The purpose of the project was to develop a Web application using GIS data that would map addresses or coordinate information and then find any hazardous areas that may fall within a given distance to this location. The geographic area of this project covers the extent of cities San Bernardino, Redlands, and Yucaipa, California.

The complexity of the behavior of parallel and distributed programs is the major reason for the difficulties in the analysis and diagnosis of their performance. Complex systems such as these have frequently been studied using models as abstractions of such systems. By capturing only the details of the system which are considered essential, a model is a replica of the complex system which is simpler and easier to understand than the real system. CHITRA92, the second generation of the performance analysis tool CHITRA, builds a continuous time semi-Markov chain to model program behavior. However, this model is limited to representing relationships between states which are only immediate predecessors or successors of each other. This project introduces and implements a new empirical model of the behavior of software programs which is able to represent dependencies between nonsequential program states. The implementation combines deterministic and probabilistic modeling and is based on the Chi Automatic Interaction Detection (CHAID) statistical technique designed for investigating categorical data. The empirical model, constructed by analyzing an ensemble of program execution sequences, is stochastic and non-Markovian in the form of an N -step Transition Matrix. The algorithm is integrated as one of the modeling subsystems of CHITRA93, the third generation of CHITRA. / Master of Science

see document / Master of Science

Master of Science

<p>A key cause of poor system performance is inherently due to the lack of understanding of system behavior. Performance problems are especially apparent in parallel and distributed programs, for which expected speedup is difficult to achieve. Theoretical models and trace visualization tools are suitable for extracting insights into the behavior of a system. Theoretical models available today work for certain types of systems and require possibly unrealistic assumptions, and hence are not considered here. Existing trace visualization tools have yielded new insights into the behavior of the sequential, parallel, and distributed programs. However, they have two inherent limitations: (1) Each tool visualizes only one execution of a program. (This is dangerous when analyzing concurrent programs, which are prone to non-deterministic behavior.) (2) The applicable domain of a visualization tool will be limited unless the tool incorporates a large variety of methods to visually display data. This is because a single display method may yield new insights into only certain systems. In addition, finding the "right" display that can provide the needed insights is a potentially time-consuming process. This project carries through the previous work-CHITRA92. This project addresses these limitations by providing the following four capabilities to analyze traces: (1) CHITRA93 analyzes a set (or ensemble) of traces to obtain the typical behavior of a system. (2) CHITRA93 incorporates three transforms to simplify ensembles by reducing either the state space size or the interval over which time is defined in the ensemble. (3) CHITRA93 builds compact summary of the dynamic behavior (or model) of a system from an ensemble. (4) CHITRA93, to avoid building models that poorly fit an ensemble, provides a suite of methods to partition ensembles into mutually exclusive, exhaustive, and homogeneous subsets so that each subset displays "similar" behavior. These methods include several visual techniques and statistical methods. Finally, a portion of the project seeks to stabilize and to produce a correct version of CHITRA.</p> / Master of Science

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