Complex Queries Optimization and Evaluation over Relational and NoSQL Data Stores in Cloud Environments - 2018


The production of giant quantity of data and the emergence of cloud computing have introduced new needs for data management. Several applications need to interact with several heterogeneous data stores relying on the type of knowledge they have to manage: relational and NoSQL (i.e., document, graph, key-worth, and column) knowledge stores. Interacting with heterogeneous data models via completely different APIs and question languages imposes difficult tasks to the developers of multiple knowledge stores applications. Indeed, the execution of advanced queries over heterogeneous data models cannot, currently, be achieved in an exceedingly declarative way as it's was with single knowledge store application, and so needs additional implementation efforts. In this Project we have a tendency to propose a mediation based mostly component to optimize and execute complex queries over multiple data stores in Cloud environments. This component is referred to as virtual knowledge store (VDS). The key ingredients of our answer are (one) a easy world schema describing the different data sources and their relationships, (2) a cost model to guage the price of the operations, (3) an inter knowledge stores parallelism execution model, and (four) a dynamic programming primarily based approach to get optimal execution arrange. Quantitative and qualitative experiments are conducted to validate our approach.

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