Volume XLII-4/W10 | Copyright
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W10, 187-192, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W10-187-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2018

12 Sep 2018

PREDICTION BASED WORKLOAD PERFORMANCE EVALUATION FOR DISASTER MANAGEMENT SPATIAL DATABASE

N. Suryana1, M. S. Rohman2, and F. S. Utomo1,3 N. Suryana et al.
  • 1Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
  • 2Department of Informatics Engineering, Universitas Dian Nuswantoro, Semarang, Indonesia
  • 3Department of Information Systems, STMIK AMIKOM Purwokerto, Purwokerto, Indonesia

Keywords: Disaster Management, Large Spatial Database, Shortest Path, Dijkstra Algorithm, Case-Based Reasoning

Abstract. This paper discusses a prediction based workload performance evaluation implementation during Disaster Management, especially at the response phase, to handle large spatial data in the event of an eruption of the Merapi volcano in Indonesia. Complexity associated with a large spatial database are not the same with the conventional database. This implies that in coming complex work loads are difficult to be handled by human from which needs longer processing time and may lead to failure and undernourishment. Based on incoming workload, this study is intended to predict the associated workload into OLTP and DSS workload performance types. From the SQL statements, it is clear that the DBMS can obtain and record the process, measure the analysed performances and the workload classifier in the form of DBMS snapshots. The Case-Based Reasoning (CBR) optimised with Hash Search Technique has been adopted in this study to evaluate and predict the workload performance of PostgreSQL. It has been proven that the proposed CBR using Hash Search technique has resulted in acceptable prediction of the accuracy measurement than other machine learning algorithm like Neural Network and Support Vector Machine. Besides, the results of the evaluation using confusion matrix has resulted in very good 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 which is optimised by Hash Search technique for determining workload data on shortest path analysis via the employment of Dijkstra algorithm. It could be useful for the prediction of the incoming workload based on the status of the predetermined DBMS parameters. In this way, information is delivered to DBMS hence ensuring incoming workload information that is very crucial to determine the smooth works of PostgreSQL.

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