Database systems maintain integrity of the stored information by ensuring that modifications to the database comply with constraints designed by the administrators. As the number of users and applications sharing a common database increases, so does the complexity of the set of constraints that originate from higher level business processes. The lack of a systematic mechanism for integrating and reasoning about a diverse set of evolving and potentially interfering policies manifested as database level constraints makes corporate policy management within relational systems a chaotic process. In this thesis we present a systematic method of mapping a broad set of process centric business policies onto database level constraints. We exploit the observation that the state of a database represents the union of all the states of every ongoing business process and thus establish a bijective relationship between progression in individual business processes and changes in the database state space. We propose graphical notations that are equivalent to integrity constraints specified in linear temporal logic of the past. Furthermore we demonstrate how this notation can accommodate a wide array of workflow patterns, can allow for multiple policy makers to implement their own process centric constraints independently using their own logical policy models, and can model check these constraints within the database system to detect potential conflicting constraints across several different business processes. A major contribution of this thesis is that it bridges several different areas of research including database systems, temporal logics, model checking, and business workflow/policy management to propose an accessible method of integrating, enforcing, and reasoning about the consequences of process-centric constraints embedded in database systems. As a result, the task of ensuring that a database continuously complies with evolving business rules governed by hundreds of processes, which is traditionally handled by an army of database programmers regularly updating triggers and batch procedures, is made easier, more manageable, and more predictable.

Thesis (Ph. D.)--University of Canterbury, 2006. / Typescript (photocopy). Includes bibliographical references (p. 166-184). Also available via the World Wide Web.

In the past few years, ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environment friendliness. In a typical scenario, the ridesharing service provider matches drivers of private vehicles or taxis to those seeking local taxicab- like transportation. Much research attention has been drawn to the optimization of travel costs in shared rides. However, other important factors in ridesharing, such as the social comfort, trust issues and revenue, have not been fully considered in the existing works. Social-aware ridesharing, which makes use of social relations among drivers and riders to address safety issues, and dynamic pricing, which dynamically determines shared ride fares, are two active research directions with important business implications. In this dissertation, we take the first step to comprehensively investigate the social-aware ridesharing queries. First, we study the problem of the top-k social-aware taxi ridesharing query. In particular, upon receiving a user's trip request, the service ranks feasible taxis in a way that integrates detour in time and passengers' cohesion in social distance. We propose a new system framework to support such a social-aware taxi-sharing service. It provides two methods for selecting candidate taxis for a given trip request. The grid-based method quickly goes through available taxis and returns a relatively larger candidate set, whereas the edge-based method takes more time to obtain a smaller candidate set. Furthermore, we design techniques to speed up taxi route scheduling for a given trip request. We propose travel-time based bounds to rule out unqualified cases quickly, as well as algorithms to find feasible cases efficiently. We evaluate our proposals using a real taxi dataset from New York City. Experimental results demonstrate the efficiency and scalability of the proposed taxi recommendation solution in real-time social-aware ridesharing services. Second, we study the problem of efficient matching of offers and requests in social-aware ridesharing. We formulate a new problem, named Assignment of Requests to Offers (ARO), that aims to maximize the number of served riders while satisfying the social comfort constraints as well as spatial-temporal constraints. We prove that the ARO problem is NP- hard. We then propose an exact algorithm for a simplified ARO problem. We further propose three pruning strategies to efficiently narrow down the searching space and speed up the assignment processing. Based on these pruning strategies, we develop two novel heuristic algorithms, the request-oriented approach and offer-oriented approach, to tackle the ARO problem. We also study the dynamic ARO problem and present a novel algorithm to tackle this problem. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches on real-world datasets. Third, we study the top-k vehicle matching in social ridesharing. In the current ridesharing research, optimizing social cohesion and revenue at the same time has not been well studied. We present a new pricing scheme that better incentivizes drivers and riders to participate in ridesharing, and then propose a novel type of Price-aware Top-k Matching (PTkM) queries which retrieve the top-k vehicles for a rider's request by taking into account both social relations and revenue. We design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. Moreover, we propose a novel index tailored to our problem to further speed up query processing. Extensive experimental results on real datasets show that our proposed algorithms achieve desirable performance for real-world deployment. The work of this thesis shows that the social-aware ridesharing query processing techniques are effective and efficient, which would facilitate ridesharing services in real world.

No description available.

