Data Mining in Germany IIM Conference, Oct. 24, 2012 Gottfried Schwarz, DLR > Lecture > Author Document > Datewww.DLR.de Chart 1.

Slides:



Advertisements
Similar presentations
Data Mining and the Web Susan Dumais Microsoft Research KDD97 Panel - Aug 17, 1997.
Advertisements

Information Society Technologies Third Call for Proposals Norbert Brinkhoff-Button DG Information Society European Commission Key action III: Multmedia.
Prof. Carolina Ruiz Computer Science Department Bioinformatics and Computational Biology Program WPI WELCOME TO BCB4003/CS4803 BCB503/CS583 BIOLOGICAL.
Department of Mathematics and Computer Science
Overview of Data Mining & The Knowledge Discovery Process Bamshad Mobasher DePaul University Bamshad Mobasher DePaul University.
Data warehouse example
MS DB Proposal Scott Canaan B. Thomas Golisano College of Computing & Information Sciences.
Purdue University Pag. 1 CS 397 Dongyan Xu Department of Computer Science and CERIAS Purdue University Office:
Web Data Management Dr. Daniel Deutch. Web Data The web has revolutionized our world Data is everywhere Constitutes a great potential But also a lot of.
Libraries and Institutional Content Management Systems
1 Tutoriály na ECML PKDD 2007 ECML/PKDD-2007 Call for Tutorials and Workshops We invite proposals for half-day tutorials and full day workshops. Proposals.
Introduction to Data Science Kamal Al Nasr, Matthew Hayes and Jean-Claude Pedjeu Computer Science and Mathematical Sciences College of Engineering Tennessee.
Big Data Course Plans at Purdue Ananth Iyer. Big Data/Analytics Coursera course on Big Data by Bill Howe claims that Big Data involves issues of
LLNL-PRES This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
GIS Lecture 1 Introduction to GIS Buildings. Poly Streams, Line Wells, Point Roads, Line Zoning,Poly MAP SHEETS.
OLAM and Data Mining: Concepts and Techniques. Introduction Data explosion problem: –Automated data collection tools and mature database technology lead.
Data Mining. 2 Models Created by Data Mining Linear Equations Rules Clusters Graphs Tree Structures Recurrent Patterns.
CS598CXZ Course Summary ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign.
gpucomputing.net is a research and development community site dedicated to fostering collaborative and interdisciplinary work on the various disciplines.
Last Words COSC Big Data (frameworks and environments to analyze big datasets) has become a hot topic; it is a mixture of data analysis, data mining,
Data Mining GyuHyeon Choi. ‘80s  When the term began to be used  Within the research community.
Laboratory for Internet Computing Harnessing Distributed, Heterogeneous Information Sources –Data integration with different formats –Extraction of information.
Extracting Key Terms From Noisy and Multi-theme Documents Maria Grineva, Maxim Grinev and Dmitry Lizorkin Institute for System Programming of RAS.
Page 1 WEB MINING by NINI P SURESH PROJECT CO-ORDINATOR Kavitha Murugeshan.
Multimedia Databases (MMDB)
Structure of Study Programmes
Information Systems Basic Core Specialization Clinical Imaging BioInformatics Public Health Computer Science Methods (formal models) Biomedical Decision.
© What do bioinformaticians do?
Chapter 1 Introduction to Data Mining
Structure of Study Programmes Bachelor of Computer Science Bachelor of Information Technology Master of Computer Science Master of Information Technology.
Web Data Management Dr. Daniel Deutch. Web Data The web has revolutionized our world Data is everywhere Constitutes a great potential But also a lot of.
Introduction to Web Mining Spring What is data mining? Data mining is extraction of useful patterns from data sources, e.g., databases, texts, web,
Information Retrieval and Web Search Lecture 1. Course overview Instructor: Rada Mihalcea Class web page:
Social Networks in Most Visible Form. Social Networking Techniques in Business Several social networking techniques can help us in reaching maximum number.
ICDM 2003 Review Data Analysis - with comparison between 02 and 03 - Xindong Wu and Alex Tuzhilin Analyzed by Shusaku Tsumoto.
Signal Processing Emphasis Group Robert Moorhead Roger King Joe Picone Nick Younan Jim Fowler Lori Bruce Jenny Du.
Last Words DM 1. Mining Data Steams / Incremental Data Mining / Mining sensor data (e.g. modify a decision tree assuming that new examples arrive continuously,
Computing Ontology Part II. So far, We have seen the history of the ACM computing classification system – What have you observed? – What topics from CS2013.
TagLearner: A P2P Classifier Learning System from Collaboratively Tagged Text Documents Haimonti Dutta 1, Xianshu Zhu 2, Tushar Muhale 2, Hillol Kargupta.
Big Data Analytics Carlos Ordonez. Big Data Analytics research Input? BIG DATA (large data sets, large files, many documents, many tables, fast growing)
27-18 września Data Mining dr Iwona Schab. 2 Semester timetable ORGANIZATIONAL ISSUES, INDTRODUCTION TO DATA MINING 1 Sources of data in business,
Mining real world data Web data. World Wide Web Hypertext documents –Text –Links Web –billions of documents –authored by millions of diverse people –edited.
DBSQL 9-1 Copyright © Genetic Computer School 2009 Chapter 9 Data Mining and Data Warehousing.
Data Visualization Michel Bruley Teradata Aster EMEA Marketing Director April 2013 Michel Bruley Teradata Aster EMEA Marketing Director.
Data and Applications Security Developments and Directions Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #15 Secure Multimedia Data.
Similarity Access for Networked Media Connectivity Pavel Zezula Masaryk University Brno, Czech Republic.
OMIS 694, Big Data Analytics
Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas Cyber Security Research on Engineering Solutions Dr. Bhavani.
WHAT IS DATA MINING?  The process of automatically extracting useful information from large amounts of data.  Uses traditional data analysis techniques.
A New Generation of Artificial Neural Networks.  Support Vector Machines (SVM) appeared in the early nineties in the COLT92 ACM Conference.  SVM have.
Image Information Mining Conference ESA-EUSC-JRC 2012 DLR Welcome and Introductory Talk Gottfried Schwarz > Lecture > Author Document > Datewww.DLR.de.
3D Motion Classification Partial Image Retrieval and Download Multimedia Project Multimedia and Network Lab, Department of Computer Science.
Indiana University School of Indiana University ECCR Summary Infrastructure: Cheminformatics web service infrastructure made available as a community resource.
CS570: Data Mining Spring 2010, TT 1 – 2:15pm Li Xiong.
Book web site:
1 Panel on Merge or Split: Mutual Influence between Big Data and HPC Techniques IEEE International Workshop on High-Performance Big Data Computing In conjunction.
Term Project Proposal By J. H. Wang Apr. 7, 2017.
MATLAB Distributed, and Other Toolboxes
Knowledge Discovery, Machine Learning, and Social Mining
Introduction C.Eng 714 Spring 2010.
Course Summary (Lecture for CS410 Intro Text Info Systems)
Datamining : Refers to extracting or mining knowledge from large amounts of data Applications : Market Analysis Fraud Detection Customer Retention Production.
Special Topics in Data Mining Applications Focus on: Text Mining
IXPUG Abstract Submission Instructions
Research Areas Christoph F. Eick
Data Warehousing and Data Mining
OMIS 665, Big Data Analytics
Parallel Analytic Systems
Dept. of Computer Science University of Liverpool
Information Retrieval and Web Design
Presentation transcript:

