Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Automaton Meet Algebra: A Hybrid Paradigm for Efficiently Processing XQuery over XML Stream

XML stream applications bring the challenge of efficiently processing queries on sequentially accessible token-based data streams. The automaton paradigm is naturally suited for pattern retrieval on tokenized XML streams, but requires patches for implementing the filtering or restructuring functionalities common for the XML query languages. In contrast, the algebraic paradigm is well-established for processing self-contained tuples. However, it does not traditionally support token inputs. This dissertation proposes a framework called Raindrop, which accommodates both the automaton and algebra paradigms to take advantage of both. First, we propose an architecture for Raindrop. Raindrop is an algebra framework that models queries at different abstraction levels. We represent the token-based automaton computations as an algebraic subplan at the high level while exposing the automaton details at the low level. The algebraic subplan modeling automaton computations can thus be integrated with the algebraic subplan modeling the non-automaton computations. Second, we explore a novel optimization opportunity. Other XML stream processing systems always retrieve all the patterns in a query in the automaton. In contrast, Raindrop allows a plan to retrieve some of the pattern retrieval in the automaton and some out of the automaton. This opens up an automaton-in-or-out optimization opportunity. We study this optimization in two types of run-time environments, one with stable data characteristics and one with fluctuating data characteristics. We provide search strategies catering to each environment. We also describe how to migrate from a currently running plan to a new plan at run-time. Third, we optimize the automaton computations using the schema knowledge. A set of criteria are established to decide what schema constraints are useful to a given query. Optimization rules utilizing different types of schema constraints are proposed based on the criteria. We design a rule application algorithm which ensures both completeness (i.e., no optimization is missed) and minimality (i.e., no redundant optimization is introduced). The experimentations on both real and synthetic data illustrate that these techniques bring significant performance improvement with little overhead

Semantics and result disambiguation for keyword search on tree data

Keyword search is a popular technique for searching tree-structured data (e.g., XML, JSON) on the web because it frees the user from learning a complex query language and the structure of the data sources. However, the convenience of keyword search comes with drawbacks. The imprecision of the keyword queries usually results in a very large number of results of which only very few are relevant to the query. Multiple previous approaches have tried to address this problem. Some of them exploit structural and semantic properties of the tree data in order to filter out irrelevant results while others use a scoring function to rank the candidate results. These are not easy tasks though and in both cases, relevant results might be missed and the users might spend a significant amount of time searching for their intended result in a plethora of candidates. Another drawback of keyword search on tree data, also due to the incapacity of keyword queries to precisely express the user intent, is that the query answer may contain different types of meaningful results even though the user is interested in only some of them. Both problems of keyword search on tree data are addressed in this dissertation. First, an original approach for answering keyword queries is proposed. This approach extracts structural patterns of the query matches and reasons with them in order to return meaningful results ranked with respect to their relevance to the query. The proposed semantics performs comparisons between patterns of results by using different types of ho-momorphisms between the patterns. These comparisons are used to organize the patterns into a graph of patterns which is leveraged to determine ranking and filtering semantics. The experimental results show that the approach produces query results of higher quality compared to the previous ones. To address the second problem, an original approach for clustering the keyword search results on tree data is introduced. The clustered output allows the user to focus on a subset of the results, and to save time and effort while looking for the relevant results. The approach performs clustering at different levels of granularity to group similar results together effectively. The similarity of the results and result clusters is decided using relations on structural patterns of the results defined based on homomor-phisms between path patterns. An originality of the clustering approach is that the clusters are ranked at different levels of granularity to quickly guide the user to the relevant result patterns. An efficient stack-based algorithm is presented for generating result patterns and constructing the clustering hierarchy. The extensive experimentation with multiple real datasets show that the algorithm is fast and scalable. It also shows that the clustering methodology allows the users to effectively retrieve their intended results, and outperforms a recent state-of-the-art clustering approach. In order to tackle the second problem from a different aspect, diversifying the results of keyword search is addressed. Diversification aims to provide the users with a ranked list of results which balances the relevance and redundancy of the results. Measures for quantifying the relevance and dissimilarity of result patterns are presented and a heuristic for generating a diverse set of results using these metrics is introduced

