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Details of Grant 

EPSRC Reference: EP/J020540/1
Title: Accelerated Real-Time Information Extraction System (ARIES)
Principal Investigator: Sezer, Professor S
Other Investigators:
Liu, Professor W Miller, Dr P Loughlin, Dr MJ
Kurugollu, Dr F O'Neill, Dr S Burns, Dr D C
McLaughlin, Dr K
Researcher Co-Investigators:
Project Partners:
Department: Electronics Electrical Eng and Comp Sci
Organisation: Queen's University of Belfast
Scheme: Standard Research
Starts: 01 October 2012 Ends: 30 September 2013 Value (£): 252,523
EPSRC Research Topic Classifications:
Computer Sys. & Architecture Digital Signal Processing
Information & Knowledge Mgmt
EPSRC Industrial Sector Classifications:
Aerospace, Defence and Marine Communications
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
09 Feb 2012 Data Intensive Systems (DaISy) Announced
Summary on Grant Application Form
Technological advances in CMOS semiconductor technology paved the way for the digital revolution. As predicted by Moore, silicon integration capability has been doubling every 18 months over the past four decades, providing the foundation for low-cost computing and memory technology.

The digitisation of information and communication technologies sparked a number of innovations revolutionising the way we compute and communicate. Ubiquitous high-bandwidth communication, enabled by WiFi and 3G/4G technologies, facilitates on-demand access to a vast amount of application and location specific information including multimedia and broadcast content, video and voice communications, email and SMS/MMS. Furthermore, it has enabled on-demand access to personalised storage and computing resources, providing the foundation for the development of cloud computing infrastructures and a wide range of online web-based services and applications. With the decreasing cost of communication and storage the Internet has also become the global communication infrastructure for a wide range of autonomous sensor technologies, referred to as the "Internet of things". Key application areas include monitoring/surveillance, smart grid, smart homes and smart cities.

Monitoring internet traffic and mining meaningful information from both the online traffic and the stored information has emerged as essential for many critical applications and services. For example resource management, market intelligence, physical and cybercrime investigations and forensics, cyber space policing, situation awareness and the monitoring of malicious behaviour for criminal and terrorist intent. As the scale, diversity and distributed nature of current and emerging data assets increases and as data becomes ever more ubiquitous and critical to decision making, effective real-time mining of useful information becomes essential.

Considering the exponential increase of internet traffic and stored data, traditional software based approaches have become inadequate and unsustainable. Performance gain achieved due to Moore's law does not keep up with the required computing bandwidth of current and near future generated data assets. Internet traffic bandwidth is doubling every 12 months while the emerging content diversity is significantly increasing mining complexity. As the enterprise becomes more data centric, with a significant increase in data assets within the public and private cloud, traditional scaling by increasing the number of computing resources can no longer be sustained due to cost and power dissipation.

Most data mining algorithms are derived by the software community and are optimised for data structures for platforms based upon the Von-Neumann architecture. An effective solution now requires a paradigm shift in the way we process data and also how we extract meaningful information from a large amount of distributed, constantly changing data that is partially stored or in-transit.

Key Findings
The ARIES project explored ways on how hardware acceleration can be used to scale big data analysis throughput in order to significantly reduce the analysis time of large unstructured data and achieve real-time data analysis capability for potential threat detection and situation awareness.

Sentiment analysis is very popular and widely used technique. Similar to most big data application, it involves the search of key patterns recognised to indicate sentiment within a portion of unstructured text. Hardware acceleration of sentiment analysis is of significant benefit, for increasing the number of key pattern and data size within a given time frame or at real time.

Our investigation has shown that offloading of complex content processing tasks onto a custom-purpose parallel processing platform (ARIES) can achieve over 60x better performance than standard software based solution. Benchmarking the content processing only, the custom-purpose content processor achieves well over 6000x acceleration, excluding the communication overhead via the PCIe Interface with the CPU.

Potential use in non-academic contexts
Significant industrial interest for the ARIES project led to a number of industrial funded projects and the customisation of the ARIES platform for commercial use by RepKnight Limited.
Impacts
No information has been submitted for this grant.
Sectors submitted by the Researcher
Information & Communication Technologies
Project URL:  
Further Information:  
Organisation Website: http://www.qub.ac.uk