28,286 research outputs found
TopX : efficient and versatile top-k query processing for text, structured, and semistructured data
TopX is a top-k retrieval engine for text and XML data. Unlike Boolean engines, it stops query processing as soon as it can safely determine the k top-ranked result objects according to a monotonous score aggregation function with respect to a multidimensional query. The main contributions of the thesis unfold into four main points, confirmed by previous publications at international conferences or workshops:
• Top-k query processing with probabilistic guarantees.
• Index-access optimized top-k query processing.
• Dynamic and self-tuning, incremental query expansion for top-k query
processing.
• Efficient support for ranked XML retrieval and full-text search.
Our experiments demonstrate the viability and improved efficiency of our approach compared to existing related work for a broad variety of retrieval scenarios.TopX ist eine Top-k Suchmaschine fĂĽr Text und XML Daten. Im Gegensatz
zu Boole\u27; schen Suchmaschinen terminiert TopX die Anfragebearbeitung,
sobald die k besten Ergebnisobjekte im Hinblick auf eine mehrdimensionale
Anfrage gefunden wurden. Die Hauptbeiträge dieser Arbeit teilen sich in
vier Schwerpunkte basierend auf vorherigen Veröffentlichungen bei internationalen
Konferenzen oder Workshops:
• Top-k Anfragebearbeitung mit probabilistischen Garantien.
• Zugriffsoptimierte Top-k Anfragebearbeitung.
• Dynamische und selbstoptimierende, inkrementelle Anfrageexpansion für Top-k Anfragebearbeitung.
• Effiziente Unterstützung für XML-Anfragen und Volltextsuche.
Unsere Experimente bestätigen die Vielseitigkeit und gesteigerte Effizienz unserer Verfahren gegenüber existierenden, führenden Ansätzen für eine weite
Bandbreite von Anwendungen in der Informationssuche
No-But-Semantic-Match: Computing Semantically Matched XML Keyword Search Results
Users are rarely familiar with the content of a data source they are
querying, and therefore cannot avoid using keywords that do not exist in the
data source. Traditional systems may respond with an empty result, causing
dissatisfaction, while the data source in effect holds semantically related
content. In this paper we study this no-but-semantic-match problem on XML
keyword search and propose a solution which enables us to present the top-k
semantically related results to the user. Our solution involves two steps: (a)
extracting semantically related candidate queries from the original query and
(b) processing candidate queries and retrieving the top-k semantically related
results. Candidate queries are generated by replacement of non-mapped keywords
with candidate keywords obtained from an ontological knowledge base. Candidate
results are scored using their cohesiveness and their similarity to the
original query. Since the number of queries to process can be large, with each
result having to be analyzed, we propose pruning techniques to retrieve the
top- results efficiently. We develop two query processing algorithms based
on our pruning techniques. Further, we exploit a property of the candidate
queries to propose a technique for processing multiple queries in batch, which
improves the performance substantially. Extensive experiments on two real
datasets verify the effectiveness and efficiency of the proposed approaches.Comment: 24 pages, 21 figures, 6 tables, submitted to The VLDB Journal for
possible publicatio
No-but-semantic-match : computing semantically matched xml keyword search results
Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of non-mapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top-k results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches. © 2017, Springer Science+Business Media, LLC
Investigation into Indexing XML Data Techniques
The rapid development of XML technology improves the WWW, since the XML data has many advantages and has become a common technology for transferring data cross the internet. Therefore, the objective of this research is to investigate and study the XML indexing techniques in terms of their structures. The main goal of this investigation is to identify the main limitations of these techniques and any other open issues.
Furthermore, this research considers most common XML indexing techniques and performs a comparison between them. Subsequently, this work makes an argument to find out these limitations. To conclude, the main problem of all the XML indexing techniques is the trade-off between the
size and the efficiency of the indexes. So, all the indexes become large in order to perform well, and none of them is suitable for all users’ requirements. However, each one of these techniques has some advantages in somehow
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