Papers by Numptik Voratas
The objective of this manuscript is just to present a list of references that will be useful for ... more The objective of this manuscript is just to present a list of references that will be useful for the researcher who wishes to work on either machine learning or query optimization. Some of the important research works related to these two areas are mentioned in the following references. I hope that the list of following references will assist the naïve researchers who are working in these domain area.
Query is a statement or group of statement that adequately execute some basic database operations... more Query is a statement or group of statement that adequately execute some basic database operations viz. " Read " , " Write " , " Delete " , and " Update ". It plays a consequential role in managing and retrieving data. In general, distributed queries are more complex and complicated as compared to centralized queries. Queries can be categorized as data creation and data destruction, Data management queries, Data control quarry, OLTP and DSS quarries. In data creation and data destruction quarries create, insert and drop quarries are used. In data management quarry data is managed and manipulate, data can be insert, delete and update. In data control query, one can save data using commit command; permission can be granted using grant command [1][2][3]. In online transaction processing (OLTP) the work analysis and query optimization is done. In decision support system (DSS) queries used to retrieve data from large database. The execution time is not predictable in DSS query. Decision support system (DSS) queries are more complex as compare to online transaction processing queries (OLTP). The running time of DSS queries are unpredictable as compare to OLTP. The process of optimization in Decision support system (DSS) queries is complex as compare to OLTP queries. A distributed DSS query is used to retrieve data from multiple sites. In online transaction processing system (OLTP); real updates are performed. However, DSS queries execute batches as compared to real time updates. Online transaction processing (OLTP) database applications are optimal for managing changing data; these applications typically have many users who are performing transaction at the same time that change real time data, in other words OLTP is a live database. On other side the tables in a decision support database are heavily indexed and the raw data is frequently preprocessed and organized to support various types of queries to be used. The OLTP and DSS queries can be differentiated on the basis of different parameters as mentioned below [1][4][5][6]: A number of heuristics have been applied in recent times, which proposed new algorithms for substantially improving the performance of a query[1][2][3]. As stated by Manik Sharma et al. (2015) there are two major types of database queries called DSS and OLTP queries. To optimize a DSS query on the basis of usage of system resources, one has to find an optimal query execution plan which minimizes the Total Costs of a query. For finding the optimal query execution plan, the costs of
Query is a statement or group of statement that adequately execute some basic database operations... more Query is a statement or group of statement that adequately execute some basic database operations viz. " Read " , " Write " , " Delete " , and " Update ". It plays a consequential role in managing and retrieving data. In general, distributed queries are more complex and complicated as compared to centralized queries. Queries can be categorized as data creation and data destruction, Data management queries, Data control quarry, OLTP and DSS quarries. In data creation and data destruction quarries create, insert and drop quarries are used. In data management quarry data is managed and manipulate, data can be insert, delete and update. In data control query, one can save data using commit command; permission can be granted using grant command [1][2][3]. In online transaction processing (OLTP) the work analysis and query optimization is done. In decision support system (DSS) queries used to retrieve data from large database. The execution time is not predictable in DSS query. Decision support system (DSS) queries are more complex as compare to online transaction processing queries (OLTP). The running time of DSS queries are unpredictable as compare to OLTP. The process of optimization in Decision support system (DSS) queries is complex as compare to OLTP queries. A distributed DSS query is used to retrieve data from multiple sites. In online transaction processing system (OLTP); real updates are performed. However, DSS queries execute batches as compared to real time updates. Online transaction processing (OLTP) database applications are optimal for managing changing data; these applications typically have many users who are performing transaction at the same time that change real time data, in other words OLTP is a live database. On other side the tables in a decision support database are heavily indexed and the raw data is frequently preprocessed and organized to support various types of queries to be used. The OLTP and DSS queries can be differentiated on the basis of different parameters as mentioned below [1][4][5][6]: A number of heuristics have been applied in recent times, which proposed new algorithms for substantially improving the performance of a query[1][2][3]. As stated by Manik Sharma et al. (2015) there are two major types of database queries called DSS and OLTP queries. To optimize a DSS query on the basis of usage of system resources, one has to find an optimal query execution plan which minimizes the Total Costs of a query. For finding the optimal query execution plan, the costs of
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Papers by Numptik Voratas