RSS-Feed en-gb TYPO3 News Wed, 19 Dec 2018 14:49:38 +0000 Wed, 19 Dec 2018 14:49:38 +0000 TYPO3 EXT:news news-2105 Fri, 10 Aug 2018 09:58:00 +0000 Data Science Conference LWDA 2018 in Mannheim https://dws.informatik.uni-mannheim.deen/news/singleview/detail/News/data-science-conference-lwda-2018-in-mannheim-1/ The Data and Web Science Group is hosting the Data Science Conference LWDA 2018 in Mannheim on August 22-24, 2018.

LWDA, which expands to „Lernen, Wissen, Daten, Analysen“ („Learning, Knowledge, Data, Analytics“), covers recent research in areas such as knowledge discovery, machine learning & data mining, knowledge management, database management & information systems, information retrieval. 

The LWDA conference is organized by and brings together the various special interest groups of the Gesellschaft für Informatik (German Computer Science Society) in this area. The program comprises of joint research sessions and keynotes as well as of workshops organized by each special interest group.

Further information can be found on the conference website:

Download the conference poster.

Other Topics - Künstliche Intelligenz I Topics - Data Mining Topics - Decision Support Topics - Web Search and IR Chris Heiner Rainer Simone
news-2045 Mon, 08 Jan 2018 12:32:58 +0000 Papers accepted at PerCom 2018 https://dws.informatik.uni-mannheim.deen/news/singleview/detail/News/papers-accepted-at-percom-2018/ We have a few papers accepted at the 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom), one of the top-tier conferences in the field of Pervasive Computing:


Main Conference


Hips Do Lie! A Position-Aware Mobile Fall Detection System
(Christian Krupitzer, Timo Sztyler, Janick Edinger, Martin Breitbach, Heiner Stuckenschmidt and Christian Becker)

NECTAR: Knowledge-based Collaborative Active Learning for Activity Recognition
(Gabriele Civitarese, Claudio Bettini, Timo Sztyler, Daniele Riboni, and Heiner Stuckenschmidt)


Satellit Events (Workshops)


Towards Systematic Benchmarking of Activity Recognition Algorithms
(Timo Sztyler, Christian Meilicke and Heiner Stuckenschmidt)

Modeling and Reasoning with ProbLog: An Application in Recognizing Complex Activities
(Timo Sztyler, Gabriele Civitarese and Heiner Stuckenschmidt)

Improving Motion-based Activity Recognition with Ego-centric Vision
(Alexander Diete, Timo Sztyler, Lydia Weiland and Heiner Stuckenschmidt)

Publications Heiner Research
news-1872 Tue, 25 Apr 2017 10:28:00 +0000 Paper accepted at IJCAI 2017 https://dws.informatik.uni-mannheim.deen/news/singleview/detail/News/paper-accepted-at-ijcai-2017/ We have a paper accepted at the 26th International Joint Conference on Artificial Intelligence (IJCAI), the premier conference in the field of AI:

  • Sanja Štajner, Simone Paolo Ponzetto and Heiner Stuckenschmidt: Automatic Assessment of Absolute Sentence Complexity.

The work presented in the paper is a collaboration between the NLP and AI groups of DWS in the context of project C4 of the Collaborative Research Center SFB 884 on "Political Economy of Reforms"



Research Publications Simone Heiner
news-1824 Fri, 24 Feb 2017 10:45:50 +0000 Journal Papers on Demand Forecasting and Process Model Management accepted https://dws.informatik.uni-mannheim.deen/news/singleview/detail/News/journal-papers-on-demand-forecasting-and-process-model-management-accepted/ Two Journal Papers from the Artificial Intelligence Group have been accepted recently

The paper " Cluster-based hierarchical demand forecasting for perishable goods by Jakob Huber Alexander Gossmann and Heiner Stuckenschmidt has been accepted for Publication in Elseviers' Expert Systems with Applications(Impact Factor . 2.981)

The Paper "Overcoming Individual Process Model Matcher Weaknesses Using Ensemble Matching" By Christian Meilicke, Henrik Leopold, Elena Kuss and Heiner Stuckenschmidt has been accepted in Elsevier's Decision Support Systems (Impact Factor 2.604)

Research Publications Heiner
news-1773 Tue, 13 Dec 2016 10:18:15 +0000 Paper accepted at ICSC 2017 https://dws.informatik.uni-mannheim.deen/news/singleview/detail/News/paper-accepted-at-icsc-2017/ Our paper on "Domain Adaptation for Automatic Detection of Speculative Sentences" by Sanja Štajner, Goran Glavas, Simone Paolo Ponzetto and Heiner Stuckenschmidt has been accepted as a full paper for the 11th International Conference on Semantic Computing (IEEE ICSC 2017).

