SaaS Level based Middleware Database Integrator Platform
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 5
Abstract
In purpose of data searching acceleration, the fastest data response is the major concern for latest cloud environment. Regarding this, the intellectual decision is to enrich the SaaS level applications. Amongst the SaaS based applications, service level database integration is the recent trend to provide the integrated view of the heterogeneous cloud databases through shared services using DBaaS. But the generic limitations interacted during the database integration are dynamic adaptability of multiple databases structure, dynamic data location identification in the concern databases, data response using the data commonality. Data migration technique and single query approach are the two individual solutions for the proposed limitations. But the side effects during data migration technique are extra space utilisation and excess time consumption. Again, the single query approach suffers from worst case time complexity for data connectivity, data aggregation and query evaluation. So, to find a suitable data response solution by eliminating these combined major issues, a graph based Middleware Database Integrator Platform or MDIP model has been proposed. This integrator platform is actually the flexible metadata representation technique for the concerned heterogeneous cloud databases. The associativity and commonality among components of multiple databases would be further helpful for efficient data searching in an integrated way. For the incorporation within the service level but not in the services, MDIP is considered as the different platform. It is applicable over any service based database integration in purpose of data response efficiency. Finally, the quality assessment using evaluated query time compared with already proposed SLDI shows better data access quality. Thus, its expertise dedication in data response can overcome summarised challenges like data adaptation flexibility, dynamic identification of data location, wastage of data storage, data accessing within minimal time span and optimised cost in presence of data consistency, data partitioning and user side scalability.
Authors and Affiliations
Sanjkta Pal
Keywords
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- EP ID EP259092
- DOI 10.14569/IJACSA.2017.080552
- Views 82
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