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Keynote Lectures

On Knowledge Discovery and Interactive Intelligent Visualization of Biomedical Data - Challenges in Human-Computer Interaction & Biomedical Informatics
Andreas Holzinger, Medical University Graz, Austria

Visualization & Data Mining for High Dimensional Datasets
Alfred Inselberg, Tel Aviv University, Israel

Issues in Combined Static and Dynamic Data Management
Daniela Nicklas, Carl von Ossietzky Universitaet Oldenburg, Germany

 

On Knowledge Discovery and Interactive Intelligent Visualization of Biomedical Data - Challenges in Human-Computer Interaction & Biomedical Informatics

Andreas Holzinger
Medical University Graz
Austria
 

Brief Bio
Andreas Holzinger is head of the Research Unit Human–Computer Interaction, Institute of Medical Informatics, Statistics and Documentation, Medical University Graz, Associate Professor of Applied Informatics at the Faculty of Computer Science, Institute of Informationsystems and Computer Media and Lecturer at the Faculty of Electrical and Information Engineering, Institute of Genomics and Bioinformatics at Graz University of Technology. He serves as consultant for the Canadian, Swiss, French and Dutch Government, for the German Excellence Initiative and as national expert in the European Commission (Lisbon Delegate 2000). Andreas, born 1963, started as an apprentice in Information Technology in 1978; while working as an industrial engineer, he resumed a parallel second-chance education, finished his PhD in Cognitive Science in 1997 and completed his second doctorate (Habilitation) in Computer Science in 2003. Since 1999 participation in leading positions in 30+ R&D multi-national projects, budget 3+ MEUR;300+ publications, 3600+ citations; Andreas was Visiting Professor in Berlin, Innsbruck, Vienna, London, and Aachen. He is passionate on bringing together Human-Computer Interaction (HCI) and Knowledge Discovery and Data Mining (KDD), with the goal of supporting human intelligence with machine intelligence – to discover new, previously unknown insights into complex biomedical data. Homepage:http://www.hci4all.at; Current main lecture:http://genome.tugraz.at/medical_informatics.shtml


Abstract
Biomedical Informatics can be defined as “the interdisciplinary field that studies and pursues the effective use of biomedical data, information and knowledge for scientific inquiry, problem solving, and decision making, motivated by efforts to improve human health.” However, professionals in the life sciences are faced with an increasing quantity of highly complex, multi-dimensional and weakly structured data. While researchers in Human-Computer Interaction (HCI) and Information Retrieval/Knowledge Discovery in Databases (IR/KDD) have long been independently working to develop methods that can support expert end users to identify, extract and understand information out of this data, it is obvious that an interdisciplinary approach to bring these two fields closer together can yield synergies in the application of these methods to weakly structured complex medical data sets. The aim is to support end users to learn how to interactively analyse information properties and to visualize the most relevant parts – in order to gain knowledge, and finally wisdom, to support a smarter decision making. The danger is not only to get overwhelmed by increasing masses of data, moreover there is the risk of modelling artifacts.



 

 

Visualization & Data Mining for High Dimensional Datasets

Alfred Inselberg
Tel Aviv University
Israel
 

Brief Bio
Alfred Inselberg received a Ph.D. in Mathematics and Physics from the University of Illinois (Champaign-Urbana) then was Research Professor there until 1966. He held research positions at IBM, where he developed a Mathematical Model of Ear (TIME Nov. 74), concurrently having joint appointments at UCLA, USC and later at the Technion and Ben Gurion University. Since 1995 he is Professor at the School of Mathematical Sciences at Tel Aviv University. He was elected Senior Fellow at the San Diego Supercomputing Center in 1996, Distinguished Visiting Professor at Korea University in 2008 and Distinguished Visiting Professor at National University of Singapore in 2011. Alfred invented and developed the multi-dimensional system of Parallel Coordinates for which he received numerous awards and patents (on Air Traffic Control, Collision-Avoidance, Computer Vision, Data Mining). The textbook "Parallel Coordinates: VISUAL Multidimensional Geometry and its Applications", Springer (October) 2009, has a full chapter on Data Mining and was acclaimed, among others, by Stephen Hawking.


