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26
Optimal Prefetching via Data Compression
, 1995
"... Caching and prefetching are important mechanisms for speeding up access time to data on secondary storage. Recent work in competitive online algorithms has uncovered several promising new algorithms for caching. In this paper we apply a form of the competitive philosophy for the first time to the pr ..."
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Cited by 262 (7 self)
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Caching and prefetching are important mechanisms for speeding up access time to data on secondary storage. Recent work in competitive online algorithms has uncovered several promising new algorithms for caching. In this paper we apply a form of the competitive philosophy for the first time to the problem of prefetching to develop an optimal universal prefetcher in terms of fault ratio, with particular applications to largescale databases and hypertext systems. Our prediction algorithms for prefetching are novel in that they are based on data compression techniques that are both theoretically optimal and good in practice. Intuitively, in order to compress data effectively, you have to be able to predict future data well, and thus good data compressors should be able to predict well for purposes of prefetching. We show for powerful models such as Markov sources and nth order Markov sources that the page fault rates incurred by our prefetching algorithms are optimal in the limit for almost all sequences of page requests.
LeZiUpdate: An InformationTheoretic Approach to Track Mobile Users in PCS Networks
, 1999
"... The complexity of the mobility tracking problem in a cellular environment has been characterized under an informationtheoretic framework. Shannon’s entropy measure is identified as a basis for comparing user mobility models. By building and maintaining a dictionary of individual user’s path update ..."
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Cited by 136 (13 self)
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The complexity of the mobility tracking problem in a cellular environment has been characterized under an informationtheoretic framework. Shannon’s entropy measure is identified as a basis for comparing user mobility models. By building and maintaining a dictionary of individual user’s path updates (as opposed to the widely used location updates), the proposed adaptive online algorithm can learn subscribers’ profiles. This technique evolves out of the concepts of lossless compression. The compressibility of the variabletofixed length encoding of the acclaimed LempelZiv family of algorithms reduces the update cost, whereas their builtin predictive power can be effectively used to reduce paging cost.
On prediction using variable order Markov models
 JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
, 2004
"... This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Cont ..."
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Cited by 103 (1 self)
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This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs). We discuss the properties of these algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to prediction quality as measured by the average logloss. We also compare classification algorithms based on these predictors with respect to a number of large protein classification tasks. Our results indicate that a “decomposed” CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in sequence prediction tasks. Somewhat surprisingly, a different algorithm, which is a modification of the LempelZiv compression algorithm, significantly outperforms all algorithms on the protein classification problems.
LeZiUpdate: An InformationTheoretic Framework for Personal Mobility Tracking
 in PCS Networks. Wireless Networks
, 2002
"... Abstract. The complexity of the mobility tracking problem in a cellular environment has been characterized under an informationtheoretic framework. Shannon’s entropy measure is identified as a basis for comparing user mobility models. By building and maintaining a dictionary of individual user’s pa ..."
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Cited by 69 (2 self)
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Abstract. The complexity of the mobility tracking problem in a cellular environment has been characterized under an informationtheoretic framework. Shannon’s entropy measure is identified as a basis for comparing user mobility models. By building and maintaining a dictionary of individual user’s path updates (as opposed to the widely used location updates), the proposed adaptive online algorithm can learn subscribers ’ profiles. This technique evolves out of the concepts of lossless compression. The compressibility of the variabletofixed length encoding of the acclaimed Lempel–Ziv family of algorithms reduces the update cost, whereas their builtin predictive power can be effectively used to reduce paging cost.
MobilityBased Predictive Call Admission Control and Bandwidth Reservation in Wireless Cellular Networks
 IEEE INFOCOM
, 2001
"... This paper presents call admission control and bandwidth reservation schemes in wireless cellular networks that have been developed based on assumptions more realistic than existing proposals. In order to guarantee the handoff dropping probability, we propose to statistically predict user mobility b ..."
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Cited by 55 (5 self)
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This paper presents call admission control and bandwidth reservation schemes in wireless cellular networks that have been developed based on assumptions more realistic than existing proposals. In order to guarantee the handoff dropping probability, we propose to statistically predict user mobility based on the mobility history of users. Our mobility prediction scheme is motivated by computational learning theory, which has shown that prediction is synonymous with data compression. We derive our mobility prediction scheme from data compression techniques that are both theoretically optimal and good in practice. In order to utilize resource more efficiently, we predict not only the cell to which the mobile will handoff but also when the handoff will occur. Based on the mobility prediction, bandwidth is reserved to guarantee some target handoff dropping probability. We also adaptively control the admission threshold to achieve a better balance between guaranteeing handoff dropping probability and maximizing resource utilization. Simulation results show that the proposed schemes meet our design goals and outperform the staticreservation and cellreservation schemes. Paper submitted to Computer Networks. This paper is based on a paper presented at IEEE Infocom 2001, Anchorage, Alaska, April 2001. Technical subject area: call admission control, bandwidth reservation, mobility prediction. Please address all correspondence to Professor Victor Leung at the above address. This work was supported by a grant from Motorola Canada Ltd., and by the Canadian Natural Sciences and Engineering Research Council under grant CRDPJ 223095. MobilityBased Predictive Call Admission Control and Bandwidth Reservation in Wireless Cellular Networks Yu 1 I.
