Keynote Speakers

Prof. Yike Guo, Imperial College London, UK

Yike Guo is a Professor of Computing Science in the Department of Computing at Imperial College London. He is the founding Director of the Data Science Institute at Imperial College, as well as leading the Discovery Science Group in the department. Professor Guo also holds the position of CTO of the tranSMART Foundation, a global open source community using and developing data sharing and analytics technology for translational medicine.

Professor Guo received a first-class honours degree in Computing Science from Tsinghua University, China, in 1985 and received his PhD in Computational Logic from Imperial College in 1993 under the supervision of Professor John Darlington. He founded InforSense, a software company for life science and health care data analysis, and served as CEO for several years before the company's merger with IDBS, a global advanced R&D software provider, in 2009.

He has been working on technology and platforms for scientific data analysis since the mid-1990s, where his research focuses on knowledge discovery, data mining and large-scale data management. He has contributed to numerous major research projects including: the UK EPSRC platform project, Discovery Net; the Wellcome Trust-funded Biological Atlas of Insulin Resistance (BAIR); and the European Commission U-BIOPRED project. He is currently the Principal Investigator of the European Innovative Medicines Initiative (IMI) eTRIKS project, a €23M project that is building a cloud-based informatics platform, in which tranSMART is a core component for clinico-genomic medical research, and co-Investigator of Digital City Exchange, a £5.9M research programme exploring ways to digitally link utilities and services within smart cities.

Professor Guo has published over 200 articles, papers and reports. Projects he has contributed to have been internationally recognised, including winning the “Most Innovative Data Intensive Application Award” at the Supercomputing 2002 conference for Discovery Net, and the Bio-IT World "Best Practices Award" for U-BIOPRED in 2014. He is a Senior Member of the IEEE and is a Fellow of the British Computer Society.

Prof. Frans Coenen, University of Liverpool, UK

Frans Coenen has a general background in AI, and has been working in the field of data mining and Knowledge Discovery in Data (KDD) for the last fifteen years. He is particularly interested in: Big Data; Social Network and Trend Mining; the mining of non-standard data sets such as Graph, Image and Document collections; and the practical application of data mining in its many forms. He currently leads a small research group (8 PhDs and 5 RAs) working on many aspect of data mining and KDD. He has some 320 refereed publications on KDD and AI related research, and has been on the programme committees for many KDD events. He is pleased to have been the founder of the UK KDD symposia series, which is now in its eleventh year. Frans Coenen is a member of the IFIP WG12.2 Machine Learning and Data Mining group, The British Computer Society (BCS) and the BCS' Specialist Group on AI (BCS-SGAI). Frans Coenen is currently professor within the Department of Computer Science at the University of Liverpool where he is the director of studies for the department's on-line MSc programmes.

Image Representation for Image Mining: A Study Focusing on Census Prediction using Satellite Image Data

Abstract:Image mining is an important element of the canon of data mining. Decision making is routinely supported by visual information and the visualisation of data. At the same time our ability to collect visual information is increasing rapidly, partly because of technological advancements and partly (and associated with the first) the increasingly reduced cost of collecting such data. Consequently the demand for utilising image data for the purpose of extracting information (image mining) is increasing in a corresponding manner. A taxonomy for image representation in the context of image mining is thus presented. The main premise being that the actual mining algorithms that may be used are well understood, it is the pre-processing of the image data that remains a challenge. The requirement for the output from this pre-processing is some image representation that us both sufficiently expressive while at the same time being compatible with the mining process to be applied. Three categories of representation are considered: statistics based, tree (graph) based and point series based. The suggested taxonomy is then analysed in further detail by considering a novel image mining application, the collection of individual household census data from Google earth satellite imagery. The representations are considered both in terms of generating census prediction models (using classification and regression) and in terms of applying such models for the purpose of larger scale census prediction.

