Dr. Christian Beecks | 24.11.2015 | 16:00 Uhr | E.2.42

Abstract

With the advent of social networks and the advancement of powerful internet-enabled mobile devices, millions of users are able to easily generate, process, and share multimedia data at billion-scale every single day. The resulting multitude and versatility of multimedia data made available in the Internet challenge todays’ data management and analysis algorithms. In many research and application areas including information retrieval, data mining, and computer vision, users are no longer satisfied with keyword-based access but want to search, browse, explore, and analyze multimedia data according to content-based characteristics. One fundamental operation underlying many data analysis algorithms is similarity search which aims at retrieving the most similar multimedia objects with respect to a query. In order to carry out similarity search for query-like multimedia objects, the way of modeling similarity is of major significance due to its impact on efficiency and effectiveness.

In this talk, I will present my ongoing research in this fascinating field and highlight future research directions. More specifically, I will show how to approach similarity between multimedia data objects by means of gradient-based signatures in order to facilitate data analysis with high efficiency and efficacy.

Christian Beecks is a postdoctoral researcher in the data management and data exploration group at RWTH Aachen University, Germany. His research interests include efficient and adaptive multimedia data analysis, distance-based multimedia indexing and query processing, and real-time data management.

Prof. Fabio Stella | 11.11.2015 | 10:00 Uhr | E.1.42

Abstract

Non stationary continuous time Bayesian networks are presented and described. They allow to model systems where conditional independence relationships are allowed to change over time at discrete points in time. They build on the main blocks of continuous time Bayesian networks and non stationary dynamic Bayesian networks. The seminar presents the problem of non-stationary structural learning for such probabilistic graphical models and describes solution algorithms for three different settings. Furthermore, we present preliminary results of non stationary structural learning of Continuous Time Bayesian Networks on the following biological datasets; drosophila saccharomyces cerevisiae and songbird.

Fabio Stella is an associate professor at the Dipartimento di Informatica, Sistemistica e Comunicazione of the Università degli Studi di Milano-Bicocca. His research focuses on models and algorithms for data analysis and decision making under uncertainty in the areas of Business Intelligence, Data and Text Mining and Computational Finance. In the winter term 2015/16 he is giving the course 625.605 – Business Intelligence in Klagenfurt for the second time.

Prof. Tadeusz Morzy | 23.10.2015 | 14:00 Uhr | E.2.69

Abstract

Many applications require processing and analyzing sequential data. Examples include the analysis of passenger traveling histories, stock market prices, purchases of customers over time, meteorological events, workflow and RFID logs, etc. Recently, issues related to warehousing and analytical processing (OLAP) of sequential data have received growing attention. Particularly, the concept of Sequence OLAP (SOLAP) has been proposed that support OLAP processing of different kinds of aggregate queries on sequential data. The main feature distinguishing SOLAP from traditional OLAP is that data sequences managed by an SOLAP system are characterized by subsequence patterns they possess. The SOLAP systems allow to group data sequences based on patterns they possess and apply aggregate functions to each group. This kind of SOLAP queries are called a sequential pattern-based aggregate (PBA) queries. The processing of PBA queries is expensive due to the fact that they require full scan of all stored sequences. The natural question is how to efficiently evaluate this kind of queries?

The talk focuses on the new index structure supporting processing of sequential pattern-based aggregate queries. The structure of the index will be presented as well as classes of pattern-based aggregate queries supported by the index will be discussed. Finally, the performance of PBA queries using the proposed index will be presented.

Tadeusz Morzy is a professor in the Computing Science Department of Poznan University of Technology. He received his M. Sc., Ph. D. and Polish Habilitation from the Technical University of Poznań, Poland. He has held visiting positions at the Loyola University, New Orleans, Klagenfurt University Austria, University La Sapienza Italy, and Free University Amsterdam. He has authored and coauthored over 100 papers on databases, data mining, and data warehousing. He is co-author of a book on „Concurrency Control in Distributed Database Systems“ by North-Holland, editor and coauthor of “Handbook on Data Management” by Springer, and author of “Data Mining: Methods and Algorithms” (in Polish). He served as General Chair of the 2nd and 16th ADBIS Conferences (1998, 2012), and has served/serves on numerous program committees of international conferences and workshops. His research interests include data mining, data warehousing, transaction processing in database and data warehouse systems, access methods and query processing for databases, database optimization and performance evaluation.

Der Rückblick zum TEWI-Kolloquium von Laurens Rook am 17.7.2015 beinhaltet die Folien:

Abstract:

In recent years many times sustainability and renewable energy consumption have been set on the agenda. However, the pressing issue how to make people reduce their amount of energy consumed – or their switching  towards green alternatives – has received far less research attention. The academic discipline of behavioral economics has much to offer to this debate. In the presentation we will summarize prior research on the role of individual differences and various pricing and framing techniques that have proven to be helpful in making people switch to green energy. We will also address challenges and future directions in behavioral energy economics.

Bio:

Laurens Rook is Assistant Professor at Delft University of Technology, the Netherlands. He received his Ph.D. from the Erasmus University Rotterdam (in 2008), and his bachelor and master’s degrees from the  University of Amsterdam, the Netherlands (in 2001; MA Thesis on Mass Psychology in Fine Art and Advertising).  His research focuses on herd and imitative behavior in creative context, and is published in the Creativity Research Journal.  His second focus is on behavioral informatics. Laurens collaborates with the Learning Agents Research Group at Erasmus (LARGE). A recent paper on using social media apps to make people consume green energy  (together with University of Connecticut, USA) was awarded best poster  award (2nd prize, the 2014 Conference on Information Systems and Technology).  He lectures on Research Methodology,   Statistics, and Group Dynamics, but also is a graduated professional artist (Academy of Arts Rotterdam, 1997) with collected work in the Municipal Archives of Rotterdam, the Netherlands, and the National Art Collection of Ireland.

