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Introduction Multimedia Information Systems 2 VU (707.025) (Web -based Visual Data Analysis in the future) SS 2016 Vedran Sabol Know-Center March 8 th 2016 March 8 th , 2016 MMIS2 VU - Introduction Vedran Sabol Overview

  1. Introduction Multimedia Information Systems 2 VU (707.025) (“Web -based Visual Data Analysis” in the future) SS 2016 Vedran Sabol Know-Center March 8 th 2016 March 8 th , 2016 MMIS2 VU - Introduction Vedran Sabol

  2. Overview • Organisational information • Goals of the course • Course topics • Practical part: projects  Topics, Deadlines  Tasks: will be given in early April • Course structure and calendar • Presentations and grading March 8 th , 2016 MMIS2 VU - Introduction 2 Vedran Sabol

  3. Course • Multimedia Information Systems 2 VU 707.025 (3.0 SSt, 5 ECTS credits) • Elective (optional) course for  Computer Science  Software Development and Business Management  Doctoral Studies • Catalogues: Multimedia Information Systems, Knowledge Technologies March 8 th , 2016 MMIS2 VU - Introduction 3 Vedran Sabol

  4. Lecturer Name: Vedran Sabol Know-Center, KTI Affiliation: Inffeldgasse 13, 6 th floor, room 082 Office: Office hours: by appointment +43 316 873 30850 Phone: Email: vsabol@know-center.at March 8 th , 2016 MMIS2 VU - Introduction 4 Vedran Sabol

  5. Language • Master course: lectures in English • Communication in German/English • If in German: please informally (Du)! • Project: German/English • Presentation: German/English March 8 th , 2016 MMIS2 VU - Introduction 5 Vedran Sabol

  6. Organization of the Course • Lectures  When: Tuesday, 10:15 – 12:45  Where: HS i9 • Registration for the course in TUGOnline until 09.03.2015 • Presence at lectures is not obligatory, but recommended(!) • Presentations ARE obligatory March 8 th , 2016 MMIS2 VU - Introduction 6 Vedran Sabol

  7. Organization of the Course • Course Homepage: http://kti.tugraz.at/staff/vsabol/courses/mmis2  Lecture slides, links to external resources • Newsgroup: tu-graz.lv.mmis2  News server: news.tu-graz.ac.at  Newsgroup is the preferred way of communication for this course  The study assistant and the lecturer will actively participate in the newsgroup March 8 th , 2016 MMIS2 VU - Introduction 7 Vedran Sabol

  8. Goals of the course (VU 707.025) • Web is man made but it behaves as a natural phenomenon  Complex system: technological and social • The Web is a technological infrastructure supporting processes of  Publishing, linking, connecting, communicating, collaborating etc. • Result: creation of huge amounts of data  Unstructured data (e.g. text, images)  Semi-structured data (e.g. resources described by rich metadata)  Network data (e.g. interlinked documents, social networks)  Multi-dimensional data sets  Semantically described data (ontologies)  Sensor and time-oriented data March 8 th , 2016 MMIS2 VU - Introduction 8 Vedran Sabol

  9. Goals of the course (VU 707.025) • Goal : learn about the structure of complex data in the Web  Social networks and processes  Semantic knowledge bases: ontologies, linked open data cloud, RDF Data Cubes  Multimedia documents described by rich metadata  Sensor and event data collected by mobile devices • Goal: learn about presenting Web content with visual means  In an suitable, easy to understand way  Using Web technologies (primarily HTML5) • Goal : comprehend the Web data as an object of analysis  Knowledge Discovery in the Web (also known as Web Mining)  Visual Analytics for the Web  Apply algorithmic and visual methods for analysis of Web data March 8 th , 2016 MMIS2 VU - Introduction 9 Vedran Sabol

  10. Goals of the course (VU 707.025) • Automated analysis: Knowledge Discovery Process  Processing chain involving: selection, preprocessing, transformation, mining and interpretation of data  Mainly an automatic process • Involve humans in the analytical process: Visual Analytics  Use visualisation to support analysis of complex data  Combining visual and automatic analysis methods • Goal : learn how to apply Visual Analytics methods in the Web  on Web data  using Web technologies  in selected Web-based scenarios March 8 th , 2016 MMIS2 VU - Introduction 10 Vedran Sabol

  11. Non-Goals (VU 707.025) • MMIS2 is not about Web programming, Web frameworks, Service-oriented or Enterprise Architectures  MMIS1 dealt with some of those issues • An advanced course on the above topics: 706.052 AK Informationssysteme (WS)  also deals with J2EE, architecture of Web applications, Data Warehousing etc. March 8 th , 2016 MMIS2 VU - Introduction 11 Vedran Sabol

