« 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
10. September 2018
Die beiden vom BMBF geförderten Big-Data-Kompetenzzentren, das BBDC und das ScaDS Dresden/Leipzig, organisieren eine 2. gemeinsame Fachtagung im Themenfeld "Big Data" im Smart Data Forum in Berlin. Die Vorstellung ausgewählter Forschungsschwerpunkte und -highlights aus drei Jahren erfolgreicher Arbeit und ein Ausblick auf die Zukunft stehen im Focus.
2-Jul-2018 until 6-Jul-2018
BBDC and ScaDS Dresden/Leipzig are organizing the fourth international big data summer school in its series (former events: 2016, 2017). We offer inspiring insights into the diverse fields of big data by selected keynotes from international experts combined with possibilities for practical sessions. The practical sessions start with a Hackathon prior to the actual school start and will be continued during the week in the tutorial-styled sessions.
Both events will take place in the University of Leipzig.
DIMA, (consortium member TUB research Group) has open positions in collaborattion with Berlin Big Data Center and the Berlin Center for Machine Learning.
Big Data Newsletter Edition #007 (Jan 2019) online now.
Big Data Newsletter Archive
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 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.