« Achieving deep big data analysis will require technological breakthroughs that unite research advances in machine learning and database management systems. »Volker Markl, Director of the BBDC
« Let the machines learn! »Klaus-Robert Müller,
Co-Director of the BBDC
Konferenz „Digital Future Science Match“,
12. Mai 2017, Kosmos Berlin
Eine Veranstaltung des Berlin Big Data Center gemeinsam mit dem Zuse Institut Berlin, dem Einstein Center Digital Future und dem Tagesspiegel.
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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 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 newsletter Big Data Research is a joint newsletter which 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), and Smart Data Forum (SDF).
Edition #001 (Nov 2016)
Edition #002 (Feb 2017)
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.
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.