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Multi-User Revocable Keyword Search Scheme for

Journal of Physics: Conference Series 2018年第3期1087卷

作者： Liu Mei Tan Xiao Lei Dong Zhen Fu Cao Jia Chen Shen School of Computer Science and Software Engineering East China Normal University Shanghai Shanghai 200062 China Secure Data Sharing in Cloud

With the rapid development of cloud computing, more and more users store their data in the cloud. Considering the privacy problem of the data sharing in the cloud, searchable encryption makes it possible to efficiently share and retrieve data in multi-user settings. The existing keyword search scheme of sharing the private key with authorized users to generate trapdoors may increase the risk of key exposure, and the scheme of encrypting the data with the secret key of each authorized user and generating a ciphertext for each user may not be suitable for data sharing between a large number of users. In this paper, we combine the secure IBBE system with the keywords search and propose the scheme to achieve safe and efficient multi-user data sharing in the cloud. Authorized users can perform keyword search directly on encrypted data without sharing the secret key, and for a shared file, the data owner only needs to encrypt one ciphertext for all authorized users. Besides, authorized users of shared files can be revoked. User authorization tables and IBBE ensure that revoked users cannot access files.

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Efficient Subgraph Query Algorithm in Large Graph

Efficient Subgraph Query Algorithm in Large Graph

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2017 2nd International Conference on Advances in Management engineering and Information Technology(AMEIT 2017)

作者： Yun HUANG Jia-ming HONG School of Data Science and Computer Sun Yat-sen University School of Software and Service Outsourcing Jishou University School of Medical Information Engineering Guangzhou University of Chinese Medicine

Subgraph query is an important problem in the research and application of large graph *** large graphs with symmetry relation substructures,the existing decomposition-join strategy always leads to low searching *** solve this problem,we proposed a new decomposition-Detection-join strategy,in which we detect symmetric relations of each sub part of the decomposition,and then determine the sequence of queries based on the detection *** experimental results show that the algorithm has much improvement in query efficiency.

关键词： Subgraph query Large graph Symmetric relation Core decomposition

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RJMIM: A new feature selection method based on joint mutual information

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Revista de la Facultad de Ingenieria 2016年第4期31卷 59-69页

作者： Peng, Liwen Liu, YongGuo Knowledge and Data Engineering Laboratory of Chinese Medicine School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

Feature selection based on information theory plays an important role in classification algorithm due to its computational efficiency and independent from classification method. It is widely used in many application areas like data mining, bioinformatics and machine learning. But drawbacks of these methods are the neglect of the feature interaction and overestimation of features significance due to the limitations of goal functions criterion. To address this problem, we proposed a new feature goal function RJMIM. The method employed joint mutual information and information interaction, which alleviates the shortcomings of overestimation of the feature significance as demonstrated both theoretically and experimentally. The experiments conducted to verify the performance of the proposed method, it compared with four well-known feature selection methods use three publically available datasets from UCI. The average classification accuracy and C4.5 classifier is used to assess the effectiveness of RJMIM method.

关键词： Classification (of information)

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21st International Conference on database Systems for Advanced Applications, DASFAA 2016

作者： Liu, Huiping Jin, Cheqing Zhou, Aoying School of Computer Science and Software Engineering Institute for Data Science and Engineering East China Normal University Shanghai China

ISBN: (纸本)9783319320489

With the increasing number of GPS-equipped vehicles, more and more trajectories are generated continuously, based on which some urban applications become feasible, such as route planning. In general, route planning aims at finding a path from source to destination to meet some specific requirements, i.e., the minimal travel time, fee or fuel consumption. Especially, some users may prefer popular route that has been travelled frequently. However, the existing work to find the popular route does not consider how to estimate the travelling cost. In this paper, we address this issue by devising a novel structure, called popular traverse graph, to summarize historical trajectories. Based on which an efficient route planning algorithm is proposed to search the popular route with minimal travel cost. The extensive experimental reports show that our method is both effective and efficient. © Springer International Publishing Switzerland 2016.

