Dr Ashiq Anjum

Find a member of staff

Job title: Lecturer in Computer Science

Email: a.anjum@derby.ac.uk
Personal website: cern.ch/anjum
Phone No: 01332 591881
Room: E514

Recent publications

• "Glueing Grids and Clouds together: A Service-oriented Approach", Ashiq Anjum, Richard Hill, Richard McClatchey, Nik Bessis, Andrew Branson, International Journal of Web and Grid Services (IJWGS) ISSN (Print): 1741-1106 (Accepted, to appear in 2012)
• "Performance Simulation of a Context Provisioning Middleware based on Empirical Measurements", Eike Steffen Reetz, Michael Knappmeyer, Saad Liaquat Kiani, Ashiq Anjum, Nik Bessis, Ralf Tonjes, Elsevier Journal of Simulation Modelling Practice and Theory, ISSN: 1569-190X (Accepted, to appear in 2012)
• "Context Caches in the Clouds", Saad L Kiani, Ashiq Anjum, Nick Antonopoulos, Kamran Munir,, Richard McClatchey, Journal of Cloud Computing: Advances, Systems and Applications, ISSN: 2192-113X (Accepted, to appear in 2012)
• "CMS Workflow Execution using Intelligent Job Scheduling and Data Access Strategies", Khawar Hasham, Antonio Delgado Peris, Ashiq Anjum, Dave Evans, Dirk Hufnage, Eduardo Huedo, José M. Hernández, Richard McClatchey, Stephen Gowdy, Simon Metson, IEEE Transactions on Nuclear Science (IEEE TNS), 2011, ISSN: 0018-9499
• "A Fault Tolerant, dynamic and Low Latency BDII Architecture", Asif Osman, Ashiq Anjum, Richard McClatchey, International Journal of Grid and Distributed Computing (IJGDC), SERSC Publishers, 2010, ISSN: 2005-4262
• "Grid infrastructures for computational neuroscience: the neuGRID example", Alberto Redolfi‌, Richard McClatchey‌, Ashiq Anjum, Alex Zijdenbos‌, David Manset, Frederik Barkhof, Christian Spenger‌, Yannik Legré‌, Lars-Olof Wahlund, Chiara Barattieri di San Pietro‌ & Giovanni B Frisoni, Journal of Future Neurology. November 2009, Vol. 4, No. 6, Pages 703-722 , DOI 10.2217/fnl.09.53. Future Science Group publishers 2009.
• "Rule-Based Querying of Distributed, Heterogeneous Data", Tom Lansdale, Peter Bloodsworth, Ashiq Anjum, Irfan Habib, Yasir Mehmood, Richard McClatchey, IETE Tech Review Journal, 2009;26:363-8
• "Dot-base62x: Building A Compact and User-friendly Text Representation Scheme of IPv6 Addresses for Cloud Computing", Zhenxing Liu, Lu Liu, James Hardy, Ashiq Anjum, Richard Hill, Nick Antonopoulos, Journal of Cloud Computing: Advances, Systems and Applications, ISSN: 2192-113X (Accepted, to appear in 2012)
• "Context Parsing, Processing and Distribution in Clouds", Saad Liaquat Kiani, Ashiq Anjum, Nik Bessis, Richard Hill and Michael Knappmeyer, Mobile Information Systems (MIS) Journal, ISSN:574-017X (Accepted, to appear in 2012)
• "An Architecture for Integrated Intelligence in Urban Management using Cloud Computing", Zaheer Khan, David Ludlow, Richard McClatchey, Ashiq Anjum, Journal of Cloud Computing: Advances, Systems and Applications, ISSN: 2192-113X (electronic version) (Accepted, to appear in 2012)
• "Data Intensive and Network Aware (DIANA) Grid Scheduling", Richard McClatchey, Ashiq Anjum, Heinz Stockinger, Arshad Ali, Ian Willers, Michael Thomas, Journal of Grid Computing, Springer Verlag, 5(1):43-64, 2007
• "Bulk Scheduling with DIANA Scheduler", Ashiq Anjum, Richard McClatchey, Arshad Ali, Ian Willers, IEEE Transactions on Nuclear Science, vol. 53, issue 6, pp. 3818-3829

Current Activities / Links with Industry

Stream Cloud Project with XAD Communications Limited, Bristol, UK(2012-2014)

The Stream Cloud project (funded by the Technology Strategy Board) aims to develop an end-to-end solution for batch and real-time analysis of video streams using cloud computing. A software library will be developed to exploit the cloud computing potential for extracting and deriving important features from recorded video streams. For large-scale monitoring of objects, the partnership will setup a computing cloud to process video metadata in real time. To reduce video stream processing time and costs, image processing algorithms will be ported on hardware accelerators such as GPGPUs. Intelligent techniques such as machine learning will be exploited to enable users to provide object recognition, search and compare the objects, and track and monitor the objects under a controlled environment. Profiles will be created to find patterns and events or unusual behaviours in the recorded video streams.