Practical Implementations of Arithmetic Coding
 IN IMAGE AND TEXT
, 1992
"... We provide a tutorial on arithmetic coding, showing how it provides nearly optimal data compression and how it can be matched with almost any probabilistic model. We indicate the main disadvantage of arithmetic coding, its slowness, and give the basis of a fast, spaceefficient, approximate arithmet ..."
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Cited by 41 (6 self)
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We provide a tutorial on arithmetic coding, showing how it provides nearly optimal data compression and how it can be matched with almost any probabilistic model. We indicate the main disadvantage of arithmetic coding, its slowness, and give the basis of a fast, spaceefficient, approximate arithmetic coder with only minimal loss of compression efficiency. Our coder is based on the replacement of arithmetic by table lookups coupled with a new deterministic probability estimation scheme.
Optimal Prediction for Prefetching in the Worst Case
, 1998
"... Response time delays caused by I/O are a major problem in many systems and database applications. Prefetching and cache replacement methods are attracting renewed attention because of their success in avoiding costly I/Os. Prefetching can be looked upon as a type of online sequential prediction, whe ..."
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Cited by 29 (5 self)
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Response time delays caused by I/O are a major problem in many systems and database applications. Prefetching and cache replacement methods are attracting renewed attention because of their success in avoiding costly I/Os. Prefetching can be looked upon as a type of online sequential prediction, where the predictions must be accurate as well as made in a computationally efficient way. Unlike other online problems, prefetching cannot admit a competitive analysis, since the optimal offline prefetcher incurs no cost when it knows the future page requests. Previous analytical work on prefetching [J. Assoc. Comput. Mach., 143 (1996), pp. 771–793] consisted of modeling the user as a probabilistic Markov source. In this paper, we look at the much stronger form of worstcase analysis and derive a randomized algorithm for pure prefetching. We compare our algorithm for every page request sequence with the important class of finite state prefetchers, making no assumptions as to how the sequence of page requests is generated. We prove analytically that the fault rate of our online prefetching algorithm converges almost surely for every page request sequence to the fault rate of the optimal finite state prefetcher for the sequence. This analysis model can be looked upon as a generalization of the competitive framework, in that it compares an online algorithm in a worstcase manner over all sequences with a powerful yet nonclairvoyant opponent. We simultaneously achieve the computational goal of implementing our prefetcher in optimal constant expected time per prefetched page using the optimal dynamic discrete random variate generator of Matias, Vitter, and Ni [Proc. 4th Annual SIAM/ACM
Switching between two universal source coding algorithms
 In Data Compression Conference
, 1998
"... This paper discusses a switching method which can be used to combine two sequential universal source coding algorithms. The switching method treats these two algorithms as blackboxes and can only use their estimates of the probability distributions for the consecutive symbols of the source sequence ..."
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Cited by 28 (1 self)
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This paper discusses a switching method which can be used to combine two sequential universal source coding algorithms. The switching method treats these two algorithms as blackboxes and can only use their estimates of the probability distributions for the consecutive symbols of the source sequence. Three weighting algorithms based on this switching method are presented. Empirical results show that all three weighting algorithms give a performance better than the performance of the source coding algorithms they combine. 1
OnLine Stochastic Processes in Data Compression
, 1996
"... The ability to predict the future based upon the past in finitealphabet sequences has many applications, including communications, data security, pattern recognition, and natural language processing. By Shannon's theory and the breakthrough development of arithmetic coding, any sequence, a 1 ..."
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Cited by 16 (6 self)
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The ability to predict the future based upon the past in finitealphabet sequences has many applications, including communications, data security, pattern recognition, and natural language processing. By Shannon's theory and the breakthrough development of arithmetic coding, any sequence, a 1 a 2 \Delta \Delta \Delta a n , can be encoded in a number of bits that is essentially equal to the minimal informationlossless codelength, P i \Gamma log 2 p(a i ja 1 \Delta \Delta \Delta a i\Gamma1 ). The goal of universal online modeling, and therefore of universal data compression, is to deduce the model of the input sequence a 1 a 2 \Delta \Delta \Delta a n that can estimate each p(a i ja 1 \Delta \Delta \Delta a i\Gamma1 ) knowing only a 1 a 2 \Delta \Delta \Delta a i\Gamma1 so that the ex...