
The world's latest scenario suggests that the database size will pilling-up epidemically. Due to this, requirement of larger database is eminent. Furthermore, these data needs to be scalable enough to support future up gradation, keeping in mind that performance is not compromised. The structure of Structured Query Language (SQL) query also plays an enormous role in gauging the performance of execution. In this paper we have suggested a model that allows checking of the query performance with respect to predefined rules, SQL benchmark, strategy of SQL tuning and data freezing. In this paper we systemized existing query formation rules which need to be followed by every query that needs to be optimized. Our approach also suggest the user an optimized query for normalization if required. If filtration is used in given query our approach will also be stating any requirement for indexing on particular column. By using any of the above approach along with avoiding unnecessary usage of data, columns, our proposed model converts the input user SQL query into an optimized SQL query and main aims to give assurance of reduced query execution time.
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
