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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  • Conference paper
    Heinis T, Ailamaki A, 2015,

    Reconsolidating Data Structures

    , Pages: 665-670
  • Conference paper
    Karpathiotakis M, Alagiannis I, Heinis T, Branco M, Ailamaki Aet al., 2015,

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

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

    Optimising Correlation Matrix Calculations on Gene Expression Data

    , BMC Bioinformatics, Vol: 15, ISSN: 1471-2105
  • 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, Vol: 2014, ISSN: 1126-6708

    We present statistically convergent profile likelihood maps obtained via globalfits of a phenomenological Minimal Supersymmetric Standard Model with 15 free parameters(the MSSM-15), based on over 250M points. We derive constraints on the modelparameters from direct detection limits on dark matter, the Planck relic density measurementand data from accelerator searches. We provide a detailed analysis of the richphenomenology of this model, and determine the SUSY mass spectrum and dark matterproperties that are preferred by current experimental constraints. We evaluate the impactof the measurement of the anomalous magnetic moment of the muon (g −2) on our results,and provide an analysis of scenarios in which the lightest neutralino is a subdominant componentof the dark matter. The MSSM-15 parameters are relatively weakly constrained bycurrent data sets, with the exception of the parameters related to dark matter phenomenology(M1, M2, µ), which are restricted to the sub-TeV regime, mainly due to the relic densityconstraint. The mass of the lightest neutralino is found to be < 1.5 TeV at 99% C.L., butcan extend up to 3 TeV when excluding the g − 2 constraint from the analysis. Low-massbino-like neutralinos are strongly favoured, with spin-independent scattering cross-sectionsextending to very small values, ∼ 10−20 pb. ATLAS SUSY null searches strongly impacton this mass range, and thus rule out a region of parameter space that is outside the reachof any current or future direct detection experiment. The best-fit point obtained after inclusionof all data corresponds to a squark mass of 2.3 TeV, a gluino mass of 2.1 TeV and a130 GeV neutralino with a spin-independent cross-section of 2.4×10−10 pb, which is withinthe reach of future multi-ton scale direct detection experiments and of the upcoming LHCrun at increased centre-of-mass energy.

  • 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: 1550-7998
  • Book chapter
    Guo Y, He S, Guo L, 2014,

    Elastic Application Container System: Elastic Web Applications Provisioning

    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.

  • Conference paper
    Guo Y, He S, Guo L, 2014,

    Cloud Resource Monitoring for Intrusion Detection

    We present a novel security monitoring framework for intrusion detection in IaaS cloud infrastructures. The framework uses statistical anomaly detection techniques over data monitored both inside and outside each Virtual Machine instance. We present the architecture of our monitoring framework and describe the implementation of the real-time monitors and detectors. We also describe how the framework is used in three different attack scenarios. For each of the three attack scenarios, we describe how the attack itself works and how it could be detected. We describe what data is monitored in our framework and how the detection is conducted using anomaly detection methods. We also present evaluation of the detection using synthetic and real data sets. Our experimental evaluation across all three scenarios shows that our tools perform well in practical situations and provide a promising direction for future research.

  • 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
    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
    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
    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
  • Journal article
    Martin J, Ringeval C, Trotta R, Vennin Vet al., 2014,

    The best inflationary models after Planck

    , Journal of Cosmology and Astroparticle Physics, Vol: 2014, ISSN: 1475-7516

    We compute the Bayesian evidence and complexity of 193 slow-roll single-field models of inflation using the Planck 2013 Cosmic Microwave Background data, with the aim of establishing which models are favoured from a Bayesian perspective. Our calculations employ a new numerical pipeline interfacing an inflationary effective likelihood with the slow-roll library ASPIC and the nested sampling algorithm MultiNest. The models considered represent a complete and systematic scan of the entire landscape of inflationary scenarios proposed so far. Our analysis singles out the most probable models (from an Occam's razor point of view) that are compatible with Planck data, while ruling out with very strong evidence 34% of the models considered. We identify 26% of the models that are favoured by the Bayesian evidence, corresponding to 15 different potential shapes. If the Bayesian complexity is included in the analysis, only 9% of the models are preferred, corresponding to only 9 different potential shapes. These shapes are all of the plateau type.

  • Journal article
    Han R, Ghanem MM, Guo L, Guo Y, Osmond M, Han R, Ghanem M, Guo L, Guo Y, Osmond Met al., 2014,

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

    , Future Generation Computer Systems, Vol: n/a, ISSN: 0167-739X

    Elasticity (on-demand scaling) of applications is one of the most important features of cloud computing. This elasticity is the ability to adaptively scale resources up and down in order to meet varying application demands. To date, most existing scaling techniques can maintain applications’ Quality of Service (QoS) but do not adequately address issues relating to minimizing the costs of using the service. In this paper, we propose an elastic scaling approach that makes use of cost-aware criteria to detect and analyse the bottlenecks within multi-tier cloud-based applications. We present an adaptive scaling algorithm that reduces the costs incurred by users of cloud infrastructure services allowing them to scale their applications only at bottleneck tiers and present the design of an intelligent platform that automates the scaling process. Our approach is generic for a wide class of multi-tier applications, and we demonstrate its effectiveness against other approaches by studying the behaviour of an example e-commerce application using a standard workload benchmark. â?� Elasticity enables adaptively scaling up and down cloud applications to meet run-time requirements. â?� We propose an approach for achieving cost-effective elasticity. â?� Cost-aware criteria are introduced. â?� Changing workloads are adapted by scaling up or down only the bottleneck components in multi-tier applications.

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