Improving OLAP analysis of multidimensional data streams via efficient compression techniques

Intelligent Techniques for Warehousing and Mining Sensor Network Data

Volumn , Issue , 2009, Pages 17-49

  Cuzzocrea, Alfredo ^a,b   Furfaro, Filippo ^b   Masciari, Elio ^a   Saccà, Domenico ^b  

^a ICAR CNR   (Italy)

^b UNIVERSITY OF CALABRIA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 79961194303     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-60566-328-9.ch002     Document Type: Chapter

References (51)

1. Acharya, S., Gibbons, P. B., Poosala, V., & Ramaswamy, S. (1999). Join synopses for approximate query answering. In Proceedings of 19th ACM Symposium on Principles of Database Systems (pp. 275-286).
2. Agarwal, S., Agrawal, R., Deshpande, P. M., Gupta, A., Naughton, J. F., Ramakrishnan, R., & Sarawagi, S. (1996). On the computation of multidimensional aggregates. In Proceedings of the 22th International Conference on Very Large Data Bases (pp. 506-521).
3. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (pp. 487-499).
4. Alert System. (2007). Retrieved from http://www.alertsystems.org
5. Ananthakrishna, R., Das, A., Gehrke, J., Korn, F., Muthukrishnan, S., & Srivastava, D. (2003). Efficient approximation of correlated sums on data streams. IEEE Transactions on Knowledge and Data Engineering, 15(3), 569-572. doi:10.1109/TKDE.2003.1198391
6. Avnur, R., & Hellerstein, J. M. (2000). Eddies: Continuously adaptive query processing. In Proceedings of the 2000 ACM International Conference on Management of Data (pp. 261-272).
7. Babcock, B., Babu, S., Datar, M., Motwani, R., & Widom, J. (2002). Models, issues in data stream systems. In Proceedings of the 21st ACM Symposium on Principles of Database Systems (pp. 1-16).
8. Babu, S., & Widom, J. (2001). Continuous queries over data streams. SIGMOD Record, 30(3), 109-120. doi:10.1145/603867.603884
9. Barbarà, D., Du Mouchel, W., Faloutsos, C., Haas, P. J., Hellerstein, J. M., & Ioannidis, Y. E. (1997). The New Jersey data reduction report. A Quarterly Bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering, 20(4), 3-45.
10. Berchtold, S., Böhm, C., & Kriegel, H.-P. (1998). The pyramid-technique: Towards breaking the curse of dimensionality. In Proceedings of the 1998 ACM International Conference on Management of Data (pp. 142-153).
11. Beyer, K., & Ramakrishnan, R. (1999). Bottom-up computation of sparse, iceberg cubes. In Proceedings of the 1999 ACM International Conference on Management of Data (pp. 359-370).
12. Bonnet, P., Gehrke, J., & Seshadri, P. (2000). Querying the physical world. IEEE Personal Communications, 7(5), 10-15. doi:10.1109/98.878531
13. Bonnet, P., Gehrke, J., & Seshadri, P. (2001). Towards sensor database systems. In Proceedings of 2nd International Conference on Mobile Data Management (pp. 3-14).
14. Buccafurri, F., Furfaro, F., Saccà, D., & Sirangelo, C. (2003). A quad-tree based multiresolution approach for two-dimensional summary data. In Proceedings of the 15th IEEE International Conference on Scientific, Statistical Database Management (pp. 127-137).
15. Cai, Y. D., Clutterx, D., Papex, G., Han, J., Welgex, M., & Auvilx, L. (2004). MAIDS: Mining alarming incidents from data streams. In Proceedings of the 2004 ACM International Conference on Management of Data (pp. 919-920).
16. Chaudhuri, S., & Dayal, U. (1997). An overview of data warehousing and OLAP technology. SIGMOD Record, 26(1), 65-74. doi:10.1145/248603.248616
