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The 2nd Big Data All Hands Meeting (BDAHM) is held in conjunction with the 2nd Smart Data Innovation Conference (SDIC). The conference is jointly organized by the SDIL, the BBDC, and ScaDS.

11 -12 October 2017, Karlsruhe, KIT, Campus South

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We are pleased to announce that Prof. Volker Markl, Head of the Database Systems and Information Management (DIMA) Group at the Technische Universität Berlin and Director of the German Research Center for Artificial Intelligence (DFKI) Intelligent Analytics for Massive Data Research Group was elected President of the VLDB Endowment.

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The paper by Andreas Kunft, Asterios Katsifodimos, Sebastian Schelter, Tilmann Rabl, and Volker Markl will be published in PVLDB Vol 10 and presented at VLDB 2018 in Rio de Janeiro, Brazil.

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The paper "Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models" by Martin Kiefer, Max Heimel, Sebastian Breß and Volker Markl will be published in PVLDB Vol 10 and will be presented at VLDB 2018 in Rio de Janeiro, Brazil

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Veranstaltungen

Big Data Summer School 2017

The Big Data Competence Centers ScaDS Dresden/Leipzig and BBDC are hosting the third international big data summer school in Germany.

August, 21st - 25th 2017

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Big Data Excellence in Germany and UK

March 1st, 2017 in Berlin

A joint event by the Berlin Big Data Center and UK Science and Innovation Network took place at Smart Data Forum.

The First Berlin Big Data Center Symposium Held in Berlin

The Berlin Big Data Center held its first Symposium on November 8th at the Smart Data Forum, located in Berlin. At this event, members of the BBDC presented the project’s interim results from two years of research.

The Big Data Newsletter

The joint newsletter Big Data Research reflects works done by Berlin Big Data Center (BBDC), Dresden/Leipzig Competence Center for Scalable Data Services and Solutions (ScaDS), Smart Data Innovation Lab (SDIL), Smart Data Forum (SDF), and the project "Assessing Big Data" (Abida).

Edition #001 (Nov 2016)

Edition #002 (Feb 2017)

Edition #003 (July 2017)

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Five Dimensions of Big Data

Big data is often defined as any data set that cannot be handled using today’s widely available mainstream techniques and technologies. The challenges of handling big data are often described using 3-Vs (volume, variety and velocity): high volume of data from a variety of data sources arriving with high velocity analysed to achieve an economic benefit. However, the 3-Vs fail to reflect complexity of “Big Data” in its entirety.

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According to the Harvard Business Review, Data Scientist is “The Sexiest Job of the 21st Century”. Data scientists are often considered to be wizards that deliver value from big data. These wizards need to have knowledge in three very distinct subject areas, namely, scalable data management, data analysis and domain area expertise. However, it is a challenge to find these jacks-of-all-trades that cover all three areas. Or, as the Wall Street Journal puts it “Big Data’s Problem is Little Talent”. Naturally, finding talented data scientists is also a requirement, if we are to put big data to good use. If data analysis were specified using a declarative language, data scientists would not have to worry about low-level programming any longer. Instead, they would be free to concentrate on their data analysis problem. The goal of the Berlin Big Data Center is to help bridge the Talent Gap of Big Data through researching and developing novel technology.

Read more about it in the article of the VLDB keynote "Breaking the Chains: On Declarative Data Analysis and Data Independence in the Big Data Era" by Volker Markl.