Efficient Mining of XML Query Patterns for Caching

Yang, Liang; Lee, Mong Li; Hsu, Wynne

doi:10.1016/b978-012722442-8/50015-x

Cited by 88 publications

(96 citation statements)

References 11 publications

(20 reference statements)

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“…To determine the next higher-up joins we scan the base joins produced: Starting with the join with largest profit, namely SJ 1 , we compute the join profits between SJ 1 and SJ 2 , and between SJ 1 and SJ 4 . The algorithm decides for joining SJ 1 and SJ 2 , because the profit is larger. Lastly SJ 4 participates in the final evaluation plan.…”

Section: Greedy Heuristic Algorithmmentioning

confidence: 99%

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Efficient evaluation of preference query processes using twig caches

Cho

Balke

2009

2009 Third International Conference on Research Challenges in Information Science

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Abstract-Facing today's information flood needs efficient means for personalization. Therefore XML query processing over large volumes of data needs to make the most out of already spent processing time by caching common (sub)expressions for reuse. This is especially promising for the new paradigm of personalized preference queries. Here a sequence of possible query relaxations is a-priori determined by the users' preferences. Structural and value-based preferences thus define a query process where predicates are progressively relaxed until a suitable set of best possible results has been retrieved. To improve evaluation times for such query processes we argue that caching intermediate join results of structural preference queries is especially effective, because subsequent queries will always be subsumed by some previously cached queries to a certain extent. In this paper we propose a structural join-based caching scheme that allows preference queries to reuse the most beneficial structural join results of all previous queries. We first design a twig cache along with effective strategies for cache management. Moreover, we present a selection algorithm for join orders using cached data and the preference-induced sequence of future queries to select optimal query evaluation plans. Our benchmark experiments show that by using our twig caches preference query processing can be essentially sped up.

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Section: Greedy Heuristic Algorithmmentioning

confidence: 99%

“…Caching this basic structure for reuse has been shown to result in significant query time improvements. Caching approaches for XML documents have studied caching frequent query patterns based on statistical considerations [1] and caching on semantic considerations [2].…”

Section: Introductionmentioning

confidence: 99%

Efficient evaluation of preference query processes using twig caches

Cho

Balke

2009

2009 Third International Conference on Research Challenges in Information Science

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“…In [10], complicated pruning techniques are incorporated to reduce the size of candidates, however the method discovers only collections of paths in ordered trees, rather than arbitrary frequent trees. Our work is also related to FastXMiner [11], discovers frequently asked query patterns and their results are intelligently arranged in the system cache in order to improve future query performance. This method is also Apriori-like, however, it only discovers frequent query patterns rooted at the root of DTD, whereas our mining algorithm does not have this limitation.…”

Section: Related Workmentioning

confidence: 99%

Discovering Minimal Infrequent Structures from XML Documents

Wang

Mamoulis

Cheung

et al. 2004

Web Information Systems – WISE 2004

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Abstract. More and more data (documents) are wrapped in XML format. Mining these documents involves mining the corresponding XML structures. However, the semi-structured (tree structured) XML makes it somewhat difficult for traditional data mining algorithms to work properly. Recently, several new algorithms were proposed to mine XML documents. These algorithms mainly focus on mining frequent tree structures from XML documents. However, none of them was designed for mining infrequent structures which are also important in many applications, such as query processing and identification of exceptional cases. In this paper, we consider the problem of identifying infrequent tree structures from XML documents. Intuitively, if a tree structure is infrequent, all tree structures that contain this subtree is also infrequent. So, we propose to consider the minimal infrequent structure (MIS), which is an infrequent structure while all proper subtrees of it are frequent. We also derive a level-wise mining algorithm that makes use of the SG-tree (signature tree) and some effective pruning techniques to efficiently discover all MIS. We validate the efficiency and feasibility of our methods through experiments on both synthetic and real data.

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“…. an XML query using one view has been extensively studied e.g., in [11,12,13], and using several views, e.g., in [14,15,16,17]. A dual problem to view-based rewriting is the automated selection of materialized views to improve the performance of a given XQuery workload.…”

Section: Introductionmentioning

confidence: 99%

“…A dual problem to view-based rewriting is the automated selection of materialized views to improve the performance of a given XQuery workload. Well-studied for relational databases [7,8], it has also attracted attention for XML queries and views [11,13,18,19].…”

Section: Introductionmentioning

confidence: 99%