17. Chen, Y., Dong, G., Han, J., Wah, B. W., & Wang, J. (2002). Multi-dimensional regression analysis of time-series data streams. In Proceedings of the 28th International Conference on Very Large Data Bases (pp. 323-334).
18. Colliat, G. (1996). OLAP, relational, and multidimensional database systems. SIGMOD Record, 25(3), 64-69. doi:10.1145/234889.234901
19. Cortes, C., Fisher, K., Pregibon, D., Rogers, A., & Smith, F. (2000). Hancock: A language for extracting signatures from data streams. In Proceedings of the 6th ACM International Conference on Knowledge Discovery and Data Mining (pp. 9-17).
20. Cuzzocrea, A. (2005). Overcoming limitations of approximate query answering in OLAP. In Proceedings of the 9th IEEE International Database Engineering, Applications Symposium (pp. 200-209).
21. Deligiannakis, A., Kotidis, Y., & Roussopoulos, N. (2003). Data reduction techniques for sensor networks. (Technical Report CS-TR-4512). UM Computer Science Department.
22. Deligiannakis, A., Kotidis, Y., & Roussopoulos, N. (2004). Compressing historical information in sensor networks. In Proceedings of the ACM International Conference on Management of Data (pp. 527-538).
23. Deligiannakis, A., Kotidis, Y., & Roussopoulos, N. (2004). Hierarchical In-network data aggregation with quality guarantees. In Proceedings of 9th International Conference on Extending Database Technology (pp. 658-675).
24. Dobra, A., Garofalakis, M., Gehrke, J., & Rastogi, R. (2002). Processing Complex aggregate queries over data streams. In Proceedings of the 2002 ACM International Conference on Management of Data (pp. 61-72).
25. Domingos, P., & Hulten, G. (2000). Mining highspeed data streams. In Proceedings of the 6th ACM International Conference on Knowledge Discovery, Data Mining (pp. 71-80).
26. Fang, M., Shivakumar, N., Garcia-Molina, H., Motwani, R., & Ullman, J. D. (1998). Computing iceberg queries efficiently. In Proceedings of the 24th International Conference on Very Large Data Bases (pp. 299-310).
27. Ganti, V., Li Lee, M., & Ramakrishnan, R. (2000). ICICLES: Self-tuning samples for approximate query answering. In Proceedings of 26th International Conference on Very Large Data Bases (pp. 176-187).
28. Gehrke, J., & Madden, S. (2004). Query processing in sensor networks. IEEE Pervasive Computing / IEEE Computer Society [and] IEEE Communications Society, 3(1), 46-55. doi:10.1109/MPRV.2004.1269131
29. Gilbert, A. C., Kotidis, Y., Muthukrishnan, S., & Strauss, M. J. (2001). Surfing wavelets on streams: One-pass summaries for approximate aggregate queries. In Proceedings of 27th International Conference on Very Large Data Bases (pp. 79-88).
30. Gonzalez, H., Han, J., Li, X., & Klabjan, D. (2006). Warehousing and analyzing massive RFID data sets. In Proceedings of the 22nd IEEE International Conference on Data Engineering (pp. 83-93).
31. Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., & Venkatrao, M. (1997). Data cube: A relational aggregation operator generalizing group-by, cross-tab and sub-totals. Data Mining and Knowledge Discovery, 1(1), 29-54. doi:10.1023/A:1009726021843
32. Guha, S., Meyerson, A., Mishra, N., Motwani, R., & O'Callaghan, L. (2003). Clustering data streams: Theory, practice. IEEE Transactions on Knowledge and Data Engineering, 15(3), 515-528. doi:10.1109/TKDE.2003.1198387
33. Han, J., Chen, Y., Dong, G., Pei, J., Wah, B. W., Wang, J., & Cai, Y. D. (2005). Stream cube: An architecture for multi-dimensional analysis of data streams. Distributed and Parallel Databases, 18(2), 173-197. doi:10.1007/s10619-005-32961
