# Service-Oriented Local And Global Visualization with Sorting On-demand for Climate Data Xusheng Xiao Huge amount of climate simulation.

## Presentation on theme: "Service-Oriented Local And Global Visualization with Sorting On-demand for Climate Data Xusheng Xiao Huge amount of climate simulation."— Presentation transcript:

Service-Oriented Local And Global Visualization with Sorting On-demand for Climate Data Xusheng Xiao xxiao2@ncsu.edu Huge amount of climate simulation data are collected from different areas (e.g., cities, countries). Climate scientists keep trying to predict the trends of the variation of climate both locally and globally. Exploring visualization of data mining (e.g., histogram) has been used more and more frequently to get a general view ahead of predicting. Climate experts would like to analyze data by navigating among levels of data ranging from the most summarized (drill-up) to the most detailed (drill-down) (e.g., drill-down shown in Figure 1). Table 1 [1] Solution 1: Service-Oriented Histogram [2] Solution 2: On-demand Sorting [3] http://csc.ncsu.edu/ NCSU Computer Science Cache data and parameters (min, max, count) locally Index data with break number (e.g., 0.5 is in the break [0, 1] ) Check whether the data in the requested breaks are sorted or not If sorted, transfer data directly If data is not sorted, sort only the data in the corresponding break and mark the break as sorted Transfer local histogram data (min, max, count) for global computation Merge data from different sources Table 2 Result Challenge References 1.http://www.esrl.noaa.gov/psd/psd3/cruises/ 2.Felix Halim, Panagiotis Karras, and Roland H.C. Yap. 2009. Fast and effective histogram construction. ACM, New York, NY, USA, 1167-1176. 3.C. A. R. Hoare. Quicksort. The Computer Journal, 5(1):10‚Äì16, January 1962. Zhe Zhang zzhang13@ncsu.edu Ye Jin yjin6@ncsu.edu Globally transferring caused problems: Time-consuming (see Table 1) Package Lost during data transfer (see Table 1) Frequently drill-up and drill-down navigation of data consumes computation resources. (e.g., scanning same data set multiple times see Table 2) Motivation Locally And Global Visualization Locally compute min, max, and count Transmitting the local min, max and count to compute global min, max and count Each data sources compute the histogram based on the global min, max and count Only transferring the computed histogram data, which is much smaller compared to all the climate data Merge the transmitted histograms to show the global histograms Figure 1: Drill-down to interval [-1,1] Here are the raw data in multiple domains have already collected, we can see the latest data sets are all for year 2008. Data Domain Single data set size Number of data sets Total Size Collecting Time In Best Case VOCALS 2008~70000 KB56~3920 MB~10 Hrs ASCOS 2008~140000 KB25~3500 MB~10 Hrs AEROSE 2008 ～ 80000 KB 36~2880 MB~7 Hrs STRATUS 2007 ～ 70000KB 21~1470 MB~5 Hrs Data Size Run Once Histogram Discovery Histogram Run log(n) Times User specified 30 Times ~1500 MB2 Mins~17 * 2 = 34 Mins60 Mins ~3000 MB4 Mins~18 * 2 = 36 Mins120 Mins ~4500 MB6 Mins~19 * 2 =38 Mins180 Mins Total time needed to discovery meaningful or user specified parameters visualization results, we need to speed up those visualization algorithms. Figure 2: System Framework

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