Browsing by Author "Ling, Tok Wang"

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    From Structure-based to Semantics-based Approach for Effective XML Keyword Search
    (2013-04-25T03:00:43Z) Le, Thuy Ngoc; Wu, Huayu; Ling, Tok Wang; Li, Luochen; Lu, Jiaheng
    Existing XML keyword search approaches can be categorized into tree-based search and graph-based search. Both of them are structure-based search because they mainly rely on the exploration of the structural features of document. Those structure-based approaches cannot fully exploit hidden semantics in XML document. This causes serious problems in processing some class of keyword queries. In this paper, we thoroughly point out mismatches between answers returned by structure-based search and the expectations of common users. Through detailed analysis of these mismatches, we show the importance of semantics in XML keyword search and propose a semantics-based approach to process XML keyword queries. Particularly, we propose to use Object Relationship (OR) graph, which fully capture semantics of object, relationship and attribute, to represent XML document and we develop algorithms based on the OR graph to return more comprehensive answers. Experimental results show that our proposed semantics-based approach can resolve the problems of the structure-based search, and significantly improve both the effectiveness and efficiency.
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    Temporal Keyword Search with Aggregates and Group-By
    (2021-07) Gao, Qiao; Lee, Mong Li; Ling, Tok Wang
    Abstract. Temporal keyword search enables non-expert users to query temporal relational databases with time conditions. However, aggregates and group-by are currently not supported in temporal keyword search, which hinders querying of statistical information in temporal databases. This work proposes a framework to support aggregate, group-by and time condition in temporal keyword search. We observe that simply combining non-temporal keyword search with aggregates, group-by, and temporal aggregate operators may lead to incorrect and meaningless results as a result of data duplication over time periods. As such, our framework utilizes Object-Relationship-Attribute semantics to identify a unique at-tribute set in the join sequence relation and remove data duplicates from this attribute set to ensure the correctness of aggregate and group-by computation. We also consider the time period in which temporal at-tributes occur when computing aggregate to return meaningful results. Experiment results demonstrate the importance of these steps to retrieve correct results for keyword queries over temporal databases.