Bart Goethals is professor at the Department of Mathematics and Computer Science of the University of Antwerp in Belgium. He leads the Data Mining lab of the Advanced Database Research and Modeling (ADReM) research group, which performs fundamental research on the structures, the basic properties and the power of languages, algorithms and methodologies for processing and analysing large quantities of data. His primary research interests are the study of data mining techniques to efficiently find interesting patterns and properties in large databases. He received the IEEE ICDM 2001 Best Paper Award and the PKDD 2002 Best Paper Award for his theoretical studies on frequent itemset mining. He was organizer and program chair of ECML PKDD 2008, program chair of SIAM DM 2010, and general chair of IEEE ICDM 2012. He organised and chaired several workshops, such as ICDM FIMI 2003, 2004, PKDD KDID 2004, SIGKDD OSDM 2005, SDM HPDM 2006, and SIGKDD UP 2010 and served on the organizing and program committees of several conferences such as ACM SIGKDD, IEEE ICDM, SIAM DM, and ECML PKDD. He is general chair of the ECML PKDD Steering Committee (2008-2011), associate editor of the Data Mining and Knowledge Discovery journal, the Knowledge and Information Systems journal and Editor-in-Chief of the ACM SIGKDD Explorations newsletter.
Title "Cartification: from Similarities to Itemset Frequencies".
Suppose we are given a multi-dimensional dataset. For every point in the dataset, we create a transaction, or cart, in which we store the k-nearest neighbors of that point for one of the given dimensions. The resulting collection of carts can then be used to mine frequent itemsets; that is, sets of points that are frequently seen together in some dimensions. Experimentation shows that finding clusters, outliers, cluster centers, or even subspace clustering becomes easy on the cartified dataset using state-of-the-art techniques in mining interesting itemsets.
Dominik Slezak received his D.Sc. (habilitation) in 2011 from Institute of Computer Science, Polish Academy of Sciences, and Ph.D. in Computer Science in 2002 from University of Warsaw, Poland. In 2005, he co-founded Infobright Inc., where he is currently working as chief scientist. He also is with Institute of Mathematics, University of Warsaw. He also used to be with University of Regina, SK, Canada, and Polish-Japanese Institute of Information Technology in Warsaw.
Dominik serves as an associate editor and editorial board member for a number of international scientific journals, including Fundamenta Informaticae, General Systems, Information Sciences, Intelligent Information Systems, Knowledge and Information Systems, and several others. He has co-edited over 20 scientific books and volumes of international conference proceedings. He has co-authored over 100 chapters and papers for books, journals, and international conferences. He has delivered plenary talks at conferences in over 10 countries. His research interests include Rough Sets, Granular Computing, Database Architectures, and Data Mining. He is also an executive member of International Rough Set Society.
Title "Rough Sets and FCA - Scalability Challenges".
Rough sets and FCA provide foundations for a number of methods useful in data mining and knowledge discovery, at the stages of data preprocessing, classification and representation. Rough-set-based and FCA-based methods are often applied together with other techniques in order to cope with real-world challenges. It is therefore important to investigate ways of extending original notions and modifying original algorithms to facilitate dealing with truly large and complex data sets. This talk attempts to categorize some ideas of how to scale rough-set-based and FCA-based methods with respect to amounts of objects and attributes, as well as types and cardinalities of attribute values. We discuss usage of analytical RDBMS engines and AI-based randomized heuristics to quickly compute approximate, yet practically meaningful results (e.g.: approximate concept lattices). We also discuss differences and similarities in algorithmic challenges related to rough sets and FCA, illustrating that they should be regarded as complementary rather than competing methodologies.
Edward M.L. Peters is the Chief Executive Officer of OpenConnect, a published author, a media commentator and a frequent speaker at industry events. As CEO, Mr. Peters sets the strategic direction of OpenConnect and oversees worldwide operations. He is the author of numerous publications ranging from scholarly peer-review journals to The Hill Congressional Newspaper and the Financial Times. Mr. Peters is also the author of The Paid-for Option, Using Process Intelligence to Close the Healthcare Knowledge Gap. In it he makes the bold statement that healthcare reform could pay for itself by applying innovation, technology and evidence-based measures to control healthcare costs.