Research Publications Heiner Simone
news-1772 Mon, 12 Dec 2016 15:12:18 +0000 Paper Accepted for PerCom 2017 https://dws.informatik.uni-mannheim.deen/news/singleview/detail/News/paper-accepted-for-percom-2017/ The Paper "Online Personalization of Cross-Subjects based Activity Recognition Models on Wearable Devices" by Timo Sztyler and Heiner Stuckenschmidt has been accepted as a full paper for the IEEE International Conference on Pervasive Computing and Communications 2017 (PerCom'17). Research Publications Heiner news-1747 Tue, 15 Nov 2016 08:14:22 +0000 mayato GmbH becomes Industry Partner of Mannheim Master in Data Science https://dws.informatik.uni-mannheim.deen/news/singleview/detail/News/mayato-gmbh-becomes-industry-partner-of-mannheim-master-in-data-science-1/ Die Analyse und Auswertung von großen, oft komplexen Datenmengen sind Schlüsselfaktoren für den wirtschaftlichen Erfolg von Unternehmen. Dafür bedarf es gut ausgebildeter Spezialistinnen und Spezialisten. Das BI-Analysten- und Beraterhaus mayato und die Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik der Universität Mannheim haben für den neuen Studiengang „Mannheim Master in Data Science“ (MMDS), der ab Februar 2017 beginnt, eine Kooperation vereinbart. Gemeinsames Ziel ist es, eine wissenschaftlich fundierte und an den Bedürfnissen der beruflichen Praxis ausgerichtete Ausbildung zum Data Scientist anzubieten. Im Rahmen des neuen Studiengangs erhalten Studentinnen und Studenten theoretische Kenntnisse in Statistik, Mathematik, Datenanalysen- und Verfahren. Dazu gehören Themen wie Datenbank-Technologien, Data Mining, Text Analytics, Machine Learning, Optimierung, Algorithmen und Datensicherheit. Die Kooperation mit mayato fördert zusätzlich den praktischen Bezug: Im Rahmen des MMDS-Studiums begleiten die Experten von mayato Abschlussarbeiten und Praxisprojekte. Darüber hinaus sind Vorträge über aktuelle Anwendungen von Data Science im Rahmen von Vorlesungen und Informationsveranstaltungen geplant.

„Die exzellente theoretische Ausbildung von Data Scientists kombiniert mit der Markterfahrung und Expertise von mayato im Bereich Data Science, Analytics, Big Data und Business Intelligence führt zu top qualifizierten Datenspezialisten, die relevante Erkenntnisse aus großen und komplexen Datenmengen gewinnen können“, sagt Eric Ecker, Leiter Geschäftsbereich Industry Analytics, mayato GmbH.

„Praktische Erfahrungen zu sammeln, ist für unsere Studentinnen und Studenten unerlässlich. Es freut uns, dass wir mit mayato einen kompetenten Partner für den neuen Studiengang gewinnen konnten“, erklärt Prof. Dr. Christian Bizer (Web Based Systems) und Prof. Dr. Heiner Stuckenschmidt (Künstliche Intelligenz) fügt hinzu: „Das Beratungshaus widmet sich dediziert dem Bereich Datenanalyse, das ist für uns ein echter Glücksgriff.“

Weiterführende Informationen:





Topics - Data Mining Projects Chris Heiner Simone
news-1720 Wed, 26 Oct 2016 09:20:00 +0000 Rim Helaoui receicves best PhD Thesis Award https://dws.informatik.uni-mannheim.deen/news/singleview/detail/News/rim-helaoui-receicves-best-phd-thesis-award/ Rim Helaoui, a former PhD Student in the Artificial Intelligence group received an award for the best PhD Thesis submitted to the faculty of Business Informatics and -mathematics in the academic year 2015/2016 for her Thesis on Human Activity recognition.

