A taxonomy of web prediction algorithms

Domènech, Josep; Ossa, B. de la; Sahuquillo, Julio; Gil, José A.; Pont, Ana

doi:10.1016/j.eswa.2012.01.140

Cited by 11 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DDG is a prediction algorithm based on web access patterns while RG is based on web structure [18]. This is the main reason why the cost of RG is lower than that of previous proposals.…”

Section: General Descriptionmentioning

confidence: 84%

Referrer Graph: A cost-effective algorithm and pruning method for predicting web accesses

Ossa

Gil

Sahuquillo

et al. 2013

Computer Communications

View full text Add to dashboard Cite

This paper presents the Referrer Graph (RG) web prediction algorithm and a pruning method for the associated graph as a low-cost solution to predict next web users accesses. RG is aimed at being used in a real web system with prefetching capabilities without degrading its performance. The algorithm learns from users accesses and builds a Markov model. These kinds of algorithms use the sequence of the user accesses to make predictions. Unlike previous Markov model based proposals, the RG algorithm di↵erentiates dependencies in objects of the same page from objects of di↵erent pages by using the object URI and the referrer in each request. Although its design permits us to build a simple data structure that is easier to handle and, consequently, needs lower computational cost in comparison with other algorithms, a pruning mechanism has been devised to avoid the continuous growing of this data structure. Results show that, compared with the best prediction algorithms proposed in the open literature, the RG algorithm achieves similar precision values and page latency savings but requiring much less computational and memory resources. Furthermore, when pruning is applied, additional and notable resource consumption savings can be achieved without degrading original performance. In order to reduce further the resource consumption, a mechanism to prune de graph has been devised, which reduces resource consumption of the baseline system without degrading the latency savings.

show abstract

“…DDG is a prediction algorithm based on web access patterns while RG is based on web structure [18]. This is the main reason why the cost of RG is lower than that of previous proposals.…”

Section: General Descriptionmentioning

confidence: 84%

Referrer Graph: A cost-effective algorithm and pruning method for predicting web accesses

Ossa

Gil

Sahuquillo

et al. 2013

Computer Communications

View full text Add to dashboard Cite

show abstract

“…The graph contains a node for each entry of the area while the edges represent the transitions from one entry to another. Our graph is trained using several lookahead window sizes W [25].…”

Section: B Selection Technique Pgsmentioning

confidence: 99%

Radio Map Prefetching for Indoor Navigation in Intermittently Connected Wi-Fi Networks

Konstantinidis

Nikolaides

Chatzimilioudis

et al. 2015

2015 16th IEEE International Conference on Mobile Data Management

View full text Add to dashboard Cite

Wi-Fi (or WLAN) based indoor navigation applications for mobiles rely on cloud-based services (s) that take care of a user's (u) localization task using structures called RadioMaps (RMs). It is imperative for u to have a stable Wi-Fi connection in order to either continuously receive location updates from s or to download RMs a priori for offline navigation. Wi-Fi networks however, suffer from intermittent connectivity due to poor network planning that results in sparse deployment of access points and effectively areas where Wi-Fi coverage cannot be guaranteed. This inherently affects the localization accuracy and therefore the navigation experience of users. In this paper, we propose an innovative framework for accurate and fast indoor localization over an intermittently connected Wi-Fi network, coined Prefetching Localization (PreLoc). In Preloc, we prioritize the download of RM records based on knowledge acquired from historic traces of other users inside the same building. Instead of downloading the complete RM from s to u, we propose a Probabilistic Group Selection (PGS) strategy, which identifies RM records that have a higher probability of being necessary to a user moving inside a target area. We have evaluated our framework using a real prototype developed in Android, as well as realistic Wi-Fi traces we collected at the University of Cyprus. Our experimental study reveals that PreLoc using PGS and conventional fingerprint-based indoor positioning algorithms can yield accuracy that is as good as using the same algorithms with a complete RM, even under scenarios of weak Wi-Fi coverage.

show abstract

“…But it suffers from a much higher state and space complexity. A lot of research has been done to improve the speed and memory requirement of these approaches, for example by pruning states [3,12,6].…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

“…The literature on this subject is extensive and there are many different approaches [6]. Two of the most popular are PPM (Prediction by Partial Matching) [2] and DG (Dependency Graph) [5].…”

Section: Introductionmentioning

confidence: 99%

Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction

Gueniche

Fournier-Viger

Tseng

2013

Advanced Data Mining and Applications

View full text Add to dashboard Cite

Abstract. Predicting the next item of a sequence over a finite alphabet has important applications in many domains. In this paper, we present a novel prediction model named CPT (C ompact P rediction T ree) which losslessly compress the training data so that all relevant information is available for each prediction. Our approach is incremental, offers a low time complexity for its training phase and is easily adaptable for different applications and contexts. We compared the performance of CPT with state of the art techniques, namely PPM (P rediction by P artial M atching), DG (Dependency Graph) and All-K-th-Order Markov. Results show that CPT yield higher accuracy on most datasets (up to 12% more than the second best approach), has better training time than DG and PPM, and is considerably smaller than All-K-th-Order Markov.

show abstract

A taxonomy of web prediction algorithms

Cited by 11 publications

References 37 publications

Referrer Graph: A cost-effective algorithm and pruning method for predicting web accesses

Referrer Graph: A cost-effective algorithm and pruning method for predicting web accesses

Radio Map Prefetching for Indoor Navigation in Intermittently Connected Wi-Fi Networks

Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction

Contact Info

Product

Resources

About