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  • 1.
    Fong, Simon
    et al.
    Department of Computer and Information Science, University of Macau.
    Wong, Raymond K.
    School of Computer Science and Engineering, University of New South Wales, Sydney.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Accelerated PSO Swarm Search Feature Selection for Data Stream Mining Big Data2016In: IEEE Transactions on Services Computing, ISSN 1939-1374, E-ISSN 1939-1374, Vol. 9, no 1, p. 33-45Article in journal (Refereed)
    Abstract [en]

    Big Data though it is a hype up-springing many technical challenges that confront both academic research communities and commercial IT deployment, the root sources of Big Data are founded on data streams and the curse of dimensionality. It is generally known that data which are sourced from data streams accumulate continuously making traditional batch-based model induction algorithms infeasible for real-time data mining. Feature selection has been popularly used to lighten the processing load in inducing a data mining model. However, when it comes to mining over high dimensional data the search space from which an optimal feature subset is derived grows exponentially in size, leading to an intractable demand in computation. In order to tackle this problem which is mainly based on the high-dimensionality and streaming format of data feeds in Big Data, a novel lightweight feature selection is proposed. The feature selection is designed particularly for mining streaming data on the fly, by using accelerated particle swarm optimization (APSO) type of swarm search that achieves enhanced analytical accuracy within reasonable processing time. In this paper, a collection of Big Data with exceptionally large degree of dimensionality are put under test of our new feature selection algorithm for performance evaluation.

  • 2.
    Fu, Zhangjie
    et al.
    Department of Computer and Software, Nanjing University of Information Science and Technology.
    Huang, Fengxiao
    Department of Computer and Software, Nanjing University of Information Science and Technology.
    Sun, Xingming
    Department of Computer and Software, Nanjing University of Information Science and Technology.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Enabling Semantic Search based on ConceptualGraphs over Encrypted Outsourced Data2019In: IEEE Transactions on Services Computing, ISSN 1939-1374, E-ISSN 1939-1374Article in journal (Refereed)
    Abstract [en]

    Currently, searchable encryption is a hot topic in the field of cloud computing. The existing achievements are mainly focused on keyword-based search schemes, and almost all of them depend on predefined keywords extracted in the phases of index construction and query. However, keyword-based search schemes ignore the semantic representation information of users’ retrieval and cannot completely match users’ search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, for the first time, we define and solve the problems of semantic search based on conceptual graphs(CGs) over encrypted outsourced data in clouding computing (SSCG).We firstly employ the efficient measure of ”sentence scoring” in text summarization and Tregex to extract the most important and simplified topic sentences from documents. We then convert these simplified sentences into CGs. To perform quantitative calculation of CGs, we design a new method that can map CGs to vectors. Next, we rank the returned results based on ”text summarization score”. Furthermore, we propose a basic idea for SSCG and give a significantly improved scheme to satisfy the security guarantee of searchable symmetric encryption (SSE). Finally, we choose a real-world dataset – ie., the CNN dataset to test our scheme. The results obtained from the experiment show the effectiveness of our proposed scheme.

  • 3.
    Wu, Weiwei
    et al.
    School of Computer Science, Southeast University, Nanjing, Jiangsu, China.
    Wang, Wanyuan
    School of Computer Science and Engeering, Southeast University, Nanjing, Jiangsu China.
    Fang, Xiaolin
    Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang China.
    Junzhou, Luo
    School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu China.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Electricity Price-aware Consolidation Algorithms for Time-sensitive VM Services in Cloud Systems2019In: IEEE Transactions on Services Computing, ISSN 1939-1374, E-ISSN 1939-1374Article in journal (Refereed)
    Abstract [en]

    Despite the salient feature of cloud computing, the cloud provider still suffers from electricity bill, which mainly comes from 1) the power consumption of running physical machines and 2) the dynamically varying electricity price offered by smart grids. In the literature, there exist viable solutions adaptive to electricity price variation to reduce the electricity bill. However, they are not applicable to serving time-sensitive VM requests. In serving time-sensitive VM requests, it is potential for the cloud provider to apply proper consolidation strategies to further reduce the electricity bill. In this work, to address this challenge, we develop electricity-price-aware consolidation algorithms for both the offline and online scenarios. For the offline scenario, we first develop an consolidation algorithm with constant approximation, which always approaches the optimal solution within a constant factor of 5. For the online scenario, we propose an $O(\log(\frac{L_{max}}{L_{min}}))$ -competitive algorithm that is able to approach the optimal offline solution within a logarithmic factor, where $\frac{L_{max}}{L_{min}}$ is the ratio of the longest length of the processing time requirement of VMs to the shortest one. Our trace-driven simulation results further demonstrate that the average performance of the proposed algorithms produce near-optimal electricity bill.

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