@article {587,
	title = {Advances in secure communication and information processing systems},
	journal = {Journal of Digital Information Management},
	volume = {13},
	year = {2015},
	note = {Export Date: 22 February 2018},
	month = {2015},
	pages = {i},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84942531727\&partnerID=40\&md5=ec068ff353213713be8f563709ba559c},
	author = {Xhafa, F. and Li, J.}
}
@article {421,
	title = {The research of algorithm for data mining based on fuzzy theory},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {327 - 334},
	abstract = {Data Mining is a new filed in data processing research. Support Vector Machine (SVM) is one of the new methods using in data mining, which has gained great applicable success. However, there are still plenty of limitations in SVM. For example, SVM won{\textquoteright}t work if its training set contains uncertain information. In order to solve the problem presented above, this paper discusses the constraining programming of fuzzy chance and the characteristic of fuzzy classification as well as its expression methods. The algorithm for classifying Support Vector Machine is also included in this paper.},
	keywords = {Algorithm, Data mining, Fuzzy classification, Fuzzy linear separable, Fuzzy programming},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84890455119\&partnerID=40\&md5=cafd40536136512fd2e0485a0240a565},
	author = {Wang, A. and Li, J.}
}
@article {422,
	title = {The research on giant complex system using intelligent decision support system},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {335 - 341},
	abstract = {Mankind is confronted with an increasingly complex system, especially the major decision issues, such as organization and management, coordination, planning, forecasting, controlling and so on. Due to the complexity of the system, many fuzzy factors and the limitations of the existing complex giant system theory, there are still many problems to be solved. The research on the giant complex system based on the theory of fuzzy decision theory theoretically had been proving the reliability of the group decision and has established "the multijudgments information" fuzzy synthesis computational method. It has designed the corresponding multipurpose intelligent decision support system IDSS.},
	keywords = {Artificial intelligence, Decision support system, Fuzzy processing, Giant complex system},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84890477476\&partnerID=40\&md5=62072c647fcaedfc91f59a55e1124f4d},
	author = {Li, J. and Wang, A.}
}
@article {397,
	title = {Research on the anti-perspective correction algorithm of QR barcode},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {202 - 206},
	abstract = {2-D (2-dimension) bar code has many advantages compared with 1-D bar code, so it has many prevalent applications in recent days. However, there{\textquoteright}re several factors that affecting the sampling process, they lead to a certain degree of deformation towards the 2-D bar code. Furthermore, this deformation may too severe for 2-D bar code to be recognized. Generally, the main stream of twisted image correcting is based on the approach of control-point transformation. So how to locate the control point accurately becomes a key factor in 2-D bar code correcting. This paper develops a QR barcode searching graph and gets the precise location of control point. Experiment shows that our algorithm has good efficiency and accuracy.},
	keywords = {Image correction, Line fitting, Perspective transformation, QR code, Searching graphics},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84880830674\&partnerID=40\&md5=4b80f450374df6601877d816dd4cc9af},
	author = {Li, J. and Wang, Y.-W. and Wang, Y. and Chen, Y. and Wang, G.}
}
@article {298,
	title = {Fusion user browsing behavior and user interests autonomous control of personalized search results sort},
	journal = {Journal of Digital Information Management},
	volume = {9},
	year = {2011},
	note = {Export Date: 10 July 2014},
	month = {2011},
	pages = {271 - 275},
	abstract = {Reference simplifies the ODP concept, establish a user hierarchical structure can be controlled autonomously interest preference expression of use interests model. Meanwhile, from the user{\textquoteright}s browsing behavior can reflect the user{\textquoteright}s interest in starting, this paper analyzes the relationship between the user{\textquoteright}s browsing behavior and user interest. Considering both the times of page visiting and the browsing speed for the degree of use interest, as well as the similarity of the page keywords and a single user interest point, the index can be obtained for evaluating whether a page meets the user interest preference. Utilizing it as search results of evaluation indexes, search results sorting. Further analysis under different integrity of user interests of description and different results processing under the way of sorting effect, through individual apiration is decided according to user{\textquoteright}spersonal interest in server-side describe file integrity of, will be willing to disclosure of personal information stored in the search engine server in order to improve the quality of the search results, sensitive personal information retained in local client, reach users privacy protection effect.},
