News from the Artificial Intelligence Group

The chair of artificial intelligence deals with the wide field of machine learning. In particular the chair concentrates on the development and implementation of learning algorithms that solve challenging problems.

ViSTA-TV in a Nutshell

The European project VistaTV had its successful final review meeting in Amsterdam, 1st of July. LS 8 contributed live stream analysis separating ads from shows in internet television. Online recommendations of shows based on user behavior have been produced based on Termset Clustering.


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Mediaday of the SMS Group in Hilchenbach

Mediaday of the SMS Group in Hilchenbach at 3. July 2014

Data Mining/ Industrie 4.0

Summary talk by Katharina Morik about "Data Mining, Big Data and Prediction Models"

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Talk at the TU Dortmund: What happens to our data? Between permanent harassment paranoia and post-privacy

Wednesday, 2. July 2014, 16:00 (s.t.) -18:30, P1-05-309
  • Kristian Kersting (Chair for artificial intelligence)
  • Sarah Küsgen (Chair for service and technology management)
  • Kai-Uwe Loser (Data security engineer of the RUB)
  • Johannes Weyer & Robin D. Fink (specific field technical sociology)

The youngest exposures of whistle-blowser Edward Snowden showed one more time the attractiveness of collecting massive data in the age of social media.

The question 'what happens to our data?', viewed from technical, economic and sociological background, will be investigated in the context of this event. The technical possibilities of modern data-mining are diverse and allow conclusions down to the individual level. Collected data from social networks are especially attractive for marketing and product design. Behind this background the protection of privacy will be assigned to new tasks.

The contributors will hold a 10-15 minutes talk each and will afterwards take part in a discussion with the audience. The event will be moderated by Johannes Weyer.

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Moving to Otto-Hahn-Str. 12

The chair for Artificial Intelligence is moving to the new building in Otto-Hahn-Str. 12. Thus, between 06/30/14 and 07/04/14 we may not be available at all times.

Talk at VigLink: Resource-aware graphical models

Prof. Morik talks at VigLink

Machine learning can help to enhance small devices. For instance, keeping the energy consumption of smart phones low is one of the major concerns of the users, as is well illustrated by various “charge your mobile” stations at public places. Where the operating systems of smart phones already offer heuristics and battery apps show consumption profiles, machine learning can do more. Predictions allow better optimizations of the operating system, prepare for particular app usages at certain points in time, or manage services such as GPS or WLAN in a context-aware and adaptive manner. This challenges learning algorithms to real-time application of their models. Moreover, it demands the models to run on the resource-restricted device without consuming more energy themselves than they save!

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Talk at NASA: Data Analytics for Sustainability

Title: Data Analytics for Sustainability

  • Speaker: Katharina Morik, Technische Universität, Dortmund
  • Date & Time: Wednesday, May 28, 2:00 pm - 3:00 pm
  • Location: Building N245 Auditorium


Sustainability has many facets and researchers from many disciplines are working onthem. Particularly knowledge discovery always considered sustainability an importanttopic (e.g., special issue on data mining for sustainability in Data Mining andKnowledge Discovery Journal, March 2012).

Host: Dr. Kamalika Das
NASA Ames Research Center
MS 269-1, PO Box 1, Moffett Field, CA 94035

PROF. MORIK continues her series of lectures at google


On Tue 05/27/2014 Prof. Katharina Morik give a talk about "Resource-aware graphical models and spatio-temporal predictions" at the Google Headquarters in Palo Alto, California, USA.