O fator temporal é uma variável natural da maioria dos sistemas de informação, pois no mundo real os eventos ocorrem de maneira dinâmica, modicando continuamente os valores dos seus objetos no decorrer do tempo. Muitos desses sistemas precisam registrar essa modicação e atribuir os instantes de tempo em que cada informação foi válida no sistema. Este trabalho reúne as características relacionadas aos Bancos de Dados Temporais e Bancos de Dados Objeto-Relacionais. O objetivo primordial é propor uma forma de implementar alguns aspectos temporais, desenvolvendo um módulo que faça parte das características e funcionalidades internas de um SGBD. O módulo temporal contempla principalmente a parte de restrições de integridade temporal que é utilizada para manter a consistência da informação temporal armazenada. Para isso, é proposto um novo tipo de dado que melhor representa as marcas temporais dos objetos. Uma parte importante para a implementação desse projeto é a utilização de um SGBD objeto-relacional que possui algumas características orientadas a objetos que permitem a extensão de seus recursos, tornando-o capaz de gerenciar alguns aspectos temporais. O módulo temporal desenvolvido torna esses aspectos temporais transparentes para o usuário. Por conseqüência, esses usuários são capazes de utilizar os recursos temporais com maior naturalidade. / The temporal factor is a natural variable of the majority of the information systems, therefore in the real world the events occur in dynamic way, modifying continuously the values of its objects in elapsing of the time. Many of these systems need to register this modication and to attribute the instants of time where each information was valid in the system. This work congregates the characteristics related to the Temporal Databases and Object-Relational Databases. The primordial objective is to consider a form to implement some temporal aspects, developing a module that is part of the characteristics and internal functionalities of a DBMS. The temporal module mainly contemplates the part of restrictions of temporal integrity that is used to keep the consistency of the stored temporal information. For this, a new data type is proposed that better represent the objects timestamps. An important part for the implementation of this project is the use of a object-relational DBMS that has some object-oriented characteristics that allow the extension of its resources, becoming capable to manage some temporal aspects. The developed temporal module becomes these transparent temporal aspects for the user. For consequence, these users are capable to use the temporal resources more naturally.

For many applications of location-based services, such as friend finding, buddy tracking,information sharing and cooperative caching in ad hoc networks, it is often important to be able to identify whether the positions of a given set of moving objects are within close proximity. To compute these kinds of proximity relations among large populations of moving objects, continuously available location position information of these objects must be correlated against each other to identify whether a given set of objects are in the specified proximity relation. In this dissertation, we state this problem, referring to it as the location constraint matching problem, both in the Euclidean space and the road network space. In the Euclidean space, we present an adaptive solution to this problem for various environments. We also study the position uncertainty associated with the constraint matching. For the road network space, where the object can only move along the edges of the road network, we propose an efficient algorithm based on graph partitioning, which dramatically restricts the search space and enhances performance. Our approaches reduce the constraint processing time by 80% for Euclidean space and by 90% for road network space respectively. The logical combination of individual constraints with conjunction, disjunction and negation results in more expressive constraint expressions than are possible based on single constraints. We model constraint expressions with Binary Decision Diagrams (BDD). Furthermore, we exploit the shared execution of constraint combinations based on the BDD modeling. All the algorithms for various aspects of the constraint processing are integrated in the research prototype L-ToPSS (Location-based Toronto Publish/Subscribe System). Through experimental study and the development of an analytical model, we show that the proposed solution scales to large numbers of constraints and large numbers of moving objects.

For many applications of location-based services, such as friend finding, buddy tracking,information sharing and cooperative caching in ad hoc networks, it is often important to be able to identify whether the positions of a given set of moving objects are within close proximity. To compute these kinds of proximity relations among large populations of moving objects, continuously available location position information of these objects must be correlated against each other to identify whether a given set of objects are in the specified proximity relation. In this dissertation, we state this problem, referring to it as the location constraint matching problem, both in the Euclidean space and the road network space. In the Euclidean space, we present an adaptive solution to this problem for various environments. We also study the position uncertainty associated with the constraint matching. For the road network space, where the object can only move along the edges of the road network, we propose an efficient algorithm based on graph partitioning, which dramatically restricts the search space and enhances performance. Our approaches reduce the constraint processing time by 80% for Euclidean space and by 90% for road network space respectively. The logical combination of individual constraints with conjunction, disjunction and negation results in more expressive constraint expressions than are possible based on single constraints. We model constraint expressions with Binary Decision Diagrams (BDD). Furthermore, we exploit the shared execution of constraint combinations based on the BDD modeling. All the algorithms for various aspects of the constraint processing are integrated in the research prototype L-ToPSS (Location-based Toronto Publish/Subscribe System). Through experimental study and the development of an analytical model, we show that the proposed solution scales to large numbers of constraints and large numbers of moving objects.

