Due to wireless communication technologies, positioning technologies, and mobile computing develop quickly, mobile services are becoming practical and important on big spatiotemporal databases management. Mobile service users move only inside a spatial network, e.g. a road network. They often issue the K Nearest Neighbor (KNN) query to obtain data objects reachable through the road network. The challenge problem of mobile services is how to efficiently answer the data objects which user interest to the corresponding mobile users. Therefore, how to effectively modeling road networks, effectively indexing, and querying on the road networks have become a popular topic. Lu et. al. have proposed a road network model that captures the real-life road networks better than previous models. Then, based on their model, they have proposed a RNG (Road Network Grid) index for speeding up the KNN query on real-life road networks. The RNG index structure is a quad-tree structure and a point-based structure. However, in their model, they divide the double track road which U-turn is allowed at some parts. This modeling does not capture the real-life road networks accurately. Since they divide the road, this makes the number of points of the graph increase. The number of times of partitioning the graph increases. It increases the execution time of constructing the index structure. The format of the leaf node of the RNG index makes the search time increase. Moreover, the query processing on the RNG index structure has to visit the root repeatedly. This condition makes the search time increase. Therefore, in this thesis, we propose a network model that captures the real-life road networks. We do not have to divide the real-life roads when we map the real-life roads into graph. We map the real-life road networks into graph directly. Then, based on our network model, we propose an EBNA (Edge-Based Nine-Area tree) index structure to make the search time of obtaining the interest edge information quickly. The EBNA index structure is an edge-based index structure. We store all of the edge information on the leaf node. We can obtain the edge information directly. Each edge information entry has a pointer point to link edges. Links of each edge entry consist a graph. This graph makes the KNN query processing visit the root only one time. From our simulation result, we show that the performance of constructing the EBNA index is better than constructing the RNG index and the performance of the KNN query processing by using EBNA index is better than the KNN query processing by using RNG index.

¡@¡@In recent years, the geographic information system (GIS) databases develop quickly and play a significant role in many applications. Many of these applications allow users to find objects with keywords and spatial information at the same time. Most researches in the spatial keyword queries only consider the exact match between the database and query with the textual information. Since users may not know how to spell the exact keyword, they make a query with the approximate-keyword, instead of the exact keyword. Therefore, how to process the approximate-keyword query in the spatial database becomes an important research topic. Alsubaiee et al. have proposed the Location-Based-Approximate-Keyword-tree (LBAK-tree) index structure which is to augment a tree-based spatial index with approximate-string indexes such as a gram-based index. However, the LBAK-tree index structure is the R*-tree based index structure. The nodes of the R*-tree have to be split and be reinserted when they get full. Due to this condition, it can not index the spatial attribute and the textual attribute at the same time. It stores the keywords in the nodes after the R*-tree is already built. Based on the R*-tree, it has to search all the children in a node to insert a new item and answer a query. Moreover, after they find the needed keywords by using the approximate index, they probe the nodes by checking the intersection of the similar keyword sets and the keywords stored in the nodes. However, the higher level the node is, the larger the number of keywords stored in the node is. It takes long time to check the intersections. And the LBAK-tree checks all the intersections even if there exits one of the intersections which is already an empty set. Therefore, in this thesis, we propose the Nine-Area-Approximate-Keyword-tree (NAAK-tree) index structure to process the spatial approximate-keyword query. We do not have to partition the space to construct the spatial index. We do not have to reinsert the children when split the nodes, so we can deal with the keywords at the same time. We can use the spatial number to find out the nodes that satisfy the spatial condition of the query. And we augment the NAAK-tree with signatures to speed up the query of the textual condition. We use the union of the bit strings of each keyword in a node to represent them in the node. Therefore, we can efficiently filter out the nodes that there is no keyword corresponding to the query by checking the signatures just one time without checking all the keywords stored in the nodes. Based on our NAAK-tree, if there exits one empty set in the similar keywords sets, we do not check all the similar keywords sets. From our simulation results, we show that the NAAK-tree is more efficient than the LBAK-tree to build the index and answer the spatial approximate-keyword query.

Moving objects database systems are the most challenging sub-category among Spatio-Temporal database systems. A database system that updates in real-time the location information of GPS-equipped moving vehicles has to meet even stricter requirements. Currently existing data storage models and indexing mechanisms work well only when the number of moving objects in the system is relatively small. This dissertation research aimed at the real-time tracking and history retrieval of massive numbers of vehicles moving on road networks. A total solution has been provided for the real-time update of the vehicles’ location and motion information, range queries on current and history data, and prediction of vehicles’ movement in the near future. To achieve these goals, a new approach called Segmented Time Associated to Partitioned Space (STAPS) was first proposed in this dissertation for building and manipulating the indexing structures for moving objects databases. Applying the STAPS approach, an indexing structure of associating a time interval tree to each road segment was developed for real-time database systems of vehicles moving on road networks. The indexing structure uses affordable storage to support real-time data updates and efficient query processing. The data update and query processing performance it provides is consistent without restrictions such as a time window or assuming linear moving trajectories. An application system design based on distributed system architecture with centralized organization was developed to maximally support the proposed data and indexing structures. The suggested system architecture is highly scalable and flexible. Finally, based on a real-world application model of vehicles moving in region-wide, main issues on the implementation of such a system were addressed.

