FRINK Linked Data Fragments server

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• W2896570434 abstract "Workload performance evaluation can be implemented during Disaster Management and especially at the response phase to handle large spatial data in the event of an eruption and in this study it is involves the merapi volcano of Indonesia. Merapi volcano is known for its biggest eruption in the world. After the occurrence of an eruption, the affected areas are isolated, and thus it is difficult to be accessed by the rescuers. It is indeed very difficult to reach the isolated area as well as to rescue the victims from the affected areas. Although specific researches have resulted in solutions to the issue, other aspects that include the sending of workload to the database needs to be taken into consideration and it is viable to result in an effective and efficient process. Besides, the shortest route could be defined timely and accurately hence enabling the victims to leave the isolated area and to reach the evacuation point safely. This research intends to study on workload performance which is crucial to support the working mechanism of Database Management System (DBMS). Literature on recent studies has made it clear that research in this particular area of interest is scarce. Therefore, the general objective of this research is to evaluate and predict workload performance of spatial DBMS associated with PostgreSQL which is different from MySQL. Based on incoming workload, this research is able to predict the associated workload into OLTP and DSS workload performance types. From the SQL statements it is clear that the DBMS is able to obtain and record the process, measure the analyzed performances and the workload classifier in the form of snapshots from the DBMS. For example, it has been proven that Dijkstra Algorithm is able to determine the shortest and the safest path. Then, all the workload that are obtained to determine the processes are recorded into one excel file. The Case Based Reasoning (CBR) optimized with Hash Search Technique has been adopted in this study for the purpose of evaluating and predicting the workload performance of PostgreSQL DBMS. Data recorded in the shortest path analysis process reveals that the evaluation and the prediction on workload performance of shortest path analysis using Dijkstra algorithm has been well implemented. It has been proven that the proposed CBR using Hash Search technique has resulted in an excellent prediction of the accuracy measurement. Besides, the results of the evaluation using confusion matrix has resulted in excellent accuracy as well as improvement in execution time. Additionally, the results of the study indicated that the prediction model for workload performance evaluation using CBR that is optimized with Hash Search technique for determining workload data on Shortest Path analysis via the employment of Dijkstra algorithm can be useful for the prediction of incoming workload based on the status of the DBMS parameters. In this way, information is delivered to DBMS hence ensuring incoming workload information is very crucial for the purpose of determining the smooth works of PostgreSQL DBMS." @default.
• W2896570434 created "2018-10-26" @default.
• W2896570434 creator A5010381232 @default.
• W2896570434 date "2017-01-01" @default.
• W2896570434 modified "2023-09-24" @default.
• W2896570434 title "Workload Performance Evaluation Of Large Spatial Database For DSS Based Disaster Management" @default.
• W2896570434 hasPublicationYear "2017" @default.
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