industrial Science, Professor, 生産技術研究所, 教授 (40161509)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
HAYASHI Hiroshi University of Tokyo, Research Associate, 生産技術研究所, 助手 (50282596)
NAKAYAMA Masaya University of Tokyo, Associate Professor, 情報基盤センター, 助教授 (90217943)
NAKANO Miyuki Univ.of Tokyo, Institute of Industrial Science, Research Associate, 生産技術研究所, 助手 (30227863)
TORII Syunichi Hitachi, Ltd.Main Researcher, ビジネスソリューション開発本部, 技術主幹
|
|---|
| Project Period (FY) |
1999 – 2000
|
| Project Status |
Completed (Fiscal Year 2000)
|
| Budget Amount *help |
¥13,600,000 (Direct Cost: ¥13,600,000)
Fiscal Year 2000: ¥6,500,000 (Direct Cost: ¥6,500,000)
Fiscal Year 1999: ¥7,100,000 (Direct Cost: ¥7,100,000)
|
|---|
| Keywords | data mining / parallel database processing / distributed processing / 並列データベース |
|---|
| Research Abstract |
In this research, we developed a parallel association rule mining algorithm and implemented the algorithm on the large multi-processors (100 processors). Then, we tried to make the proposed algorihtm fit for a practical use.
At the first year, we designed a parallel association rule mining algorithm considering the taxonomy for sequence data. We adopt a hashing method to the candidate rules so that we can easily achieve scalable performance on the environment of large number of processors. Then, the preliminary experiment was done by using the PC cluster. After the experiment, we investigated a run time load balancing method considering the taxonomy class and the frequency.
At the second year, we implemented our parallel association rule mining algorithm by SQL and executed our algorithm on the PC cluster and the yendor DBMS.Then, we investigated the pracical use of our algorithm on the large DBMS engine. Comparing our results with the special mining program written in C, we showed that the performance of SQL data mining algorithm with several nodes is almost equal to that of the special program. At last, we clarify the effectiveness of SQL parallel mining algorithm by considering reducing ratio of SQL algorithm to the special mining program.
|