关键词： Travel time

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TSCluWin: Trajectory stream clustering over sliding window 21st

TSCluWin: Trajectory stream clustering over sliding window

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21st International Conference on database Systems for Advanced Applications, DASFAA 2016

作者： Mao, Jiali Song, Qiuge Jin, Cheqing Zhang, Zhigang Zhou, Aoying Institute for Data Science and Engineering School of Computer Science and Software Engineering East China Normal University Shanghai China

ISBN: (纸本)9783319320489

The popularity of GPS-embedded devices facilitates online monitoring of moving objects and analyzing movement behaviors in a real-time manner. Trajectory clustering acts as one of the most important trajectory analysis tasks, and the researches in this area have been studied extensively in the recent decade. Due to the rapid arrival rate and evolving feature of stream data, little effort has been devoted to online clustering trajectory data streams. In this paper, we propose a framework that consists of two phases, including a micro-clustering phase where a number of micro-clusters represented by compact synopsis data structures are incrementally maintained, and a macro-clustering phase where a small number of macro-clusters are generated based on micro-clusters. Experimental results show that our proposal is both effective and efficient to handle streaming trajectories without compromising the quality. © Springer International Publishing Switzerland 2016.

关键词： Trajectories

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Real-time personalized taxi-sharing 21st

Real-time personalized taxi-sharing

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21st International Conference on database Systems for Advanced Applications, DASFAA 2016

作者： Duan, Xiaoyi Jin, Cheqing Wang, Xiaoling Zhou, Aoying Yue, Kun School of Computer Science and Software Engineering Institute for Data Science and Engineering East China Normal University Shanghai China School of Information Science and Engineering Yunnan University Kunming China

ISBN: (纸本)9783319320489

Taxi-sharing is an efficient way to improve the utility of taxis by allowing multiple passengers to share a taxi. It also helps to relieve the traffic jams and air pollution. It is common that different users may have different attitudes towards the taxi-sharing scheduling plan, such as the fee to be paid and the additional time to the destination. However, this property has not been paid enough attention to in the traditional taxi-sharing systems-the traditional focus is how to decrease the travel *** study the problem of personalized taxi-sharing in this paper, with the consideration of each passenger’s preference in payment, travel time and waiting time. We first define the satisfaction degree of each party involved in the scheduling plan, based on which two goals are defined to evaluate the overall plan, including MaxMin and MaxSum. Subsequently, we devise a two-phase framework to deal with this problem. The statistical information gathered during the offline phase will be used to hasten query processing during the online phase. Experimental reports upon the real dataset illustrate the effectiveness and efficiency of the proposed method. © Springer International Publishing Switzerland 2016.

关键词： Scheduling

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SmartVM: A Multi-Layer Microservice-Based Platform for Deploying SaaS

SmartVM: A Multi-Layer Microservice-Based Platform for Deplo...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xi Zheng Jiaojiao Jiang Yuqun Zhang Yao Deng Min Fu Tianlei Zheng Xiao Liu Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia School of Information Technology Deakin University Melbourne Australia Data61 CSIRO Sydney Australia School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (纸本)9781538637913

With the advent of software-as-a-Service (SaaS), SaaS developers are facing many challenges associated with the multi-tenancy and the dramatically increased number of users. In order to achieve resource-optimized, on-demand dynamic scaling across multiple tenants, and reduce costs, in this paper, a new platform, named SmartVM, is created to enable SaaS developer to create, customize, and deploy SaaS solutions in a multi-tier microservice-based manner. We develop an e-commerce SaaS prototype to evaluate effectiveness and efficiency of SmartVM. The results show that the SmartVM deployments outperforms the conventional monolithic and microservice deployments in smart monitoring, cost reduction, and resource optimization.

