Title: Ultra-Scalable and Ultra-Efficient Integrated and Visual Big Data Analytics
Acronym: LeanBigData
Abstract:
LeanBigData targets at building an ultra-scalable and ultra-efficient integrated big data platform addressing important open issues in big data analytics. Current big data infrastructure scale to large amounts of data and system sizes, however, in a very inefficient way consuming disproportionally high resources per data item processed. Furthermore, the lack of integrated big data management technologies to process streaming events and different workloads over stored data results in the complexity to integrate disparate big data systems and the overhead of copying data across systems. What is more, data analysis cycles to refine queries and identify facts of interest take hours, days, or weeks, whereas business processes demand today shorter cycles. LeanBigData will address these issues by:
Delivering ultra-scalable big data management systems: NoSQL key-value data store, a distributed CEP system, and a distributed SQL query engine.
Providing an integrated big data platform to avoid the inefficiencies and delays introduced by current ETL-based integration approaches of disparate technologies.
Supporting an end-to-end big data analytics solution removing the main sources of delays in data analysis cycles
DKMS Role: Participant
DKMS contribution: The lab will participate in the design and implementation of real world use cases for LeanBigData validation. In particular, will contribute in the architecture and design of different components for the social network use case. The lab having extensive experience in algorithm research will contribute advanced algorithms for social network analytics and will integrate them in the social network use case validating the LeanBigData technologies and platform.
Contact points: Marta Patiño Martinez
Tel.: +34 91 3367452
Start/End Date: From 2014-02-01 to 2017-01-31
Funding: oject ID: 619606, Funded under: FP7-ICT, Topic(s): ICT-2013.4.2 – Scalable data analytics