A seasoned technology executive, Mr. Peters has been responsible for guiding several organizations to market leadership positions and creating value for shareholders. He led the LBO (with Golden Gate Capital) of DataDirect Technologies (spin-off from MERANT) and its subsequent sale to Progress Software (PRGS). He has also held sr. executive positions with Progress Software, Cerebellum Software and INTERSOLV, where he led the data connectivity business unit to become the leading ISV in the data access middleware market.
A recognized business leader, Mr. Peters is special visiting professor and chairman of the OpenConnect research chair in Business Process Discovery and Analytics at Katholieke Universiteit Leuven in Belgium. He also serves on the board of a number of organizations and companies, including Alex Brown Center for Entrepreneurship at the University of Maryland in Baltimore, and the Computer and Communication Industry Association. Mr. Peters has also received numerous awards recognizing his leadership including the 2004 Maryland Technology Council, Entrepreneur of the Year, IBM/GUIDE International President's Award, the R/A/D Award for Excellence in Repository-Based Application Development and a Lehigh University Williams Prize. He was also an Ernst & Young Entrepreneur of the Year Award Finalist in 2003, 2004 and 2010.
Mr. Peters holds a Bachelor of Arts in government and a Master of Science in industrial engineering from Lehigh University. He has also attended the Columbia University Graduate School of Business, the Stanford University Graduate School of Business Executive Program for Growing Companies and is a graduate of the Entrepreneurial Management Program at Carnegie-Mellon University.
Title "Processes are concepts, aren't they?".
Discovery is an information / technical approach to the important managerial problem of decision making under not only uncertainty, but also actually, \unknown unknowns". Formal Concept Analysis (FCA) is an elegant mathematically grounded theory that complements Data Discovery particularly well, especially for data with no predefined structure. It allows for discovering emergent, a-priori unknown concepts in these data. Process Discovery deals with emergent behavior in business workflows. In its current state-of-the-art, Process Discovery combines machine-learning techniques utilizing Hidden Markov Model (HMM) representations of Processes. In one of our research lines, we investigate how FCA can improve and complement Process Discovery. Although the inclusion of temporal data and events in FCA allows for the creation of "early warning" and trend-models, HMM's are needed for a deep understanding of the processes and their intrinsic complexities. However, FCA can assist significantly in the post-processing and understanding of HMM's, in particular in the presence of process variations and exceptions. But FCA allows also for the detection of recurrent, coherent Process steps, which can be considered "service steps" in business processes. Ultimately, an appropriate mathematical representation of HMM's should allow for the application of algebraic extensions of FCA for discovering Processes and their variations as mathematical concepts as well. Some initial work on Process patterns gives inspiring research directions. Real-life case materials from Healthcare Administration, Customer Contact-Center's and Financial Services illustrate this keynote lecture.
Luc De Raedt received his degree in Computer Science (Licentiaat Informatica) from the Katholieke Universiteit Leuven (Belgium) in 1986 and his Ph.D. in Computer Science (Dr. Informatica) from the same university in 1991. He worked at the Department of Computer Science of the Katholieke Universiteit Leuven (Belgium) from 1986 till 1999, where he held positions as teaching assistant (1986-1991), post-doctoral researcher of the Fund for Scientific Research, Flanders (1991-1999), part-time assistant professor (1993-1998) and part-time associate professor (1998-1999). From 1999 till 2006 he was a (full) Professor (C4) of Computer Science of the Albert-Ludwigs-University Freiburg and chair of the Machine Learning and Natural Language Processing Lab research group. Since then, he has taken up a (full) research professorship (BOF) at the Department of Computer Science of the Katholieke Universiteit Leuven (Belgium), where he joined the lab for Declarative Languages and Artificial Intelligence. He lectures at the Katholieke Universiteit Leuven, and has also lectured at the Universities of Freiburg, Basel and Sienna.
