| Autor: |
Ardagna CA; Department of Computer Science, Università degli Studi di Milano, Milano, Italy., Bena N; Department of Computer Science, Università degli Studi di Milano, Milano, Italy., Hebert C; SAP Labs France, Mougins, France., Krotsiani M; Department of Computer Science, City, University of London, London, United Kingdom., Kloukinas C; Department of Computer Science, City, University of London, London, United Kingdom., Spanoudakis G; Department of Computer Science, City, University of London, London, United Kingdom. |
| Abstrakt: |
Big data management is a key enabling factor for enterprises that want to compete in the global market. Data coming from enterprise production processes, if properly analyzed, can provide a boost in the enterprise management and optimization, guaranteeing faster processes, better customer management, and lower overheads/costs. Guaranteeing a proper big data pipeline is the holy grail of big data, often opposed by the difficulty of evaluating the correctness of the big data pipeline results. This problem is even worse when big data pipelines are provided as a service in the cloud, and must comply with both laws and users' requirements. To this aim, assurance techniques can complete big data pipelines, providing the means to guarantee that they behave correctly, toward the deployment of big data pipelines fully compliant with laws and users' requirements. In this article, we define an assurance solution for big data based on service-level agreements, where a semiautomatic approach supports users from the definition of the requirements to the negotiation of the terms regulating the provisioned services, and the continuous refinement thereof. |
