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Keynote Lectures

The Geometry and Topology of Data Analysis
Herbert Edelsbrunner, IST, Austria

Resource-Aware Data Analysis
Katharina Morik, TU Dortmund University, Germany

Social Business Intelligence - OLAP Applied to User Generated Contents
Matteo Golfarelli, University of Bologna, Italy

Model-driven Development of Multi-View Modelling Tools - The MUVIEMOT Approach
Dimitris Karagiannis, University of Vienna, Austria

 

The Geometry and Topology of Data Analysis

Herbert Edelsbrunner
IST
Austria
 

Brief Bio
Herbert Edelsbrunner is Professor at the Institute of Science and Technology Austria.  He graduated from the Graz University of Technology, Austria, in 1982, he was faculty at the University of Illinois at Urbana-Champaign from 1985 through 1999, and Arts and Sciences Professor at Duke University from 1999 to 2012. He co-founded Geomagic in 1996, a software company in the field of Digital Shape Sampling and Processing, which got recently integrated into the 3D printing market.
His research areas are algorithms, computational geometry, computational topology, data analysis, and applications to biology.  He has published three textbooks in the general area of computational geometry and topology.  In 1991, he received the Alan T. Waterman Award from the US National Science Foundation.  In 2006, he received an honorary degree from the Graz University of Technology.  He is a member of the American Academy of Arts and Sciences, of the Germany Academy of Sciences (the Leopoldina), of the Academia Europaea, and a corresponding member of the Austrian Academy of Sciences.


Abstract
Major inroads into the analysis of high-dimensional data have followed initially low-dimensional geometric and topological constructions.  I was personally involved in the following two such developments.
  A. The alpha shapes developed during the 80s and 90s of last century make the connection between a finite sample and the
     space the sample represents.
  B. Persistent homology developed during the last 15 years extends the classical notion of homology to quantify the scale at which a shape has holes.
Both of these concepts can be generalized in many directions, including into higher dimensions, where they can be used to   reason about seemingly non-geometric objects, including medical profiles, images, and text documents.



 

 

Resource-Aware Data Analysis

Katharina Morik
TU Dortmund University
Germany
 

Brief Bio
Katharina Morik is full professor for computer science at the TU Dortmund University, Germany. She earned her Ph.D. (1981) at the University of Hamburg and her habilitation (1988) at the TU Berlin. Starting with natural language processing, her interest moved to machine learning ranging from inductive logic programming to statistical learning, then to the analysis of very large data collections, high-dimensional data, and resource awareness. Her aim to share scientific results supports strongly open source developments. For instance, RapidMiner started out at her lab, which continues to contribute to it. Since 2011 she is leading the collaborative research center SFB876 on resource-aware data analysis, an interdisciplinary center comprising 12 projects, 19 professors, and about 50 Ph D students or Postdocs. She was one of those starting the IEEE International Conference on Data Mining together with Xindong Wu, and was chairing the program of this conference in 2004. She was the program chair of the European Conference on Machine Learning (ECML) in 1989 and one of the program chairs of ECML PKDD 2008. She is in the editorial boards of the international journals “Knowledge and Information Systems” and “Data Mining and Knowledge Discovery”.


Abstract

Algorithms are designed in consideration of restricted resources. These have been computing time, memory space, and computing nodes. With big data, large computing centers and cloud computing have become popular, but they do not diminish the requirements for space- and time-efficient algorithms. The programming scheme of map and reduce is tailored for processing big data in large computer centers. In contrast, sensor networks, smartphones and other mobile devices cannot rely on a stable access to a computer center, hence stressing the memory constraints and bringing to attention additional resources, namely communication bandwidth and energy. Resource-aware data analysis brings together cyber-physical systems and big data analytics. Learning algorithms are designed to exploit massively parallel hardware, to process data in a stream, and to interact with central capacities in a smart, adaptive way.
This talk presents some results on resource-aware data analysis for smart phones. On the one hand, apps are inspected with respect to their energy consumption. This allows users to adapt their behavior to available resources. However, we want to move beyond that. The operating system, the memory organization, and the wireless communication of devices are to be guided by predictions. Based on predictions, the right time to send and receive data, to prefetch files into cache, and to bundle files for submission can be determined, so to maximize the battery duration of smart phones.