Abstract
A dataset with M items has 2^M subsets anyone of which may be the one which satisfies our objectives. With a good data display and interactivity our fantastic pattern-recognition can cut great swaths searching through this combinatorial explosion and also extract insights from the visual patterns. These are the core reasons for data visualization. With parallel coordinates the search for relations in multivariate datasets is transformed into a 2-D pattern recognition problem. The foundations are developed interlaced with applications. Guidelines and strategies for knowledge discovery are illustrated on several real datasets (financial, process control, credit-score, intrusion-detection etc) one with hundreds of variables. A geometric classification algorithm is presented and applied to complex datasets. It has low computational complexity providing the classification rule explicitly and visually. The minimal set of variables required to state the rule (features) is found and ordered by their predictive value. Multivariate relations can be modeled as hypersurfaces and used for decision support. A model of a (real) country’s economy reveals sensitivies, impact of constraints, trade-offs and economic sectors unknowingly competing for the same resources. An overview of the methodology provides foundational understanding; learning the patterns corresponding to various multivariaterelations. These patterns are robust in the presence of errors and that is good news for the applications. We stand at the threshold of breaching the gridlock of multidimensional visualization. The parallel coordinates methodology has been applied to collision avoidance and conflict resolution algorithms for air traffic control (3 USA patents), computer vision (1 USA patent), data mining (1 USA patent), optimization, decision support and elsewhere.



 

 

Issues in Combined Static and Dynamic Data Management

Daniela Nicklas
Carl von Ossietzky Universitaet Oldenburg
Germany
 

Brief Bio
Daniela Nicklas is a Junior Professor for Database and Internet technologies at the Carl von Ossietzky Universität Oldenburg since 2008. Besides her research on context-aware applications and context modeling, she works on data stream processing and sensor-based systems within the application domains of transportation, energy, and ambient environments. She received her PhD (Dr. rer. nat.) in Computer Science 2008 from the University of Stuttgart, working within the Center of Collaboration (SFB 627) "Nexus" under the supervision of Prof. Dr. Bernhard Mitschang. The main contribution if her thesis was architecture and a spatial data model for large-scale integration of context data for location-based, mobile applications. After that, she worked for two years as PostDoc in that Center of Project, co-leading a project on context-aware workflows together with Prof. Dr. Frank Leymann. In 2008, she received an IBM Exploratory Stream Analytics Innovation award for "Data Stream Technology for Future Energy Grid Control". Besides being a committee member in many programme committees of pervasive computing and database conferences and workshops, she helped organizing international conference, like e.g., the Annual IEEE International Conferences on Pervasive Computing and Communications (PerCom) as Vice Program Chair in 2010. She is also a member of the editorial boards of the Elsevier Pervasive Computing and Communication Journal, the International Journal of Pervasive Computing and Communications (Emerald), and the Datenbankspektrum (German Journal on Databases).


Abstract
With the upcoming widespread availability of sensors, more and more applications depend on physical phenomena. Up-to-date real world information is embedded into business processes, in production environments, or in mobile applications, so that such context-aware applications can adapt their behavior to the current situation of their user or environment. Another example are so-called SCADA systems (supervisory control and data acquisition), where complex installations (e.g., energy grids or power plants) are monitored and controlled. In general, data can be managed either by a database management system (DBMS), or directly by the application. The first approach has many advantages: information demands can be declared by queries and kept separately from the application. When the information demand changes, only the query has to be changed (which dramatically decreases software maintenance costs). In addition, a DBMS can optimize the query execution, so that the requested data is retrieved efficiently from the systems. However, if applications depend on real-world data, the amount of data, the update rate, and low latency requirements often prevent the storage in a DBMS. Thus, the data streams from the sensors are managed directly by applications, with all its drawbacks. The goal of data stream management is to provide the same flexibility and data independence for data streams as for stored data. However, despite of performance advantages, DSMS represent no general solution since applications often require the persistent storage of continuous query results as well as a combined processing of current and historical stream data. The performance of integrated database methods, on the other hand, is limited due to the expensive data management of traditional DBMS. A naïve solution would be to federate or integrate both types of systems. However, a closer look shows that such a federation raises many open questions



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