  12. Non-Goals (VU 707.025) • MMIS2 is not about providing a comprehensive overview of Knowledge Discovery and Visual Analytics methods • Advanced courses on the above topics  707.003 Knowledge Discovery and Data Mining 1 (VO, winter semester)  707.004 Knowledge Discovery and Data Mining 2 (VU, summer semester)  710.220 Visual Analytics (VU, summer semester) March 8 th , 2016 MMIS2 VU - Introduction 12 Vedran Sabol

  13. Topics of the course (VU 707.025) • Automatic Web data analysis  The Knowledge Discovery (KDD) process  Data selection and cleaning, feature engineering, data mining algorithms…  Discussion of selected data mining algorithms (e.g. clustering)  Applications on text, graph and sensor data • Recommendation User Interfaces  Recommenders as ahead of time information retrieval engines  Adaptive visualisation interfaces for metadata-rich recommendations  Examples using a browser plug-in March 8 th , 2016 MMIS2 VU - Introduction 13 Vedran Sabol

  14. Topics of the course (VU 707.025) • Visual Analytics for Web Data  Combined automatic and visual analysis – human in the loop  Information landscapes  Social network visualization  Ordination and layout algorithms • Visualisation of Semantic Data (RDF)  Introduction to RDF  Geo-spatial and temporal data  Using semantics to automate visualisation  Visual ontology alignment March 8 th , 2016 MMIS2 VU - Introduction 14 Vedran Sabol

  15. Topics of the course (VU 707.025) • High-dimensional data visualisation  Multi-visualisation interfaces  View coordination  RDF Data Cube Visualisation  Visual metaphors for multidimensional data • Visual exploration of sensor and time-oriented data  Scalable sensor-data visualization  Visualisation of multiple sensor channels  Interactive exploration techniques for sensor data March 8 th , 2016 MMIS2 VU - Introduction 15 Vedran Sabol

  16. Example - Geovisualisation • Which is the happiest city in the USA?  http://onehappybird.com/2013/02/18/where-is-the-happiest-city-in-the-usa/ • Sentiment detection to extract “happiness” from geo -tagged tweets • Geo- visualisation with colour coding to convey “happiness” March 8 th , 2016 MMIS2 VU - Introduction 16 Vedran Sabol

  17. Example – EEXCESS uRank • Content-based exploration of recommendations • Significantly easier to use than list scanning change weights pick keywords Re-ranking of documents Inspection: highlight keywords in content March 8 th , 2016 MMIS2 VU - Introduction 17 Vedran Sabol

  18. Example – EEXCESS Recommendation Dashboard • Multiple visualisations  Timeline  GeoView  BarChart • Filtering of recommendations • Organising recommendations in collections March 8 th , 2016 MMIS2 VU - Introduction 18 Vedran Sabol

  19. Practical Part – Project (VU 707.025) • Implement a Web-based system for visual data analysis  Team work: groups of 2-3 students • Topical areas 1. Visual exploration of network data (AFEL EU Project) • Social network data 2. Automated visualisation of semantic data (AFEL and CODE EU projects) • Ontologies, multi-dimensional data sets (RDF-cubes) 3. Visualisation of recommender results (EEXCESS EU project) • Recommendations incl. content and metadata (time stamps, geo- references…) 4. Visualisation of sensor data (MoreGrasp EU project) • Sensor data from mobile devices, industrial sensors, bio-med sensors etc.  Project tasks will be given in the lecture on 12.04.2016 • Attendance highly recommended! March 8 th , 2016 MMIS2 VU - Introduction 19 Vedran Sabol

  20. Practical Part – Project (VU 707.025)  TeachCenter: for all matters concerning practicals  https://tugtc.tugraz.at/wbtmaster/courseMain.htm?707025  Detailed information on the practicals, development environment etc.  Registration for projects, presentation slots etc.  Will be set up over the following days • Announcement in a newsgroup posting March 8 th , 2016 MMIS2 VU - Introduction 20 Vedran Sabol

  21. Practical Part – Tasks (VU 707.025) • Team building: group member names, chosen project • Project plan: goals, time estimate, group member responsibilities • Implementation: working, well-documented code • Project report: scientific paper-like document  Title + Abstract  Motivation and goals (which problem you are solving for the chosen data)  Description of your solution: methodology, algorithms, design, use case  Discussion and outlook: what worked well, what could be improved  References: software libraries, data sets, papers…  Length: 6 pages for groups of three students, 4 pages for groups of two  Format: Springer LNCS • http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0 March 8 th , 2016 MMIS2 VU - Introduction 21 Vedran Sabol

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