Luc De Raedt's research interests are in Artificial Intelligence, Machine Learning and Data Mining, as well as their applications. He is currently working on probabilistic logic learning (sometimes called statistical relational learning), which combines probabilistic reasoning methods with logical representations and machine learning, the integration of constraint programming with data mining and machine learning principles, the development of programming languages for machine learning, and analyzing graph and network data. He is also interested in applications of these methods to chemo- and bio-informatics, to natural language processing, vision, robotics and action- and activity learning.
He has been a coordinator or a principle investigator in several European and national projects, such as the ESPRIT III and IV projects on Inductive Logic Programming I and II, the EU FP 5 and 6 IST FET projects on Application of Probabilistic Inductive Logic Programming II, the ESPRIT IV project Aladin, the EU FP 5 IST FET project cInQ (Consortium on Inductive Querying), the EU FP 6 FET project IQ (Inductive Querying) and the recent EU FP 7 projects BISON (Bisociation Networks) and FIRST-MM (Flexible Skill Acquisition and Intuitive Robot Tasking for Mobile Manipulation in the Real World).
He was program (co)-chair of the 7th European Conference on Machine Learning (1994, Catania, Sicily), the 5th International Workshop on Inductive Logic Programming (1995, Leuven, Belgium), the key organiser and co-chair of the program committees of the 5th European Conference on Principles and Practice of Knowledge Discovery in Databases and the 12th European Conference on Machine Learning. These conferences were co-located in Freiburg, September 2001. In 2005, he chaired (together with Stefan Wrobel) the program committee of the 22nd International Conference on Machine Learning. He is an area editor of the Theory and Practice of Logic Programming, an action editor for the Journal of Machine Learning Research and the Machine Learning Journal, a former member of the advisory board and a former associate editor of the Journal of Artificial Intelligence Research, a member of the editorial boards of New Generation Computing, AI Communications, Intelligent Data Analysis, Informatica, Data Mining and Knowledge Discovery, and the Journal of Applied Logic. He was an elected and founding member of the board of the International Machine Learning Society from 2004-2011. In 2005, he was elected as an ECCAI fellow. In 2012, he will be the program chair of the 20th European Conference on Artificial Intelligence (ECAI 2012) in Montpellier.
Title "Declarative Modeling for Machine Learning and Data Mining".
Despite the popularity of machine learning and data mining today, it remains challenging to develop applications and software that incorporates machine learning or data mining techniques. This is because machine learning and data mining have focussed on developing high-performance algorithms for solving particular tasks rather than on developing general principles and techniques. I propose to alleviate these problems by applying the constraint programming methodology to machine learning and data mining and to specify machine learning and data mining problems as constraint satisfaction and optimization problems. What is essential is that the user be provided with a way to declaratively specify what the machine learning or data mining problem is rather than having to outline how that solution needs to be computed. This corresponds to a model + solver-based approach to machine learning and data mining, in which the user specifies the problem in a high level modeling language and the system automatically transforms such models into a format that can be used by a solver to efficiently generate a solution. This should be much easier for the user than having to implement or adapt an algorithm that computes a particular solution to a specific problem. I shall illustrate this using our results on constraint programming for itemset mining and probabilistic programming.
Dr. Hsinchun Chen is McClelland Professor of Management Information Systemsat the University of Arizona. He received the B.S. degree from the NationalChiao-Tung University in Taiwan, the MBA degree from SUNY Buffalo, and thePh.D. degree in Information Systems from the New York University. Dr. Chen had served as a Scientific Counselor/Advisor of the National Library of Medicine (USA), Academia Sinica (Taiwan), and National Library of China (China). Dr. Chen is a Fellow of IEEE and AAAS. He received the IEEE Computer Society 2006 Technical Achievement Award, the 2008 INFORMS Design Science Award, the MIS Quarterly 2010 Best Paper Award, and the IEEE 2011 Research Achievement and Leadership Award in Intelligence and Security Informatics. He is also a finalist of the AZ Tech Council's Governor's Innovation of the Year Award in 2011.