 

Prof. Dr. Hong Zhu, Oxford Brookes University, United Kingdom

Hong Zhu obtained his BSc, MSc and PhD degrees in Computer Science from Nanjing University, China, in 1982, 1984 and 1987, respectively. He worked at Nanjing University 1987 to November 1998. From October 1990 to December 1994 while on leave from Nanjing University, he was a research fellow at Brunel University and the Open University, UK. He joined Oxford Brookes University, UK, in November 1998 as a Senior Lecturer in Computing and became a Professor of Computer Science in October 2004. Prof. Zhu chairs the Applied Formal Methods Research Group of the Department of Computing and Communication Technologies. He is a senior member of IEEE Computer Society, a member of British Computer Society, ACM, and China Computer Federation. His research interests are in the area of software development methodologies, including formal methods, agent-orientation, automated software development, foundation of software engineering, software design, modelling and testing methods, Software-as-a-Service, etc. He has published 2 books and more than 180 research papers in journals and international conferences. He has been a conference program committee chair of SOSE 2012 and ICWS 2015, etc., a conference general chair of SOSE 2013, MobileCloud 2014, MS 2016, EDGE 2017, etc. He is a member of the editorial board of the journal of Software Testing, Verification and Reliability, Software Quality Journal, International Journal of Big Data Intelligence, and the International Journal of Multi-Agent and Grid Systems.

Big SaaS: The Driving Force for Software Engineering Paradigm Shift

Abstract: The past few years have seen a rapid growth of the so-called Big SaaS, i.e. large-scale software-as-a-service applications. They are large in terms of the sizes of the program code, the volume of data stored and processed, the number of users and tenants resides in the system, as well as the value produced. The development, operation and maintenance of Big SaaS have imposed grave challenges to almost all aspects of software engineering due to their high complexity and the high demand on system performance and reliability. Existing solutions have been offered by the industry, where the most notable ones include the microservices architecture, the container technology, and the DevOps methodology and corresponding automated tools. Their wide adoption by the industry has in fact significantly changed software engineering practice. Consequently, a paradigm shift in software engineering has already been taking place. However, as the heart of any software engineering paradigm, a programming language that matches the new paradigm is still yet to arrive. In this talk, we will review the current industrial practices in software engineering of Big SaaS and identifying the key characteristics of the emerging new paradigm. Based on the review, we examine the existing programming paradigms as the candidate for the engineering of Big SaaS applications. Finally, we report our research on a novel programming language called CAOPLE, which stands for Caste-centric Agent-Oriented Programming LanguagE. It was initially designed and implemented for autonomous internet-based applications as a part of our agent-oriented software engineering methodology. It turns out to be particularly suitable for writing software in microservices architecture. We also report an integrated development environment for CAOPLE called CIDE, which targets the development of distributed cloud applications in microservices architecture and supports the DevOps principles.

 

