This thesis implements a part of the front-end software, the Optimizer, of the distributed database system being developed at Virginia Tech. The Optimizer generates a strategy for optimal query processing, and it presents and analyzes a given query by means of query trees and query graphs. This thesis develops PASCAL procedures that implement quantitative and qualitative rules to select query graphs requiring minimum communication costs. To develop the rules, the size of the temporary files generated after performing each required operation is estimated. The focus of this work is on the implementation of a new technique for estimating the temporary file sizes. Detailed discussion of this implementation is presented and illustrated with a complete example, followed by a comparison with one of the existing methods proposed by Dwyer. / M.S.

A methodology is presented for evaluating the performance of database update schemes. The methodology uses the M/Hr/1 queueing model as a basis for this analysis and makes use of the history of how data is used in the database. Parameters have been introduced which can be set based on the characteristics of a specific system. These include update to retrieval ratio, average file size, overhead, block size and the expected number of items in the database. The analysis is specifically directed toward the support of derived data within the relational model. Three support methods are analyzed. These are first examined in a central database system. The analysis is then extended in order to measure performance in a distributed system. Because concurrency is a major problem in a distributed system, the support of derived data is analyzed with respect to three distributive concurrency control techniques -- master/slave, distributed and synchronized. In addition to its use as a performance predictor, the development of the methodology serves to demonstrate how queueing theory may be used to investigate other related database problems. This is an important benefit due to this lack of fundamental results in the area of using queueing theory to analyze database performance.

Traffic accidents are of concern for traffic safety; 1.25 million deaths are reported each year. Hence, it is crucial to have access to real-time data and rapidly detect or predict accidents. Predicting the occurrence of a highway car accident accurately any significant length of time into the future is not feasible since the vast majority of crashes occur due to unpredictable human negligence and/or error. However, rapid traffic incident detection could reduce incident-related congestion and secondary crashes, alleviate the waste of vehicles’ fuel and passengers’ time, and provide appropriate information for emergency response and field operation. While the focus of most previously proposed techniques is predicting the number of accidents in a certain region, the problem of predicting the accident occurrence or fast detection of the accident has been little studied. To address this gap, we propose a deep learning approach and build a deep neural network model based on long short term memory (LSTM). We apply it to forecast the expected speed values on freeways’ links and identify the anomalies as potential accident occurrences. Several detailed features such as weather, traffic speed, and traffic flow of upstream and downstream points are extracted from big datasets. We assess the proposed approach on a traffic dataset from Sacramento, California. The experimental results demonstrate the potential of the proposed approach in identifying the anomalies in speed value and matching them with accidents in the same area. We show that this approach can handle a high rate of rapid accident detection and be implemented in real-time travelers’ information or emergency management systems. / M.S. / Rapid traffic accident detection/prediction is essential for scaling down non-recurrent conges- tion caused by traffic accidents, avoiding secondary accidents, and accelerating emergency system responses. In this study, we propose a framework that uses large-scale historical traffic speed and traffic flow data along with the relevant weather information to obtain robust traffic patterns. The predicted traffic patterns can be coupled with the real traffic data to detect anomalous behavior that often results in traffic incidents in the roadways. Our framework consists of two major steps. First, we estimate the speed values of traffic at each point based on the historical speed and flow values of locations before and after each point on the roadway. Second, we compare the estimated values with the actual ones and introduce the ones that are significantly different as an anomaly. The anomaly points are the potential points and times that an accident occurs and causes a change in the normal behavior of the roadways. Our study shows the potential of the approach in detecting the accidents while exhibiting promising performance in detecting the accident occurrence at a time close to the actual time of occurrence.

The main campus of Virginia Polytechnic Institute and State University (Virginia Tech) has a current parking inventory of approximately 14,310 parking spaces in 88 surface lots. Of the available parking spaces 92% are designated for the use of Virginia Tech students, faculty and staff members. The share of visitor usage of these parking spaces is approximately 2%. The objective of this thesis is to evaluate the current visitor parking system and develop techniques to enhance the parking facility operations. The current visitor parking demand is determined by conducting a field evaluation of the visitor parking in five parking lots. Personal and follow-up interviews were conducted with university visitors to determine their satisfaction levels with the existing parking facilities. While the survey results demonstrated that 52% of visitors rated the parking services to be good or very good, the study also showed that approximately 28% of the visitors did not know the location of the most convenient parking lot to access their destination and almost 6% were not satisfied with the parking services offered by Virginia Tech. Apart from this, about 32% of the visitors had to visit at least 2 parking lots before finding a parking space. These figures clearly demonstrate a need for a better management of the parking lots to serve the visitors in an efficient way. In an attempt to enhance the visitor parking system a web-based interactive framework is proposed. This framework identifies the best possible parking lot(s) for a selected destination considering various factors like - distance of the parking lot to the destination and typical occupancy of the parking lot at specific times of the day and other relevant factors. The web-based interactive system is designed to provide the visitor with two or more choices for the parking in order to arrive at their desired destination. / Master of Science

This thesis describes a computing environment WBCSim and its experiment management component. WBCSim is a web-based simulation system used to increase the productivity of wood scientists conducting research on wood-based composite and material manufacturing processes. This experiment management component integrates a web-based graphical front end, server scripts, and a database management system to allow scientists to easily save, retrieve, and perform customized operations on experimental data. A detailed description of the system architecture and the experiment management component is presented, along with a typical scenario of usage. / Master of Science

The B-tree is a one of the more popular methods in use today for indexes and inverted files in database management systems. The traditional implementation of a Bâ tree uses many pointers (more than one per key), which can directly affect the performance of the B-tree. A general method of file organization and access (called Dynanic Address Computation) has been described by Cook that can be used to implement B-trees using no pointers. A minimal amount of storage (in addition to the keys) is required. An implementation of Dynamic Address Computation and a B-tree management package is described. Analytical performance measures are derived in an attempt to understand the performance characteristics of the B-tree. It is shown that the additional costs associated with Dynamic Address Computation result in an implementation that is competitive with traditional implementations only for small applications. For very large B-trees, additional work is required to make the performance acceptable. Some examples of possible modifications are discussed. / Master of Science

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The information systems literature is rich with studies of database organization and its impact on machine, programmer, and administrative efficiency. Little attention, however, has been paid to the impact of database organization on end-user interactions with computer systems. This research effort addressed this increasingly important issue by examining the effects of database organization on the ability of end-users to locate and extract desired information. The study examined the impact of normalization levels of external relational database schema on end-user query success. It has been suggested in the literature that end-user query success might be improved by presenting external schema in lower level normal forms. This speculation is based on an analytical study of one particular class of query, queries involving join operations. The research presented here provides empirical support for this assertion. However, the implicit assumption that all other queries are neutral in their bias toward a particular level of normalization was found to be false. A class of queries requiring decomposition of prejoined relations was identified which strongly biases normalized relations. Thus, no particular normalization level was shown to dominate unless assumptions were made as to the class of query being formulated. Evidence from field research may be required to completely resolve the issue. The study also examined the interaction effects between normalization levels and other key variables known to impact query success. Significant interactions with user skill and the complexity of the query being made were found. The level of normalization did not impact high skilled users making easy queries or low skilled users making difficult queries. The impact of these interactions, as well as the main effects of the related variables, on query syntax and logic errors holds important implications for database administrators as well as those involved with the development of database query languages. / Ph. D.