Enabling near-term prediction of status for intelligent transportation systems: Management techniques for data on mobile objects

Location Dependent Queries (LDQs) benefit from the rapid advances in communication and Global Positioning System (GPS) technologies to track moving objects\u27 locations, and improve the quality-of-life by providing location relevant services and information to end users. The enormity of the underlying data maintained by LDQ applications - a large quantity of mobile objects and their frequent mobility - is, however, a major obstacle in providing effective and efficient services. Motivated by this obstacle, this thesis sets out in the quest to find improved methods to efficiently index, access, retrieve, and update volatile LDQ related mobile object data and information. Challenges and research issues are discussed in detail, and solutions are presented and examined. --Abstract, page iii

Continuous Spatial Query Processing in Mobile Information Systems

Nowadays, many mobile applications provide location-based services that allow users to access location-related information from anywhere, whenever they desire. A moving user can issue queries to access information about moving or static objects. Continuous spatial query processing systems are used for this type of application. We propose two query processing strategies for location based services. The objectives of our strategies are to reduce: (1) the server workload, (2) the data transmission cost and (3) the query response time, for location-based services while providing an answer for a continuous region query. We compare our first strategy with a brute-force strategy and found that our strategy can significantly reduce the server workload and data transmission cost over the brute-force method. We compare our improved strategy with the original strategy and brute-force strategy. The experimental results show that the improved strategy achieves lower query response time than the original and brute-force strategy

Moving Query Monitoring In Spatial Network Environments

Kaveh Madani and Jay Lund have developed a new model that can be used for large-scale hydropower planning studies in California with reasonable computational effort and time. The Energy-Based Hydropower Optimization Model (EBHOM) is a non-linear optimization model that finds the reservoir operations and hydropower generation which maximizes hydropower revenues. EBHOM provides planning and management insights about hydropower systems with minimal computational effort. The run time of the model is low and its application to different systems is not costly. The effects of on-peak and off-peak pricing on the hydropower operations are considerable. EBHOM uses a novel method to incorporate the effects of variable pricing on the operations. This is important for hydropower systems that are operated with fluctuating hourly prices. Recently, EBHOM has been improved to also consider the climate change effects on hydropower demand and pricing. The improved EBHOM (EBHOM 2.0) benefits from an Artificial Neural Networks (ANN) module that estimates the changes in hourly hydropower pricing in response to temperature changes

Moving Query Monitoring in Spatial Network Environments

Moving queries over mobile objects are an important type of query in moving object database systems. In recent years, there have been quite a few works in this area. Due to the high frequency in location updates and the expensive cost of continuous query processing, server computation capacity and wireless communication bandwidth are the two limiting factors for large-scale deployment of moving object database systems. Many techniques have been proposed to address the server bottleneck including one using distributed servers. To address both scalability factors, distributed query processing techniques have been considered. These schemes enable moving objects to participate in query processing to substantially reduce the demand on server computation, and wireless communications associated with location updates. Most of these techniques, however, assume an open-space environment. Since Euclidean distance is different from network distance, techniques designed specifically for an open space cannot be easily adapted for a spatial network. In this paper, we present a distributed framework which can answer moving query over moving objects in a spatial network. To illustrate the effectiveness of the proposed framework, we study two representative moving queries, namely, moving range queries and moving k-nearest-neighbor queries. Detailed algorithms and communication mechanisms are presented. The simulation studies indicate that the proposed technique can significantly reduce server workload and wireless communication cost