Prof. Tsvi Kuflik | 8th October 2015 | 16:00 | E.1.42

Abstract:
Over the years, the area of user modeling (and later on recommendation systems) produced a variety of user modeling techniques. These techniques were developed for modeling and representing the users in order to better understand their needs and provide them with personalized services. The common techniques in use are collaborative filtering and content/feature based, while in specific domains we can find also case-based, demographic and overlay approaches. However, the knowledge represented by these techniques is quite limited. In recent years, with the advent of web 2.0 and the social and semantic web, personal information becomes widely available online in various forms. This poses opportunities as well as major challenges for the classical user modeling approaches – how to make use of this information to enhance user modeling? As a potential solution to the problem, we are exploring the idea of graph-based user modeling representation, as an integrative framework that enables standard and simple representation of users‘ characteristics, not limited to a specific technique. In various studies we demonstrated the potential benefits of this approach and it’s possible contribution to user modeling and recommender systems. The talk will briefly present the general idea of graph-based user modeling as well as research results that demonstrate its contribution to a variety of domains and scenarios.

Short c.v.
Prof. Tsvi Kuflik heads the Information Systems Dept. at The University of Haifa. Over the past ten years, the focus of his work was on ubiquitous user modeling applied to cultural heritage. In the course of his work, a “Living Lab” has been developed at the University of Haifa – a museum visitors’ guide system was developed for the Hecht museum. It is available for visitors on a daily basis and serves also as a test bed for experimenting with novel technologies in the museum. Currently, the system is being used for research on Social Signal Processing where signals transmitted by devices carried by the visitors are used for modeling group behavior, in order to reason about the state of the group visit. Another research direction focusses on the use of intelligent user interfaces in ubiquitous computing within the “living lab”. Where issues like interaction with large, situated displays; interrupt management; navigation support; temporal and lifelong aspects of ubiquitous user modeling are studied. Tsvi got BSc. and MSc. In computer science and PhD. In information systems from Ben-Gurion University of the Negev, Israel. Over the years Tsvi collaborated with local and international researchers, supervised graduate students working with him on this research, organized the PATCH workshops series (Personal Access To Cultural Heritage) and published about 200 scientific papers, out of them 30 papers about this specific research. Tsvi is also a distinguished ACM scientist and a senior IEEE member.

Markus Schedl | Thursday, 1st October 2015 | 14:00 | E.2.42

Abstract:
Social media represent an unprecedented source of information about every topic of our daily lives. Since music plays a vital role for almost everyone, information about music items and artists is found in abundance in user-generated data. In this talk, I will report on our recent research on exploiting social media to extract music-related information, aiming to improve music retrieval and recommendation. More precisely, I will elaborate on the following questions:

• Which factors are important to human perception of music?
• How to extract and annotate music listening events from social media, in particular microblogs?
• What can this kind of data tell us about the music taste of people around the world?
• How to make accessible music listening data from social media in an intuitive way?
• How to build music recommenders tailored to user characteristics?

Bio:
Markus Schedl is an associate professor at the Johannes Kepler University Linz / Department of Computational Perception. He graduated in Computer Science from the Vienna University of Technology and earned his Ph.D. in Technical Sciences from the Johannes Kepler University Linz. Markus further studied International Business Administration at the Vienna University of Economics and Business Administration as well as at the Handelshögskolan of the University of Gothenburg, which led to a Master’s degree.
Markus (co-)authored more than 100 refereed conference papers and journal articles (among others, published in ACM Multimedia, SIGIR, ECIR, IEEE Visualization; Journal of Machine Learning Research, ACM Transactions on Information Systems, Springer Information Retrieval, IEEE Multimedia). Furthermore, he is associate editor of the Springer International Journal of Multimedia Information Retrieval and serves on various program committees and reviewed submissions to several conferences and journals (among others, ACM Multimedia, ECIR, IJCAI, ICASSP, IEEE Visualization; IEEE Transactions of Multimedia, Elsevier Data & Knowledge Engineering, ACM Transactions on Intelligent Systems and Technology, Springer Multimedia Systems).
His main research interests include web and social media mining, information retrieval, multimedia, and music information research.
Since 2007, Markus has been giving several lectures, among others, „Music Information Retrieval“, „Exploratory Data Analysis“, „Multimedia Search and Retrieval“, „Learning from User-generated Data“, „Multimedia Data Mining“, and „Intelligent Systems“. He further spent guest lecturing stays at the Universitat Pompeu Fabra, Barcelona, Spain, the Utrecht University, the Netherlands, the Queen Mary, University of London, UK, and the Kungliga Tekniska Högskolan, Stockholm, Sweden.

Contact Details:
Dr. Markus Schedl
Deptartment of Computational Perception
Johannes Kepler University
Altenberger Straße 69
4040 Linz, Austria
Tel.: +43 732 2468 1512
e-mail: markus.schedl@jku.at
Website: http://www.cp.jku.at/people/schedl
Full publication record is available at http://www.cp.jku.at/people/schedl/publications.html