Sequential pattern mining is a computationally challenging task since algorithms have to generate and/or test a combinatorially explosive number of intermediate subsequences. In order to reduce complexity, some researchers focus on the task of mining closed sequential patterns. This not only results in increased efficiency, but also provides a way to compact results, while preserving the same expressive power of patterns extracted by means of traditional (non-closed) sequential pattern mining algorithms. In this paper, we present CloFAST, a novel algorithm for mining closed frequent sequences of itemsets. It combines a new data representation of the dataset, based on sparse id-lists and vertical id-lists, whose theoretical properties are studied in order to fast count the support of sequential patterns, with a novel one-step technique both to check sequence closure and to prune the search space. Contrary to almost all the existing algorithms, which iteratively alternate itemset extension and sequence extension, CloFAST proceeds in two steps. Initially, all closed frequent itemsets are mined in order to obtain an initial set of sequences of size 1. Then, new sequences are generated by directly working on the sequences, without mining additional frequent itemsets. A thorough performance study with both real-world and artificially generated datasets empirically proves that CloFAST outperforms the state-of-the-art algorithms, both in time and memory consumption, especially when mining long closed sequences

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Recently, there has been increased interest in the extraction of structured data from the web (both "Surface" Web and"Hidden" Web). In particular, in this paper we focus on the automatic extraction of Web Lists. Although this task has been studied extensively, existing approaches are based on the assumption that lists are wholly contained in a Web page.They do not consider that many websites span their listing on several Web Pages and show for each of these only a partial view. Similar to databases, where a view can represent a subset of the data contained in a table, they split a logical list in multiple views (view lists). Automatic extraction of logical lists is an open problem. To tackle this issue we propose an unsupervised and domain-independent algorithm for logical list extraction. Experimental results on real-life and data-intensive Web sites confirm the effectiveness of our approach.

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Process mining is a research discipline that aims to discover, monitor and improve real processing using event logs. In this paper we tackle the problem of next activity prediction/recommendation via "nested prediction model" learning, that is, we first identify recurrent and frequent sequences of activities and then we learn a prediction model for each frequent sequence. The key principle underlying the design of the proposed solution is in the ability to process massive logs by means of a parallel and distributed solution (by exploiting the Spark parallel computation framework) which can make reasonable decisions in the absence of perfect models. Indeed, given the classical threshold for minimum support and a user-specified error bound, our approach exploits the Chernoff bound to mine "approximate" frequent sequences with statistical error guarantees on their actual supports. Experiments on real-world log data prove the effectiveness of the proposed approach.

A sitemap represents an explicit specification of the design concept and knowledge organization of a website and is therefore considered as the website’s basic ontology. It not only presents the main usage flows for users, but also hierarchically organizes concepts of the website. Typically, sitemaps are defined by webmasters in the very early stages of the website design. However, during their life websites significantly change their structure, their content and their possible navigation paths. Even if this is not the case, webmasters can fail to either define sitemaps that reflect the actual website content or, vice versa, to define the actual organization of pages and links which do not reflect the intended organization of the content coded in the sitemaps. In this paper we propose an approach which automatically generates sitemaps. Contrary to other approaches proposed in the literature, which mainly generate sitemaps from the textual content of the pages, in this work sitemaps are generated by analyzing the Web graph of a website. This allows us to: i) automatically generate a sitemap on the basis of possible navigation paths, ii) compare the generated sitemaps with either the sitemap provided by the Web designer or with the intended sitemap of the website and, consequently, iii) plan possible website re-organization. The solution we propose is based on closed frequent sequence extraction and only concentrates on hyperlinks organized in “Web lists”, which are logical lists embedded in the pages. These “Web lists” are typically used for supporting users in Web site navigation and they include menus, navbars and content tables. Experiments performed on three real datasets show that the extracted sitemaps are much more similar to those defined by website curators than those obtained by competitor algorithms.

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