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Suffix Tree Based Prediction for Pervasive Computing Environments

Suffix Tree Based Prediction for Pervasive Computing Environments. The architecture of a PCS. Information dissemination in a PCS. Mobile Hosts (MH) #MHosts >> #Servers. Uplink bandwidth << Downlink bandwidth. Downlink Communication Bandwidth. Base Station. Information System (server).

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Suffix Tree Based Prediction for Pervasive Computing Environments

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  1. Suffix Tree Based Prediction for Pervasive Computing Environments

  2. The architecture of a PCS Panhellenic Conference on Informatics, 11-13 November 2005

  3. Information dissemination in a PCS Mobile Hosts (MH) #MHosts>> #Servers Uplink bandwidth << Downlink bandwidth Downlink Communication Bandwidth Base Station Information System (server) Wireless Cell Panhellenic Conference on Informatics, 11-13 November 2005

  4. Roaming: Where is the mobile? • The mobile can freely roam inside the coverage area of the cellular system • Arises the need for location management • location update • location prediction Panhellenic Conference on Informatics, 11-13 November 2005

  5. Querying: What data will be requested? • The mobile can request any data available in the information system • Arises the need for • Proactively pushing them into the broadcast channel • Proactively sending them to the next-to-visit base station Panhellenic Conference on Informatics, 11-13 November 2005

  6. Predict: Position & Information Needs • Why is the location prediction useful? • effective solutions to the mobility tracking/prediction problem can reduce update and paging costs, freeing the network from excessive signaling traffic [bd02]. • Why is the request prediction useful? • Accurate data request prediction results in effective prefetching [nkm03], which combined with a caching mechanism [km04], can reduce user-perceived latencies as well as server and network loads [bd02] A. Bhattacharya and S. K. Das, LeZi-Update: An information-theoretic framework for personal mobility tracking in PCS networks, ACM/Kluwer Wireless Networks, 8(2-3), pp. 121 – 135, 2002. [nkm03] A. Nanopoulos, D. Katsaros, Y. Manolopoulos, A data mining algorithm for generalized Web prefetching,IEEE Transactions on Knowledge and Data Engineering, 15(5), pp. 1155–1169,2003. [km04] D. Katsarosand Y. Manolopoulos, Web caching in broadcast mobile wireless environments,IEEE Internet Computing,8 (3), pp. 37 – 45,2004. Panhellenic Conference on Informatics, 11-13 November 2005

  7. Where is prediction based? • Both of the aforementioned problems are related to the ability of the underlyingnetwork to • record, • learn and, subsequently • predict the mobile's “behaviour”,i.e., its movements or its information needs • The success of the prediction ispresupposed and is boost by the fact that mobile users exhibit some degree ofregularity in their movement and/or in their access patterns • This regularity may be apparent in the behaviour of each individual client or inclient groups. Panhellenic Conference on Informatics, 11-13 November 2005

  8. Location prediction  Request prediction • These issues had been treated in isolation, but pioneering works ([vk96] and [bd02]) are paving the way for treating both problems in an homogeneous fashion • Use methods for data compression (thus, characterized as “information-theoretic”), in carrying out prediction. • They model the respective state space as finite alphabets comprised of discrete symbols • In the mobility tracking scenario, the alphabet consists of all possible sites (cells) where the client has ever visited or might visit (assuming that the number of cells in the coverage area is finite) • In the request prediction scenario, the alphabet consists of all the data objects requested by the client plus the objects that might be requested in the future (assuming that the objects come from a database and thus their number is finite) [vk96] J. S. Vitter and P. Krishnan, Optimal prefetching via data compression,Journal of the ACM, 43 (5), pp. 771–793, 1996. Panhellenic Conference on Informatics, 11-13 November 2005

  9. 4 Families of predictors • PPM: Prediction by Partial Match • LZ78: Lempel-Ziv 1978 • PST: Probabilistic Suffix Tree • CTW: Context –Tree Weighting Panhellenic Conference on Informatics, 11-13 November 2005

  10. The PPM predictor • Running sequence: aabacbbabbacbbc Panhellenic Conference on Informatics, 11-13 November 2005

  11. The LZ78 predictor • Running sequence: aabacbbabbacbbc Enhanced Panhellenic Conference on Informatics, 11-13 November 2005

  12. The PST predictor • Running sequence: aabacbbabbacbbc Panhellenic Conference on Informatics, 11-13 November 2005

  13. The CTW predictor (1/3) • Running bin sequence: 010|11010100011 • Krichevsky-Trofimov estimator: Panhellenic Conference on Informatics, 11-13 November 2005

  14. The CTW predictor (2/3) Panhellenic Conference on Informatics, 11-13 November 2005

  15. The CTW predictor (3/3) Panhellenic Conference on Informatics, 11-13 November 2005

  16. Discrete Sequence Prediction Problem • At any given time instance t (meaning that t symbols xt, xt-1, ...,x1 haveappeared, in reverse order) calculate the conditional probability where • This model introduces stationaryMarkov chain, since the probabilities are not time-dependent • The outcome of the predictoris a ranking of the symbols according to their P. The predictors which usesuch kind of prediction models are termed Markov predictors Panhellenic Conference on Informatics, 11-13 November 2005

  17. The STP algorithm [em92] A. Ehrenfeucht and J. Mycielski, A pseudorandom sequence – How random is it?,American Mathematical Monthly, 99 (4), pp. 373–375, 1992. Panhellenic Conference on Informatics, 11-13 November 2005

  18. Candidate predictions An example execution of STP • Suppose that the sequence of symbols seen so far is the following:s124=abcdefgabcdklmabcdexabcd$ • The largest suffix which appear somewhere is the seq is abcd, and s124=abcdefgabcdklmabcdexabcd$ • Let α = 0.5, thus we use a portion of abcd, half of it: cd • Appearances of cd in the sequence are: s124=abcdefgabcdklmabcdexabcd$ • Since e appears most of the times, the final outcome of the prediction is: e Panhellenic Conference on Informatics, 11-13 November 2005

  19. Proof of concept of STP (1/2) • Definition. The ratio of symbols returned by the predictor that indeed matchwith the next event/symbol in the sequence, divided by the total number of symbolsreturn by the predictor defines the predictionprecision Panhellenic Conference on Informatics, 11-13 November 2005

  20. Proof of concept of STP (2/2) • Definition. The total number of symbols return by the predictor divided by the total number ofevents/symbols of the sequence defines the predictionoverhead Panhellenic Conference on Informatics, 11-13 November 2005

  21. Thank you for your attention! Any questions ? Panhellenic Conference on Informatics, 11-13 November 2005

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