Relational computation for mining association rules from XML data

Liu, Hong-Cheu; Zeleznikow, John

doi:10.1145/1099554.1099613

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…More recently the problem has been investigated in the XML context [6], [31], [24], [8], [10], [19], [33]. In [31], Wan and Dobbie use XQuery [29] to extract association rules from simple XML documents.…”

Section: Comparison With Other Workmentioning

confidence: 99%

Data Mining for XML Query-Answering Support

Mazuran

Quintarelli

Tanca

2012

IEEE Trans. Knowl. Data Eng.

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Abstract-Extracting information from semistructured documents is a very hard task, and is going to become more and more critical as the amount of digital information available on the Internet grows. Indeed, documents are often so large that the data set returned as answer to a query may be too big to convey interpretable knowledge. In this paper, we describe an approach based on Tree-Based Association Rules (TARs): mined rules, which provide approximate, intensional information on both the structure and the contents of Extensible Markup Language (XML) documents, and can be stored in XML format as well. This mined knowledge is later used to provide: 1) a concise idea-the gist-of both the structure and the content of the XML document and 2) quick, approximate answers to queries. In this paper, we focus on the second feature. A prototype system and experimental results demonstrate the effectiveness of the approach.

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Section: Comparison With Other Workmentioning

confidence: 99%

Data Mining for XML Query-Answering Support

Mazuran

Quintarelli

Tanca

2012

IEEE Trans. Knowl. Data Eng.

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“…The problem of association rule mining was initially proposed in [1] and many implementations of the algorithms, downloadable from [12], were developed in the database literature. More recently the problem has been investigated in the XML context [6], [31], [24], [8], [10], [19], [33]. In [31], Wan and Dobbie use XQuery [29] to extract association rules from simple XML documents.…”

Section: Comparison With Other Workmentioning

confidence: 99%

Data Mining For XML Query-Answering Support

Kulkarni¹,

Kaneria²,

Kothawade³

et al. 2012

GRA

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XML has become a defacto standard for storing, sharing and exchanging information across heterogeneous platforms. The XML content is growing day by day in rapid pace. Enterprises need to make queries on XML databases frequently. As huge XML data is available, it is challenging task to extract required data from XML database. It is computationally expensive to answer queries without any support. Towards this, in this paper we present a technique known as Tree-based Association Rules (TARs) mined rules that provide required information on structure and content of XML file and the TARs are also stored in XML format. The mined knowledge (TARs) used later for XML query answering support. This enables quick and accurate answering. We also developed a prototype application to demonstrate the efficiency of the proposed system. The empirical results are very positive and query answering is expected to be useful in real time applications.

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