Prof. Alfredo Cuzzocrea, University of Trieste, Italy

Alfredo Cuzzocrea is currently Associate Professor in Computer Science Engineering at the DIA Department, University of Trieste, Italy. He is also habilitated as Full Professor in Computer Science Engineering by the the French National Scientific Habilitation of the National Council of Universities (CUN) under the egira of Ministry of Higher Education and Research (MESR). Previously, he has been Senior Researcher at the Institute of High Performance Computing and Networking of the Italian National Research Council, Italy, and Adjunct Professor at the University of Calabria, Italy. He got the habilitatation as Associate Professor in Computer Science Engineering by the Italian National Scientific Habilitation of the Italian Ministry of Education, University and Research (MIUR). During the past, he has also been Adjunct Professor at the University of Catanzaro “Magna Graecia”, Italy, Adjunct Professor at the University of Messina, Italy, and Adjunct Professor at the University of Naples “Federico II”, Italy. Previously, he was Adjunct Professor at the University of Naples “Parthenope”, Italy. He holds 36 visiting positions worldwide (Europe, USA, Asia, Australia). He serves as Springer Fellow Editor. He serves as Elsevier Ambassador. He holds several roles in international scientific societies, steering committees for international conferences, and international panels, some of them having directional responsibility. He served as Panel Leader and Moderator in international conferences. He served as Invited Speaker in several international conferences worldwide (Europe, USA, Asia). He is member of scientific boards of several PhD programs worldwide (Europe, Asia, Australia). He serves as Editor for the Springer series “Communications in Computer and Information Science”. He covers a large number of roles in international journals, such as Editor-In-Chief, Associate Editor, Special Issue Editor (including JCSS, IS, KAIS, FGCS, DKE, INS, BigData Research). He edited more than 30 international books and conference proceedings. He is member of editorial advisory boards of several international books. He covers a large number of roles in international conferences, such as General Chair, Program Chair, Workshop Chair, Local Chair, Liaison Chair and Publicity Chair (including CSE, ODBASE, DaWaK, DOLAP, ICA3PP, ICEIS, APWeb, SSTDM, IDEAS, IDEAL). He served as Session Chair in a large number of international conferences (including EDBT, CIKM, DaWaK, DOLAP, ADBIS). He serves as Review Board Member in a large number of international journals (including TODS, TKDE, TKDD, TSC, TIST, TSMC, THMS, JCSS, IS, KAIS, FGCS, DKE, INS). He serves as Review Board Member in a large number of international books. He serves as Program Committee Member in a very large number of international conferences (including VLDB, ICDE, EDBT, CIKM, IJCAI, KDD, ICDM, PKDD, SDM). His current research interests include multidimensional data modeling and querying, data stream modeling and querying, data warehousing and OLAP, OLAM, XML data management, Web information systems modeling and engineering, knowledge representation and management models and techniques, Grid and P2P computing, privacy and security of very large databases and OLAP data cubes, models and algorithms for managing uncertain and imprecise information and knowledge, models and algorithms for managing complex data on the Web, models and algorithms for high-performance distributed computing and architectures. He is author or co-author of more than 340 papers in international conferences (including EDBT, CIKM, SSDBM, MDM, DaWaK, DOLAP), international journals (including JCSS, IS, KAIS, DKE, INS) and international books (mostly edited by Springer). He is also involved in several national and international research projects, where he also covers responsibility roles.

Scalable OLAP-Based Big Data Analytics over Cloud Infrastructures: Models, Issues, Algorithms

Abstract: Starting from the combination of two emerging research areas, namely OLAP-based big data analytics tools and Cloud infrastructures, this paper focuses the attention on so-called scalable OLAP-based big data analytics tools, by providing literature overview and two state-of-the-art research contributions of   recent   years.   Acting   as   fundamental   components,   these solutions are likely to be integrated in larger OLAP-based big data analytics tools of the future


Invited Speaker

Presenter: Arinze Akutekwe

Arinze Akutekwe is a Data Scientist with Fujitsu Services, a global IT and Telecommunications company. His studied Information Systems and computer Science at masters and PhD levels respectively and has several publications in the area of artificial intelligence, machine learning and data mining as well as experience in IT and systems administration. His current focus at Fujitsu is developing artificial intelligence and big data solutions for industrial applications. He has worked on various projects ranging from sentiment analysis, predictive maintenance, image recognition and many more.

 

Title: Artificial Intelligence-Driven Big Data Solutions: An Industrial Perspective

Abstract: Artificial intelligence (AI) is currently changing the way we live with huge impacts on our day-to-day lives. Big data is daily being generated in high volume, velocity and wide varieties from mobile devices, wearable and internet of things. The integration of AI and machine learning capabilities with big data are set to drastically change the business landscapes and business models of various industries. A recent survey by Fortune 500 showed that in 2017, 81 percent of CEOs believe that AI and big data will have positive impact on their organisation, a number up from 54 percent in 2016, but how? In this presentation, we look at AI-driven big data solutions as applied in the industry. We discuss current and trending methods in deep learning and predictive analytics with practical examples to specific industry domains.

