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Xiao Q.,Jiangxi University of Finance and Economics | Xiao Q.,Jiangxi Key Laboratory of Data and Knowledge Engineering
Tehnicki Vjesnik | Year: 2016

Collaboration has become main stream and trend in interdisciplinary fields. In research collaboration organizations, to evaluate the contributions of researchers to the organization and then to identify core researchers is an important issue to carry out performance appraisal and crisis management of brain drain. Scientific research collaboration network is a basic model to investigate this question, but under the context of increasingly complex collaborative behaviour, it shows its limitations for semantic representations. In this paper, by introducing hypernetwork, a more powerful modelling tool than traditional network, and taking scientific paper co-authorship as object to construct scientific research collaboration hypernetwork (SRCH), we measure the importance of researchers in two aspects, as collaborative relationship structure and collaborative achievement value from a hypernetwork perspective. An additive weighting method with adjustable parameters is utilized to integrate the evaluation indicators of the two aspects, and then the synthetical importance evaluation of researchers is obtained. Analysis of data instance verifies that our node importance measure for scientific research collaboration from hypernetwork perspective is reasonable and effective. © 2016, Strojarski Facultet. All rights reserved.

Liu D.,Jiangxi University of Finance and Economics | Liu D.,Jiangxi Key Laboratory of Data and Knowledge Engineering | Wu S.,Songjiang Branch of Shanghai Rural Commercial Bank | Lan Y.,Gannan Medical University | And 4 more authors.
Soft Computing | Year: 2013

Extensible Markup Language (XML) is a simple, flexible text format derived from SGML, which is originally designed to support large-scale electronic publishing. Nowadays XML plays a fundamental role in the exchange of a wide variety of data on the Web. As XML allows designers to create their own customized tags, enables the definition, transmission, validation, and interpretation of data between applications, devices and organizations, lots of works in soft computing employ XML to take control and responsibility for the information, such as fuzzy markup language, and accordingly there are lots of XML-based data or documents. However, most of mobile and interactive ubiquitous multimedia devices have restricted hardware such as CPU, memory, and display screen. So, it is essential to compress an XML document/element collection to a brief summary before it is delivered to the user according to his/her information need. Query-oriented XML text summarization aims to provide users a brief and readable substitution of the original retrieved documents/elements according to the user's query, which can relieve users' reading burden effectively. We propose a query-oriented XML summarization system QXMLSum, which extracts sentences and combines them as a summary based on three kinds of features: user's queries, the content of XML documents/elements, and the structure of XML documents/elements. Experiments on the IEEE-CS datasets used in Initiative for the Evaluation of XML Retrieval show that the query-oriented XML summary generated by QXMLSum is competitive. © 2012 Springer-Verlag Berlin Heidelberg.

Liu D.,Jiangxi University of Finance and Economics | Liu D.,Jiangxi Key Laboratory of Data and Knowledge Engineering | Wan C.,Jiangxi University of Finance and Economics | Wan C.,Jiangxi Key Laboratory of Data and Knowledge Engineering | And 4 more authors.
Information Sciences | Year: 2013

Text-centric (or document-centric) XML document retrieval aims to rank search results according to their relevance to a given query. To do this, most existing methods mainly rely on content terms and often ignore an important factor - the XML tags and paths, which are useful in determining the important contents of a document. In some previous studies, each unique tag/path is assigned a weight based on domain (expert) knowledge. However, such a manual assignment is both inefficient and subjective. In this paper, we propose an automatic method to infer the weights of tags/paths according to the topical relationship between the corresponding elements and the whole documents. The more the corresponding element can generalize the document's topic, the more the tag/path is considered to be important. We define a model based on Average Topic Generalization (ATG), which integrates several features used in previous studies. We evaluate the performance of the ATG-based model on two real data sets, the IEEECS collection and the Wikipedia collection, from two different perspectives: the correlation between the weights generated by ATG and those set by experts, and the performance of XML retrieval based on ATG. Experimental results show that the tag/path weights generated by ATG are highly correlated with the manually assigned weights, and the ATG model significantly improves XML retrieval effectiveness. © 2013 Elsevier Inc. All rights reserved.

Liao G.,Jiangxi University of Finance and Economics | Liao G.,Jiangxi Key Laboratory of Data and Knowledge Engineering | Wu L.,Jiangxi Water Conservancy Project Administration Bureau of Ganfu Plain | Wan C.,Jiangxi University of Finance and Economics | Wan C.,Jiangxi Key Laboratory of Data and Knowledge Engineering
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | Year: 2012

In recent years, a large amounts of uncertain data are emerging due to the wide usage of new technologies such as wireless sensor networks and radio frequency identification. Considering the uncertainty of uncertain data streams, a new kind of probability frequent pattern tree-PFP-tree and a probability frequent pattern mining method-PFP-growth are proposed in this paper. PFP-growth uses transactional uncertain data stream model and a time-based probability decay window model to find probability frequent patterns through calculating expected supports. The main characteristics of PFP-growth include: 1)Because the contributions on the expected supports of items arriving at different time within a window may be different, a time-based probability decay window model is used to improve mining precision ratios; 2)In order to enhance retrieval speed on PFP-tree, an item index table and a transaction index table are designed; 3)A pruning algorithm is designed to delete the nodes which are not possible to be frequent patterns, to reduce greatly the overhead of both time and space; 4)A transaction probability list is set for every node to meet the requirement that some data items may have different probabilities in different transactions. Experimental results have shown that the PFP-growth method can not only ensure a higher mining precision ratio, but also need less processing time and storage space than the existing methods.

Liao G.,Jiangxi University of Finance and Economics | Liao G.,Jiangxi Key Laboratory of Data and Knowledge Engineering | Wu L.,Jiangxi University of Finance and Economics | Wu L.,Jiangxi Key Laboratory of Data and Knowledge Engineering | And 3 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

In recent years, large amounts of uncertain data are emerged with the widespread employment of the new technologies, such as wireless sensor networks, RFID and privacy protection. According to the features of the uncertain data streams such as incomplete, full of noisy, non-uniform and mutable, this paper presents a probability frequent pattern tree called PFP-tree and a method called PFP-growth, to mine probability frequent patterns based on probability damped windows. The main characteristics of the suggested method include: (1) adopting time-based probability damped window model to enhance the accuracy of mined frequent patterns; (2) setting an item index table and a transaction index table to speed up retrieval on the PFP-tree; and (3) pruning the tree to remove the items that cannot become frequent patterns;. The experimental results demonstrate that PFP-growth method has better performance than the main existing schemes in terms of accuracy, processing time and storage space. © 2011 Springer-Verlag.

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