	keywords = {Degree of User Interest, Personalized Search, Privacy protection, Sort Search Results, User Interest Model, User{\textquoteright}s Browsing Behaviors},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84855413177\&partnerID=40\&md5=42962ce43dcfb6f7b617b32ec5cecdcd},
	author = {Li, J. and Wang, H. and Guo, Y.}
}
@article {185,
	title = {A novel graph containment query algorithm on graph databases},
	journal = {Journal of Digital Information Management},
	volume = {7},
	year = {2009},
	note = {Export Date: 10 July 2014},
	month = {2009},
	pages = {143 - 151},
	abstract = {Nowadays, efficient graph query processing method is coming more and more important along with the structured data accumulating. Given a graph database, a graph containment query retrieves the graphs from the database that are subgraphs of the query graph. In this paper, an algorithm is proposed, which is founded on a CFG (Closed Frequent subgraph) based index, to answer graph containment query. CFG is a set of special frequent subgraph of a graph database. When query processes through this index, it gets back a smaller candidate answer set than other methods which means much less subgraph isomorphism calculation. Both theoretical analysis and experimental evaluation result shows that the proposed method not only efficiently prunes the search branches in the feature index, but also effectively reduces the subgraph isomorphism test between the query graph and the indexed features.},
	keywords = {Frequent subgraphs, Graph databases, Graph indexing, Graph querying},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-73249130115\&partnerID=40\&md5=c53fbe52cb4f2481146df6768914245f},
	author = {Li, X. and Zhang, W. and Li, J.}
}
@article {113,
	title = {Clustering frequent graph patterns},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Export Date: 10 July 2014},
	month = {2007},
	pages = {335 - 346},
	abstract = {In recent years, graph mining has attracted much attention in the data mining community. Several efficient frequent subgraph mining algorithms have been recently proposed. However, the number of frequent graph patterns generated by these graph mining algorithms may be too large to be effectively explored by users, especially when the support threshold is low. In this paper, we propose to summarize frequent graph patterns by a much smaller number of representative graph patterns. Several novel concepts such as δ-cover graph, jump value and δ-jump pattern are proposed for efficiently summarizing frequent graph patterns. Based on the property of all δ-jump patterns being representative graph patterns, we propose two efficient algorithms for summarizing frequent graph patterns, RP-FP and RP-GD. The RP-FP algorithm computes representative graph patterns from a set of closed frequent graph patterns, whereas the RP-GD algorithm directly mines representative graph patterns from graph databases. The RP-FP has tight approximation (summarization) bound but higher computational complexity. The RP-GD has no approximation bound guarantee but is far more efficient. We conducted extensive experimental studies using various real and synthetic datasels. Experimental results show that RP-FP and RP-GD are able to obtain compact summarization in both real and synthetic graph databases. When the number of closed graph patterns is very large, RP-GD is much more efficient than RP-FP, while achieving comparable summarization quality.},
	keywords = {Closed graph pattern, Frequent graph pattern, Graph mining, Representative graph pattern, δ-jump pattern},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-49149118250\&partnerID=40\&md5=cae15cbd3bb5b1a9e3f57a95dbe295b5},
	author = {Liu, Y. and Li, J. and Zhu, J. and Gao, H.}
}
@article {27,
	title = {Continuously evaluating approximate similarity search on high dimensional data stream},
	journal = {Journal of Digital Information Management},
	volume = {4},
	year = {2006},
	note = {Export Date: 10 July 2014},
	month = {2006},
	pages = {82 - 86},
	abstract = {In many applications, including online video monitoring system, it is often necessary to find out the most similar sequence, from a sequence set in database, for the high dimensional data stream. Due to its high computation complexity and the restrictions of query processing on data stream, continuously retrieving the similar content on high dimensional data stream is very challenging. The evaluating algorithm is expected to be very fast to match data stream arrival rate. To address this problem, we propose a method called CVNN in this paper. In our method, the sequences in database are represented into the compact summaries, namely CVs, which can be stored in memory. An online algorithm is conducted to transform data stream into a small number of CVs continuously, at the same time the nearest neighbor query is processed periodically based on the similarity of CVs. Experimental results show that our method is fairly effective.},
	keywords = {Data stream, High dimensional space, Query processing, Similarity Search, Video indexing},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33748267270\&partnerID=40\&md5=da6323dddf161caf008918184cbec115},
	author = {Wang, W. and Li, J. and Ai, C. and Shi, S.}
}