Algorithms for XML filtering

In a publish-subscribe system based on XML filtering, the subscriber profiles are usually specified by filters written in the XPath language. The system processes the stream of XML documents and delivers to subscribers a notification or the content of those documents that match the filters. The number of interested subscribers and their stored profiles can be very large, thousands or even millions. In this case, the scalability of the system is critical. In this thesis, we develop several algorithms for XML filtering with linear XPath expressions. The algorithms are based on a backtracking Aho-Corasick pattern-matching automaton (PMA) built from "keywords" extracted from the filters, where a keyword is a maximal substring consisting only of XML element names. The output function of the PMA indicates which keyword occurrences of which filter are recognized at a given state. Our best results have been obtained by using a dynamically changing output function, which is dynamically updated during the processing of the input document. We have conducted an extensive performance study in which we compared our filtering algorithms with YFilter and the lazy DFA, two well-known automata-based filtering methods. With a non-recursive XML data set, PMA-based filtering is tens of times more efficient than YFilter and also significantly more efficient than the lazy DFA. With a slightly recursive data set PMA-based filtering has the same performance as the lazy DFA and it is significantly more efficient than YFilter. We have also developed an optimization method called filter pruning. This method improves the performance of filtering by utilizing knowledge about the XML document type definition (DTD) to simplify the filters. The optimization algorithm takes as input a DTD and a set of linear XPath filters and produces a set of pruned linear XPath filters that contain as few wildcards and descendant operators as possible. With a non-recursive data set and with a slightly recursive data set the filter-pruning method yielded a tenfold increase in the filtering speed of the PMA-based algorithms and a hundredfold increase with YFilter and the lazy DFA. Filter pruning can also increase the filtering speed in the case of highly recursive data sets

mSpace meets EPrints: a Case Study in Creating Dynamic Digital Collections

In this case study we look at issues involved in (a) generating dynamic digital libraries that are on a particular topic but span heterogeneous collections at distinct sites, (b) supplementing the artefacts in that collection with additional information available either from databases at the artefact's home or from the Web at large, and (c) providing an interaction paradigm that will support effective exploration of this new resource. We describe how we used two available frameworks, mSpace and EPrints to support this kind of collection building. The result of the study is a set of recommendations to improve the connectivity of remote resources both to one another and to related Web resources, and that will also reduce problems like co-referencing in order to enable the creation of new collections on demand

Evaluating linear XPath expressions by pattern-matching automata

Abstract: We consider the problem of efficiently evaluating a large number of XPath expressions, especially in the case when they define subscriber profiles for filtering of XML documents. For each document in an XML document stream, the task is to determine those profiles that match the document. In this article we present a new general method for filtering with profiles expressed by linear XPath expressions with child operators (/), descendant operators (//), and wildcards ( * ). This new filtering algorithm is based on a backtracking deterministic finite automaton derived from the classic Aho-Corasick pattern-matching automaton. This automaton has a size linear in the sum of the sizes of the XPath filters, and the worst-case time bound of the algorithm is much less than the time bound of the simulation of linear-size nondeterministic automata. Our new algorithm has a predecessor that can handle child and descendant operators but not wildcards, and has been shown to be extremely efficient when a documenttype definition (DTD) has been used to prune out all the wildcards and most of the descendant operators. But in some cases, such as when the DTD is highly recursive, it may not be possible to prune out all wildcards without producing a too large set of filters. Then it is important to have the full generality of an evaluation algorithm, as presented in this article, that can also handle wildcards

A Prime Number Approach to Matching an XML Twig Pattern including Parent-Child Edges

Twig pattern matching is a core operation in XML query processing because it is how all the occurrences of a twig pattern in an XML document are found. In the past decade, many algorithms have been proposed to perform twig pattern matching. They rely on labelling schemes to determine relationships between elements corresponding to query nodes in constant time. In this paper, a new algorithm TwigStackPrime is proposed, which is an improvement to TwigStack (Bruno et al., 2002). To reduce the memory consumption and computation overhead of twig pattern matching algorithms when Parent-Child (P-C) edges are involved, TwigStackPrime efficiently filters out a tremendous number of irrelevant elements by introducing a new labelling scheme, called Child Prime Label (CPL). Extensive performance studies on various real-world and artificial datasets were conducted to demonstrate the significant improvement of CPL over the previous indexing and querying techniques. The experimental results show that the new technique has a superior performance to the previous approaches