Arinze Akutekwe is a Data Scientist with Fujitsu Services, a global IT and Telecommunications company. His studied Information Systems and computer Science at masters and PhD levels respectively and has several publications in the area of artificial intelligence, machine learning and data mining as well as experience in IT and systems administration. His current focus at Fujitsu is developing artificial intelligence and big data solutions for industrial applications. He has worked on various projects ranging from sentiment analysis, predictive maintenance, image recognition and many more.

 

Invited Speaker

Presenter: Dr Goksel Misirli, (The School of Computing and Mathematics, Keele University, UK)

I am a lecturer at the School of Computing and Mathematics, Keele University. Before joining to Keele University, I worked as a postdoctoral researcher at Newcastle University, where I also obtained my PhD from the School of Computing Science. My PhD thesis title was “Data integration strategies for informing computational design in synthetic biology”. Previously, I worked in different companies including Ericsson and Reed Business Information as a software engineer, and I have a BSc in Control and Computer Engineering. I particularly enjoy doing research at the boundaries of computing and biology. My research includes the development of novel algorithms, computational design paradigms and frameworks, utilising large, heterogeneous and complex datasets. Semantic Web, ontologies, biological networks, data integration and mining, visualisation, computational workflows, data and model-driven design approaches are some of the areas that I explore. Particularly, I demonstrated the utility of these approaches in synthetic and systems biology, to computationally design biological systems, and to gain biological insights.

Title: Data-driven design of biological systems

Abstract: Applying computational approaches have become an integral part of many disciplines. As the amount of data produced increase all the time, it becomes ever more challenging to analyse data and to inform subsequent processes. Particularly, biological sciences benefit from information-driven experiments. Data are also extremely important to create novel biological applications. DNA synthesis has already become a routine. This process allows us to encode user-specified genetic information, which can then be used to introduce unnatural desired behaviours at the cellular level. Applications already include the development of biological sensors, the bioremediation of harmful molecules, the development of drug delivery systems, the creation of DNA-based information storages and the exploitation of DNA computing with the aim of solving computationally expensive problems. In this approach, simple DNA components from different organisms can be joined together to create complex systems. However, engineering biological systems is often challenging. Biological systems can be very complex and they can have huge design spaces. Here, we will explore how data-driven methodologies that are already applied in computing science can be used to integrate and mine useful biological information. We will then demonstrate the application of such information in creating predictive models to explore large design spaces of biological systems through computational simulations. These approaches are particularly useful in order to apply engineering principles to biology and to adapt design-build-test cycles for developing desired, predictable and complex biological systems.

 

Invited Speaker

Presenter: John Gordon

Title: Opportunities and Challenges for Consultants in Cloud and Big Data Technologies

Abstract: QA Consulting has been delivering Enterprise and Mission Critical solutions to the Public and Private Sectors for almost 20 years. BAE SYSTEMS, Deutsche Bank, Disney, Fujitsu, Santander, Siemens, and TalkTalk are few of the key clients of QA consulting.  QA consultancy works with industry-leading software partners to deliver services to some of the UK’s most recognisable organisations. Our key partners include; Amazon WebServices, Adobe, CLOUDERA, HORTONWORKS and ORACLE. I will talk about QA Consultancy’s unique academy training model which underpins Centre of Excellence Enablement Programme to enable our customers to grow capability to build, deploy and support their mission critical applications and solutions. We provide five main training pathways in Programming, Devops, Cloud computing, Big data and Cyber security. I will talk about what these each training pathways entails with an emphasis on cloud and big data. These training programmes provide opportunities to graduates to become IT Consultant and work in the latest in-demand technologies for two years. Specifically, I will also talk about what we look for in consultants and what is expected of them over a two-year engagement. Finally, I will present a few case studies of consultants we have had that have completed the academy training model and have deployed successfully to client site.