Get-Together mit den Erstsemestern des Master Wirtschaftsinformatik Research Group Data and Web Science

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1 Get-Together mit den Erstsemestern des Master Wirtschaftsinformatik Research Group Data and Web Science Universität Mannheim DWS Research Group Slide 1

2 Teaching Overview The DWS Group offers the following courses for master students: Semantic Web Technologies Web Mining Text Analytics Data Mining and Matrices Web Data Integration Data Mining II Web Search / IR Large-Scale Data Management Knowledge Management Decision Support Databases II Data Mining I Offered this semester. Universität Mannheim DWS Research Group Slide 2

3 Data and Web Science Research Group 5 Professors 8 Post-docs 19 PhD students Universität Mannheim DWS Research Group Slide 3

4 Data and Web Science Research Group Research Goal Overall Research Goal: Understand heterogeneous data in order to improve applications using knowledge Information Extraction Data Integration Data Mining & Reasoning Integrated Schema.org Data Integrated Web Tables and Text Data Applications Web Search Data Analytics Recommender Systems Process Management Universität Mannheim DWS Research Group Slide 4

5 Data and Web Science Research Group Research Areas - Artificial Intelligence (Prof. Heiner Stuckenschmidt) knowledge representation formalisms and reasoning techniques for information extraction and integration - Data Analysis (Prof. Rainer Gemulla) methods for analyzing and mining large datasets as well as their practical realizations and applications - Natural Language Processing (Prof. Simone Ponzetto) knowledge acquisition from heterogeneous Web sources and its application to text understanding and search Universität Mannheim DWS Research Group Slide 5

6 Data and Web Science Research Group Research Areas - Web-based Systems (Prof. Chris Bizer) technical and empirical questions concerning the evolution of the World Wide Web from a medium for the publication of documents into a global dataspace - Web Data Mining (Prof. Dr. Heiko Paulheim) using web data as background knowledge in data mining, and data mining methods to create and improve large-scale knowledge bases Universität Mannheim DWS Research Group Slide 6

7 Data and Web Science Research Group Webpage Universität Mannheim DWS Research Group Slide 7

8 Teaching offer for Master students (HWS 2016) - IE 500: Data Mining 1 Discover interesting patterns in data (classification, clustering, etc.) - IE 560: Decision Support Formulate and algorithmically solve decision making problems - IE 650: Semantic Web Technologies Understand the vision of the Semantic Web - IE 661: Text Analytics Automatically process natural language - CS 560: Large-Scale Data Management Fundamental concepts and paradigms for large-scale data management - IE 670: Web Data Integration Consolidate data from heterogeneous sources into a single representation Universität Mannheim DWS Research Group Slide 8

9 IE 500: Data Mining 1 - We are drowning in data, but starving for knowledge. (John Naisbitt, 1982) But how do we get from data to knowledge? - Data mining is nothing else than torturing the data until it confesses. (Fred Menger, year unknown) Universität Mannheim DWS Research Group Slide 9

10 IE 500: Data Mining 1 - Lecture contents the basics of torturing data : Classification: Will your bank grant you a loan? Frequent pattern mining: Which products to place together in a supermarket to maximize customer purchases? Text Mining: Do students on Twitter like or dislike this lecture? Clustering: How to automatically organize your MP3 collection? - Student project: Torture some data of your choice. - Teaching staff: Prof. Dr. Heiko Paulheim (Lectures) Oliver Lehmberg (Exercises) Universität Mannheim DWS Research Group Slide 10

11 IE 560: Decision Support - Decision-making is an important part of all science-based professions, where specialists apply their knowledge in a given area to make informed decisions. - In the Lecture, we look at models that help to formulate and algorithmically solve decision making problems, i.e. that find a solution that maximizes the expected benefit of the outcome. - Topics include: Planning Problems and Algorithms Probabilistic Graphical Models Decision Theory and Decision Networks Game Theory and Mechanism Design Universität Mannheim DWS Research Group Slide 11

12 IE 560: Decision Support action = arg max a EU ( a e) Universität Mannheim DWS Research Group Slide 12

13 IE 560: Decision Support - Lecture: Mondays 12:00 13:30, B6, A Exercises: Mondays 13:45 15:15, B6, A Teaching staff: Prof. Dr. Heiner Stuckenschmidt (Lecture) Dr. Melisachew Wudage Chekol (Exercises) - Literature: Stuart Russel and Peter Norvig: Artificial Intelligence A modern Approach. Pearson Chapters 2,7,10,11 and A Reader will be available Universität Mannheim DWS Research Group Slide 13

14 IE 650: Semantic Web Technologies - Understanding the vision of the Semantic Web - Acquaintance with foundations of W3C standards for building semantic web applications Data Integration and Access: XML, RDF and SPARQL Knowledge Representation: RDFS and OWL - Overview of advanced topics in data and knowledge representation Ontology Management: Engineering, Learning and Alignment - Programming skills and IT competence: practical usage of technologies for building semantic web applications Universität Mannheim DWS Research Group Slide 14

