Grid computing seeks to aggregate computing resources within an enterprise and leverage on resources you don't own for compute-intensive applications. Geo-rectification is a process for correcting spatial location and orientation of a satellite image. This paper focuses on the parallelization of the computeintensive satellite image geo-rectification problem on a cluster grid. We discuss our approach to data and task partitioning, visualization technique and the archival of data. The computational tasks include wrapping satellite positional data to compensate the earth curvature, and consist of several steps such as image re-sampling, resolution conversion and image matching. Experimental results obtained using commodity PCs are discussed. IntroductionGrid computing [11,13] seeks to efficiently coordinate the sharing of geographically distributed computing resources, thereby bringing supercomputing power to its users. Unlike cluster computing [4,5,26] that is more constrained to computation on a local area networked of processors, grid computing enables applications to utilize resources that are spread across wide area networks. , architecture for matching grid application requirements to a set of heterogeneous grid resources is proposed. Our grid middleware, called ALiCE (Adaptive and scaLable Internet-based Computing Engine), is a portable software technology for developing and deploying general-purpose grid applications and systems. ALiCE aggregates and virtualises computer resources on the Internet/intranet into one computing environment through a platform-independent consumer-producer resource-sharing model, and

Spatial data mining is the extraction of implicit knowledge, spatial relations or other patterns not explicitly stored in spatial database. The focus of this paper is placed on the information derivation of spatial data. Geographical coordinates of hot spots in forest fire regions, which are extracted from the satellite images, are studied and used in the detection of likely fire points. Due to the saturation of spectral band, there are false alarms in the derived data set. We use clustering and Hugh transformation to determine regular patterns in the derived hotspots and classify them as false alarms on the assumption that fires usually do not spread in regular patterns such as in a straight line. This project demonstrates the application of spatial data mining to reduce false alarm from the set of hot spots derived from NOAA images.

Excessive rollback recoveries due to overoptimistic event execution in Time Warp simulators often degmde their runtime performance. This paper presents a twosided throttling scheme to dynamically adjust the event execution speed of Time Warp simulators. The proposed throttle is based on a new concept called global progress window, which allows the individual simulation process to be positioned on a global time scale, thereby to accelemte OT suspend their event execution. As each simulation process can be throttled to a steady state, excessive rollback recoveries due to causality er- MTS can be avoided. To quantify the effect of rollbacks and for purpose of comparing different Time Warp implementations, we propose two new measures called RPE (number of Rollback events Per committed Event), and 8 (relative Eflectiveness in reducing rollback overhead).OUT implementation results show that the proposed throttle effectively regulates the proceeding of each simulation process, resulting in a significant reduction in rollback thrashing and elapsed time.

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Excessive rollback recoveries due to overoptimistic event execution in Time Warp simulators often degmde their runtime performance. This paper presents a two-