He is author/editor of 20 books, 25 book chapters, 230 SCI journal articles, and 140 refereed conference articles covering Web computing, search engines, digital library, intelligence analysis, biomedical informatics, data/text/web mining, and knowledge management. His recent books include: Dark Web (2012); Sports Data Mining (2010); Infectious Disease Informatics (2010); Terrorism Informatics (2008); Mapping Nanotechnology Knowledge and Innovation (2008), Digital Government (2007); Intelligence and Security Informatics for International Security (2006); and Medical Informatics (2005), all published by Springer. Dr. Chen's publication productivity in Information Systems was ranked #8 in a bibliometric study in (CAIS 2005) and #9 in (EJIS, 2007); and he was ranked #1 in Digital Library research in a study in (IP&M 2005), #1 in JASIST publication for 1998-2007 in (JASIST 2008) and #5 in h-index in IEEE Intelligent Systems publications for 1986-2010 in (IEEE IS 1010). His overall h-index is 60, which is among the top three in the MIS.
He is Editor in Chief (EIC) of the ACM Transactions on Management Information Systems (ACM TMIS) and Springer Security Informatics (SI) Journal. He serves on ten editorial boards including: IEEE Intelligent Systems, ACM Transactions on Information Systems, IEEE Transactions on Systems, Man, and Cybernetics, Journal of the American Society for Information Science and Technology, Decision Support Systems, and International Journal on Digital Library. He has been an advisor for major NSF, DOJ, NLM, DOD, DHS, and other international research programs in digital library, digital government, medical informatics, and national security research. Dr. Chen is founding director of Artificial Intelligence Lab and Hoffman E-Commerce Lab. The UA Artificial Intelligence Lab, which houses 20+ researchers, has received more than $30M in research funding from NSF, NIH, NLM, DOD, DOJ, CIA, DHS, and other agencies (90 grants, 40 from NSF).
Dr. Chen has also produced 30 Ph.D. students who are placed in major academic institutions around the world. The Hoffman E-Commerce Lab, which has been funded mostly by major IT industry partners, features one of the most advanced e-commerce hardware and software environments in the College of Management. Dr. Chen is conference co-chair of ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2004 and has served as the conference/program co-chair for the past eight International Conferences of Asian Digital Libraries (ICADL), the premiere digital library meeting in Asia that he helped develop. Dr. Chen is also (founding) conference co-chair of the IEEE International Conferences on Intelligence and Security Informatics (ISI) 2003-present. The ISI conference, which has been sponsored by NSF, CIA, DHS, and NIJ, has become the premiere meeting for international and homeland security IT research.
Dr. Chen's COPLINK system, which has been quoted as a national model for public safety information sharing and analysis, has been adopted in more than 3500 law enforcement and intelligence agencies. The COPLINK research had been featured in the New York Times, Newsweek, Los Angeles Times, Washington Post, Boston Globe, and ABC News, among others. The COPLINK project was selected as a finalist by the prestigious International Association of Chiefs of Police (IACP)/Motorola 2003 Weaver Seavey Award for Quality in Law Enforcement in 2003. COPLINK research has recently been expanded to border protection (BorderSafe), disease and bioagent surveillance (BioPortal), and terrorism informatics research (Dark Web), funded by NSF, DOD, CIA, and DHS. In collaboration with selected international terrorism research centers and intelligence agencies, the Dark Web project has generated one of the largest databases in the world about extremist/terrorist-generated Internet contents (web sites, forums, blogs, and multimedia documents).
Dark Web research supports link analysis, content analysis, web metrics analysis, multimedia analysis, sentiment analysis, and authorship analysis of international terrorism contents. The project has received significant international press coverage, including: Associated Press, USA Today, The Economist, NSF Press, Washington Post, Fox News, BBC, PBS, Business Week, Discover magazine, WIRED magazine, Government Computing Week, Second German TV (ZDF), Toronto Star, and Arizona Daily Star, among others. Dr. Chen is also a successful entrepreneur. He is the founder of the Knowledge Computing Corporation (KCC), a university spin-off IT company and a market leader in law enforcement and intelligence information sharing and data mining. KCC was acquired by i2, the industry leader in intelligence analytics and fraud detection, in 2009. The combined i2/KCC company was acquired by IBM in 2011 for $500M.