Machine learning can help to enhance small devices. For instance, keeping the energy consumption of smart phones low is one of the major concerns of the users, as is well illustrated by various “charge your mobile” stations at public places. Where the operating systems of smart phones already offer heuristics and battery apps show consumption profiles, machine learning can do more. Predictions allow better optimizations of the operating system, prepare for particular app usages at certain points in time, or manage services such as GPS or WLAN in a context-aware and adaptive manner. This challenges learning algorithms to real-time application of their models. Moreover, it demands the models to run on the resource-restricted device without consuming more energy themselves than they save!
In the talk, graphical models are presented that face these challenges. Using Conditional Random Fields (CRF) for the prediction of files that the user will fetch next on her smart phone can be used by the operating system for organizing the memory. Analyzing groups of apps running on the smart phone may estimate the energy consumption over time.
A novel spatio-temporal random field (STRF) has been implemented, smoothing the temporal changes and distributing the optimization. This graphical model has been used to predict app usage over time. In another application, it has been combined with a trip planner resulting in smart routing for smart cities. In order to run graphical models on very restricted devices, even those withoutvfloating point calculation, one computing with integer values only has been developed. The integer approximation of graphical models shows good accuracy and speed-up and opens up novel applications on resource-restricted devices.



Sustainability has many facets and researchers from many disciplines are working on them. Particularly knowledge discovery always considered sustainability an important topic (e.g., special issue on data mining for sustainability in Data Mining and Knowledge Discovery Journal, March 2012).

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Prof. Morik gives a talk about 'Data Analytics for Sustainability' at the University of Maryland, Baltimore County on Thursday 22 May 2014.


Sustainability has many facets and researchers from many disciplines are working on them. Particularly knowledge discovery always considered sustainability an important topic (e.g., special issue on data mining for sustainability in Data Mining and Knowledge Discovery Journal, March 2012).

  • Environmental tasks include risk analysis concerning floods, earthquakes, fires, and other disasters as well as the ability to react to them in order to guarantee resilience. The climate is certainly of influence and the debate on climate change received quite some attention.
  • Energy efficiency demands energy-aware algorithms, operating systems, green computing. System operations are to be adapted to a predicted user behavior such that the required processing is optimized with respect to minimal energy consumption.
  • Engineering tasks in manufacturing, assembly, material processing, and waste removal or recycling offer opportunities to save resources to a large degree. Adding the prediction precision of learning algorithms to the general knowledge of the engineers allows for surprisingly large savings.

Global reports on the millennium goals and open government data regarding sustainability are publicly available. For the investigation of influence factors, however, data analytics is necessary. Big data challenges the analysis to create data summaries. Moreover, the prediction of states is necessary in order to plan accordingly. In this talk, two case studies will be presented. Disaster management in case of a flood combines diverse sensor data streams for a better traffic administration. A novel spatiotemporal random field approach is used for smart routing based on traffic predictions. The other case study is in engineering and saves energy in the steel production based on the multivariate prediction of the processing end-point by the regression support vector machine.

11:00am-12:30pm, Thursday 22 May 2014, ITE 456, UMBC

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Call for Papers - MLDM 2015

MLDM 2015

11th International Conference on Machine Learning and Data Mining

July 11 - 24, 2015, Freie Hansestadt Hamburg, Germany

This congress will feature three events the 11th International Conference on Machine Learning and Data Mining MLDM, the 15 th Industrial Conference on Data Mining ICDM ( www.data-mining-forum.de), and the 10 th International Conference on Mass Data Analyisis of Signals and Images MDA (www.mda-signals.de). Workshops and Tutorial will also be given.

  • Submission of papers: January 15th, 2015
  • Notification of acceptance: February 28, 2015
  • Submission of camera-ready copy: April 5th, 2015
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Katharina Morik at the University Vienna

Dortmunder postdoc Wouter Duivesteijn wins C.J. Kok Jury Award 2013.
Prof. Dr. Dr. h. c. Monika Henzinger und Prof. Dr. Katharina Morik with some participants of the college, where Katharina Morik gives a course “Data Analytics”.

More than 1 year after the faculty of computer science at the TU Dortmund has conferred an honorary doctorate to Monika Henzinger, Professor at the University of Vienna, Katharina Morik gives a course on "Data Analytics" in the context of the interdisciplinary college at the computer science of the University of Vienna and also presented in a well-attended colloquium lecture results of the SFB876: "Big Data Analytics and Astrophysics".

Workshop: Needles In a Stream of Hay (NISH2014)

Workshop collocated with INFORMATIK 2014, September 22-26, Stuttgart, Germany.