关键词： Containers Monitoring Business software as a service Australia Scalability Measurement

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Accuracy estimation of link-based similarity measures and its application

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Frontiers of Computer science 2016年第1期10卷 113-123页

作者： Yinglong ZHANG Cuiping LI Chengwang XIE Hong CHEN School of Software East China Jiaotong University Nanchang 330045 China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education and Department of Computer Science Renmin University of China Beijing 100872 China Intelligent Optimization and Information Processing Laboratory East China Jiaotong University Nanchang 330013 China

Link-based similarity measures play a significant role in many graph based applications. Consequently, mea- suring node similarity in a graph is a fundamental problem of graph data mining. Personalized PageRank （PPR） and Sim- Rank （SR） have emerged as the most popular and influen- tial link-based similarity measures. Recently, a novel link- based similarity measure, penetrating rank （P-Rank）, which enriches SR, was proposed. In practice, PPR, SR and P-Rank scores are calculated by iterative methods. As the number of iterations increases so does the overhead of the calcula- tion. The ideal solution is that computing similarity within the minimum number of iterations is sufficient to guaran- tee a desired accuracy. However, the existing upper bounds are too coarse to be useful in general. Therefore, we focus on designing an accurate and tight upper bounds for PPR, SR, and P-Rank in the paper. Our upper bounds are designed based on the following intuition： the smaller the difference between the two consecutive iteration steps is, the smaller the difference between the theoretical and iterative similar- ity scores becomes. Furthermore, we demonstrate the effec- tiveness of our upper bounds in the scenario of top-k similar nodes queries, where our upper bounds helps accelerate the speed of the query. We also run a comprehensive set of exper- iments on real world data sets to verify the effectiveness and efficiency of our upper bounds.

关键词： personalized PageRank SimRank P-Rank up-per bound

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ListMerge: Accelerating top-k aggregation queries over large number of lists 21st

ListMerge: Accelerating top-k aggregation queries over large...

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21st International Conference on database Systems for Advanced Applications, DASFAA 2016

作者： Zhang, Shile Sun, Chao He, Zhenying School of Software Engineering Fudan University Shanghai China School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China

ISBN: (纸本)9783319320489

Sorted list is widely used to feature indexing in a variety of applications, such as multimedia database and information retrieval. Answering top-k aggregation queries on a set of lists plays an increasingly important role in these domains. Unfortunately the existing solutions, such as threshold-style (TA-style) algorithms, do not guarantee superior performance on a large number of lists. In this paper, we introduce a merge-based strategy, called ListMerge, to accelerating TA-style algorithms. ListMerge exploits a critical observation to TA-style algorithms: if aggregation functions are monotone and distributive, it is much more efficient that merging several lists together, then applying a TA-style algorithm. This observation also inspires the development of our cost model, which can evaluate the best number of merged lists. Experimental results show that ListMerge could outperform the baseline algorithms up to 4-20 times in synthetic datasets generated by various distributions. © Springer International Publishing Switzerland 2016.

关键词： Computers

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Pattern Learning Based Parallel Ant Colony Optimization

Pattern Learning Based Parallel Ant Colony Optimization

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xiaotian Jin Wenbo Zheng Shaocong Mo Yili Qu Xin Jin Jiangwei Zhou Pengfei Duan Tao Zheng School of Computer Science and Technology Wuhan University of Technology Wuhan China School of Software Engineering Xi'an Jiaotong University Xi'an China School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Management Huazhong University of Science and Technology Wuhan China School Of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Wuhan University of Technology Wuhan Hubei CN School of Computer Science and Technology Wuhan University of of Technology Wuhan China

ISBN: (纸本)9781538637913

Ant colony optimization (ACO) can be used to solve complex optimization problems in engineering, economic management and military strategy. Most of these are NP hard problems, which are difficult to solve with traditional methods. An improved parallel ACO algorithm based on pattern learning is proposed in this paper. It extracts parameters automatically to reduce solution space and enhance calculation efficiency. Various parameters in the algorithm are analyzed, and a refining strategy is formed according to ACO's characteristics. The parallel ACO algorithm is carried out under the MIC/CPU architecture, and it can significantly enhance performance.

关键词： Microwave integrated circuits Urban areas Parallel processing Layout Computer architecture Computer science Support vector machines

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