Data Mining in Germany IIM Conference, Oct. 24, 2012 Gottfried Schwarz, DLR > Lecture > Author Document > Datewww.DLR.de Chart 1

German “Data Mining” in Wikipedia and Google Wikipedia: [English] data mining = [German] Data-Mining Google (Oct. 23, 2012): “Data Mining” Germany  34.0 million hits “Data Mining” Germany business  16.5 million hits “Data Mining” Germany economics  9.8 million hits “Data Mining” Germany “computer science”  7.4 million hits “Data Mining” Germany chemistry  6.5 million hits “Data Mining” Germany geoscience  5.7 million hits “Data Mining” Germany physics  4.9 million hits > Lecture > Author Document > Datewww.DLR.de Chart 2

My Impressions: Data Mining in Germany University institutes: computer science (code and tool development) economics / business administration (teaching, use of existing tools) mathematics / statistics (development of methods) natural sciences (special data mining applications) Research establishments MPI INF, Fraunhofer IAIS, KIT, DLR (application of data mining) Authorities and organizations Webpage of the German Federal Statistical Office: “Data Mining”  3 hits Webpage of the German Research Community (DFG):  17 hits Industry Google: “data mining” BMW | Siemens  686,000 | 840,000 hits Google: “Gartner report” “data mining” Germany  188,000 hits > Lecture > Author Document > Datewww.DLR.de Chart 3

Our Work: Image Information Mining (Satellite Images) “Image processing” versus “image mining” Image processing example: Take images of forested areas. How much biomass do we have? Image pre-processing, classification and analysis, model support Image mining example: Search for North Sea images with oil slicks. How much improvement do we have over the years? Feature extraction, active learning, semantic annotation, candidate image retrieval and analysis > Lecture > Author Document > Datewww.DLR.de Chart 4

European Conference on Data Mining (ECDM’12) Parallel and distributed data mining algorithms Data streams mining Graph mining Spatial data mining Text video, multimedia data mining Web mining Pre-processing techniques Visualization Security and information hiding in data mining Databases Bioinformatics Biometrics Image analysis Financial modeling Forecasting Classification Clustering Social Networks Educational data mining Core Data Mining Topics Data Mining Applications > Lecture > Author Document > Datewww.DLR.de Chart 5

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2012) - Tutorials Advanced Topics in Data Stream Mining Advanced Topics in Ensemble Learning Decomposing Binary Matrices: Where Linear Algebra Meets Combinatorial Data Mining Mining Deep Web Repositories PAC-Bayesian Analysis and Its Applications Random Projections for Machine Learning and Data Mining: Theory and Applications Probabilistic Modeling of Ranking Understanding and Managing Cascades on Large Graphs > Lecture > Author Document > Datewww.DLR.de Chart 6

Keynote Lectures during this Conference Human-machine communication Data Mining in Astrophysics Establishing a Challenge Evaluation program for the IIM Community Detection of Compound Structures in Very High Spatial Resolution Images Visual Analytics East Japan Great Earthquake and Tsunami: Detection of hazards by polarimetric SAR > Lecture > Author Document > Datewww.DLR.de Chart 7

Outlook: How does all this fit together? Collaboration between satellite image miners and other communities? For instance, advanced methods of machine learning, knowledge discovery in databases, and remote sensing? Do we have common goals? > Lecture > Author Document > Datewww.DLR.de Chart 8