Dr. Chen has also received numerous awards in information technology and knowledge management education and research including: AT&T Foundation Award, SAP Award, the Andersen Consulting Professor of the Year Award, the University of Arizona Technology Innovation Award, and the National Chiao-Tung University Distinguished Alumnus Award. He was also named Distinguished Alumnus by SUNY Buffalo. Dr. Chen had served as a keynote or invited speaker in major international security informatics, medical informatics, information systems, knowledge management, and digital library conferences and major international government meetings (NATO, UN, EU, FBI, CIA, DOD, DHS). He is a Distinguished/Honorary Professor of several major universities in Taiwan and China (including Chinese Academy of Sciences and Shanghai Jiao Tong University) and was named the Distinguished University Chair Professor of the National Taiwan University. Dr. Chen had served as the Program Chair of the International Conference on Information Systems (ICIS) 2009, held in Phoenix, Arizona.
Title "Dark Web: Exploring and Mining the Dark Side of the Web".
This talk will review the emerging research in Terrorism Informatics based on a web mining perspective. Recent progress in the internationally renowned Dark Web project will be reviewed, including: deep/dark web spidering (web sites, forums, Youtube, virtual worlds), web metrics analysis, dark network analysis, web-based authorship analysis, and sentiment and affect analysis for terrorism tracking. In collaboration with selected international terrorism research centers and intelligence agencies, the Dark Web project has generated one of the largest databases in the world about extremist/terrorist-generated Internet contents (web sites, forums, blogs, and multimedia documents). Dark Web research has received significant international press coverage, including: Associated Press, USA Today, The Economist, NSF Press, Washington Post, Fox News, BBC, PBS, Business Week, Discover magazine, WIRED magazine, Government Computing Week, Second German TV (ZDF), Toronto Star, and Arizona Daily Star, among others. For more Dark Web project information, please see: http://ai.eller.arizona.edu/research/terror/.
Paul Elzinga received his MSc degree in Econometrics from the University of Groningen in 1984 and his MSc degree in Knowledge and Information engineering from the Middlesex University of London in 1995. Paul Elzinga received his PhD degree in Economics and Business science at the University of Amsterdam in 2011. Paul Elzinga started his career with developing information systems for the Groningen Police Department in 1983. He developed many information systems of various kinds, from dispatching systems to knowledge based systems for monitoring potential criminals. He was information architect at the national police department from 2002 to 2005 and designed the XML based national full text inquiry database where all incidents and all criminal records of the Dutch police are made available for information analysis. In 2005 he continued his career at the Amsterdam-Amstelland Police Department where he worked on knowledge- based information systems and started his PhD entitled “Formalizing the concepts of crimes and criminals” in 2007. At this moment he is working as project leader in several innovative data- and text-mining projects within different police organizations and is actively involved in the cooperation between KU Leuven and Higher School of Economics in Moscow where the FCA-based software CORDIET is developed to be used by several national and regional police force units in the Netherlands.
Title "Can concepts reveal criminals?".
In 2005 the Amsterdam-Amstelland police introduced Intelligence-led Policing as a management paradigm. The goal of ILP is to optimally use the information which becomes available after police patrols, motor vehicle inspections, video camera recordings, etc. to prevent crimes where possible and optimally allocate available resources. This policy has resulted in an increasing number of textual reports, video materials, etc. every year. Until now, this vast amount of information was not easily accessible because good analysis methods were missing and as a consequence hardly used by the criminal intelligence departments. In the first part of this talk I will give a short overview of traditional statistical methods such as hot spot analysis which have been used to make this information accessible and steer police actions. In the second part of this talk I will present using some real life cases how FCA was used to identify criminals involved in human trafficking, terrorism, robberies, etc. In the third part of this talk I would like to evoke a lively discussion on the potential of FCA related algorithms and methods for analyzing textual reports, internet data such as twitter feeds, browsing behavior of visitors of radical Islamic websites, etc.
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Contact person: Poelmans Jonas
E-mail address: Jonas.Poelmans@econ.kuleuven.be
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