Thesis (Ph.D.)--University of Western Sydney, 2009. / A thesis presented to the University of Western Sydney, College of Health and Science, School of Computing and Mathematics, in fulfilment of the requirements for the degree of Doctor of Philosophy. Includes bibliographies.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 125-131). Also available in electronic version. Access restricted to campus users.

Informações temporais estão presentes numa ampla gama de aplicações. Praticamente qualquer aplicação possui pelo menos um campo que contém dados temporais como datas ou timestamps. Entretanto, bancos de dados tradicionais não tem um suporte amplo para armazenamento e consulta sobre esse tipo de dados eficientemente, e SGBDs com suporte nativo para dados temporais raramente estão disponíveis para os desenvolvedores de sistemas. Na maior parte do tempo, bases de dados comuns são usadas para armazenar dados das aplicações, e quando dados temporais são necessários, eles são gerenciados utilizando o pobre suporte oferecido por SGBDs relacionais tradicionais. Dito isso, o projetista da base de dados precisa confiar em um bom design de esquema para que a dificuldade natural enfrentada ao lidar com dados temporais possa ser minimizada. Enquanto algumas escolhas de design podem parecer óbvias, outras são difíceis de avaliar apenas com uma análise superficial, necessitando experimentação antes de serem aplicadas ou não. Por exemplo, em vários casos pode ser difícil de medir o quanto uma determinada escolha de design vai afetar o consumo de espaço em disco, e quanto essa mesma escolha afetará a performance geral. Esse tipo de informação é necessária para que o projetista da base de dados seja capaz de determinar se , por exemplo, o aumento no consumo de espaço em disco gerado por uma escolha específica é aceitável por conta da melhora de performance que ela oferece. O problema é que não há estudo que analise as escolhas de design disponíveis, fazendo uma análise através de dados concretos. Mesmo quando é fácil identificar, dentre duas escolhas, qual tem performance melhor em um determinado critério, é difícil mensurar o quão melhor a escolha melhor se sai, e se algum efeito colateral trazido por ela é aceitável. Ter dados concretos para suportar esse tipo de decisão permite ao projetista da base de dados fazer escolhas que se enquadram melhor no contexto da sua aplicação. O objetivo desse trabalho é analisar algumas escolhas de design comuns para representar e gerenciar dados temporais em SGBDs relacionais tradicionais, provendo direcionamento sobre qual alternativa se enquadra melhor em cada situação onde dados temporais são necessários. Dados concretos sobre cada uma das alternativas estudadas são gerados e analisados e conclusões são obtidas a partir deles. / Temporal information is present on a wide range of applications. Almost every application has at least one field that contains temporal data like dates or timestamps. However, traditional databases don’t have a comprehensive support to storage and query this kind of data efficiently, and DBMS with native support for temporal data are rarely available to system developers. Most of the time, regular databases are used to store application data and when temporal data is needed, it is handled using the poor support offered by standard relational DBMS. That said, the database designer must rely on good schema design so that the natural difficulty faced when dealing with temporal data on standard relational DBMS can be minimized. While some design choices may seem obvious, others are difficult to evaluate just by looking at them, therefore needing experimentation prior to being applied or not. For example, in several cases it might be difficult to measure how much will a specific design choice affect the disk space consumption, and how much will this same design choice affect overall performance. This kind of information is needed so that the database designer will be able to determine if, for example, the increased disk space consumption generated by a given choice is acceptable because of the performance enhancement it gives. The problem is that there is no study that analyses the design choices available, analyzing them through concrete data. Even when it is easy to see which of two design choices perform better in a given criterion, it is hard to see how better the better choice does, and if any other side-effect it has is acceptable. Having concrete data to support this kind of decision allows the database designer to make the choices that suits his application’s context best. The objective of this work is to analyze several common design choices to represent and handle different kinds of temporal data on standard SQL DBMS, providing guidance on which alternative suits best each situation where temporal data is required. Concrete data about each of the studied alternatives are generated and analyzed, and conclusions are drawn from them.