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TU Berlin

Inhalt des Dokuments

Talks DIMA Research Seminar

Talks  WS18/19
Talk/Location
Lecturer/Subject
15.10.2018
4 pm
EN 719
Prof. Dr. Birgit Beck
"
Some Philosophical Considerations Regarding “AI”"

27.11.2018
4 pm
Smart Data Forum
(Salzufer 6, Eingang
Otto-Dibelius-Strasse,
10587 Berlin)

Prof. Renée J. Miller,  Northeastern University
"Open Data Integration"
17.12.2018
4 pm
EN 719
Dr. Alberto Lerner, eXascale Infolab at the University of Fribourg, Switzerland
"The Case for Network-Accelerated Query Processing"

Dr. Alberto Lerner, eXascale Infolab at the University of Fribourg, Switzerland

Location:
TU Berlin, EN building, seminar room EN 719 (7th floor), Einsteinufer 17, 10587 Berlin

Title:
The Case for Network-Accelerated Query Processing

Abstract:
The fastest plans in MPP databases are usually those with the least amount of data movement across nodes. That‘s because data does not get processed while in transit. The network switches that connect MPP nodes are hard-wired to strictly perform packet-forwarding logic. In a recent paradigm shift, however, network devices are becoming “programmable.” The quotes here are cautionary. Switches had not become general purpose computers suddenly. But now the set of tasks they can perform can be encoded in software—and that means such switch can be instructed to manipulate the data it is forwarding.

In this talk we explore this programmability to accelerate OLAP queries. We found that we can offload onto the switch some very common and expensive query patterns. Moving data through networking equipment can hence for the first time contribute to query execution. Our preliminary results show that we can improve response times on even the best agreed upon plans by more than 2x using 25 Gbps networks. We also see the promise of linear performance improvement with faster speeds. The use of programmable switches can open new possibilities of architecting rack- and datacenter-sized database systems, with implications across the stack.
 
Bio:
Alberto Lerner is a Senior Researcher at the eXascale Infolab at the University of Fribourg, Switzerland. His interests revolve around systems that explore closely coupling of hardware and software in order to realize untapped performance and/or functionality. Previously, he spent years in the industry consulting for large, data-hungry verticals such as finance and advertisement. He had also been part of the teams behind a few different database engines: IBM‘s DB2, working on robustness aspects of the query optimizer, Google‘s Bigtable, on elasticity aspects, and MongoDB, on general architecture. Alberto received his Ph.D. from ENST - Paris (now ParisTech), having done his thesis research work at INRIA/Rocquencourt and NYU. He‘s also done post-doctoral work at IBM Research (both at T.J. Watson and Almaden).

Prof. Renée J. Miller, Northeastern University

Location:
Smart Data Forum (Salzufer 6, Eingang Otto-Dibelius-Strasse, 10587 Berlin)

Title:
Open Data Integration

Abstract:
Open Data plays a major role in open government initiatives. Governments around the world are adopting Open Data Principles promising to make their Open Data complete, primary, and timely. These properties make this data tremendously valuable to data scientists. However scientists generally do not have a priori knowledge about what data is available (its schema or content), but will want to be able to use Open Data and integrate it with other public or private data they are studying. Traditionally, data integration is done using a framework called “query discovery” where the main task is to discover a query (or transformation script) that transforms data from one form into another. The goal is to find the right operators to join, nest, group, link, and twist data into a desired form. In this talk, I introduce a new paradigm for thinking about Open Data Integration where the focus is on “data discovery”, but highly efficient internet-scale discovery that is heavily query-aware. As an example, a join-aware discovery algorithm finds datasets, within a massive data lake, that join (in a precise sense of having high containment) with a known dataset. I describe a research agenda and recent progress in developing scalable query-aware data discovery algorithms.

Bio: 
Renée J. Miller is a University Distinguished Professor of Computer Science at Northeastern University. She is a Fellow of the Royal Society of Canada, Canada’s National Academy of Science, Engineering and the Humanities. She received the US Presidential Early Career Award for Scientists and Engineers (PECASE), the highest honor bestowed by the United States government on outstanding scientists and engineers beginning their careers. She received an NSF CAREER Award, the Ontario Premier’s Research Excellence Award, and an IBM Faculty Award. She formerly held the Bell Canada Chair of Information Systems at the University of Toronto and is a fellow of the ACM. Her work has focused on the long-standing open problem of data integration and has achieved the goal of building practical data integration systems. She and her co-authors (Fagin, Kolaitis and Popa) received the (10 Year) ICDT Test-of-Time Award for their influential 2003 paper establishing the foundations of data exchange. Professor Miller has led the NSERC Business Intelligence Strategic Network and was elected president of the non-profit Very Large Data Base Foundation. She received her PhD in Computer Science from the University of Wisconsin, Madison and bachelor’s of science degrees in Mathematics and Cognitive Science from MIT.

Prof. Dr. Birgit Beck, TU Berlin, FG Ethik und Technikphilosophie

Title:

Some Philosophical Considerations Regarding “AI”


Abstract:

In today’s society, the notion of “artificial intelligence” is ubiquitous. Recently, there are voices from
science, politics and economy calling for “ethical guidelines” regarding AI. Although ethical guidelines
are certainly a good thing to have, it appears necessary, first and foremost, to determine what exactly
the object of such guidelines would be.
The present talk addresses this question by scrutinising the meaning of “artificial intelligence” and
assumes on the basis of some exemplary instances of “AI” that the notion of “artificial intelligence”
simpliciter is a vague and, therefore, misleading term.


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