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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 11    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 11, November 2011
Visualization Approach to Effective Decision Making on Hydrological Data
Full Text(PDF, 3000)  PP.  
Author(s)
Faudziah Ahmad, Khairul Bariah Ahmad, Azliza Othman
KEYWORDS
Temporal, Visualization, Hydrology Data, Decision making.
ABSTRACT
Temporal data is by nature arranged according to the sequence of time where the order of the data is very significant. Thus in order to visualize a temporal data, the order of the data has to be preserve that will show certain trends or temporal patterns. Most visualization technique however uses technical visual representation such as bar chart and line graph. This approach is suitable and can be easily comprehended only by technical users. In order to reduce the learning curve in understanding the prototype develop and facilitate decision making, metaphor based visualization approach was used for representing temporal hydrological data. To evaluate the correct of decision making similarity test was conducted by using data mining approach, specifically incorporating case-based reasoning. The test case or new data was compared with the case extracted from previous operation data and the case closely was examined by exploring the detailed data. Results were evaluated through usability testing and similarity testing. The prototype was demonstrated to a group of users specifically three DID staff involved with the dam operation directly and indirectly. The feedbacks received from the users are positive where the interface objects used took a short time for them to learn and understand due to the familiarity of the representation. One look at the map, it will give them the overall picture of the situation patterns of the dam water level and rainfall around the catchments area according to the time frame chosen. The metaphorical representation based visualization is used as a basis to represent temporal and multi-variate data using icon based technique and colour code to enhance interface usability and usefulness. This type of representation can be easily understood by a non-expert from the domain. The visualization actually assists users in the process of decision-making by representing the patterns in form close to the mental model of a user by using metaphor. This help speed up data exploration thus decision-making process. In critical situation speed and accuracy is vital in the decision making process.
References
[1] Aguilar-Ruiz, J., & Ferrer-Troyano, F, Visual data mining. Journal of Universal Computer Science, Vol.11, No.11, 2005, pp.1749–1751.

[2] Bederson, B. B. and Shneiderman, B. The Craft of Information Visualization: Reading and Reflections, Morgan Kaufmann Oublishers, 2003.

[3] Bertini, E. and Lalane, D., Investigating and Reflecting on the Integration of Automatic Data Analysis and Visualization in Knowledge Discovery, SIGKDD Explorations, Vol.11, No.2, 2010, pp.9-18.

[4] Brodlie, K.W., Duce D.A., Gallop, J.R., Walton, J.P.R.B. and Wood, J.D., Distributed and Collaborative Visualization, Computer Graphics Forum, Vol. 23, No.2, 2004, pp.223-251.

[5] Demsar, U., Data mining of geospatial data: combining visual and automatic method. Ph.D. Thesis, Royal Institute of Technology (KTH), 2006.

[6] House, D., Bair, A. and Ware, C., On the optimization of visualizations of complex phenomena, Visualization. VIS 05. IEEE, 2005, pp. 87- 94, 23-28 Oct. 2005.

[7] Kiem, D.A., Information Visualization and Visual Data Mining. IEEE Transactions on Visualization and Computer Graphics. Vol. 8, No. 1, 2002.

[8] Norwawi, N. 2004. Computational RPD Model based on Temporal Data Mining Approach in a Multiagent Environment for Flood Emergency Response Operation . PhD Thesis, Universiti Utara Malaysia, 2004.

[9] Preece, J., Rogers, J., and Sharp, S., Interaction Design: beyond human-computer interaction. 2nd Ed., John Wiley & Sons, Inc. 2007.

[10] Robinson, N. and Shapcott, M., Data Mining Information Visualization – Beyond Charts and Graphs, Proceedings of the 6th International Conference on IEEE Information Visualization, 2002.

[11] Schneidewind, J., Sips, M., & Keim, D. A., An automated approach for the optimization of pixel-based visualizations. Information Visualization, Vol.6, No.1, 2007, pp.75–88.

[12] Shneiderman, B. & Plainsant, C., Designing the User Interface: Strategies for effective Human-Computer Interaction, 4th edition. Addison Wesley, 2005.

[13] Spence, R., Information Visualization, Addison-Wesley, 2001.

[14] Staiano, A., Ciaramella, A., Raiconi, G., Tagliaferri, R., Amato, R., Longo, G., Miele, G., Donalek, C., Data visualization methodologies for data mining systems in bioinformatics, Proceedings IEEE International Joint Conference on Neural Networks, Vol.1, 2005, pp.143- 148.

[15] Teoh, S. T., Zhang, K., Tseng, S.-M., Ma, K.-L., & Wu, S. F., Combining visual and automated data mining for near-real-time anomaly detection and analysis, Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security, 2004, pp.35–44.

[16] Ware, C., Information Visualization: Perception for Design: Second Edition. Morgan Kaufmann Publishers, 2004.

[17] Zhao, K., Liu, B., Tirpak, T.M., Xiao, W., A Visual Data Mining Framework for Convenient Identification of Useful Knowledge, Fifth IEEE International Conference on Data Mining, 2005, pp.530-537.

[18] Tanin, E. Zhang, R. and Kulik, L., Spatio-temporal Database Research at University of Melbourne, SIGMOD Record, 2009, Vol.38, No.3, pp.35-39.

[19] Zakaria, Z., Knowledge Discovery Query Language (KDQL), Proceedings of the 12th Wseas International Conference on Computers, 2008, pp.497-519.

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