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STREAMLINE

STREAMLINE

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EC-Contrib. €

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 STREAMLINE project word cloud

Explore the words cloud of the STREAMLINE project. It provides you a very rough idea of what is the project "STREAMLINE" about.

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Project "STREAMLINE" data sheet

The following table provides information about the project.

Coordinator
RISE RESEARCH INSTITUTES OF SWEDEN AB 

Organization address
address: BRINELLGATAN 4
city: BORAS
postcode: 501 15
website: www.ri.se

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Sweden [SE]
 Project website http://h2020-streamline-project.eu/
 Total cost 3˙291˙294 €
 EC max contribution 3˙291˙294 € (100%)
 Programme 1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
 Code Call H2020-ICT-2015
 Funding Scheme RIA
 Starting year 2015
 Duration (year-month-day) from 2015-12-01   to  2018-11-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    RISE RESEARCH INSTITUTES OF SWEDEN AB SE (BORAS) coordinator 186˙140.00
2    DEUTSCHES FORSCHUNGSZENTRUM FUR KUNSTLICHE INTELLIGENZ GMBH DE (KAISERSLAUTERN) participant 725˙521.00
3    RISE SICS AB SE (KISTA) participant 562˙910.00
4    INTERNET MEMORY RESEARCH SAS FR (MONTREUIL) participant 501˙103.00
5    ALTICE LABS SA PT (AVEIRO) participant 431˙452.00
6    MAGYAR TUDOMANYOS AKADEMIA SZAMITASTECHNIKAI ES AUTOMATIZALASI KUTATOINTEZET HU (BUDAPEST) participant 418˙197.00
7    ROVIO ENTERTAINMENT OY FI (ESPOO) participant 373˙832.00
8    NMUSIC SA PT (LECA DO BALIO) participant 92˙137.00
9    MEO-SERVICOS DE COMUNICACOES E MULTIMEDIA SA PT (LISBOA) participant 0.00

Map

 Project 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 etab

 Deliverables

List of deliverables.
Combined Data at Rest and Data in Motion Analysis Platform v1 Documents, reports 2020-01-23 09:37:44
Field trials and Evaluation v1 Demonstrators, pilots, prototypes 2020-01-23 09:37:44
Dissemination Roadmap & Project Website Documents, reports 2020-01-23 09:37:44
Design and Implementation v1 Documents, reports 2020-01-23 09:37:44
Flink deployment software Demonstrators, pilots, prototypes 2020-01-23 09:37:44
Project Plan Period 1 Documents, reports 2020-01-23 09:37:44
Annual Report, Quality Assurance and Evaluation Period 1 Documents, reports 2020-01-23 09:37:44
Flink Real Time Stream Mining Library v1 Documents, reports 2020-01-23 09:37:44
Status report on dissemination activities Period 1 Documents, reports 2020-01-23 09:37:43
Flink Real Time Stream Mining Library v3 Documents, reports 2020-01-23 09:37:43
Design and Implementation v2 Documents, reports 2020-01-23 09:37:42
Status report on dissemination activities Period 2 Documents, reports 2020-01-23 09:37:42
Combined Data at Rest and Data in Motion Analysis Platform v3 Documents, reports 2020-01-23 09:37:43
Use case report for actionable knowledge extraction from text information Documents, reports 2020-01-23 09:37:42
Design and Implementation v3 Documents, reports 2020-01-23 09:37:42
Combined Data at Rest and Data in Motion Analysis Platform v2 Documents, reports 2020-01-23 09:37:42
Annual Report, Quality Assurance and Evaluation Period 2 Documents, reports 2020-01-23 09:37:43
Field Trials and Implementation v3 Demonstrators, pilots, prototypes 2020-01-23 09:37:42
Flink on Hops/Hadoop Demonstrators, pilots, prototypes 2020-01-23 09:37:43
Flink interactive environment Demonstrators, pilots, prototypes 2020-01-23 09:37:43
Flink Real Time Stream Mining Library v2 Documents, reports 2020-01-23 09:37:43
Project Plan Period 2 Documents, reports 2020-01-23 09:37:42
Field Trials and Evaluation v2 Demonstrators, pilots, prototypes 2020-01-23 09:37:42
A high level declarative language for ML Documents, reports 2020-01-23 09:37:43

Take a look to the deliverables list in detail:  detailed list of STREAMLINE deliverables.

 Publications

year authors and title journal last update
List of publications.
2017 Philipp M. Grulich, Tilmann Rabl, Volker Markl, Csaba Sidló, Andras Benczur
STREAMLINE - Streamlined Analysis of Data at Rest and Data in Motion
published pages: , ISSN: , DOI:
20th International Conference on Extending Database Technology (EDBT), 2017 2020-01-23
2017 Andreas Kunft, Asterios Katsifodimos, Sebastian Schelter, Tilmann Rabl, Volker Markl
Blockjoin: efficient matrix partitioning through joins
published pages: 2061-2072, ISSN: 2150-8097, DOI:
Proceedings of the VLDB Endowment - Proceedings of the 43rd International Conference on Very Large Data Bases 10/13 2020-01-23
2018 Quoc-Cuong To, Juan Soto, Volker Markl
A survey of state management in big data processing systems
published pages: 847-872, ISSN: 1066-8888, DOI: 10.1007/s00778-018-0514-9
The VLDB Journal 27/6 2020-01-23
2017 Erzsébet Frigó, Róbert Pálovics, Domokos Kelen, Levente Kocsis, András A. Benczúr
Alpenglow: Open Source Recommender Framework with Time-aware Learning and Evaluation
published pages: , ISSN: , DOI:
RecSys 2017 poster 2020-01-23
2018 Ferenc Béres, Róbert Pálovics, Anna Oláh, András A. Benczúr
Temporal walk based centrality metric for graph streams
published pages: , ISSN: 2364-8228, DOI: 10.1007/s41109-018-0080-5
Applied Network Science 3/1 2020-01-23
2019 Behrouz Derakhshan, Alireza Rezaei Mahdiraji, Tilmann Rabl, and Volker Markl
Continuous Deployment of Machine Learning Pipelines
published pages: , ISSN: , DOI:
22nd International Conference on Extending Database Technology (EDBT), 2019 2020-01-23
2017 Erzsébet Frigó, Róbert Pálovics, Domokos Kelen, Levente Kocsis, András A. Benczúr
Online ranking prediction in non-stationary environments
published pages: , ISSN: , DOI:
RecTemp 2017 – workshop on reasoning on temporal aspects in user modeling in conjunction with RecSys 2017 2020-01-23
2016 András A. Benczúr, Róbert Pálovics, Márton Balassi, Volker Markl, Tilmann Rabl, Juan Soto, Björn Hovstadius, Jim Dowling,Seif Haridi
Towards Streamlined Big Data Analytics
published pages: 31-32, ISSN: , DOI:
ERCIM News 107 2020-01-23
2018 András A. Benczúr, Levente Kocsis, Róbert Pálovics
Online Machine Learning in Big Data Streams
published pages: , ISSN: , DOI:
2020-01-23
2019 Jonas Traub Philipp Grulich, Alejandro Rodríguez Cuéllar Sebastian Breß Asterios Katsifodimos Tilmann Rabl Volker Markl
Efficient Window Aggregation with General Stream Slicing
published pages: , ISSN: , DOI:
22nd International Conference on Extending Database Technology (EDBT), 2019 2020-01-23

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