TY - GEN
T1 - VPSF
T2 - 10th International Conference on Database and Expert Systems Applications, DEXA 1999
AU - Kim, Jeong Ki
AU - Chang, Jae Woo
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1999.
PY - 1999
Y1 - 1999
N2 - In this paper, we propose a Vertically-partitioned Parallel Signature File (VPSF) method which can partition a signature file vertically. Our VPSF method uses an extendable hashing technique for dynamic environment and uses a frame-sliced signature file technique for efficient retrieval. Our VPSF method also can eliminate the data skew and the execution skew by allocating each frame to a processing node. To prove the efficiency of our VPSF method, we compare its performance with those of the conventional parallel signature file methods, i.e., HPSF and HF, in terms of retrieval time, storage overhead, and insertion time. The experiment runs on several distributions with normal, half, and double standard deviations of the real data. The result shows that our VPSF achieves about 40% better retrieval performance than the HF in all cases. In addition, we show that our VPSF gains about 20~50% improvement in retrieval time, compared with the HF and HPSF on record sets with the half deviation. As a result, our VPSF generally outperforms on retrieval performance when the records of a database are uniform in size.
AB - In this paper, we propose a Vertically-partitioned Parallel Signature File (VPSF) method which can partition a signature file vertically. Our VPSF method uses an extendable hashing technique for dynamic environment and uses a frame-sliced signature file technique for efficient retrieval. Our VPSF method also can eliminate the data skew and the execution skew by allocating each frame to a processing node. To prove the efficiency of our VPSF method, we compare its performance with those of the conventional parallel signature file methods, i.e., HPSF and HF, in terms of retrieval time, storage overhead, and insertion time. The experiment runs on several distributions with normal, half, and double standard deviations of the real data. The result shows that our VPSF achieves about 40% better retrieval performance than the HF in all cases. In addition, we show that our VPSF gains about 20~50% improvement in retrieval time, compared with the HF and HPSF on record sets with the half deviation. As a result, our VPSF generally outperforms on retrieval performance when the records of a database are uniform in size.
UR - https://www.scopus.com/pages/publications/84947904184
U2 - 10.1007/3-540-48309-8_15
DO - 10.1007/3-540-48309-8_15
M3 - Conference paper
AN - SCOPUS:84947904184
SN - 3540664483
SN - 9783540664482
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 166
EP - 175
BT - Database and Expert Systems Applications - 10th International Conference, DEXA 1999, Proceedings
A2 - Bench-Capon, Trevor J. M.
A2 - Soda, Giovanni
A2 - Tjoa, A. Min
PB - Springer Verlag
Y2 - 30 August 1999 through 3 September 1999
ER -