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Project Title
LEADS: Large-Scale Elastic Architecture for Data as a Service
Internal ID
27796
Principal Investigator
Felber, Pascal  
Rivière, Etienne  
Halalai, Raluca  
Pasin, Marcelo  
Sobe, Anita  
Sutra, Pierre  
Status
Completed
Start Date
October 1, 2013
End Date
September 30, 2015
Organisations
Institut d'informatique  
Project Web Site
http://www.leads-project.eu/
Identifiants
https://libra.unine.ch/handle/20.500.14713/2469
-
https://libra.unine.ch/handle/123456789/1651
Keywords
Cloud Computing Data as a Service Big Data Storage Geo-distribution Dependability
Description
LEADS is a research project funded by the European Commission under the FP7. Three universities and four companies team up to build and demonstrate a novel cloud service model named Data-as-a-Service, on top of an innovative infrastructure based on multiple energy-conscious micro-clouds.

LEADS works on answering the demand of companies wishing to exploit the wealth of public data available on today's Internet. Large IT-oriented companies can crawl, store, and query large amounts of data in their own premises. Small companies and companies that are not on the Internet analytics business may still want to:
- Analyze the Web graph to extract business intelligence;
- Match company-specific private data to public data;
- Monitor in real-time public data evolution and detect trends, identify opinion leaders, etc.;
- Propose novel data-enabled services: complex graph analytics, real-time data aggregation, ...

Yet, many companies may not be able or willing to crawl, store and process in-house, because of its associated high cost, complexity, or simply because they miss the necessary expertise. Can these companies rely on larger ones for accessing and processing public content? Querying capabilities, and data freshness and comprehensiveness would depend on the provider's good will. Also, there are little guarantees on confidentiality. Data is power, and power is seldom willingly shared.

LEADS proposes Data-as-a-Service as a solution to the need for small actors to take advantage of big public data, by mutualizing the costs of extracting, storing and processing public data, while offering rich and extensible possibilities, including privacy-protecting querying on public and private data including data updated in real-time, and more.
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