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
    Gómez-Romero J, Molina-Solana M, Oehmichen A, Guo Yet al., 2018,

    Visualizing large knowledge graphs: A performance analysis

    , Future Generation Computer Systems, Vol: 89, Pages: 224-238, ISSN: 0167-739X

    © 2018 Elsevier B.V. Knowledge graphs are an increasingly important source of data and context information in Data Science. A first step in data analysis is data exploration, in which visualization plays a key role. Currently, Semantic Web technologies are prevalent for modeling and querying knowledge graphs; however, most visualization approaches in this area tend to be overly simplified and targeted to small-sized representations. In this work, we describe and evaluate the performance of a Big Data architecture applied to large-scale knowledge graph visualization. To do so, we have implemented a graph processing pipeline in the Apache Spark framework and carried out several experiments with real-world and synthetic graphs. We show that distributed implementations of the graph building, metric calculation and layout stages can efficiently manage very large graphs, even without applying partitioning or incremental processing strategies.

  • JOURNAL ARTICLE
    Gómez-Romero J, Molina-Solana M, Ros M, Ruiz MD, Martin-Bautista MJet al., 2018,

    Comfort as a service: A new paradigm for residential environmental quality control

    , Sustainability (Switzerland), Vol: 10

    © 2018 by the author. This paper introduces the concept of Comfort as a Service (CaaS), a new energy supply paradigm for providing comfort to residential customers. CaaS takes into account the available passive and active elements, the external factors that affect energy consumption and associated costs, and occupants' behaviors to generate optimal control strategies for the domestic equipment automatically. As a consequence, it releases building occupants from operating the equipment, which gives rise to a disruption of the traditional model of paying per consumed energy in favor of a model of paying per provided comfort. In the paper, we envision a realization of CaaS based on several technologies such as ambient intelligence, big data, cloud computing and predictive computing. We discuss the opportunities and the barriers of CaaS-centered business and exemplify the potential of CaaS deployments by quantifying the expected energy savings achieved after limiting occupants' control over the air conditioning system in a test scenario.

  • JOURNAL ARTICLE
    Song J, Fan S, Lin W, Mottet L, Woodward H, Wykes MD, Arcucci R, Xiao D, Debay J-E, ApSimon H, Aristodemou E, Birch D, Carpentieri M, Fang F, Herzog M, Hunt GR, Jones RL, Pain C, Pavlidis D, Robins AG, Short CA, Linden PFet al., 2018,

    Natural ventilation in cities: the implications of fluid mechanics

    , BUILDING RESEARCH AND INFORMATION, Vol: 46, Pages: 809-828, ISSN: 0961-3218
  • JOURNAL ARTICLE
    Jahani E, Sundsøy P, Bjelland J, Bengtsson L, Pentland AS, de Montjoye Y-Aet al., 2017,

    Improving official statistics in emerging markets using machine learning and mobile phone data

    , EPJ Data Science, Vol: 6
  • JOURNAL ARTICLE
    Molina-Solana M, Birch D, Guo Y-K, 2017,

    Improving data exploration in graphs with fuzzy logic and large-scale visualisation

    , APPLIED SOFT COMPUTING, Vol: 53, Pages: 227-235, ISSN: 1568-4946
  • JOURNAL ARTICLE
    Molina-Solana M, Ros M, Dolores Ruiz M, Gomez-Romero J, Martin-Bautista MJet al., 2017,

    Data science for building energy management: A review

    , RENEWABLE & SUSTAINABLE ENERGY REVIEWS, Vol: 70, Pages: 598-609, ISSN: 1364-0321
  • JOURNAL ARTICLE
    Steele JE, Sundsøy PR, Pezzulo C, Alegana VA, Bird TJ, Blumenstock J, Bjelland J, Engø-Monsen K, de Montjoye Y-A, Iqbal AM, Hadiuzzaman KN, Lu X, Wetter E, Tatem AJ, Bengtsson Let al., 2017,

    Mapping poverty using mobile phone and satellite data

    , Journal of The Royal Society Interface, Vol: 14, Pages: 20160690-20160690, ISSN: 1742-5689
  • 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
    Creswell A, Bharath AA, 2016,

    Task Specific Adversarial Cost Function.

    , CoRR, Vol: abs/1609.08661
  • 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
  • JOURNAL ARTICLE
    McGinn D, Birch D, Akroyd D, Molina-Solana M, Guo Y, Knottenbelt WJet al., 2016,

    Visualizing Dynamic Bitcoin Transaction Patterns

    , BIG DATA, Vol: 4, Pages: 109-119, ISSN: 2167-6461
  • JOURNAL ARTICLE
    Taquet M, Quoidbach J, de Montjoye Y-A, Desseilles M, Gross JJet al., 2016,

    Hedonism and the choice of everyday activities

    , Proceedings of the National Academy of Sciences, Vol: 113, Pages: 9769-9773, ISSN: 0027-8424
  • JOURNAL ARTICLE
    de Montjoye YKJV, Rocher L, Pentland AS, 2016,

    bandicoot: an open-source Python toolbox to analyze mobile phone metadata

    , Journal of Machine Learning Research, Vol: 17, ISSN: 1532-4435

    bandicoot is an open-source Python toolbox to extract more than 1442 features from standard mobile phone metadata. bandicoot makes it easy for machine learning researchers and practitioners to load mobile phone data, to analyze and visualize them, and to extract robust features which can be used for various classification and clustering tasks. Emphasis is put on ease of use, consistency, and documentation. bandicoot has no dependencies and is distributed under MIT license

  • CONFERENCE PAPER
    Heinis T, Ailamaki A, 2015,

    Reconsolidating Data Structures.

    , Publisher: OpenProceedings.org, Pages: 665-670

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