Lehrangebot. Fachgebiet Informationssysteme Prof. Dr. Norbert Fuhr

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1 Lehrangebot Fachgebiet Informationssysteme Prof. Dr. Norbert Fuhr N. Fuhr, U. Duisburg-Essen Lehrangebot 1

2 Lehrangebot des FG Informationssysteme Datenbanken Internet-Suchmaschinen Information Retrieval Information Mining Information Engineering

3 Internet-Suchmaschinen/ Einf. in Information Retrieval (Bachelor) Information Retrieval (Master)

4 The web how it all began

5 Internet Search Engines Applications Web Search Engines

6 Internet Search Engines Applications Web Search Engines Internet Shops

7 Internet Search Engines Applications Web Search Engines Internet Shops Digital Libraries

8 Internet Search Engines Applications Web Search Engines Internet Shops Digital Libraries Multimedia Web Services (FlickR, itunes, LastFM, YouTube)

9 Internet Search Engines Applications Web Search Engines Internet Shops Digital Libraries Multimedia Web Services (FlickR, itunes, LastFM, YouTube)

10 Internet-Suchmaschinen: Fragestellungen Verständnis welche Dokumente sind suchbar? Das Suchwort steht gar nicht auf der Seite? 'Rathaus Duisburg' 'Duisburger Rathaus'?

11 Internet-Suchmaschinen: Fragestellungen Verständnis welche Dokumente sind suchbar? Das Suchwort steht gar nicht auf der Seite? 'Rathaus Duisburg' 'Duisburger Rathaus'? Suchkompetenz Operatoren:..., *, OR, NOT, inurl:, filetype: ähnliche Seiten Suchoptionen

12 Internet-Suchmaschinen: Fragestellungen Verständnis welche Dokumente sind suchbar? Das Suchwort steht gar nicht auf der Seite? 'Rathaus Duisburg' 'Duisburger Rathaus'? Suchkompetenz Operatoren:..., *, OR, NOT, inurl:, filetype: ähnliche Seiten Suchoptionen Anbietersicht Wie kommt meine Seite bei Google nach oben? Wie setze ich eine eigene Suchmaschine auf? Wie sollte ich das Benutzerinterface gestalten?

13 Learning to Rank

14 Learning to Rank Fuhr 1992

15 Apple vs. Samsung 2014 US patent [2000]

16 Apple vs. Samsung 2014 US patent [2000] [Fuhr et al. 1995]

17 Supporting Query Formulation Google 2010-

18 Supporting Query Formulation Google Schaefer, Fuhr et al. 2005

19 IR User Interface Evaluation Eyetracking experiments to test usability of search and collaboration functionalities User experiments on suggestion dialog User experiments to compare variants for result item presentations User experiments to compare variants for result list presentations (tabs vs. single grouped list)

20 Modelling Interactive Retrieval

21 Modelling Interactive Retrieval

22 Information Mining Tasks: Classification Numeric prediction Associations Clustering Applications in Business Intelligence Smart Home Self-Driving Cars Big Data Analytics.

23 Classification Examples Will there be rain tomorrow?

24 Classification Examples Will there be rain tomorrow? Will the applicant cause a traffic accident next year?

25 Classification Examples Will there be rain tomorrow? Will the applicant cause a traffic accident next year?

26 Classification Examples Will there be rain tomorrow? Will the applicant cause a traffic accident next year? Will the client be able to pay back his loan?

27 Classification Examples Will there be rain tomorrow? Will the applicant cause a traffic accident next year? Will the client be able to pay back his loan? Is there a traffic jam?

28 Classification Examples Will there be rain tomorrow? Will the applicant cause a traffic accident next year? Will the client be able to pay back his loan? Is there a traffic jam?

29 Examples for numeric prediction How many rolls will be sold tomorrow?

30 Examples for numeric prediction How many rolls will be sold tomorrow? How many tourists /business travellers will need a hotel romm in our city at a specific date?

31 Examples for numeric prediction How many rolls will be sold tomorrow? How many tourists /business travellers will need a hotel romm in our city at a specific date? How many people will want to fly from A to B on xx.xx?

32 Examples for numeric prediction How many rolls will be sold tomorrow? How many tourists /business travellers will need a hotel romm in our city at a specific date? How many people will want to fly from A to B on xx.xx?

33 Examples for associations Shopping basket analysis: Men buying diapers, often also shop for beer Transaction analysis of credit cards, customer card, Payback cards

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36 Internet of Things

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38 Information Engineering Information Life Cycle Information Behavior, Seeking, Searching Modelle, Methoden: Information Retrieval, Ontologien, Evaluierung Systeme: Digitale Bibliotheken, Enterprise Search, Recommender, Social Media

39 RTG: User-Centred Social Media User GRK 2167 System Crowd

40 RTG: User-Centred Social Media User Trust, Bias & Decision Support GRK 2167 System Crowd

41 RTG: User-Centred Social Media User Trust, Bias & Decision Support GRK 2167 System Collaboration Crowd

42 RTG: User-Centred Social Media User Trust, Bias & Decision Support GRK 2167 System Collaboration Crowd Privacy & Awareness

43 RTG: User-Centred Social Media User Trust, Bias & Decision Support GRK 2167 System Collaboration Crowd Privacy & Awareness

44 Professoren des Graduiertenkollegs GRK 2167 Duisburg, 10. Februar

45 Lehrangebot Datenbanken (Bachelor) Internet-Suchmaschinen (Bachelor) Information Retrieval (Master) Information Mining (Master) Information Engineering (Master) Studien/Praxisprojekte Seminare Bachelor-/Masterarbeiten

46 Prüfungsergebnisse Master Ang. Inf., Komedia, BWL

47 Prüfungsergebnisse Master ISE Ang. Inf., Komedia, BWL