@article {574,
	title = {The research of scale-free sensor network topology evolution based on cloud computing},
	journal = {Journal of Digital Information Management},
	volume = {13},
	year = {2015},
	note = {Cited By :1Export Date: 22 February 2018},
	month = {2015},
	pages = {266 - 275},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84942511265\&partnerID=40\&md5=9a5bc2cf6892f518d2988276246f2859},
	author = {He, Y. and Zhang, W. and Jiang, N. and Li, L. and Luo, X.}
}
@article {399,
	title = {Fault prediction of nonlinear system using time series novelty estimation},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {207 - 212},
	abstract = {Many complex nonlinear systems have the characteristics such as difficult modeling, dangerous testing, and high cost to experiment with fault. Aim at these points, a new kind of novelty estimation method based on an on-line Least Square Support Vector Regression (LS-SVR) algorithm is presented for fault prediction of nonlinear system in this paper. Firstly, a set of characteristic token values, which represent normal state of time series at the beginning, are selected as the training data to establish the on-line LS-SVR model. Then, the output values of the on-line model are analyzed to estimate novel pattern. Once the novel pattern is affirmed, the fault of system is forecasted immediately. The proposed method needs neither the math model of system nor the fault training data and primary knowledge, and it is believed with good real-time capability. The results of simulation on Continuous Stirred Tank Reactor (CSTR) show the efficiency.},
	keywords = {Fault prediction, Nonlinear system, Novelty estimation, Time series},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84880818113\&partnerID=40\&md5=5fe267d630eb652afa900d99483c0dcf},
	author = {Su, S. and Zhang, W.}
}
@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.}
}