15 IE 650: Semantic Web Technologies - Lecture: Tuesdays , B6, A Exercise: Fridays, , B6, A Teaching staff: Prof. Dr. Heiko Paulheim (Lecture) Exercises: Dr. Anna Lisa Gentile, André Melo (Exercises) - Prerequisites: basic programming skills (e.g. Java) - Passing the course Team project: applications of semantic technologies (50%) Written exam: passing with grade 4.0 or better (50%) - Optional: ungraded weekly assignments (mostly theoretical) and tutorial lessons to help you preparing for the written exam Universität Mannheim DWS Research Group Slide 15

16 IE 660: Text Analytics - Methods to automatically process natural language from a computational / algorithmic perspective Key topics - Modeling the role and function of words within sentence structure (morphology, syntax/parsing) - Understanding meaning in context (computational semantics) - Applications Information extraction Machine Translation Topic modeling Universität Mannheim DWS Research Group Slide 16

17 IE 660: Text Analytics - Lecture: Tuesday 15:30 17:00, B6, A Exercises: Wednesday 13:45 15:15, B6, A Course personnel: Prof. Dr. Simone Paolo Ponzetto Dr. Stefano Faralli, Dr. Sanja Štajner - Literature: D. Jurafsky, J. H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics (2nd ed.), Prentice-Hall, C. Manning and H. Schütze, Foundations of Statistical Natural Language Processing, MIT Press. Cambridge, MA: May Universität Mannheim DWS Research Group Slide 17

18 CS 704: Process Mining Seminar Topic Discovery, conformance, and enhancement of business processes more information is provided on our website: Goals Read, understand, and explore scientific literature Summarize a current research topic in a concise report (10-15 pages) Requirements No programming skills are required but they might be helpful Lectures such as Process Management are recommended Schedule Select your preferred topics and register by Sep 11 Organizers Prof. Dr. Heiner Stuckenschmidt Timo Sztyler (timo@informatik.uni-mannheim.de) Universität Mannheim DWS Research Group Slide 18

19 CS 704: Demand Forecasting Seminar - Topic: demand forecasting methods and approaches Forecasting during Promotions, Forecasting with Neural Networks, Bayesian Structural Time Series Forecasting, Short-term Forecasting (e.g. load, traffic), Hierarchical Forecasting, Combinatorial Forecasting, Judgmental Forecasting, etc. - Goals read, understand, and explore scientific literature summarize a current research topic in a concise report (15 pages) - Registration Deadline: Organizers Prof. Dr. Heiner Stuckenschmidt Jakob Huber (jakob@informatik.uni-mannhem.de) Universität Mannheim DWS Research Group Slide 19

20 CS 707: Data and Web Science Seminar Learn about recent advancements in data and web science Read, understand, explore, present, and peer review scientific literature This term: Statistical relational learning in the context of information extraction and knowledge graphs Topics: (open) information extraction, matrix/tensor factorizations, graphical models, rule mining, graph mining, learning textual patterns, Check out website and register until Wednesday (Sep 7) Instructors: R. Gemulla, Y. Wang, K. Gashteovski Universität Mannheim DWS Research Group Slide 20

21 CS 709: Digital Humanities Seminar Topic Topics at the border between text mining and (computer-assisted) humanities more information is provided on our website: Goals Read, understand, and explore scientific literature Summarize a current research topic in a concise report (10-15 pages) Requirements No programming skills are required but they might be helpful Lectures such as Text Analytics are recommended Schedule Select your preferred topics and register by Sep 11 Organizers Prof. Dr. Simone Paolo Ponzetto Federico Nanni (federico@informatik.uni-mannheim.de) Universität Mannheim DWS Research Group Slide 21

22 Team Project Your Personal Assistant for Health Care Topic Goals Your Personal Assistant for Health Care in Everyday Live Recognize relevant activities during the daily routine Development of an application (Android) that provides recommendations Requirements Programming skills are required (e.g., Java) Lectures such as databases, algorithms and data structures, data mining and computer science are recommended Schedule The project starts in the first week of October (12 month, 6 slots) Organizers Prof. Dr. Heiner Stuckenschmidt Timo Sztyler (timo@informatik.uni-mannheim.de) Universität Mannheim DWS Research Group Slide 22

23 Team Project Factual Text Analytics Extract factual structured knowledge from large unstructured text corpora Develop a system to analyze, visualize, and explore facts Instructors: R. Gemulla, Y. Wang, K. Gashteovski Example: Who built the pyramids? Universität Mannheim DWS Research Group Slide 23

24 Team Project Intelligent System Maintenance Master Team Project: Mining and Analyzing Data for Intelligent System Maintenance Content: Development of a Industry 4.0 information system which mines process data of tool machines in factories and visualize usage and maintenance information Goal: Big data analysis of machines to improve user experience and industrial maintenance processes Project Set-Up: Organizers: Prof. Dr. Simone Paolo Ponzetto / Dr. Christian Bartelt / Fabian Burzlaff Evaluation Partner: Trumpf GmbH Duration: 6 Month (planned) Size: 4-5 Master Students with technical study foci Prerequisites: Comprehensive Programming Skills (e.g. in Java) and/or Frontend Development Universität Mannheim DWS Research Group Slide 24

25 The DWS group continuously hires good students. - To work on: Data and Web Mining projects Information Extraction and Integration projects Knowledge Representation and Reasoning projects Implement open source tools h/month contracts are possible. - Contact PostDoc or Professor responsible for the project/area that you are interested in. Include CV and overview about your marks. - Good start for writing your master thesis within group. Universität Mannheim DWS Research Group Slide 25