Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

Search or filter publications

Filter by type:

Filter by publication type

Filter by year:

to

Results

  • Showing results for:
  • Reset all filters

Search results

  • CONFERENCE PAPER
    Arulkumaran K, Dilokthanakul N, Shanahan M, Bharath AAet al., 2016,

    Classifying Options for Deep Reinforcement Learning.

  • JOURNAL ARTICLE
    Bertone G, Calore F, Caron S, Ruiz R, Kim JS, Trotta R, Weniger Cet al., 2016,

    Global analysis of the pMSSM in light of the Fermi GeV excess: prospects for the LHC Run-II and astroparticle experiments

    , JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS, ISSN: 1475-7516
  • JOURNAL ARTICLE
    Ma Z-B, Yang Y, Liu Y-X, Bharath AAet al., 2016,

    Recurrently Decomposable 2-D Convolvers for FPGA-Based Digital Image Processing

    , IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, Vol: 63, Pages: 979-983, ISSN: 1549-7747
  • CONFERENCE PAPER
    Heinis T, Ailamaki A, 2015,

    Reconsolidating Data Structures.

    , Publisher: OpenProceedings.org, Pages: 665-670
  • JOURNAL ARTICLE
    Heinis T, Ham DA, 2015,

    On-the-Fly Data Synopses: Efficient Data Exploration in the Simulation Sciences

    , SIGMOD RECORD, Vol: 44, Pages: 23-28, ISSN: 0163-5808
  • CONFERENCE PAPER
    Karpathiotakis M, Alagiannis I, Heinis T, Branco M, Ailamaki Aet al., 2015,

    Just-In-Time Data Virtualization: Lightweight Data Management with ViDa.

    , Publisher: www.cidrdb.org
  • CONFERENCE PAPER
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Associating Locations Between Indoor Journeys from Wearable Cameras

    , 13th European Conference on Computer Vision (ECCV), Publisher: SPRINGER-VERLAG BERLIN, Pages: 29-44, ISSN: 0302-9743
  • JOURNAL ARTICLE
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Appearance-based indoor localization: A comparison of patch descriptor performance

    , PATTERN RECOGNITION LETTERS, Vol: 66, Pages: 109-117, ISSN: 0167-8655
  • CONFERENCE PAPER
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Indoor Localisation with Regression Networks and Place Cell Models.

    , Publisher: BMVA Press, Pages: 147.1-147.1
  • CONFERENCE PAPER
    Tauheed F, Heinis T, Ailamaki A, 2015,

    THERMAL-JOIN: A Scalable Spatial Join for Dynamic Workloads.

    , Publisher: ACM, Pages: 939-950
  • JOURNAL ARTICLE
    Guo Y, He S, Guo L, 2014,

    Enhancing Cloud Resource Utilisation using Statistical Analysis

    Resource provisioning based on virtual machine (VM) has been widely accepted and adopted in cloud computing environments. A key problem resulting from using static scheduling approaches for allocating VMs on different physical machines (PMs) is that resources tend to be not fully utilised. Although some existing cloud reconfiguration algorithms have been developed to address the problem, they normally result in high migration costs and low resource utilisation due to ignoring the multi-dimensional characteristics of VMs and PMs. In this paper we present and evaluate a new algorithm for improving resource utilisation for cloud providers. By using a multivariate probabilistic model, our algorithm selects suitable PMs for VM re-allocation which are then used to generate a reconfiguration plan. We also describe two heuristics metrics which can be used in the algorithm to capture the multi-dimensional characteristics of VMs and PMs. By combining these two heuristics metrics in our experiments, we observed that our approach improves the resource utilisation level by around 8% for cloud providers, such as IC Cloud, which accept user-defined VM configurations and 14% for providers, such as Amazon EC2, which only provide limited types of VM configurations.

  • JOURNAL ARTICLE
    Han R, Ghanem MM, Guo L, Guo Y, Osmond Met al., 2014,

    Enabling cost-aware and adaptive elasticity of multi-tier cloud applications

    , FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, Vol: 32, Pages: 82-98, ISSN: 0167-739X
  • BOOK CHAPTER
    He S, Guo L, Guo Y, 2014,

    Elastic application container system: Elastic web applications provisioning

    , Handbook of Research on Demand-Driven Web Services: Theory, Technologies, and Applications, Pages: 376-398, ISBN: 9781466658851

    © 2014 by IGI Global. All rights reserved. Cloud applications have been gaining popularity in recent years for their flexibility in resource provisioning according to Web application demands. The Elastic Application Container (EAC) system is a technology that delivers a lightweight virtual resource unit for better resource efficiency and more scalable Web applications in the Cloud. It allows multiple application providers to concurrently run their Web applications on this technology without worrying the demand change of their Web applications. This is because the EAC system constantly monitors the resource usage of all hosting Web applications and automatically reacts to the resource usage change of Web applications (i.e. it automatically handles resource provisioning of the Web applications, such as scaling of the Web applications according to the demand). In the chapter, the authors firstly describe the architecture, its components of the EAC system, in order to give a brief overview of technologies involved in the system. They then present and explain resource-provisioning algorithms and techniques used in the EAC system for demand-driven Web applications. The resource-provisioning algorithms are presented, discussed, and evaluated so as to give readers a clear picture of resource-provisioning algorithms in the EAC system. Finally, the authors compare this EAC system technology with other Cloud technologies in terms of flexibility and resource efficiency.

  • JOURNAL ARTICLE
    Heinis T, 2014,

    Data analysis: approximation aids handling of big data.

    , Nature, Vol: 515
  • JOURNAL ARTICLE
    Martin J, Ringeval C, Trotta R, Vennin Vet al., 2014,

    The best inflationary models after Planck

    , JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS, ISSN: 1475-7516

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-t4-html.jsp Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=607&limit=15&respub-action=search.html Current Millis: 1513156858825 Current Time: Wed Dec 13 09:20:58 GMT 2017