Research Heiner Rim
news-1634 Wed, 29 Jun 2016 08:47:38 +0000 New Degree Program Mannheim Master in Data Science https://dws.informatik.uni-mannheim.deen/news/singleview/detail/News/new-degree-program-mannheim-master-in-data-science-1/ The School of Business Informatics and Mathematics together with the School of Social Sciences start to offer the new degree program Mannheim Master in Data Science in Spring 2017. The degree program equips students with solid theoretical foundations as well as the necessary practical skills to obtain operational insights from large and complex data sets. 

The acquisition and utilization of large quantities of data nowadays influences all areas of our daily life. The analysis and interpretation of large and often complex datasets - called Data Science - is a key factor for the economic success of businesses, the improvement of processes in public administration, as well as the advancement of science. The central obstacle that hinders companies, governments, and research institutions from realizing a larger number of Big Data projects is the lack of well-trained specialists who have the ability to integrate, analyze and interpret large amounts of data. With its new degree program “Mannheim Master in Data Science“ (MMDS), the University of Mannheim contributes as one of the first universities in Germany to closing this education gap.

Due to the traditionally strong quantitative and empirical orientation of Mannheim’s social sciences as well as due to the focus of Mannheim’s business informatics and mathematics on analyzing large data sets, the University of Mannheim provides the ideal environment for educating data scientists. The new degree program teaches students both the fundamental knowledge about methods of empirical social research and explorative data mining, and the skills to apply this knowledge on large data sets in practice. The program is highly interdisciplinary and is run as a collaboration of University of Mannheim’s Data and Web Science Group, Institute of Business Informatics, Department of Sociology, Department of Political Science, and Institute of Mathematics.  

The MMDS degree program starts in the spring term of 2017 with 25 students per year. The program is financially supported by the second stage of the “Master 2016” extension program of the state of Baden-Württemberg. The target audience of the degree program are graduates of technically oriented Bachelor programs such as business informatics, business mathematics, informatics, mathematics and statistics, as well as graduates of quantitatively oriented Bachelor programs in social and economic sciences such as business administration, political sciences, sociology, and economics.

Structure and Content

The program is structured into the five blocks Fundamentals, Data Management, Data Analytics, Projects and Seminars, and the Master’s Thesis.

Fundamentals: The goal of the fundamentals block is to align the previous knowledge of students from different degree programs. Graduates from computer science and mathematics acquire the required knowledge in empirical research (in particular, data collection and multivariate statistics). Graduates from the social sciences and other fields acquire the required knowledge in computer science (in particular, programming and database technology).

Data Management: One of the central challenges in the Big Data area is to handle the enormous amount, speed, heterogeneity, and quality of the data collected in industry, the public sector, and science. The Data Management block covers methods and concepts for obtaining, storing, integrating, managing, querying, and processing large amounts of data. The block includes courses on modern data management technology (such as parallel database systems, Spark, and NoSQL databases), data integration, information retrieval and search, software engineering, and algorithms.

Data Analytics: The Data Analytics block forms the core of the study program. It provides courses ranging from data mining, machine learning, and decision support, over text analytics and natural language processing, to advanced social science methods such as cross-sectional and longitudinal data analysis. The range of methodological courses is enhanced by courses on optimization, visualization, mathematics and information, and algebraic statistics.

Projects and Seminars: The Projects and Seminars block introduces students to independent research and teaches the skills necessary to successfully participate in and contribute to larger data science projects. The block consists of research seminars, individual projects, team projects, as well as data science competitions. The projects are conducted jointly with industrial partners and/or support ongoing research efforts of participating institutes.

Master Thesis: In the master thesis, students apply what they learned throughout the program. The master thesis has a duration of 6 months. Students are encouraged to write their thesis either in the context of research projects conducted by participating institutes or together with an industrial partner.

More Information

More information about the degree program and how to apply for the program is found at



Chris Rainer Simone Heiner Projects
news-1618 Thu, 02 Jun 2016 10:52:16 +0000 Paper Accepted for UbiComp 2016 https://dws.informatik.uni-mannheim.deen/news/singleview/detail/News/paper-accepted-for-ubicomp-2016/ The Paper "Unsupervised Recognition of Interleaved Activities of Daily Living through Ontological and Probabilistic Reasoning" by Daniele Riboni, Timo Sztyler, Gabriele Civitarese, and Heiner Stuckenschmidt has been accepted as a full paper for the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016).

Publications Research Heiner