IC3K is a joint conference composed of three concurrent conferences: KDIR, KEOD and KMIS. These three conferences are always co-located and held in parallel. Keynote lectures are plenary sessions and can be attended by all IC3K participants.
Dieter A. Fensel, University Innsbruck, Austria
Title: Available Soon
Frans Coenen, University of Liverpool, United Kingdom
Title: Mining Satellite Images for Census Data Collection: A Study Using the Google Static Maps Service
Dieter A. Fensel
In 1989, Prof. Dr. Dieter Fensel earned both his Master in Social Science (Free University of Berlin) and his Master in Computer Science (Technical University of Berlin). In 1993, he was awarded his Doctoral degree in Economic Science, Dr. rer. pol., from the University of Karlsruhe. And in 1998 he received his Habilitation in Applied Computer Science. Throughout his doctoral and post-doctoral career, Prof. Dr. Fensel has held positions at the University of Karlsruhe (AIFB), the University of Amsterdam (UvA), and the Vrije Universiteit Amsterdam (VU). In 2002, he took a chair at the Institute for Computer Science, Leopold Franzens University of Innsbruck, Austria. In 2003, he became the Scientific Director of the Digital Enterprise Research Institute (DERI) at the National University of Ireland, Galway, receiving a large grant acquired from Science Foundation Ireland (SFI) and in 2006 he became the Director of the Digital Enterprise Research Institute (DERI) at the Leopold Franzens University of Innsbruck, Austria. In 2007, he founded the Semantic Technology Institute International (STI2), which is organized as a collaborative association of interested scientific, industrial and governmental parties of the world wide Semantic Web and Service community that share a common vision. End of 2007, DERI Innsbruck was renamed to STI Innsbruck. STI International already counts 30 associate members from all over the world. His current research interests are around the usage of semantics in 21st century computer science.
He has published over 200 papers via scientific books and journals, conferences, and workshop contributions. He has co-organized over 200 academic workshops and conferences. He is an Associated Editor fourteen scientific journals/publications. He has been an executive member in than 50 international and national research projects with a total volume of more than 40 Million Euro. Furthermore, he worked as scientific or project coordinator in several projects, such as Larck (IP), SOA4All (IP), KnowledgeWeb (NoE) or Tripcom (Strep). His academic experience is not however restricted to academia, having taught over fifty courses at various levels of education, from professional academies and technical colleges to universities and scientific conferences. Topics include: Formal Specification Languages, Software Engineering, Data Warehouse, World Wide Web, Electronic Commerce, Agent-based Information Access, Semantic Web and Ontologies. He has supervised over 50 master theses and PhDs and is a recipient of the Carl-Adam-Petri-Award of the Faculty of Economic Sciences from the University of Karlsruhe (2000). Dieter Fensel has contributed to more than 10 books as an author or editor.
Mining Satellite Images for Census Data Collection: A Study Using the Google Static Maps Service
University of Liverpool
Frans Coenen has many years of experience working in the filed of AI and Knowledge Discovery in Data (KDD). He is currently particularly interested in: Social Networkand Trend Mining; the mining of non-standard data sets such a Graph, Image and document bases; and the practical application of datamining in its many forms. He currently leads a small research group (13 PhDs and 2 RAs) working on many aspect of data mining and KDD. He has been on the programme committees for many KDD events and is pleased to have been the founder of the UK KDD symposia series which is now in its sixth year. He is also very honoured to have been invited to present a two week lecture series on Havana, Cuba, on behalf of CENATAV. Frans Coenen is a member of the IFIP WG12.2 --- Machine Learning and Data Mining group.
In the context of AI Frans Coenen is a member of the The British Computer Society (BCS) Specialist Group in AI (BCS-SGAI). He has been involved, in various capacities (chair, co-chair, technical programme chair, deputy technical programme chair) with the British Computer Societies (BCS) annual international AI conferences (ES99, ES2000, ES2001, ES2002 and AI2003 to AI2009). For many years (1998-2009) he was a key figure on the BCS-SGAI committee, which he was invited to join; and was founder and first editor of Expert Update, the SGAI magazine.He is also on the editorial board for the Journal of Knowledge and Information systems and for the Knowledge Engineering review
Frans Coenen's current funded research is founded on a European project and two Innovate UK projects KTPs (Knowledge Transfer Projects), Hit Search and YSTC. The Hit Search project is concerned with internet brand reputation management; a product, "Littlebirdie", has recently been launched. The YSTC project is directed at customer-client matching using internet based resources.
Frans Coenen has some 300 refereed research papers published in international journals and conference proceedings (plus several books).
Previous research includes: (1) the industry funded KDD in FM (Knowledge Discovery in Data for Facilities Management) project (completed in December 2001) and (2) the DGKBIS EPSRC funded project which was awarded top ratings by EPSRC --- ALPHA 5 for scientific and technological merit, and ALPHA 5 for management and use of resources, (3) the EPSRC funded DOMAIN project, (4) the "stoves" Foresight-link project and (5) several TCS/KTP projects (Tachograph, Prima, Transglobal, Deeside, Racewood).
He has made presentations at many international conferences and workshops.
Census collection is a common practice throughout the world. However, the process is expensive and resource intensive. This is especially the case in areas which feature poor communication and transportation networks. A cost effective alternative is to use high-resolution satellite imagery to obtain a census approximation at a significantly reduced cost. This can be achieved by building a predictor that can label households, that feature in satellite image data, according to “family” size. The fundamental idea is to segment satellite images so as to obtain satellite sub-images describing individual households and to represent these segmentations in a manner conducive to household “family” size prediction. A number of representations are considered: graph-based, histogram based and texture based. By pairing each represented household with known census data, namely family size, a predictor can be constructed to predict household size according to the nature of each representation. The generated predictor can then be used to provide a quick and easy mechanism for the approximate collection of census data that does not require significant resource. To generate the desired predictor training data was obtained by collecting “on ground” census data and matching this to satellite imagery. The test site for the work was a collection of villages lying in the Ethiopian hinterland. The operation of the proposed predictor was evaluated using test data collected in the same manner as the training data, and by utilizing the predictor in the context of a "large scale" study for an area of the Ethiopian hinterland for which the population had been previously recorded.