Abstract
One considers the problem of estimating the most frequent values in a data stream. In many cases an approximate answer may be enough. A novel algorithm is presented to approximate the most frequent values using a mixed approach between counter-based techniques and sketch-based ones. The algorithm is then used to find the most frequent destinations of calls by individual customers of telecommunications operators. The use of fast and small footprint algorithms is critical due to the huge number of customers to check and approximate answers are enough in most situations. The problem is that such detection needs to be performed for each individual customer and kept up to date at all times. This paper presents telecommunications customer’s behavior to justify the use of approximate algorithms. Although used in this paper on telecommunications this algorithm may well be used in other contexts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bertsekas, D.P.: Dynamic Programming and Optimal Control, vol. 1. Athena Scientific, Belmont (1995)
Cormode, G., Muthukrishnan, S.: What’s Hot and What’s Not: TrackingMost Frequent Items Dynamically. In: Proceedings of the 22nd ACM PODS Symposium on Principles of Database Systems, pp. 296–306 (2003)
Datar, M., Gionis, A., Indyk, P., Motwani, R.: Maintaining Stream Statistics Over Sliding Windows. SIAM Journal on Computing 31(6) (2002)
Demaine, E., López-Ortiz, A., Munro, J.: Frequency Estimation of Internet Packet Streams with Limited Space. In: Proceedings of the 10th ESA Annual European Symposium on Algorithms, pp. 348–360 (2002)
Dimitropoulos, X., Hurley, P., Kind, A.: Probabilistic Lossy Counting: An efficient algorithm for finding heavy hitters. ACM SIGCOMM Computer Communication Review 38(1) (January 2008)
Estan, C., Varghese, G.: New directions in traffic measurement and accounting. In: Proceedings of SIGCOMM 2002. ACM Press, New York (2002); Also: UCSD technical report CS2002-0699 (February 2002); available electronically
Estan, C., Varghese, G.: New Directions in Traffic Measurement and Accounting: Focusing on the Elephants, Ignoring the Mice. ACM Trans. Comput. Syst. 21(3), 270–313 (2003)
Estan, C., Varghese, G., Fisk, M.: Bitmap algorithms for counting active flows on high speed links. Technical Report CS2003-0738, UCSD (March 2003)
Fan, L., Cao, P., Almeida, J., Broder, A.: Summary Cache: A Scalable Wide-Area Web Cache Sharing Protocol. IEEE/ACM Transactions on Networking 8(3), 281–293 (2000), doi:10.1109/90.851975
Flajolet, P., Martin, N.: Probabilistic Counting Algorithms for Data Base Applications. Journal of Computer and System Sciences 31(2) (October 1985)
Homem, N., Carvalho, J.: Finding top-k destinations in telecommunications, Information Sciences, INS-D-09-158 (under review)
Manku, G., Motwani, R.: Approximate Frequency Counts over Data Streams. In: Proceedings of the 28th ACM VLDB International Conference on Very Large Data Bases, pp. 346–357 (2002)
Metwally, A., Agrawal, D., Abbadi, A.: Efficient Computation of Frequent and Top- k Elements in Data Streams, Technical Report 2005-23, University of California, Santa Barbara (September 2005)
Misra, J., Gries, D.: Finding Repeated Elements. Science of Computer Programming 2, 143–152 (1982)
Whang, K., Vander-Zanden, B., Taylor, H.: A Linear-Time Probabilistic Counting Algorithm for Database Applications. ACM Transactions on Database Systems 15(2) (June 1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Homem, N., Carvalho, J.P. (2010). Estimating Top-k Destinations in Data Streams. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Computational Intelligence for Knowledge-Based Systems Design. IPMU 2010. Lecture Notes in Computer Science(), vol 6178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-14049-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14048-8
Online ISBN: 978-3-642-14049-5
eBook Packages: Computer ScienceComputer Science (R0)