Distributed threshold querying of general functions by a difference of monotonic representation

Proceedings of the VLDB Endowment

Volumn 4, Issue 2, 2010, Pages 46-57

  Sagy, Guy ^a   Keren, Daniel ^b   Sharfman, Izchak ^a   Schuster, Assaf ^a  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^b UNIVERSITY OF HAIFA   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DIGITAL STORAGE; OBJECT DETECTION;

DATABASE RELATIONS; DISTRIBUTED DATA; EVENT-TRIGGERING; GENERAL FUNCTIONS; MONOTONIC FUNCTIONS; SCORING FUNCTIONS; THRESHOLD QUERIES; THRESHOLDING;

QUERY PROCESSING;

EID: 84862656283     PISSN: None     EISSN: 21508097     Source Type: Conference Proceeding    
DOI: 10.14778/1921071.1921072     Document Type: Article

References (39)

1. http://gregsadetsky.com/aol-data/.
2. http://www.netflix.com/.
3. W. T. Balke, W. Nejdl, W. Siberski, and U. Thaden. Progressive distributed top-k retrieval in peer-to-peer networks. In ICDE Conf., pages 174-185, 2005.
4. S. Borzsonyi, D. Kossmann, and K. Stocker. The skyline operator. In ICDE Conf., pages 421-430, 2001.
5. P. Cao and Z. Wang. Efficient top-k query calculation in distributed networks. In PODC Conf., pages 206-215, 2004.
6. D. Caragea, A. Silvescu, and V. Honavar. A framework for learning from distributed data using sufficient statistics and its application to learning decision trees. Int. J. Hybrid Intell. Syst., 1(2):80-89, 2004.
7. N. L. Carothers. Real Analysis. Cambridge University Press, 2000.
8. K. C.-C. Chang and S. won Hwang. Minimal probing: Supporting expensive predicates for top-k queries. In SIGMOD Conf., pages 346-357, 2002.
9. D. W.-L. Cheung and Y. Xiao. Effect of data skewness in parallel mining of association rules. In PAKDD Conf., pages 48-60, 1998.
10. A. Das Sarma, A. Lall, D. Nanongkai, and J. Xu. Randomized multi-pass streaming skyline algorithms. Proc. VLDB Endow., 2(1):85-96, 2009.
11. R. Fagin, A. Lotem, and M. Naor. Optimal aggregation algorithms for middleware. In PODS Conf., pages 102-113, 2001.
12. C. Giannella, K. Liu, T. Olsen, and H. Kargupta. Communication efficient construction of decision trees over heterogeneously distributed data. In ICDM Conf., pages 67-74, 2004.
13. P. Godfrey, R. Shipley, and J. Gryz. Algorithms and analyses for maximal vector computation. VLDB Journal, 16(1):5-28, 2007.
14. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., 2005.
15. L. Huang, X. Nguyen, M. N. Garofalakis, J. M. Hellerstein, M. I. Jordan, A. D. Joseph, and N. Taft. Communication-efficient online detection of network-wide anomalies. In INFOCOM conf., pages 134-142, 2007.
16. A. Jain, J. M. Hellerstein, S. Ratnasamy, and D. Wetherall. A wakeup call for internet monitoring systems: The case for distributed triggers. In HotNets Workshop, 2004.
17. R. Keralapura, G. Cormode, and J. Ramamirtham. Communication-efficient distributed monitoring of thresholded counts. In SIGMOD, pages 289-300, 2006.
18. D. Kossmann, F. Ramsak, and S. Rost. Shooting stars in the sky: an online algorithm for skyline queries. In VLDB Conf., pages 275-286, 2002.
19. A. Lakhina, M. Crovella, and C. Diot. Diagnosing network-wide traffic anomalies. In SIGCOMM Conf., pages 219-230, 2004.
20. D. D. Lewis, Y. Yang, T. G. Rose, and F. Li. Rcv1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5(4):361-397, 2004.
21. A. Marian, N. Bruno, and L. Gravano. Evaluating top-k queries over web-accessible databases. In ICDE Conf., pages 369-381, 2002.
22. S. Michel, P. Triantafillou, and G. Weikum. Klee: a framework for distributed top-k query algorithms. In VLDB Conf., pages 637-648, 2005.
23. S. Michel, P. Triantafillou, and G. Weikum. Minerva∞: A scalable efficient peer-to-peer search engine. In Middleware, pages 60-81, 2005.
24. C. Olston, J. Jiang, and J. Widom. Adaptive filters for continuous queries over distributed data streams. In SIGMOD Conf., pages 563-574, 2003.
25. D. Papadias, Y. Tao, G. Fu, and B. Seeger. Progressive skyline computation in database systems. ACM Trans. Database Syst., 30(1):41-82, 2005.
26. B.-H. Park and H. Kargupta. Distributed data mining: Algorithms, systems, and applications. Lawrence Erlbaum Associates, 2002.
27. S. Ramaswamy. Extreme data mining. In SIGMOD Conf., pages 1-2. ACM, 2008.
28. T. Rose, M. Stevenson, and M. Whitehead. The Reuters Corpus Volume 1 - from Yesterday's News to Tomorrow's Language Resources. In LREC conf., pages 29-31, 2002.
29. A. Schuster and R. Wolff. Communication-efficient distributed mining of association rules. In SIGMOD Conf., pages 473-484, 2001.
30. I. Sharfman, A. Schuster, and D. Keren. A geometric approach to monitoring threshold functions over distributed data streams. ACM Trans. Database Syst., 32(4), 2007.
31. I. Sharfman, A. Schuster, and D. Keren. Shape sensitive geometric monitoring. In PODS Conf., pages 301-310, 2008.
32. T. Suel, C. Mathur, J. wen Wu, J. Zhang, A. Delis, M. Kharrazi, X. Long, and K. Shanmugasundaram. Odissea: A peertopeer architecture for scalable web search and information retrieval. In WebDB Workshop, 2002.
33. K.-L. Tan, P.-K. Eng, and B. C. Ooi. Efficient progressive skyline computation. In VLDB Conf., pages 301-310, 2001.
34. D. Xin, J. Han, and K. C.-C. Chang. Progressive and selective merge: computing top-k with ad-hoc ranking functions. In SIGMOD Conf., pages 103-114, 2007.
35. H. Xiong, S. Shekhar, P.-N. Tan, and V. Kumar. Exploiting a support-based upper bound of pearson's correlation coefficient for efficiently identifying strongly correlated pairs. In KDD, pages 334-343, 2004.
36. H. Yu, H.-G. Li, P. Wu, D. Agrawal, and A. E. Abbadi. Efficient processing of distributed top-k queries. In DEXA Conf., pages 65-74, 2005.
37. J. Zhang and J. Feigenbaum. Finding highly correlated pairs efficiently with powerful pruning. In CIKM Conf., pages 152-161, 2006.
38. Z. Zhang, S. won Hwang, K. C.-C. Chang, M. Wang, C. A. Lang, and Y.-C. Chang. Boolean + ranking: querying a database by k-constrained optimization. In SIGMOD Conf., pages 359-370, 2006.
39. Q. Zhao, M. Ogihara, H. Wang, and J. Xu. Finding global icebergs over distributed data sets. In PODS Conf., pages 298-307, 2006.