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DSC/e Symposium: April 21th, 2017: Lars Age, Facets of Data Analytics

Data Science Center Eindhoven

Vrijdag 21 april 2017 van 13:30 tot 18:30 (CEST)

DSC/e Symposium: April 21th, 2017: Lars Age, Facets of...

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DSC/e Symposium: April 21th, 2017: Lars Age, Facets of Data Analytics 20 apr. 2017 Gratis  

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On Friday April 21, 2017 the DSC/e will organize a symposium in honor of Lars Arge, who will receive an honorary doctorate from the TU/e. The symposium is free, however registration is necessary

Title: I/O-efficient Algorithms for flood risk analysis using massive terrain data 

Lars Arge (Aarhus University, Denmark) is a pioneer in the area of memory-hierarchy efficient algorithms, especially I/O-efficient algorithms for problems involving massive datasets. He has worked on massive dataset problems in many fundamental areas, with an emphasis on I/O-efficient algorithms and data structures for problems with practical applications in geographical information systems and spatial databases. Arge is the founder and director of the Center for Massive Data Algorithmics (MADALGO), a world-leading Danish research center, and he is chairman of the board of SCALGO, a company that brings cutting-edge massive terrain data-processing technology to market. Lars Arge currently also serves as the Secretary General of the Danish Royal Academy of Sciences and Letters.

The work of Arge fits well with research going on within the DSC/e, in particular with the work of the research groups Algorithms (led by prof. Mark de Berg) and Applied Geometric Algorithms (led by prof. Bettina Speckmann). The symposium will feature lectures by three prominent researchers from the DSC/e who will showcase some of the research from the DSC/e and, of course, a lecture by Lars Arge about his work.

 Abstract:
Improvements of mapping technologies have resulted in a major increase in both the amount and quality of terrain data being acquired. However, as in many other big data applications, this has revealed scalability problems with existing terrain processing software. Often these problems are a result of the underlying algorithms not taking the hierarchical structure of memory systems into account - especially the large difference in the access time of main memory and disk. In this presentation, we will discuss the above problems further and describe some of our work on overcoming the problems in the context of flood risk analysis using massive detailed terrain data. More precisely we will discuss so-called I/O-efficient algorithms - that is, algorithms that try to minimize movement of data between internal memory and disk - for a number of flood risk analysis problems. We will also discuss how this work has led to a successful start-up company.

 


PROGRAM

Date and time: Friday, April 10, 13:30 – 18:00

Location: Auditorium TU/e, room AUD 4 

13:30 – 13:35

Welcome by Mark de Berg

13:35 – 14:15

Remco van der Hofstad

Citation Netweroks as a Window to Science: a Case Study

14:15 – 14:55

Wil van der Aalst

Learning Hybrid Process Models from Events: Process Discovery Without Faking Confidence

14:55 – 15:35

Bettina Speckmann

Geometric Algorithms for Complex Moving Objects

15:35 – 16:00

Coffee break

16:00 – 17:00

Lars Arge
I/O-efficient Algorithms for flood risk analysis using
massive terrain data

17:00 – 18:00

Drinks



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Waar en wanneer


Technische Universiteit Eindhoven
Auditorium room AUD 4
De Zaale
5612 AZ Eindhoven
Nederland

Vrijdag 21 april 2017 van 13:30 tot 18:30 (CEST)


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Organisator

Data Science Center Eindhoven

The Data Science Center Eindhoven (DSC/e) is TU/e’s response to the growing volume and importance of data. 90% of the data in the world today has been created in the last two years alone and the world's data will grow by 50 times in the next 10 years.

Moreover, human and organizational activities are intertwined with the digital universe. Therefore, data science is growing in importance and becoming an integral part of different types of engineering and scientific research.

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DSC/e Symposium: April 21th, 2017: Lars Age, Facets of Data Analytics
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