EVENTS

ADS Coffee & Data: Visual Analytics

Details

July 07, 2017 - 09:00
Meetup

Venue

52.3544534
4.955359499999986

ADS Coffee & Data offers the opportunity for researchers and business to share their knowledge and give insight on a central theme, specifically on Friday 07 July this will be Visual Analytics. We are honoured to have one of the founders and leading researcher of Visual Analytics, Professor Daniel A. Keim, present at this Meetup. There is also the chance to network before and after over a cup of coffee.

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Date: Friday 07 July 2017

Time: 09:00-11:15

Location: Science Park 904, UvA (Room: C1.110 – 1st floor)

Registration on Meetup

09:00-09:15 Coffee

Introduction & Chair: Marcel Worring, Professor in Data Science for Business Analytics (Amsterdam Business School), Director and Associate Professor, Informatics Institute, (UvA)

09:15-10:00: Daniel A. Keim, Professor and Head of the Information Visualization and Data Analysis Research Group in the Computer Science Department of the University of Konstanz, Germany will present on:

“Power of Visual Analytics: Unlocking the Value of Big Data” (see abstract below) 

Daniel A. Keim has been actively involved in data analysis and information visualization research for more than 20 years and developed a number of novel visual analysis techniques for very large data sets (further details below). 

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10:00-10:25: Presentations from Research Students from the master course Information Visualization from the Artificial Intelligence and Data Science programmes (4 x 5 minutes each)

Ronja Brettschneider – “BikeBustle: Visualizing Seattle’s Bicycle Flow”

Julius Roeder – “Visualizing the Global Terrorism Database”

Cheryl Zandvliet – “Visualizing the Dutch Parliamentary Voting Behavior”

Steven Swagman – “EuroGo – Visualizing European Go”

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10:25-10:50: Björn Þór Jónsson, Associate Professor, Computer Science Department, IT University of Copenhagen will present on:

“Towards Scalable Multimedia Analytics” (see abstract below) 

Björn’s research is within the broad field of Multimedia Analytics, applying Multi-dimensional Analysis concepts and techniques to multimedia. Over the last decade, his research has focused primarily on the performance of very largescale content based multimedia retrieval.

10:50 – 11:15 Wrap-up, discussion & coffee

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Abstracts

DANIEL A. KEIM “Power of Visual Analytics: Unlocking the Value of Big Data”

Never before in history is data generated and collected at such high volumes as it is today. For the analysis of large data sets to be effective, it is important to include the human in the data exploration process and combine the flexibility, creativity, and general knowledge of the human with the enormous storage capacity and the computational power of today’s computers. Visual Analytics helps to deal with the flood of information by integrating the human in the data analysis process. Presenting data in an interactive, graphical form often fosters new insights, encouraging the formation and validation of new hypotheses for better problem-solving and gaining deeper domain knowledge. Visual analytics techniques have proven to be of high value in exploratory data analysis. They are especially powerful for the first steps of the data exploration process, namely understanding the data and generating hypotheses about the data, but they also significantly contribute to the actual knowledge discovery by guiding the search using visual feedback.

In putting visual analysis to work on big data, it is not obvious what can be done by automated analysis and what should be done by interactive visual methods. In dealing with massive data, the use of automated methods is mandatory – and for some problems it may be sufficient to only use fully automated analysis methods, but there is also a wide range of problems where the use of interactive visual methods is necessary. The presentation discusses when it is useful to combine visualization and analytics techniques and it will also discuss the options how to combine techniques from both areas. Examples from a wide range of application areas illustrate the benefits of visual analytics techniques.

Short Bio: DANIEL A. KEIM is professor and head of the Information Visualization and Data Analysis Research Group in the Computer Science Department of the University of Konstanz, Germany. He has been actively involved in data analysis and information visualization research for more than 20 years and developed a number of novel visual analysis techniques for very large data sets. He has been program co-chair of the IEEE InfoVis and IEEE VAST as well as the ACM SIGKDD conference, and he is chair of the IEEE VAST steering committee. He has been coordinator of the German Science Foundation funded Strategic Research Initiative “Scalable Visual Analytics” and scientific coordinator of the European Commission funded Coordination Action “Visual Analytics – Mastering the Information Age (VisMaster)”. Dr. Keim got his Ph.D. and habilitation degrees in computer science from the University of Munich. Before joining the University of Konstanz, Dr. Keim was associate professor at the University of Halle, Germany and Senior Technology Consultant at AT&T Shannon Research Labs, NJ, USA.

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Information Visualization Masters Students

– Ronja Brettschneider – “BikeBustle: Visualizing Seattle’s Bicycle Flow”

We present a multiple view visual analytics tool aimed at helping users to gain insight from bike flow data in the city of Seattle. The application features spatial information on a map, represents bike flow information in a color gradient-enabled chord diagram, and is filterable by temporal as well as by population- and weather-specific properties.

– Julius Roeder – “Visualizing the Global Terrorism Database”

In order to help both scientists and non-scientists to obtain deeper insight into terrorism, we propose an interactive system which visualizes the Global Terrorism Database and provides an easily accessible, qualitative view of a large terrorism dataset, that could be used by a wide audience.

– Cheryl Zandvliet – “Visualizing the Dutch Parliamentary Voting Behavior” 

The field of visual analytics provides people with effective means to understand the growing mass of data. This is applied on data of Dutch parties’ voting behavior. With the assistance of the Data Visualization Catalogue, three base visualizations are adjusted and combined allowing users to analyze and understand party behavior.

– Steven Swagman – “EuroGo – Visualizing European Go” 

In the last 20 years, the European Go Federation has gathered details about tournaments and players, however, only query-able through a website. We present a visualization for this data showing the rise in popularity of Go in Europe through storytelling in a multiview visualization. Comprising of tournaments shown on a map, the visualization allows the user to interactively explore and find tournament and country-specific details through time.

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Björn Þór Jónsson – “Towards Scalable Multimedia Analytics”

The field of multimedia analytics has been proposed as an umbrella term for a range of techniques to explore and gain insight from multimedia collections.  Recently, we proposed to integrate data management techniques to achieve scalable multimedia analytics, and presented a set of research questions to tackle in this field. In this talk we will present two scalability projects that address a) large-scale off-line processing of multimedia, via cluster-based computing, and b) large-scale interactive learning of image preferences, via multi-core processing and compression. We end with a discussion on the importance and challenges of scalability.

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Registration is free but please do so in advance through Meet-up. The event will be in English and is open to all.

Amsterdam Data Science (ADS) accelerates data science research by connecting, sharing and showcasing world-class technology, expertise and talent from Amsterdam on a regional, national and international level. Our research enables business and society to better gather, store, analyse and present data in order to gain valuable insights and make informed decisions.