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.  

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  • 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
  • JOURNAL ARTICLE
    Martin J, Ringeval C, Trotta R, Vennin Vet al., 2014,

    Compatibility of Planck and BICEP2 results in light of inflation

    , PHYSICAL REVIEW D, Vol: 90, ISSN: 2470-0010
  • JOURNAL ARTICLE
    Nie L, Yang X, Adcock I, Xu Z, Guo Yet al., 2014,

    Inferring Cell-Scale Signalling Networks via Compressive Sensing

    , PLOS ONE, Vol: 9, ISSN: 1932-6203
  • JOURNAL ARTICLE
    Strege C, Bertone G, Besjes GJ, Caron S, Ruiz de Austri R, Strubig A, Trotta Ret al., 2014,

    Profile likelihood maps of a 15-dimensional MSSM

    , JOURNAL OF HIGH ENERGY PHYSICS, ISSN: 1029-8479
  • JOURNAL ARTICLE
    Wang M, Zhang W, Ding W, Dai D, Zhang H, Xie H, Chen L, Guo Y, Xie Jet al., 2014,

    Parallel Clustering Algorithm for Large-Scale Biological Data Sets

    , PLOS ONE, Vol: 9, ISSN: 1932-6203
  • CONFERENCE PAPER
    Wang S, Pandis I, Emam I, Johnson D, Guitton F, Oehmichen A, Guo Yet al., 2014,

    DSIMBench: A benchmark for microarray data using R

    , Pages: 47-56, ISSN: 0302-9743

    © Springer International Publishing Switzerland 2014. Parallel computing in R has been widely used to analyse microarray data. We have seen various applications using various data distribution and calculation approaches. Newer data storage systems, such as MySQL Cluster and HBase, have been proposed for R data storage; while the parallel computation frameworks, including MPI and MapReduce, have been applied to R computation. Thus, it is difficult to understand the whole analysis workflows for which the tool kits are suited for a specific environment. In this paper we propose DSIMBench, a benchmark containing two classic microarray analysis functions with eight different parallel R workflows, and evaluate the benchmark in the IC Cloud testbed platform.

  • JOURNAL ARTICLE
    Wang S, Pandis I, Johnson D, Emam I, Guitton F, Oehmichen A, Guo Yet al., 2014,

    Optimising parallel R correlation matrix calculations on gene expression data using MapReduce

    , BMC BIOINFORMATICS, Vol: 15, ISSN: 1471-2105
  • JOURNAL ARTICLE
    Wang S, Pandis I, Wu C, He S, Johnson D, Emam I, Guitton F, Guo Yet al., 2014,

    High dimensional biological data retrieval optimization with NoSQL technology

    , BMC GENOMICS, Vol: 15, ISSN: 1471-2164
  • JOURNAL ARTICLE
    Yang X, Guo Y, Guo L, 2014,

    An iterative parameter estimation method for biological systems and its parallel implementation

    , CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, Vol: 26, Pages: 1249-1267, ISSN: 1532-0626
  • JOURNAL ARTICLE
    Birch D, Kelly PHJ, Field AJ, Simondetti Aet al., 2013,

    Computationally unifying urban masterplanning

    , Proceedings of the ACM International Conference on Computing Frontiers, CF 2013

    Architectural design, particularly in large scale master planning projects, has yet to fully undergo the computational revolution experienced by other design-led industries such as automotive and aerospace. These industries use computational frameworks to undertake automated design analysis and design space exploration. However, within the Architectural, Engineering and Construction (AEC) industries wend no such computational platforms. This precludes the rapid analysis needed for quantitative design iteration which is required for sustainable design. This is a current computing frontier. This paper considers the computational solutions to the challenges preventing such advances to improve architectural design performance for a more sustainable future. We present a practical discussion of the computational challenges and opportunities in this industry and present a computational framework "HierSynth" with a data model designed to the needs of this industry. We report the results and lessons learned from applying this framework to a major commercial urban master planning project. This framework was used to automate and augment existing practice and was used to undertake previously infeasible, designer lead, design space exploration. During the casestudy an order of magnitude more analysis cycles were undertaken than literature suggests is normal; each occurring in hours not days.

  • CONFERENCE PAPER
    Birch D, Liang H, Ko J, Kelly P, Field A, Mullineux G, Simondetti Aet al., 2013,

    Multidisciplinary Engineering Models: Methodology and Case Study in Spreadsheet Analytics

    , European Spreadsheet Risks Interest Group 14th Annual Conference (EuSpRIG 2013), Publisher: EuSpRIG, Pages: 1-12
  • JOURNAL ARTICLE
    Georgatos F, Ballereau S, Pellet J, Ghanem M, Price N, Hood L, Guo YK, Boutigny D, Auffray C, Balling R, Schneider Ret al., 2013,

    Computational infrastructures for data and knowledge management in systems biology

    , Vol: 1, Pages: 377-397

    © Springer Science+Business Media Dordrecht 2013. The volume, complexity and heterogeneity of data originating from high throughput functional genomics technologies have created challenges and opportunities for Information technology (IT) departments. These increased demands have also led to increasing costs for IT infrastructure such as necessary computing power and storage devices, as well as further costs for manpower effort, required for maintenance. This chapter describes some of the challenges for computational analysis infrastructure, including bottlenecks and most pressing needs that have to be addressed to effectively support the development of systems biology and its application in medicine.

  • JOURNAL ARTICLE
    Gibeon D, Batuwita K, Osmond M, Heaney LG, Brightling CE, Niven R, Mansur A, Chaudhuri R, Bucknall CE, Rowe A, Guo Y, Bhavsar PK, Chung KF, Menzies-Gow Aet al., 2013,

    Obesity-Associated Severe Asthma Represents a Distinct Clinical Phenotype Analysis of the British Thoracic Society Difficult Asthma Registry Patient Cohort According to BMI

    , CHEST, Vol: 143, Pages: 406-414, ISSN: 0012-3692

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