34. Han, J., & Kamber, M. (2000). Data mining: Concepts and techniques. Morgan Kaufmann.
35. Han, J., Pei, J., Dong, G., & Wang, K. (2001). Efficient computation of iceberg cubes with complex measures. In Proceedings of the 2001 ACM International Conference on Management of Data (pp. 1-12).
36. Henzinger, M. R., Raghavan, P., & Rajagopalan, S. (1998). Computing on data streams (Technical Report 1998-011). Digital Systems Research Center.
37. Ho, C.-T., Agrawal, R., Megiddo, N., & Srikant, R. (1997). Range queries in OLAP data cubes. In Proceedings of the 1997 ACM International Conference on Management of Data (pp. 73-88).
38. Intanagonwiwat, C., Estrin, D., Govindan, R., & Heidemann, J. S. (2002). Impact of network density on data aggregation in wireless sensor networks. In Proceedings of the 22nd IEEE International Conference on Distributed Computing Systems (pp. 457-458).
39. Ives, Z. G., Levy, A. Y., & Weld, D. S. (2000). Efficient evaluation of regular path expressions on streaming XML data (Technical Report UWCSE-2000-05-02). University of Washington.
40. Li, X., Han, J., & Gonzalez, H. (2004). High-dimensional OLAP: A minimal cubing approach. In Proceedings of the 30th International Conference on Very Large Data Bases (pp. 528-539).
41. Madden, S., & Franklin, M. J. (2002). Fjording the stream: An architecture for queries over streaming sensor data. In Proceedings of the 18th IEEE International Conference on Data Engineering (pp. 555-566).
42. Madden, S., Franklin, M. J., & Hellerstein, J. M. (2002). TAG: A Tiny AGgregation service for ad-hoc sensor networks. ACM SIGOPS Operating Systems Review, 36, 131-146. doi:10.1145/844128.844142
43. Madden, S., & Hellerstein, J. M. (2002). Distributing queries over low-power wireless sensor networks. In Proceedings of the 2002 ACM International Conference on Management of Data (pp. 622).
44. Madden, S., Szewczyk, R., Franklin, M. J., & Culler, M. J. (2002). Supporting aggregate queries over ad-hoc wireless sensor networks. In Proceedings of the 4th IEEE Workshop on Mobile Computing and Systems, Applications (pp. 49-58).
45. Manku, G. S., & Motwani, R. (2002). Approximate frequency counts over data streams. In Proceedings of the 28th International Conference on Very Large Data Bases (pp. 346-357).
46. Qiao, L., Agrawal, D., & El Abbadi, A. (2002). RHist: Adaptive summarization over continuous data streams. In Proceedings of the 11th ACM International Conference on Information and Knowledge Management (pp. 469-476).
47. Vassiliadis, P., & Sellis, T. K. (1999). A survey of logical models for OLAP databases. SIGMOD Record, 28(4), 64-69. doi:10.1145/344816.344869
48. Xin, D., Han, J., Cheng, H., & Li, X. (2006). Answering top-k queries with multi-dimensional selections: The ranking cube approach. In Proceedings of the 32th International Conference on Very Large Data Bases (pp. 463-475).
49. Yao, Y., & Gehrke, J. (2003). Query Processing in Sensor Networks. In Proceedings of the 1st International Conference on Innovative Data Systems Research. Retireved from http://www-db.cs.wisc.edu/cidr/cidr2003/program/p21.pdf
50. Zhang, D., Gunopulos, D., Tsotras, V. J., & Seeger, B. (2002). Temporal aggregation over data streams using multiple granularities. In Proceedings of 8th International Conference on Extending Database Technology (pp. 646-663).
51. Zhao, Y., Deshande, P. M., & Naughton, J. F. (1997). An array-based algorithm for simultaneous multidimensional aggregates. In Proceedings of the 1997 ACM International Conference on Management of Data (pp. 159-170).