Super-EGO: Fast multi-dimensional similarity join

VLDB Journal

Volumn 22, Issue 4, 2013, Pages 561-585

  Kalashnikov, Dmitri V ^a  

^a Irvine   (United States)

Author keywords

Epsilon join; Euclidean space; Multi dimensional join; Similarity join

Indexed keywords

DATA-DRIVEN APPLICATIONS; EMPIRICAL STUDIES; EUCLIDEAN SPACES; HIGH-DIMENSIONAL; PARALLEL VERSION; SIMILARITY JOIN; STATE-OF-THE-ART TECHNIQUES; SYNTHETIC DATASETS;

HARDWARE; INFORMATION SYSTEMS;

ALGORITHMS;

EID: 84880618427     PISSN: 10668888     EISSN: 0949877X     Source Type: Journal    
DOI: 10.1007/s00778-012-0305-7     Document Type: Article

References (27)

1. Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In FOCS, (2006).
2. Böhm, C., Braunmüller, B., Krebs, F., Kriegel, H.-P.: Epsilon grid order: an algorithm for the similarity join on massive high-dimensional data. In SIGMOD, (2001).
3. Böhm, C., Kriegel, H.-P.: A cost model and index architecture for the similarity join. In ICDE, (2001).
4. Brinkhoff, T., Kriegel, H.-P., Seeger, B:. Efficient processing of spatial joins using R-trees, In SIGMOD (1993).
5. Bryan, B., Eberhardt, F., Faloutsos, C.: Compact similarity joins, In ICDE (2008).
6. Casey, S. D.: How to determine the effectiveness of hyper-threading technology with an application. Intel Technol. J. 6(1), (2009).
7. Cheema, M. A., Lin, X., Wang, H., Wang, J., Zhang, W.: A unified approach for computing top-k pairs in multidimensional space, In ICDE, pp. 1031-1042 (2011).
8. Chen, Z. S., Kalashnikov, D. V., Mehrotra, S.: Exploiting context analysis for combining multiple entity resolution systems, In SIGMOD (2009).
9. Corral, A., Manolopoulos, Y., Theodoridis, Y., Vassilakopoulos, M.: Closest pair queries in spatial databases, In SIGMOD (2000).
10. Dittrich, J.-P., Seeger, B.: Gess: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces. In KDD, (2001).
11. Elmasri, R., Navathe, S. B.: Fundamentals of Database Systems, 3rd edn. Addison-Wesley, Longman (2000).
12. Hjaltason, G. R., Samet, H.: Incremental distance join algorithms for spatial databases. In SIGMOD, (1998).
13. Jolliffe, I.: Principal component analysis. Encyclopedia of Statistics in, Behavioral Science, (2005).
14. Kalashnikov, D., Prabhakar, S.: Similarity join for low- and high-dimensional data, pp. 26-28. In DASFAA, Mar (2003).
15. Kalashnikov, D. V., Mehrotra, S.: Domain-independent data cleaning via analysis of entity-relationship graph. ACM Trans. Database Syst. (ACM TODS) 31(2), 716-767 (2006).
16. Kalashnikov, D. V., Prabhakar, S.: Fast similarity join for multi-dimensional data. Inf. Syst. J. 32(1), 160-177 (2007).
17. Koudas, N., Sevcik, K. C.: High dimensional similarity joins: algorithms and performance evaluation. In ICDE, (1998).
18. Lieberman, M. D., Sankaranarayanan, J., Samet, H.: A fast similarity join algorithm using graphics processing units, In ICDE (2008).
19. Lo, M.-L., Ravishankar, C. V.: Spatial hash-joins. In SIGMOD, (1996).
20. Nuray-Turan, R., Kalashnikov, D. V., Mehrotra, S., Yu, Y.: Attribute and object selection queries on objects with probabilistic attributes. ACM Trans. Database Syst. (ACM TODS), 37(1), Feb. (2012).
21. Patel, J. M., DeWitt, D. J.: Partition based spatial-merge join. In SIGMOD, (1996).
22. Schneider, D. A., DeWitt, D. J.: A performance evaluation of four parallel join algorithms in a shared-nothing multiprocessor environment. In SIGMOD, (1989).
23. Shafer, J. C., Agrawal, R.: Parallel algorithms for high-dimensional similarity joins for data mining applications. In VLDB, (1997).
24. Shim, K., Srikant, R., Agrawal, R.: High-dimensional similarity joins, In ICDE (1997).
25. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley Longman Publishing Co., Inc., Boston (2005).
26. Tao, Y., Yi, K., Sheng, C., Kalnis, P.: Efficient and accurate nearest neighbor and closest pair search in high-dimensional space. ACM Trans. Database Syst., 35(3), (2010).
27. Vernica, R., Carey, M. J., Li, C.: Efficient parallel set-similarity joins using mapreduce, In SIGMOD (2010).