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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    Deng X, Ghanem M, Guo Y, 2011,

    On Sample Selection Bias in Large-Scale Online Stream Mining: a Model Indexing Approach

    Dustdar S, Guo Y, Satzger B, Truong H-Let al., 2011,

    Principles of Elastic Processes

    , IEEE INTERNET COMPUTING, Vol: 15, Pages: 66-71, ISSN: 1089-7801
    Guo Y, Munro REJ, Kalaitzopoulos D, Grigoriadis Aet al., 2011,

    The ForeSee (4C) approach for integrative analysis in gene discovery.

    , Methods Mol Biol, Vol: 760, Pages: 53-71

    The development of high-throughput experimental techniques has made measurements for virtually all kinds of cellular components possible. Effective integration and analysis of this diverged information to produce insightful knowledge is central to biological study today. In this chapter, we present a methodology for building integrative analytical workbenches using the workflow technology. We focus on the field of gene discovery through the combined study of transcriptomics, genomics and epigenomics, although the methodology is generally applicable to any omics-data analysis for biomarker discovery. We illustrate the application of the methodology by presenting our study on the identification of aberrant genomic regions, genes and/or their regulatory elements with their implications for breast cancer research. We also discuss the challenges and opportunities brought by the latest development of the next generation sequencing technology.

    Han R, Guo L, Guo Y, He Set al., 2011,

    A deployment platform for dynamically scaling applications in the cloud

    , Pages: 506-510

    Simplifying the process of deploying applications is almost essential in the cloud. However, existing techniques can automate applications' initial deployment but have not yet adequately addressed their dynamic scaling problems. In this paper, a deployment platform to enable a novel dynamic scaling technique is introduced. This platform employs: (i) an extensible specification that describes all aspects of applications; (ii) a flexible analytical model that determines how many servers to be deployed for an application in each scaling. The platform's ability to handle dynamic workloads and to scale applications quickly enough to maintain the response time target is demonstrated. © 2011 IEEE.

    Han R, Yang X, Rowe A, Guo Yet al., 2011,

    Formal Modelling and Performance Analysis of Clinical Pathway

    , Pages: 1-5
    He S, Guo L, Guo Y, 2011,

    Real time elastic cloud management for limited resources

    , Pages: 622-629

    An Infrastructure-as-a-Service (IaaS) provider is usually assumed to own a large data centre with significant computational resources. For a small or medium sized Internet Data Centre (IDC), offering cloud computing service is a nature of business model but there are technical barriers which need to be resolved. One of the key issues is ineffective resource management given such an IDC usually has only limited resource. In this paper, we propose an efficient resource management solution specially designed for helping small and medium sized IaaS cloud providers to better utilise their hardware resources with minimum operational cost. Such an optimised resource utilisation is achieved by a well-designed underlying hardware infrastructure, an efficient resource scheduling algorithm and a set of migrating operations of VMs. © 2011 IEEE.

    He S, Guo L, Guo Y, 2011,

    Elastic application container

    , Pages: 216-217

    The computing resource level architecture allows end-users to directly control its underlying computer resources, such as VM (virtual machine) operations, scaling, networking, etc. However, setting up and maintaining a working environment is complex and time consuming for end-users and resource management is also a heavy-weight task for the providers. In contrast, the application resource level architecture automatically controls its underlying computer resources so that end-users can concentrate on their core business. In this paper, we propose a new architecture called Elastic Application Container (EAC) that enables the end-users to efficiently develop and deliver light-weight, elastic, multi-tenant, and portable applications. The EAC is an abstract representation which hides all its abstractions of the underlying VMs. We believe that our EAC architecture has the potential to become the foundation of future application resource level model in this research area. © 2011 IEEE.

    Li Q, Guo Y, 2011,

    Optimization of Resource Scheduling in Cloud Computing

    , 12th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Publisher: IEEE COMPUTER SOC, Pages: 315-320, ISSN: 2470-8801
    Liang H, Birch D, 2011,

    Extraction and Analysis Methodology for Supporting Complex Sustainable Design

    , 18th International Conference on Engineering Design (ICED11)
    Liang H, Birch D, 2011,

    Supporting Complex and Sustainable Ecocity Design Using Extraction and Analysis Methodology (EAM)

    , Ecocity World Summit, Montreal
    Ma Y, Guo Y, Tian X, Ghanem Met al., 2011,

    Distributed Clustering-Based Aggregation Algorithm for Spatial Correlated Sensor Networks

    , Sensors Journal, IEEE, Vol: 11, Pages: 641 -648-641 -648, ISSN: 1530-437X
    Parkinson H, Sarkans U, Kolesnikov N, Abeygunawardena N, Burdett T, Dylag M, Emam I, Farne A, Hastings E, Holloway E, Kurbatova N, Lukk M, Malone J, Mani R, Pilicheva E, Rustici G, Sharma A, Williams E, Adamusiak T, Brandizi M, Sklyar N, Brazma Aet al., 2011,

    ArrayExpress update-an archive of microarray and high-throughput sequencing-based functional genomics experiments

    , NUCLEIC ACIDS RESEARCH, Vol: 39, Pages: D1002-D1004, ISSN: 0305-1048
    Wu C, Guo Y, 2011,

    Detecting insecure person in crowd with human sensor

    , Procedia Computer Science, Vol: 5, Pages: 788-792

    We consider the problem of detecting potential dangerous people (i.e. terrorist) in a crowded but closed environment such as airport (and railway station). Instead of conventional sensor, we proposed to use human (passengers) themselves as sensors to detect and identify the target, processing the information from environment with human brain and their cognitive capability. Passengers report the identified issues as tweets with their mobile phone. The monitoring system collects the tweets and classifies their credibility through semantic analysis and learning. Then a temporal model is applied to compute the overall credibility of event. After the event is identified, photos and text description in tweets are combined with sensors (like camera) to identify and track the target people with range image motion detection algorithm. The work is currently in progress of designing model and prototype system. © 2011 Published by Elsevier Ltd.

    Yi-Ke Guo LG, 2011,

    IC Cloud: Enabling Compositional Cloud

    , International Journal of Automation and Computing, Vol: 8, Pages: 269-269
    Baroukh C, Rowe A, Guo Y, 2010,

    Process Calculi for Systems Biology and Applications in Severe Asthma

    , IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), Publisher: IEEE COMPUTER SOC, Pages: 217-222, ISSN: 2163-6966

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