Keynote Speakers
 

Abstract

Binary data have been occupying a special place in the domain of data analysis. Analysis of binary data sets, however, generally leads to NP-complete/hard problems. Consequently, the focus here is on effective heuristics for reducing the problem size.
Matrix factorization or factor analysis is an important task helpful in the analysis of high dimensional real world data. There are several well known methods and algorithms for factorization of real data but many application areas including information retrieval, pattern recognition and data mining require processing of binary rather than real data. Unfortunately, the methods used for real matrix factorization fail in the latter case. In this paper we introduce background for binary matrix factorization.
In order to perform object recognition (no matter which one) it is necessary to learn representations of the underlying characteristic components. Such components correspond to object-parts, or features. These data sets may comprise discrete attributes, such as those from market basket analysis, information retrieval, and bioinformatics, as well as continuous attributes such as those in scientific simulations, astrophysical measurements, and sensor networks.
The feature extraction if applied on binary datasets, addresses many research and application fields, such as association rule mining, market basket analysis, discovery of regulation patterns in DNA microarray experiments, etc. So called bars problem is used as the benchmark. Set of artificial signals generated as a Boolean sum of given number of bars is analyzed by these methods. Here we will concentrate on the case of black and white pictures of bars combinations represented as binary vectors, so the complex feature extraction methods are unnecessary.
Many applications in computer and system science involve analysis of large scale and often high dimensional data. When dealing with such extensive information collections, it is usually very computationally expensive to perform some operations on the raw form of the data. Therefore, suitable methods approximating the data in lower dimensions or with lower rank are needed. In the following, we focus on the factorization of hight-dimensional binary data or high order binary tensors.

Bio

Prof. RNDr. Václav Snášel, CSc. received the Ph.D. degree in Algebra from the Masaryk University Brno in 1991. Currently he is a full professor of the Faculty of Electrical Engineering and Computer Science at VSB-Technical University of Ostrava. From 2001 he is a visiting scientist in the Institute of Computer Science, Academy of Sciences of the Czech Republic. From 2003 he is vice-dean for Research and Science at Faculty of Electrical Engineering and Computer Science.
V. Snášel has published more than 350 papers and books on data modelling, Ontology, Knowledge Management, Databases, Multimedia, Information Retrieval, Neural Networks, Data Compression and File Organization. His research interests include also information retrieval, semistructured data, evolutionary computing and indexing methods. He supervised many Ph.D. students, and Ph.D. students outside Czech Republic (Jordan, Yemen, Slovakia, Ukraine and Vietnam). According to the Erdös Number Project, his Erdös number is 3. He is a member of IEEE, ACM, SIAM and AMS.

He participated in the project MS-2000 Mossbauer spectrometer for Russian space mission "MARS-96" see http://www.mossp2000.com/index.html

V. Snášel is Editor in Chief of the following journals:
International Journal of Grid and Utility Computing (IJGUC)
International Journal of Autonomic Computing (IJAC)

He has recently served as a Chair, International Chair and member of program committees of a number of international conferences, e.g. Sofsem, (Springer), ADBIS (Springer), ICDIM (IEEE), ECIR (Springer), ISDA (IEEE), CISIM (IEEE), NAFIPS (IEEE), Co-Chair,Intelligent Web Interaction Workshop 2007, Silicon Valley, USA, (IEEE/ACM), CIS (IEEE), Chair, DEXA - ETID 2007, 2008, (IEEE), ICCIT 2008, (IEEE), ICCSA 2008, (Springer), General Chair, CISIM 2008, (IEEE), SMCia 2008, (IEEE), IAS 2008, (IEEE), AWIC 2008, (Springer), Chair, DEXA - ETID 2008, (IEEE), International Chair, ISDA 2008, (IEEE), HIS 2008, (IEEE), ADBIS 2008 (Springer), etc.
He is a member of scientific board of Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czech Republic, Faculty of Science, Palacky University, Olomouc, Czech Republic and Faculty of Informatics and Statistics, University of economics, Prague, Czech Republic.

 

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