Finding the frequent items in streams of data

Communications of the ACM

Volumn 52, Issue 10, 2009, Pages 97-105

  Cormode, Graham ^a   Hadjieleftheriou, Marios ^a  

^a AT AND T LABS RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STREAM; EMPIRICAL EVIDENCE; FREQUENT ITEMS; INDUSTRIAL SYSTEMS; OTHER APPLICATIONS;

EID: 70349694196     PISSN: 00010782     EISSN: 15577317     Source Type: Journal    
DOI: 10.1145/1562764.1562789     Document Type: Article

References (26)

1. Alon, N., Matias, Y., Szegedy, M. the space complexity of approximating the frequency moments. in ACM Symposium on Theory of Computing, (1996), 20-29.
2. Journal version in J. Comp. Syst. Sci. 58 (1999), 137-147.
3. Arasu, A., Manku, G.S. approximate counts and quantiles over sliding windows. in ACM Principles of Database Systems (2004).
4. Bhattacharrya, S., Madeira, A., Muthukrishnan, S., Ye, T. how to scalably skip past streams. in Scalable Stream Processing Systems (SSPS) Workshop with ICDE 2007 (2007).
5. Bhuvanagiri, L., Ganguly, S., Kesh, D., Saha, C. simpler algorithm for estimating frequency moments of data streams. in ACM-SIAM Symposium on Discrete Algorithms (2006).
6. Bose, P., Kranakis, E., Morin, P., Tang, Y. Bounds for frequency estimation of packet streams. in SIROCCO (2003).
7. Boyer, R.S., Moore, J.s. a fast majority vote algorithm. technical report icsca-cMP-32, institute for computer science, university of texas (feb. 1981).
8. Boyer, R.S., Moore, J.s. MJrty-a fast majority vote algorithm. in Automated Reasoning: Essays in Honor of Woody Bledsoe, automated reasoning series. Kluwer academic Publishers, 1991, 105-117.
9. Chakrabarti, A., Cormode, G., Mcgregor, A. a near-optimal algorithm for computing the entropy of a stream. in ACM-SIAM Symposium on Discrete Algorithms (2007).
10. Charikar, M., Chen, K., Farach-colton, M. finding frequent items in data streams. in Proceedings of the International Colloquium on Automata, Languages and Programming (ICALP) (2002).
11. Cormode, G., Hadjieleftheriou, M. finding frequent items in data streams. in International Conference on Very Large Data Bases (2008).
12. Cormode, G., Korn, F., Muthukrishnan, S., Johnson, T., Spatscheck, O. srivastava, D. holistic uDafs at streaming speeds. in ACM SIGMOD International Conference on Management of Data (2004), 35-46.
13. Cormode, G., Muthukrishnan, S. an improved data stream summary: the count-min sketch and its applications. J. Algorithms 55, 1 (2005), 58-75.
14. Datar, M., Gionis, A., Indyk, P., Motwani, R. Maintaining stream statistics over sliding windows. in ACM-SIAM Symposium on Discrete Algorithms (2002).
15. Demaine, E., López-ortiz, A., Munro, J.i. Frequency estimation of internet packet streams with limited space. in European Symposium on Algorithms (ESA) (2002).
16. Fischer, M., Salzburg, S. finding a majority among n votes: solution to problem 81-5. J. Algorithms 3, 4 (1982), 376-379.
17. Gilbert, A.C., Kotidis, Y. , Muthukrishnan, S., Strauss, M. how to summarize the universe: Dynamic maintenance of quantiles. in International Conference on Very Large Data Bases (2002), 454-465.
18. Jayram, T.S., Mcgregor, A., Muthukrishnan, S., Vee, E. estimating statistical aggregates on probabilistic data streams. in ACM Principles of Database Systems (2007).
19. Karp, R., Papadimitriou, C., Shenker, S. a simple algorithm for fnding frequent elements in sets and bags. ACM Trans. Database Syst. 28 (2003), 51-55.
20. Manku, G., Motwani, R. approximate frequency counts over data streams. in International Conference on Very Large Data Bases (2002).
21. Manku, G.S. frequency counts over data streams. http://www.cse.ust.hk/ vldb2002/vlDB2002-proceedings/slides/s10P03slides.pdf (2002).
22. Metwally, A., Agrawal, D., Abbadi, A.E. effcient computation of frequent and top-k elements in data streams. in International Conference on Database Theory (2005).
23. Misra, J., Gries, D. finding repeated elements. Sci. Comput. Programming 2 (1982), 143-152.
24. Pike, D., Dorward, S., Griesemer, R., Quinlan, S. interpreting the data: Parallel analysis with sawzall. Dyn. Grids Worldwide Comput. 13, 4 (2005), 277-298.
25. Thorup, M., Zhang, Y. tabulation-based 4-universal hashing with applications to second moment estimation. in ACM-SIAM Symposium on Discrete Algorithms (2004).
26. Venkataraman, S., Song, D.X., Gibbons, P.B., Blum, A. new streaming algorithms for fast detection of superspreaders. in Network and Distributed System Security Symposium NDSS (2005).