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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  • Journal article
    Heinis T, 2014,

    Data analysis: Approximation aids handling of big data

    , Nature, Vol: 515, Pages: 198-198
  • 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

    , the 40th International Conference on Very Large Databases (VLDB 2014)
  • 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, ISSN: 1471-2164
  • Conference paper
    Lee C-H, Birch D, Wu C, Silva D, Tsinalis O, Li Y, Yan S, Ghanem M, Guo Yet al., 2013,

    Building a Generic Platform for Big Sensor Data Applications

    , 1st IEEE International Conference on Big Data (BigData2013)

    The drive toward smart cities alongside the rising adoption of personal sensors is leading to a torrent of sensor data. While systems exist for storing and managing sensor data, the real value of such data is the insight which can be generated from it. However there is currently no platform which enables sensor data to be taken from collection, through use in models to produce useful data products. The architecture of such a platform is a current research question in the field of Big Data and Smart Cities. In this paper we explore five key challenges in this field and provide a response through a sensor data platform “Concinnity” which can take sensor data from collection to final product via a data repository and workflow system. This will enable rapid development of applications built on sensor data using data fusion and the integration and composition of models to form novel workflows. We summarize the key features in our approach, exploring how this enables value to be derived from sensor data efficiently.

  • Journal article
    Strege C, Bertone G, Feroz F, Fornasa M, Ruiz de Austri R, Trotta Ret al., 2013,

    Global fits of the cMSSM and NUHM including the LHC Higgs discovery and new XENON100 constraints


    We present global fits of the constrained Minimal Supersymmetric Standard Model (cMSSM) and the Non-Universal Higgs Model (NUHM), including the most recent CMS constraint on the Higgs boson mass, 5.8 fb−1 integrated luminosity null Supersymmetry searches by ATLAS, the new LHCb measurement of BR(bar Bs → μ+μ−) and the 7-year WMAP dark matter relic abundance determination. We include the latest dark matter constraints from the XENON100 experiment, marginalising over astrophysical and particle physics uncertainties. We present Bayesian posterior and profile likelihood maps of the highest resolution available today, obtained from up to 350M points. We find that the new constraint on the Higgs boson mass has a dramatic impact, ruling out large regions of previously favoured cMSSM and NUHM parameter space. In the cMSSM, light sparticles and predominantly gaugino-like dark matter with a mass of a few hundred GeV are favoured. The NUHM exhibits a strong preference for heavier sparticle masses and a Higgsino-like neutralino with a mass of 1 TeV. The future ton-scale XENON1T direct detection experiment will probe large portions of the currently favoured cMSSM and NUHM parameter space. The LHC operating at 14 TeV collision energy will explore the favoured regions in the cMSSM, while most of the regions favoured in the NUHM will remain inaccessible. Our best-fit points achieve a satisfactory quality-of-fit, with p-values ranging from 0.21 to 0.35, so that none of the two models studied can be presently excluded at any meaningful significance level.

  • Journal article
    Gow AM, Chung KF, Gibeon D, Guo Y, Batuwita K, Osmond M, Heaney L, Brightling C, Niven R, Mansur A, Chaudhuri R, Bucknall C, Rowe A, Bhavsar Pet al., 2013,

    Obesity associated severe asthma represents a distinct clinical phenotype – Analysis of the British Thoracic Society Difficult Asthma Registry patient cohort according to body mass index

    , CHEST Journal
  • Journal article
    Rustici G, Kolesnikov N, Brandizi M, Burdett T, Dylag M, Emam I, Farne A, Hastings E, Ison J, Keays M, Kurbatova N, Malone J, Mani R, Mupo A, Pereira RP, Pilicheva E, Rung J, Sharma A, Tang YA, Ternent T, Tikhonov A, Welter D, Williams E, Brazma A, Parkinson H, Sarkans Uet al., 2013,

    ArrayExpress update-trends in database growth and links to data analysis tools

    , NUCLEIC ACIDS RESEARCH, Vol: 41, Pages: D987-D990, ISSN: 0305-1048
  • Journal article
    Hsu C-H, Lin C-Y, Ouyang M, Guo YKet al., 2013,

    Biocloud: Cloud Computing for Biological, Genomics, and Drug Design

  • Conference paper
    Wu C, Guo Y, 2013,

    Enhanced User Data Privacy with Pay-by-Data Model

    , IEEE International Conference on Big Data (Big Data), Publisher: IEEE, ISSN: 2639-1589
  • Conference paper
    Han R, Nie L, Ghanem MM, Guo Yet al., 2013,

    Elastic Algorithms for Guaranteeing Quality Monotonicity in Big Data Mining

    , IEEE International Conference on Big Data (Big Data), Publisher: IEEE, ISSN: 2639-1589
  • Journal article
    Ma Y, Guo Y, Silva D, Tsinalis O, Wu Cet al., 2013,

    Elastic Information Management for Air Pollution Monitoring in Large-Scale M2M Sensor Networks

  • Conference paper
    Stougiannis A, Pavlovic M, Tauheed F, Heinis T, Ailamaki Aet al., 2013,

    Data-driven neuroscience: enabling breakthroughs via innovative data management

    , Pages: 953-956
  • Conference paper
    Nobari S, Tauheed F, Heinis T, Karras P, Bressan S, Ailamaki Aet al., 2013,

    TOUCH: in-memory spatial join by hierarchical data-oriented partitioning

    , Pages: 701-712
  • Conference paper
    Stougiannis A, Tauheed F, Heinis T, Ailamaki Aet al., 2013,

    Accelerating spatial range queries

    , Pages: 713-716
  • Conference paper
    Pavlovic M, Tauheed F, Heinis T, Ailamaki Aet al., 2013,

    GIPSY: joining spatial datasets with contrasting density

    , Pages: 11:1-11:12

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