Index structures for structured documents

Lee, Yong Kyu; Yoo, Seong-Joon; Yoon, Kyoungro; Berra, P. Bruce

doi:10.1145/226931.226950

Cited by 58 publications

(22 citation statements)

References 12 publications

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“…While this approach can be very effective in some cases, a downside is that users have to learn a complex query language and understand the schema of underlying XML. There are other approaches stemmed from the work done on structured documents from the perspective of information retrieval [11,12,[16][17][18]. Indexing structures for documents are discussed in [7] and for structured documents in [12].…”

Section: Related Workmentioning

confidence: 99%

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An efficient index structure for XML based on generalized suffix tree

Liang

et al. 2007

Information Systems

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Section: Related Workmentioning

confidence: 99%

“…There are other approaches stemmed from the work done on structured documents from the perspective of information retrieval [11,12,[16][17][18]. Indexing structures for documents are discussed in [7] and for structured documents in [12]. Post files and signature files have been used only for searching literal terms.…”

Section: Related Workmentioning

confidence: 99%

An efficient index structure for XML based on generalized suffix tree

Liang

et al. 2007

Information Systems

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“…This scheme allows the ancestral relationship between nodes to be found by performing a simple calculation. Since the document is modeled as a complete k-ary tree, space is wasted by the insertion of spare identifiers [32]. Extensions of this scheme [36] partially drop the completeness constraint so the number of children a node may have is recomputed for every level of the tree.…”

Section: Xml Searchmentioning

confidence: 99%

Using a relational database for scalable XML search

2007

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XML is a flexible and powerful tool that enables information and security sharing in heterogeneous environments. Scalable technologies are needed to effectively manage the growing volumes of XML data. A wide variety of methods exist for storing and searching XML data; the two most common techniques are conventional tree-based and relational approaches. Tree-based approaches represent XML as a tree and use indexes and path join algorithms to process queries. In contrast, the relational approach utilizes the power of a mature relational database to store and search XML. This method relationally maps XML queries to SQL and reconstructs the XML from the database results. To date, the limited acceptance of the relational approach to XML processing is due to the need to redesign the relational schema each time a new XML hierarchy is defined. We, in contrast, describe a relational approach that is fixed schema eliminating the need for schema redesign at the expense of potentially longer runtimes. We show, however, that these potentially longer runtimes are still significantly shorter than those of the tree approach.Using a relational database for scalable XML search 147 We use a popular XML benchmark to compare the scalability of both approaches. We generated large collections of heterogeneous XML documents ranging in size from 500 MB to 8 GB using the XBench benchmark. The scalability of each method was measured by running XML queries that cover a wide range of XML search features on each collection. We measure the scalability of each method over different query features as the collection size increases. In addition, we examine the performance of each method as the result size and the number of predicates increase. Our results show that our relational approach provides a scalable approach to XML retrieval by leveraging existing relational database optimizations. Furthermore, we show that the relational approach typically outperforms the tree-based approach while scaling consistently over all collections studied.

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“…This subsection establishes a structured index method using the concept of a complete tree structure of the K-ary Tree (Dongwook, Hyuncheol, & Honglan, 1998;Lee Yoo, Yoon, & Berra, 1996) to identify rapidly the most valuable design cases as references according to the functional requirement-based query model. The primary method is to apply the UID (Unique Element Identifiers) equation to reduce the number of index entries during the search and thus increase the efficiency of the search for similar design cases.…”

Section: Searching Matching and Ranking Similar Design Casesmentioning

confidence: 99%