 

 

Social Business Intelligence - OLAP Applied to User Generated Contents

Matteo Golfarelli
University of Bologna
Italy
 

Brief Bio

Matteo Golfarelli received the Ph.D. degree for his work on autonomous agents in 1998 from the University of Bologna. Since 2005, he is an associate professor in the same University, teaching information systems, database systems, and data mining. He has published more than 90 papers in refereed journals and international conferences in the fields of pattern recognition, mobile robotics, multi-agent systems, and business intelligence that is now his main research field. Within this area, in the last 15 years he explored many relevant topics such as collaborative and pervasive BI, temporal Data Warehouses, physical and conceptual Data Warehouse design. In particular he proposed the Dimensional Fact Model a conceptual model for Data Warehouse systems that is widely used in both academic and industrial contexts. His current research interests include distributed and semantic data warehouse systems, and social business intelligence and open data warehouses. He joined several research projects on the above areas and has been involved in the PANDA thematic network of the European Union concerning pattern-base management systems.


Abstract

The huge quantity of information, talks, posts, and papers available on the web cannot be ignored by companies. Being aware in near-real time of hot topics and opinions about a product or a topic is strategic for taking better decisions. Unfortunately, this information is totally or partially unstructured, thus it is difficult to be exploited. Most of the commercial solutions are "closed" applications and most of the services are one-shot projects rather than stable monitoring systems that enable a limited exploitation of the information. Practitioners often refer to this family of tools as Opinion Mining software, Sentiment Analysis Software, or Brand Reputation Software. Many companies would prefer a solution that could be integrated in the enterprise information systems and that could be considered as yet another data flow to be included in the Business Intelligence platform and to be queried with the traditional tools that are well-known to the users.

Social business intelligence is the discipline of combining corporate data with user-generated content to let decision-makers improve their business based on the trends perceived from the environment. Setting up a Social BI architecture requires contributions by several areas of computer science such as Information Retrieval, Text Mining, Database, Ontology and Artificial Intelligence The keynote will describe the features of a Social BI architecture, it will survey the research issues related to it and it will go into details about database and big data issues that would allow to create BI like capabilities.



 

 

Model-driven Development of Multi-View Modelling Tools - The MUVIEMOT Approach

Dimitris Karagiannis
University of Vienna
Austria
 

Brief Bio
Dimitris Karagiannis is head of the research group knowledge engineering at the University of Vienna. His main research interests include knowledge management, modelling methods and meta-modelling. Besides his engagement in national and EU-funded research projects Dimitris Karagiannis is the author of research papers and books on Knowledge Databases, Business Process Management, Workflow-Systems and Knowledge Management. He serves as expert in various international conferences and is presently on the editorial board of Business & Information Systems Engineering (BISE), Enterprise Modelling and Information Systems Architectures and the Journal of Systems Integration. He is member of IEEE and ACM and is on the executive board of GI as well as on the steering committee of the Austrian Computer Society and its Special Interest Group on IT Governance. Recently he started the Open Model Initiative (www.openmodels.at) in Austria. In 1995 he established the Business Process Management Systems Approach (BPMS), which has been successfully implemented in several industrial and service companies, and is the founder of the European software- and consulting company BOC (http://www.boc-group.com), which implements software tools based on the meta-modelling approach.


Abstract
As the complexity of modern computer and enterprise systems is ever increasing due to emerging technologies and the need to integrate different systems, modelling tools, designed to encourage modellers in creating models according to the complex reality are of rising importance. Multi-view modelling methods (MVMM) can cope with this complexity by providing visualization, decomposition, and specialization functionality. The creation of a model is decomposed into the creation of several views and integrating them in order to derive the whole model of the system. Keeping the multiple views consistent and providing suitable visualization means is vital for applicability and usability of MVMMs. By contrast, when designing such tools, one is forced to adopt conventional software engineering approaches. The paper at hand tries to contribute filling that research gap by introducing a model-driven approach, tailored to the specifics of designing multi-view modelling tools. A prototypical implementation of the approach enables automatic generation of modelling tools for MVMM using the ADOxx meta modelling platform.



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