This workshop focuses on the area where two branches of data analysis research meet: data stream mining, and local exceptionality detection.

Local exceptionality detection is an umbrella term describing data analysis methods that strive to find the needle in a hay stack: outliers, frequent patterns, subgroups, etcetera. The common ground is that a subset of the data is sought where something exceptional is going on: finding the needles in a hay stack.

Data stream mining can be seen as a facet of Big Data analysis. Streaming data is not necessarily big in terms of volume per se but instead it can be in terms of the high troughput rate. Gathering data for analyzing is infeasible so the relevant data of a data point has to be extracted when it arrives.


Submissions are possible as either a full paper or extended abstract. Full papers should present original studies that combine aspects of both the following branches of data analysis:

stream mining: extracting the relevant information from data that arrives at such a high throughput rate, that analysis or even recording of records in the data is prohibited;
local exceptionality mining: finding subsets of the data where something exceptional is going on.

In addition, extended abstracts may present position statements or results of original studies concerning only one of the aforementioned branches.

Full papers can consist of a maximum of 12 pages; extended abstracts of up to 4 pages, following the LNI formatting guidelines. The only accepted format for submitted papers is PDF. Each paper submission will be reviewed by at least two members of the program committee.

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NEM Position Paper of Big and Open Data

"NEM position papers are documents giving the NEM Initiative view on any subject related to the networked electronic media area. The NEM position papers typically include: letters of advice to the Commission, formal opinions submitted to the Commissioner, submissions to regulatory bodies, or any other formal statement of this nature, as well as further views of the NEM community on various technological, societal, and policy issues related to NEM." Source: www.nem-initiative.org

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Many companies hope for big data

Our students at LS 8 learn exactly what is in demand at many companies.

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Dortmunder postdoc Wouter Duivesteijn wins C.J. Kok Jury Award 2013.

Dortmunder postdoc Wouter Duivesteijn wins C.J. Kok Jury Award 2013.

Annually, the Faculty of Science at Leiden University, the Netherlands, grants the C.J. Kok Jury Award for the best PhD thesis of the past year. All institutes within the faculty (astronomy, physics, mathematics, computer science, chemistry, pharmacy, biology, and environmental sciences) are given the opportunity to nominate candidates for the award.

 Out of a pool of over 120 dissertations, the C.J. Kok Jury Award 2013 was won by Wouter Duivesteijn, with his thesis "Exceptional Model Mining". Notably, this is the first time ever that the award (existing since 1971) has been bestowed upon a computer scientist.

Book Announcement: RapidMiner: Data Mining Use Cases and Business Analytics Applications

The book "RapidMiner: Data Mining Use Cases and Business Analytics Applications" has been published on 6 November, 2013 by Chapman and Hall/CRC

"In this book, case studies communicate how to analyze databases, text collections, and image data. … How the given data are transformed to meet the requirements of the method is illustrated by screenshots of RapidMiner. The RapidMiner processes and datasets described in the case studies are published on the companion web page of this book. The inspiring applications may be used as a blueprint and a justification of future applications."
—From the Foreword by Professor Dr. Katharina Morik, Technical University of Dortmund

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SFB/LS8 Paper receives Award at the European Conference on Machine Learning and Priciples and Practice of Knowledge Discovery in Databases

ECML presentationThe paper Spatio-Temporal Random Fields: Compressible Representation and Distributed Estimation by Nico Piatkowski, Sankyun Lee and Katharina Morik is the winner of this year's ECMLPKDD 2013 machine learning best student paper award. The ceremony took place on Monday, September 23rd, in Prague (www.ecmlpkdd2013.org).

The article has been selected out of 182 papers for the journal publication. With an acceptance rate of 7% there were 14 accepted journal publications. 124 papers were selected out of 460 submissions for the proceedings (acceptance rate 26%). From 138 accepted submissions alltogether 4 won the award for best paper. The above article from Nico Piatkowski, Sankyun Lee und Katharina Morik is one of these.

