XML data can be represented by a tree or graph and the query processing for XML data requires the structural information among nodes. Designing an efficient labeling scheme for the nodes of Order-Sensitive XML trees i...XML data can be represented by a tree or graph and the query processing for XML data requires the structural information among nodes. Designing an efficient labeling scheme for the nodes of Order-Sensitive XML trees is one of the important methods to obtain the excellent management of XML data. Previous labeling schemes such as region and prefix often sacrifice updating performance and suffer increasing labeling space when inserting new nodes. To overcome these limitations, in this paper we propose a new labeling idea of separating structure from order. According to the proposed idea, a novel Prime-based Middle Fraction Labeling Scheme(PMFLS) is designed accordingly, in which a series of algorithms are proposed to obtain the structural relationships among nodes and to support updates. PMFLS combines the advantages of both prefix and region schemes in which the structural information and sequential information are separately expressed. PMFLS also supports Order-Sensitive updates without relabeling or recalculation, and its labeling space is stable. Experiments and analysis on several benchmarks are conducted and the results show that PMFLS is efficient in handling updates and also significantly improves the performance of the query processing with good scalability.展开更多
With the development of Internet, frequent pattern mining has been extendedto more complex patterns like tree mining and graph mining. Such applications arise in complexdomains like bioinformatics, web mining, etc. In...With the development of Internet, frequent pattern mining has been extendedto more complex patterns like tree mining and graph mining. Such applications arise in complexdomains like bioinformatics, web mining, etc. In this paper, we present a novel algorithm, namedChopper, to discover frequent subtrees from ordered labeled trees. An extensive performance studyshows that the newly developed algorithm outperforms TreeMiner V, one of the fastest methodsproposed previously, in mining large databases. At the end of this paper, the potential improvementof Chopper is mentioned.展开更多
基金supported by the National Science Foundation of China(Grant No.61272067,61370229)the National Key Technology R&D Program of China(Grant No.2012BAH27F05,2013BAH72B01)+1 种基金the National High Technology R&D Program of China(Grant No.2013AA01A212)the S&T Projects of Guangdong Province(Grant No.2016B010109008,2014B010117007,2015A030401087,2015B010109003,2015B010110002)
文摘XML data can be represented by a tree or graph and the query processing for XML data requires the structural information among nodes. Designing an efficient labeling scheme for the nodes of Order-Sensitive XML trees is one of the important methods to obtain the excellent management of XML data. Previous labeling schemes such as region and prefix often sacrifice updating performance and suffer increasing labeling space when inserting new nodes. To overcome these limitations, in this paper we propose a new labeling idea of separating structure from order. According to the proposed idea, a novel Prime-based Middle Fraction Labeling Scheme(PMFLS) is designed accordingly, in which a series of algorithms are proposed to obtain the structural relationships among nodes and to support updates. PMFLS combines the advantages of both prefix and region schemes in which the structural information and sequential information are separately expressed. PMFLS also supports Order-Sensitive updates without relabeling or recalculation, and its labeling space is stable. Experiments and analysis on several benchmarks are conducted and the results show that PMFLS is efficient in handling updates and also significantly improves the performance of the query processing with good scalability.
文摘With the development of Internet, frequent pattern mining has been extendedto more complex patterns like tree mining and graph mining. Such applications arise in complexdomains like bioinformatics, web mining, etc. In this paper, we present a novel algorithm, namedChopper, to discover frequent subtrees from ordered labeled trees. An extensive performance studyshows that the newly developed algorithm outperforms TreeMiner V, one of the fastest methodsproposed previously, in mining large databases. At the end of this paper, the potential improvementof Chopper is mentioned.
