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TOPiCo : detecting most frequent items from multiple high-rate event streams

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  1. Wong, R. C.-W., and Fu, A. W.-C. Mining top-k frequent itemset from data streams.Journal of Data Mining and Knowledge Discovery 13, 2 (2006), 193--217.
  2. Weigert, S., Hiltunen, M. A., and Fetzer, C. Community-based analysis of netflow for early detection of security incidents. InProceedings of the 25th International Conference on Large Installation System Administration(Berkeley, CA, USA, 2011), LISA'11, USENIX Association.
  3. Wang, X., Candan, K. S., and Song, J. Complex pattern ranking (cpr): Evaluating top-k pattern queries over event streams. InProceedings of the 5th ACM International Conference on Distributed Event-based System(New York, NY, USA, 2011), DEBS '11, ACM, pp. 279--290.
  4. Vitter, J. S. Random sampling with a reservoir.ACM Transactions on Mathematical Software 11, 1 (1985).
  5. Tudoran, R., Nano, O., Santos, I., Costan, A., Soncu, H., Bougé, L., and Antoniu, G. Jetstream: Enabling high performance event streaming across cloud data-centers. InProceedings of the 8th ACM International Conference on Distributed Event-Based Systems(New York, NY, USA, 2014), DEBS '14, ACM, pp. 23--34.
  6. Theobald, M., Weikum, G., and Schenkel, R. Top-k query evaluation with probabilistic guarantees. InProceedings of the Thirtieth International Conference on Very Large Data Bases - Volume 30(2004), VLDB '04, VLDB Endowment, pp. 648--659.
  7. Singh, S., Estan, C., Varghese, G., and Savage, S. Automated worm fingerprinting. InProceedings of the 6th Conference on Symposium on Operating Systems Design & Implementation - Volume 6(Berkeley, CA, USA, 2004), OSDI'04, USENIX Association, pp. 4--4.
  8. Sacha, J., and Montresor, A. Identifying frequent items in distributed data sets.Computing 95, 4 (Apr. 2013), 289--307.
  9. Misra, J., and Gries, D. Finding repeated elements.Sci. Comput. Program. 2, 2 (1982), 143--152.
  10. Michel, S., Triantafillou, P., and Weikum, G. KLEE: A Framework for Distributed Top-k Query Algorithms.VLDB '05 - Proceedings of the 31st VLDB conference(2005), 637--648.
  11. Manjhi, A., Shkapenyuk, V., Dhamdhere, K., and Olston, C. Finding (recently) frequent items in distributed data streams. InProceedings of the 21st International Conference on Data Engineering(Washington, DC, USA, 2005), ICDE '05, IEEE Computer Society, pp. 767--778.
  12. Lahiri, B., and Tirthapura, S. Identifying frequent items in a network using gossip.Journal of Parallel and Distributed Computing 70, 12 (2010), 1241--1253.
  13. Lahiri Bibudh, Chandrashekar Jaideep, Tirthapura Srikanta, Space-efficient tracking of persistent items in a massive data stream, 10.1145/2002259.2002294
  14. Ilyas, I. F., Beskales, G., and Soliman, M. A. A survey of top-k query processing techniques in relational database systems.ACM Comput. Surv. 40, 4 (Oct. 2008), 11:1--11:58.
  15. Hirzel, M. Partition and compose: Parallel complex event processing. InProceedings of the 6th ACM International Conference on Distributed Event-Based Systems(New York, NY, USA, 2012), DEBS '12, ACM, pp. 191--200.
  16. Guntzer J., Balke W.-T., Kiessling W., Towards efficient multi-feature queries in heterogeneous environments, 10.1109/itcc.2001.918866
  17. Guerrieri Alessio, Montresor Alberto, Velegrakis Yannis, Top-k Item Identification on Dynamic and Distributed Datasets, Lecture Notes in Computer Science (2014) ISBN:9783319098722 p.270-281, 10.1007/978-3-319-09873-9_23
  18. Fagin Ronald, Lotem Amnon, Naor Moni, Optimal aggregation algorithms for middleware, 10.1145/375551.375567
  19. Fagin, R., Kumar, R., and Sivakumar, D. Comparing top k lists. InProceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms(Philadelphia, PA, USA, 2003), SODA '03, Society for Industrial and Applied Mathematics, pp. 28--36.
  20. Demaine Erik D., López-Ortiz Alejandro, Munro J. Ian, Frequency Estimation of Internet Packet Streams with Limited Space, Algorithms — ESA 2002 (2002) ISBN:9783540441809 p.348-360, 10.1007/3-540-45749-6_33
  21. Culhane, W., Jayaram, K. R., and Eugster, P. Fast, expressive top-k matching. InProceedings of the 15th International Middleware Conference(New York, NY, USA, 2014), Middleware '14, ACM, pp. 73--84.
  22. Cormode, G., and Muthukrishnan, S. An improved data stream summary: The count-min sketch and its applications.J. Algorithms 55, 1 (Apr. 2005), 58--75.
  23. Cao Pei, Wang Zhe, Efficient top-K query calculation in distributed networks, 10.1145/1011767.1011798
  24. Brenna, L., Gehrke, J., Hong, M., and Johansen, D. Distributed event stream processing with non-deterministic finite automata. InProceedings of the Third ACM International Conference on Distributed Event-Based Systems(New York, NY, USA, 2009), DEBS '09, ACM, pp. 3:1--3:12.
  25. Babcock Brian, Olston Chris, Distributed top-k monitoring, 10.1145/872757.872764
  26. Arlitt, M., and Jin, T. A workload characterization study of the 1998 world cup web site.Network, IEEE 14, 3 (2000), 30--37.
Bibliographic reference Schiavoni, Valerio ; Riviere, Etienne ; Sutra, Pierre ; Felber, Pascal ; Matos, Miguel ; et. al. TOPiCo : detecting most frequent items from multiple high-rate event streams.9th ACM International Conference on Distributed Event-Based Systems (Oslo, Norway, du 29/6/2015 au 3/7/2015). In: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems - DEBS '15, ACM Press2015
Permanent URL http://hdl.handle.net/2078.1/213816