EDBT/ICDT 2014 Call for Workshops

On the last day of EDBT/ICDT 2014, 28. March 2014, there are some workshops. More information about formatting guidelines and registration can be found here.

Deadline: 7. December

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EDBT/ICDT 2014 Joint Conference: Call for papers

The International Conference on Extending Database Technology is a leading international forum for database researchers, practitioners, developers, and users to discuss cutting-edge ideas, and to exchange techniques, tools, and experiences related to data management. Data management is an essential enabling technology for scientific, engineering, business, and social communities. Data management technology is driven by the requirements of applications across many scientific and business communities, and runs on diverse technical platforms associated with the web, enterprises, clouds and mobile devices. The database community has a continuing tradition of contributing with models, algorithms and architectures, to the set of tools and applications enabling day-to-day functioning of our societies. Faced with the broad challenges of today's applications, data management technology constantly broadens its reach, exploiting new hardware and software to achieve innovative results.

EDBT 2014 invites submissions of original research contributions, as well as descriptions of industrial and application achievements, and proposals for tutorials and software demonstrations. We encourage submissions relating to all aspects of data management defined broadly, and particularly encourage work on topics of emerging interest in the research and development communities.

Deadline: 15. October 2013

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LS8 at the International Broadcasting Convention (IBC) with the EU project Vista-TV

The highly respected conference with an exhibition, IBC, takes place in Amsterdam and Vista-TV is one of the exhibitors. In the Future Zone, Vista-TV presents realtime analytics of Internet-TV use. (more)

"With more than 50,000+ attendees from more than 160 countries, IBC combines a highly respected and peer-reviewed conference with an exhibition that exhibits more than 1,400 leading suppliers of state of the art electronic media technology...
Run by the industry, for the industry, IBC is owned by six industry partners that represent both exhibitors and visitors." (http://www.ibc.org/page.cfm/link=628)
Vista-TV provides users with real-time recommendations of shows and an excellent overview of the current TV program that eases the selection of the channel. In addition, for the producers of shows and for marketing companies, Vista-TV offers a real-time statistics of watching behavior. How many use the smartphone, the computer or the large TV screen for watching Internet-TV right now? In which region are the watching users located? From which channel to which other channel do users switch frequently? All these real-time analyses respect the privacy of the users and do not allow to trace a specific user. The statistics, however, is a source of valuable information.

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Fußball-Analyse mit dem streams Framework - TechniBall gewinnt Audience-Award!

In enger Zusammenarbeit mit dem Technion (Israel Institute of Technology) entstand basierend auf dem *streams* Framework ein System zur Echtzeitanalyse von Fußball-Daten für den Wettbewerb der diesjährigen DEBS Konferenz. Aufgabe der Challenge war die Berechnung von Statistiken über das Lauf- und Spielverhalten der Spieler, die mit Bewegungs- und Ortungssensoren des RedFIR Systems (Fraunhofer) augestattet wurden.
Im Rahmen des Wettbewerbs entwickelte der Lehrstuhl 8 zusammen mit dem Technion das "TechniBall" System auf Basis des *streams* Frameworks von Christian Bockermann. TechniBall ist in der Lage, die erforderlichen Statistiken deutlich schneller als in Echtzeit (mehr als 250.000 Events pro Sekunde) zu verarbeiten und wurde vom Publikum des Konferenz zum Gewinner des DEBS Challenge 2013 gekürt.

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"Machine Learning and Knowledge Discovery in Databases" as one of the top 50% most downloaded eBooks at Springer

Since its online publication on Sep 04, 2008 there has been a total of 11732 chapter downloads of "Machine Learning and Knowledge Discovery in Databases". In 2012 it is still one of the top 50% most downloaded eBooks in the relevant Springer eBook Collection with 1055 downloads.

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BBC about the project Vista TV

The BBC blog about the project Vista-TV in which Libby Miller shows visualizations of user behavior. (more...  )

UBICOMM 2013: Call for papers

The goal of the International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, UBICOMM 2013, is to bring together researchers from the academia and practitioners from the industry in order to address fundamentals of ubiquitous systems and the new applications related to them. The conference will provide a forum where researchers shall be able to present recent research results and new research problems and directions related to them. The conference seeks contributions presenting novel research in all aspects of ubiquitous techniques and technologies applied to advanced mobile applications.