The purpose of this research was first to study the Wildland-Urban Interface and Wildland-Urban Intermix (WUI) fire problem, and then to design, develop and implement improved fire assessment and fire protection features for structures in the these interface fire-prone areas. The findings included that several areas of the world are prone to devastating fires that claim lives and destroy property, and their fire problems continue to exacerbate. None of these compare to the property loss experienced in Southern California due to its vast development in fire prone areas. It is because of the continuing huge property loss and frequency of major WUI fires that Southern California was selected as the concentration for research and the case studies used in this paper. However, the results of the research are applicable to other interface fire-prone areas in the world. The author is motivated by a need to dramatically improve our ability to effectively deal with what is no longer a fire “threat,” but the reality that people have chosen to live in an area of the world in which wildland fires are part of natural forest dynamics. To reduce the economic and social impacts of these inevitable fires, we need to understand the causes of fire damage, and establish methods to minimize damage when fires occur. This thesis proposes several fire protection strategies for increased fire resiliency and safety of individuals. Following a search of fire history and analysis, three related fire assessment matrixes were synthesized (see Chapter Five). The Fire Profile Index is the principal fire assessment matrix. It was developed empirically and applied to historical fire spreads for a sense of accuracy. The intended users of the Fire Profile Index are design professionals, public agencies charged with oversight for development in the WUI, insurance agencies, building and landscape contractors, homeowners, potential homeowners, residents and fire service professionals. From the Fire Profile Index two derivative special-use matrixes were established for use by diverse groups. The first of these matrixes, the Developers Guide, is intended for design professionals, public agencies, insurance agencies, and building and landscape contractors. The second matrix is the WUI Fire Assessment Guide, whose intended users are those concerned with development in high fire hazard areas, who should have a fundamental knowledge of fire behavior. This group includes fire agencies, developers, homeowners, potential homeowners and insurance companies. This thesis contributes to increased residential structure fire resistiveness and occupant fire safety in the WUI, by proposing site-specific fire assessment and corresponding design features in both structures and landscapes. Chapter Seven covers the development of noncombustible fire shields to divert airflow and diminish flames and embers blown towards structures. Wind tunnel modeling research was conducted at the Aerospace Program’s wind tunnel at California Polytechnic State University, San Luis Obispo.

The approximate nearest neighbor(ANN) search over high dimensional data has become an unavoidable service for online applications. Fast and high-quality results of unknown queries are the largest challenge that most algorithms faced with. Locality Sensitive Hashing(LSH) is a well-known ANN search algorithm while suffers from inefficient index structure, poor accuracy in distributed scheme. The traditional index structures have most significant bits(MSB) problem, which is their indexing strategies have an implicit assumption that the bits from one direction in the hash value have higher priority. In this thesis, we propose a new content-based index called Random Draw Forest(RDF), which not only uses an adaptive tree structure by applying the dynamic length of compound hash functions to meet the different cardinality of data, but also applies the shuffling permutations to solve the MSB problem in the traditional LSH-based index. To raise the accuracy in the distributed scheme, we design a variable steps lookup strategy to search the multiple step sub-indexes which are most likely to hold the mistakenly partitioned similar objects. By analyzing the index, we show that RDF has a higher probability to retrieve the similar objects compare to the original index structure. In the experiment, we first learn the performance of different hash functions, then we show the effect of parameters in RDF and the performance of RDF compared with other LSH-based methods to meet the ANN search. / Thesis / Master of Science (MSc)

A Temporal database is one that supports some aspect of time distinct from user defined time. Over the last two decades interest in the field of temporal databases has increased significantly, with contributions from many researchers. However, the lack of efficient access methods is perhaps one of the reasons why commercial RDBMS vendors have been reluctant to adopt the advances in temporal database research. Therefore, an obvious research question is: can we develop more robust and more efficient access methods for temporal databases than the existing ones? This thesis attempts to address this question, and the main contributions of this study are summarised as follows: We investigated different representations of 'now' and how the modelling of current time influences the efficiency of accessing 'now relative' temporal data. A new method, called the 'Point' approach, is proposed. Our approach not only elegantly models the current time but also significantly outperforms the existing methods. We proposed a new index structure, called a Virtual Binary tree (VB-tree), based on spatial representation of interval data and a regular triangular decomposition of this space. Further, we described a sound and complete query algorithm. The performance of the algorithm is then evaluated both asymptotically and experimentally with respect to the state-of-the-art in the field. We claim that the VB-tree requires less space and uses fewer disk accesses than the currently best known structure - the RI-tree.

In this paper, we develop a novel index structure to support efficient approximate k-nearest neighbor (KNN) query in high-dimensional databases. In high-dimensional spaces, the computational cost of the distance (e.g., Euclidean distance) between two points contributes a dominant portion of the overall query response time for memory processing. To reduce the distance computation, we first propose a structure (BID) using BIt-Difference to answer approximate KNN query. The BID employs one bit to represent each feature vector of point and the number of bit-difference is used to prune the further points. To facilitate real dataset which is typically skewed, we enhance the BID mechanism with clustering, cluster adapted bitcoder and dimensional weight, named the BID⁺. Extensive experiments are conducted to show that our proposed method yields significant performance advantages over the existing index structures on both real life and synthetic high-dimensional datasets. / Singapore-MIT Alliance (SMA)

This master’s thesis deals with the impact of atmospheric turbulence on phase changes of a free space optical signal. This problematic is investigated by the interferometric method. A part of the thesis is focused on the phenomenon of atmospheric turbulence. Then the physical effect of interference is discussed and optical interferometers are described. The experimental part of the thesis is focused on measurement of phase shift of optical signal by interferometric method. The result of the thesis is to determine how phase shift of an optical beam depends on the strength of turbulence.