Owing to important applications such as mining web page traversal sequences, many algorithms have been introduced in the area of sequential pattern mining over the last decade, most of which have also been modified to support concise representations like closed, maximal, incremental or hierarchical sequences. This article presents a taxonomy of sequential pattern-mining techniques in the literature with web usage mining as an application. This article investigates these algorithms by introducing a taxonomy for classifying sequential pattern-mining algorithms based on important key features supported by the techniques. This classification aims at enhancing understanding of sequential pattern-mining problems, current status of provided solutions, and direction of research in this area. This article also attempts to provide a comparative performance analysis of many of the key techniques and discusses theoretical aspects of the categories in the taxonomy.

http://cs.uwindsor.ca/~cezeife/acmsurvey_paper.pdf

A taxonomy of sequential pattern mining algorithms

Community detection in networks: A multidisciplinary review

Sequences of events, items, or tokens occurring in an ordered metric space appear often in data and the requirement to detect and analyze frequent subsequences is a common problem Sequential Pattern Mining arose as a subfield of data mining to focus on this field This article surveys the approaches and algorithms proposed to date

Sequential pattern mining -- approaches and algorithms

With the growing popularity of shared resources, large volumes of complex data of different types are collected automatically. Traditional data mining algorithms generally have problems and challenges including huge memory cost, low processing speed, and inadequate hard disk space. As a fundamental task of data mining, sequential pattern mining (SPM) is used in a wide variety of real-life applications. However, it is more complex and challenging than other pattern mining tasks, i.e., frequent itemset mining and association rule mining, and also suffers from the above challenges when handling the large-scale data. To solve these problems, mining sequential patterns in a parallel or distributed computing environment has emerged as an important issue with many applications. In this article, an in-depth survey of the current status of parallel SPM (PSPM) is investigated and provided, including detailed categorization of traditional serial SPM approaches, and state-of-the art PSPM. We review the related work of PSPM in details including partition-based algorithms for PSPM, apriori-based PSPM, pattern-growth-based PSPM, and hybrid algorithms for PSPM, and provide deep description (i.e., characteristics, advantages, disadvantages, and summarization) of these parallel approaches of PSPM. Some advanced topics for PSPM, including parallel quantitative/weighted/utility SPM, PSPM from uncertain data and stream data, hardware acceleration for PSPM, are further reviewed in details. Besides, we review and provide some well-known open-source software of PSPM. Finally, we summarize some challenges and opportunities of PSPM in the big data era.

pdf/a-survey-of-parallel-sequential-pattern-mining-44ac1eve0n.pdf

A Survey of Parallel Sequential Pattern Mining

Markov models have been widely used for modelling users' navigational behaviour in the Web graph, using the transitional probabilities between web pages, as recorded in the web logs. The recorded users' navigation is used to extract popular web paths and predict current users' next steps. Such purely usage-based probabilistic models, however, present certain shortcomings. Since the prediction of users' navigational behaviour is based solely on the usage data, structural properties of the Web graph are ignored. Thus important - in terms of pagerank authority score - paths may be underrated. In this paper we present a hybrid probabilistic predictive model extending the properties of Markov models by incorporating link analysis methods. More specifically, we propose the use of a PageRank-style algorithm for assigning prior probabilities to the web pages based on their importance in the web site's graph. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches.

/pdf/web-path-recommendations-based-on-page-ranking-and-markov-qocg2ndwez.pdf

Web path recommendations based on page ranking and Markov models

In this paper we present the first approach for incremental order-preserving maintenance of XQuery views. Our technique is based on an algebraic representation of the XQuery view query called XAT. The XML algebra has ordered bag semantics; hence most of the operators logically are order preserving. We propose an order-encoding mechanism that migrates the XML algebra to (non-ordered) bag semantics, no longer requiring most of the operators to be order-aware. This way operators become distributive over update operations. This transformation brings the problem of maintaining XML views closer to the problem of maintaining views in other (unordered) data models. We have implemented our view maintenance technique on top of RAINBOW, the XML data management system developed at WPI. Our experimental results confirm that incremental XML view maintenance is significantly faster than complete recomputation.

/pdf/order-sensitive-view-maintenance-of-materialized-xquery-4prmggint9.pdf

Order-Sensitive View Maintenance of Materialized XQuery Views

Modern data sources, including structural and semi-structural sources, often export XML views over base data, and at times may materialize their views by storing the XML query result to provide faster data access. It is typically more efficient to maintain a view by incrementally propagating the base changes to the view than by re-computing it from scratch. Techniques for the incremental maintenance of relational views have been extensively studied in the literature. However, the maintenance of views created using XQuery is as of now unexplored. In this paper we propose an algebraic approach for incremental XQuery view maintenance. In our approach, an update to the XML source is transformed into a set of well defined update primitives which are propagated through the XML algebra tree. This algebraic update propagation process generates incremental update primitives to be applied to the result view. We briefly discuss our XQuery view maintenance system implementation. Our experiments confirm that incremental view maintenance is indeed faster than re-computation.

An algebraic approach for incremental maintenance of materialized XQuery views

Materializing the contents of views has important applications including providing fast access to derived database repositories, optimizing query processing based on cached results, and increasing availability. Maintaining the consistency between materialized views and their base data in the presence of source updates is important to ensure that the materialized views are up-to-date. The straightforward solution for this problem is to recompute the view from scratch over the updated sources.

/pdf/incremental-maintenance-of-materialized-xquery-views-4547t1xx7e.pdf

Incremental Maintenance of Materialized XQuery Views

This paper presents a novel approach for search engine results clustering that relies on the semantics of the retrieved documents rather than the terms in those documents. The proposed approach takes into consideration both lexical and semantics similarities among documents and applies activation spreading technique in order to generate semantically meaningful clusters. This approach allows documents that are semantically similar to be clustered together rather than clustering documents based on similar terms. A prototype is implemented and several experiments are conducted to test the prospered solution. The result of the experiment confirmed that the proposed solution achieves remarkable results in terms of precision.

/pdf/semantic-clustering-of-search-engine-results-25xc552u9y.pdf

Semantic Clustering of Search Engine Results

This paper presents a hybrid method for user query reformation and classification depending on fuzzy semantic-based approach and KNN classifier. The overall processes of the system are: query preprocessing, fuzzy membership calculation and query classification and reformation. Classification is performed using KNN classifier not just by keyword-based semantic but using a sentence-level semantics. A semantic model - EnrichSearch - has been developed for applying this approach. After classification, user's query is reformulated to be submitted to a search engine which gives better results than submitting the original query to the search engine. Experiments show significant enhancement in search results over traditional keyword-based search engines' results.

A hybrid method for user query reformation and classification

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