Deadline: 17. May 2013

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Stellen für studentische Hilfskräfte

An der TU Dortmund, Fakultät für Informatik am Lehrstuhl VIII sind ab sofort Stellen für Studentische Hilfskräfte im Umfang von bis zu 10 Wochenstunden zu besetzen. (more...  )

TechniBall - Solution for the DEBS Challenge 2013

LS8 analysis football games in realtime! Each player is equipped with a sensor and so is the ball. The streams framework from LS8 is coupled with the Esper event recognition of Technion. (more...  )

Data stream algorithms for TV Recommendations - ViSTA TV Coding Camp at Lehrstuhl 8

Fernsehen über das Internet (IP-TV) spielt eine immer größere Rolle in der heutigen Medienlandschaft. Größere Programmvielfalt, Fernsehen auf mobilen Geräten, oder Mediatheken sind nur ein paar Vorzüge de neuen Fernsehwelt. Um das TV-Erlebnis für jeden Zuschauer zu optimieren ist im Hintergrund jede Menge Hightech gefragt. Das EU-Projekt ViSTA-TV erforscht das TV-Verhalten von Benutzern, sucht nach ähnlichen Sendungen und versucht so, dem Zuschauer das bestmögliche Programm zu empfehlen. Von der Lieblingssendung zu interessanten Dokumentationen oder die neuesten Trends - in der Fülle der Angebote wird für jeden Zuschauer das richtige gefunden.

Das Projekt ViSTA-TV ist ein Gemeinschaftsprojekt der Universitäten Zürich, Amsterdam und des Lehrstuhl 8 der Informatik der TU Dortmund, sowie den Unternehmen BBC, Zattoo und der Dortmunder Firma Rapid-I. Ziel des Projektes ist die Analyse des Fernsehverhaltens von IPTV Nutzern um z.B. Empfehlungen von Sendungen möglichst genau an die Bedürfnisse und Vorlieben der Zuschauer anzupassen. Dafür wird das Ein- und Umschaltverhaltens der Benutzer, sowie Eigenschaften des Video-Signals (zB. Werbungserkennung) analysiert.

Eine Herausforderung stellt dabei die große Datenrate von Video-Daten, die in Echtzeit analysiert werden müssen. Dazu wurde die Datenstrom-Umgebung „streams“, die von Christian Bockermann am Lehrstuhl 8 entwickelt wurde, um die Fähigkeit der Video-Analyse erweitert. Dies ermöglicht die gleichzeitige Analyse von Video-Daten mit dem dazugehörigen Umschaltverhalten aus Log-Daten. Die Ergebnisse werden dann innerhalb eines Empfehlungssystems weiter verarbeitet um Nutzern einen maßgeschneiderten Blick auf das TV-Angebot zu bieten.

Mit im Blick haben die Forscher aus Dortmund dabei natürlich auch die Integra-tion weiterer Datenquellen, wie DBpedia, elektronische Fernsehzeitschriften oder die beliebte Internet Movie Database (imdb). Im Sinne des „Big Data“ Gedankens, werden alle diese Informationen zeitnah analysiert und lassen so auch Informationen über Schauspieler, Nachrichten oder aktuelle Trends auf Twitter und facebook mit in die Empfehlungen einfließen.

Coding-Camp an der TU

In dieser Woche findet im an der TU Dortmund das zweite Coding-Camp zum ViSTA-TV Projekt statt. Dabei stehen insbesondere die Integration der Module der Projektpartner im Mittelpunkt. Das Ziel des Coding-Camp ist ein erster lauffähiger Prototyp des Projektes, der Programmempfehlungen an Zuschauer über Handy-Apps anbietet.

