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STREAMLINE

Objective

STREAMLINE will address the competitive advantage needs of European online media businesses (EOMB) by delivering fast reactive analytics suitable in solving a wide array of problems, including addressing customer retention, personalised recommendation, and more broadly targeted services. STREAMLINE will develop cross-sectorial analytics drawing on multi-source data originating from online media consumption, online games, telecommunications services, and multilingual web content.

STREAMLINE partners face big and fast data challenges. They serve over 100 million users, offer services that produce billions of events, yielding over 10 TB of data daily, and possess over a PB of data at rest. Their business use-cases are representative of EOMB, which cannot be handled efficiently & effectively by state-of-the-art technologies, as a consequence of system and human latencies.
System latency issues arise due to the lack of appropriate (data) stream-oriented analytics tools and more importantly the added complexity, cost, and burden associated with jointly supporting analytics for both “data at rest” and “data in motion.” Human latency results from the heterogeneity of existing tools and the low level programming languages required for development using an inordinate number of boilerplate codes that are system specific (e.g. Hadoop, SolR, Esper, Storm, and databases) and a plethora of scripts required to glue systems together.

Our research and innovation actions, include addressing the challenges brought on by system and human latencies. In this regard, STREAMLINE will:
1. Develop a high level declarative language and user-interface, and corresponding automatic optimisation, parallelisation, and system adaptation technologies that reduce the programming expertise required by data scientists, thereby enabling them to more freely focus on domain specific matters.
2. Overcome the complexity of the so-called ‘lambda architecture’ by delivering simplified operations that jointly support “data at rest” and “data in motion” in a single system and is compatible with the Hadoop ecosystem.
3. Develop fast reactive machine learning technologies based on distributed parameter servers and fully distributed asynchronous and approximate algorithms for fast results at high input rates.

The impact of developing a European open source tool for analysing “data at rest” and “data in motion” in a single system featuring a high level declarative language and a fast reactive machine learning library is much wider than just the recommender, ad targeting, and customer retention applications that the industrial partners in STREAMLINE will use to demonstrate the business value of our work for the data economy. Our open source tools will help Europe, in general, since they lower the big data analytics skills barrier, broaden the reach of data analytics tools, and are applicable to diverse market sectors, including healthcare, manufacturing, and transportation. Thereby, enabling a broad number of European SMEs in other markets to explore and integrate these technologies into their businesses. At the same time, STREAMLINE will provide a solid foundation for big data leadership in Europe, by providing an open-source platform ready to be used by millions of stakeholders in companies, households, and government.

The STREAMLINE consortium comprises world-renowned scientists and innovators in the areas of database systems (DFKI), distributed systems (SICS), and machine learning (SZTAKI) who have won many international awards, hold 18 patents collectively, and have founded and advised nine startups. Complementing the research excellence are four leading European enterprises in the data economy, in the areas of global telecommunication services (e.g. Internet, IPTV, mobile, and landline networks) (PT), games and entertainment (Rovio), media content streaming (NMusic), and web-scale data extraction and business analytics (IMR), with P
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Call for proposal

H2020-ICT-2015
See other projects for this call

Coordinator

RISE RESEARCH INSTITUTES OF SWEDEN AB
Address
Brinellgatan 4
501 15 Boras
Sweden
Activity type
Research Organisations
EU contribution
€ 186 140

Participants (8)

DEUTSCHES FORSCHUNGSZENTRUM FUR KUNSTLICHE INTELLIGENZ GMBH
Germany
EU contribution
€ 725 521,88
Address
Trippstadter Strasse 122
67663 Kaiserslautern
Activity type
Research Organisations
MEO-SERVICOS DE COMUNICACOES E MULTIMEDIA SA

Participation ended

Portugal
EU contribution
€ 0
Address
Av Fontes Pereira De Melo 40
1069 300 Lisboa
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
SZAMITASTECHNIKAI ES AUTOMATIZALASI KUTATOINTEZET
Hungary
EU contribution
€ 418 197,44
Address
Kende Utca 13-17
1111 Budapest
Activity type
Research Organisations
NMUSIC SA

Participation ended

Portugal
EU contribution
€ 92 137,50
Address
Rua Da Lionesa 446, Edifício C6
4465-671 Leca Do Balio
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
INTERNET MEMORY RESEARCH SAS
France
EU contribution
€ 501 103,60
Address
Rue De La Revolution 45 Ter
93100 Montreuil
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
ROVIO ENTERTAINMENT OY
Finland
EU contribution
€ 373 832,25
Address
Keilaranta 7
02150 Espoo
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
ALTICE LABS SA
Portugal
EU contribution
€ 431 452,03
Address
Rua Jose Ferreira Pinto Basto
3810 106 Aveiro
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
RISE SICS AB

Participation ended

Sweden
EU contribution
€ 562 910,29
Address
Po Box 1263
164 29 Kista
Activity type
Research Organisations