Dynamically querying possibilistic XML data

Liu, Jian; Ma, Zongmin; Qv, Qiulong

doi:10.1016/j.ins.2013.11.011

Cited by 12 publications

(2 citation statements)

References 36 publications

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“…Inability to detect all the structural relationships is another weakness of the range based labeling schemes. • Prefix-based labeling schemes: In the schemes in this category [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], each node label involves the label of its ancestor besides its own. The structural relationships are determined by looking at the labels.…”

Section: Related Workmentioning

confidence: 99%

A new effective method for labeling dynamic XML data

Khanjari

Gaeini

2018

J Big Data

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IntroductionXML is a semi-structural and standard document format to exchange data. Elements in XML documents are regular and there are structural relationships between them [1]. In fact, processing queries in XML should recognize these structural relationships. They also should determine the order of elements in a document. Node labeling in XML data is one way to increase the efficiency of query processing. Labeling means allocating a unique identifier to each node in XML documents [2]. A labeling scheme encompasses traversal or browsing the document, analyzing the elements, and assessing available relationships between elements. So, it should generate small enough labels in order to be processed efficiently both in initial label assigning as well as when queries are issued.A challenging problem of the existing labeling schemes is the need for relabeling nearly all the existing nodes after inserting new nodes in XML documents. While update in XML data is a usual operation in many real-world applications, e.g. stream data, relabeling will influence the query performance, especially when large-size labels are assigned Abstract Query processing based on labeling dynamic XML documents has gained more attention in the past several years. An efficient labeling scheme should provide small size labels keeping the simplicity of the exploited algorithm in order to avoid complex computations as well as retaining the readability of structural relationships between nodes. Moreover, for dynamic XML data, relabeling the nodes in XML updates should be avoided. However, the existing schemes lack the capability of supporting all of these requirements. In this paper, we propose a new labeling scheme which assigns variable-length labels to nodes in dynamic XML documents. Our method employs the FibLSS encoding scheme that exploits the properties of the Fibonacci sequence to provide variable-length node labels of appropriate size. In XML updating process, we add a new section only in the new node's label without relabeling the existing nodes while keeping the order of nodes as well as preserving the structural relationships. Our labeling method is scalable as it is not subject to overflow, and as the number of nodes to be labeled increases exponentially, the size of labels grows linearly, which makes it suitable for big datasets. It also has the best performance in computational processing costs compared to existing approaches. The results of the experiments confirm the advantages of our proposed method in comparison to state-of-the-art techniques.

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Section: Related Workmentioning

confidence: 99%

A new effective method for labeling dynamic XML data

Khanjari

Gaeini

2018

J Big Data

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“…For the probabilistic XML data, the data models [1], [2] and query methods [3], [4], [5] on probabilistic XML documents have been studied. And for the fuzzy XML data, the researchers have also proposed some models [6], [7] for the representation of fuzzy information and query methods [8], [9], [10] on fuzzy XML documents. Ma and Yan [7] propose a fuzzy XML data model by introducing the possibility distributions to represent two types of fuzziness.…”

Section: Introductionmentioning

confidence: 99%

An Object-Oriented Approach of Keyword Querying over Fuzzy XML

Li¹

2016

CIT. J. comput. inf. technol.

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As the fuzzy data management has become one of the main research topics and directions, the question of how to obtain the useful information by means of keyword query from fuzzy XML documents is becoming a subject of an increasing needed investigation. Considering the keyword query methods on crisp XML documents, smallest lowest common ancestor (SLCA) semantics is one of the most widely accepted semantics. When users propose the keyword query on fuzzy XML documents with the SLCA semantics, the query results are always incomplate, with low precision, and with no possibilities values returned. Most of keyword query semantics on XML documents only consider query results matching all keywords, yet users may also be interested in the query results matching partial keywords. To overcome these limitations, in this paper, we investigate how to obtain more comprehensive and meaningful results of keyword querying on fuzzy XML documents. We propose a semantics of object-oriented keyword querying on fuzzy XML documents. First, we introduce the concept of "object tree", analyze different types of matching result object trees and find the "minimum result object trees" which contain all keywords and "result object trees" which contain partial keywords. Then an object-oriented keyword query algorithm ROstack is proposed to obtain the root nodes of these matching result object trees, together with their possibilities. At last, experiments are conducted to verify the effectiveness and efficiency of our proposed algorithm. ACM CCS (2012) Classification: Information systems→ Data management systems → Query languages

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An approach of top-k keyword querying for fuzzy XML

2017

Computing

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Dynamically querying possibilistic XML data

Cited by 12 publications

References 36 publications

A new effective method for labeling dynamic XML data

A new effective method for labeling dynamic XML data

An Object-Oriented Approach of Keyword Querying over Fuzzy XML

An approach of top-k keyword querying for fuzzy XML

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