Jugend Forscht: Regionalwettbewerb in Dortmund

Am 19. Februar findet in Dortmund der Regionalwettbewerb Jugend forscht statt. In den Räumen der DASA Arbeitswelt Austellung präsentieren die jungen Nachwuchsforscher ihre Ideen und Arbeiten in verschiedenen Forschungsgebieten der Jury. Für das Gebiet Mathematik/Informatik ist mit Christian Bockermann auch der Lehrstuhl 8 der Fakultät für Informatik und ein Mitarbeiter im Projekt C1 des SFB in der Jury vertreten.

Book on Managing and Mining Sensor Data published

The book Managing and Mining Sensor Data has been published as an ebook and will be available as hardcover from 28th of February 2013. The book has been supported by the collaborative research center by the authors Marco Stolpe (project B3, Artificial Intelligence) and the guest researcher Kanishka Bhaduri. They contributed the chapter on Distributed Data Mining in Sensor Networks.

Especially sensor networks provide data at different, distributed locations. For an efficient analysis new technologies need to calculate results even if communication ressources are constrained.

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Stellenausschreibung: Entwicklung einer prozessdatenbasierten realzeitlichen Parameteradaptierung in automatisierten Produktionsprozessen

Im Anwendungsfall energie- und ressourcenintensiver Industrien besteht die Herausforderung darin, steigende Produktqualität bei gleichzeitiger Reduzierung von Kosten und Produktionszeiten zu realisieren. Prinzipien und Methoden von Qualitätsmanagement- und Produktionssystemen nach dem Vorbild der japanischen Automobilindustrie rücken dabei als vorrangiges Leitbild branchenübergreifend in den Mittelpunkt. Als ein wesentliches Element des TPS leistet das Prinzip einer prozessimmanenten Qualitätskontrolle, auch bekannt unter den Begriffen Jidoka oder Autonome Automation, einen entscheidenden Beitrag. Jedoch ist das Jidoka-Prinzip im Fall automatisierter, verketteter Produktionsprozesse, wie sie beispielsweise in der Stahlindustrie vorzufinden sind, auf konventionellem Weg nicht ohne weiteres realisierbar.

Ziel dieses Promotionsvorhabens ist die Entwicklung und Validierung einer Systematik zur Ausschussminimierung und Produktqualitätsoptimierung im Kontext starr verketteter, automatisierter Produktionsprozesse. Ein möglicher Ansatz stellt dabei das Konzept der Advanced Process Control dar. Zentraler Gedanke ist dabei die realzeitliche, prozessdatenbasierte Überwachung und Auswertung von Produktionsprozessen mit dem Ziel, kurzfristige Prozessschwankungen ausgleichen und somit die Produktqualität sicherstellen zu können. Das Promotionsvorhaben soll für das oben skizzierte Produktionssystem einen Ansatz entwickeln, der basierend auf der automatisierten Auswertung von Prozessparametern entscheidet, ob die Qualität des aktuell bearbeiteten Produkts den Spezifikationen entspricht oder ob und in welcher Form eine Anpassung der Prozessparameter erforderlich und realzeitlich möglich ist, um die Qualitäts­spezifikationen zu erfüllen. Alternativ besteht eine weitere Entscheidungsmöglichkeit darin, das Produkt nicht weiter zu bearbeiten, wenn die Qualitätsabweichung durch Anpassung des Produktionsprozessablaufes nicht korrigiert werden kann.

Die Durchführung des Vorhabens umfasst neben der Entwicklung des theoretischen Konzeptes, eine simulationsbasierte Validierung sowie in enger Kooperation mit der Deutsche Edelstahlwerke GmbH am Standort Witten die Integration des Konzeptes in die betrieblichen Produktionsabläufe. Zur Lösung der Aufgabe soll auf den Einsatz modernster Data Mining-Techniken zurückgegriffen werden.

Betreuer: Prof. Deuse

Bewerbungen ab sofort an:

Dipl.-Wirt.-Ing. Uta Spörer
Tel.: +49 (231) 755 – 5787
Fax: +49 (231) 755 – 5772
E-Mail: spoerer@gsoflog.de
Mo- Do: 8:30 - 12:30 Uhr


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