Adaptive burst detection in a stream engine

Proceedings of the ACM Symposium on Applied Computing

Volumn , Issue , 2009, Pages 1511-1515

  Karnstedt, Marcel ^a   Klan, Daniel ^a   Pölitz, Christian ^a   Sattler, Kai Uwe ^a   Franke, Conny ^b  

^a ILMENAU UNIVERSITY OF TECHNOLOGY   (Germany)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BURST DETECTION; DATA STREAM; LANGUAGE LEVELS; NONSTATIONARY DATA; OPERATOR LEVELS; QUERY FORMULATION; QUERY OPERATORS; REAL-WORLD; SENSOR DATA; STREAM DATA; TIME SERIES FORECASTING;

HYDRAULICS; SENSORS; TECHNOLOGICAL FORECASTING; TIME SERIES;

COMPUTER SCIENCE;

EID: 72949091646     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1529282.1529619     Document Type: Conference Paper

References (14)

1. ARASU, A., BABU, S., AND WIDOM, J. The CQL Continuous Query Language: Semantic Foundations and Query Execution. Tech. rep., University of Stanford, 2003.
2. CHATFIELD, C., AND YAR, M. Holt-Winters Forecasting: Some Practical Issues. The Statistician, Special Issue: Statistical Forecasting and Decision-Making 37, 2 (1988), 129-140.
3. ERGÜN, F., MUTHUKRISHNAN, S., AND SAHINALP, S. C. Sublinear methods for detecting periodic trends in data streams. In LATIN (2004).
4. HINNEBURG, A., HABICH, D., AND KARNSTEDT, M. Analyzing Data Streams by Online DFT. In IWKDDS-2006 (2006), pp. 67-76.
5. KEOGH, E., LONARDI, S., AND CHI' CHIU, B. Y. Finding surprising patterns in a time series database in linear time and space. In KDD'02 (2002), pp. 550-556.
6. KLEINBERG, J. M. Bursty and hierarchical structure in streams. Data Min. Knowl. Discov. 7, 4 (2003), 373-397.
7. PAPADIMITRIOU, S., BROCKWELL, A., AND FALOUTSOS, C. AWSOM: Adaptive, Hands-Off Stream Mining. In VLDB 2003 (2003), pp. 560-571.
8. SHAHABI, C., TIAN, X., AND ZHAO, W. Tsa-tree: A wavelet-based approach to improve the efficiency of multi-level surprise and trend queries on time-series data. In SSDBM'00 (2000), p. 55.
9. SHASHA, D., AND ZHU, Y. High Performance Discovery in Time Series: Techniques and Case Studies. Springer, 2004.
10. VLACHOS, M., MEEK, C., VAGENA, Z., AND GUNOPULOS, D. Identifying Similarities, Periodicities and Bursts for Online Search Queries. In SIGMOD 2004 (2004), pp. 131-142.
11. WANG, M., MADHYASTHA, T. M., CHAN, N. H., PAPADIMITRIOU, S., AND FALOUTSOS, C. Data mining meets performance evaluation: Fast algorithms for modeling bursty traffic. In ICDE (2002).
12. YAMANISHI, K., AND TAKEUCHI, J.-I. A Unifying Framework for Detecting Outliers and Change Points from Non-stationary Time Series Data. In SIGKDD 2002 (2002), pp. 676-681.
13. ZHANG, X., AND SHASHA, D. Better burst detection. In ICDE '06 (2006), pp. 146-149.
14. ZHU, Y., AND SHASHA, D. Efficient Elastic Burst Detection in Data Streams. In SIGKDD 2003, Washington, D.C., USA (2003), pp. 336-345.