There has been a growth of a class of databases known as the Not only SQL (NoSQL) databases in recent years. Its quick growth has been fueled by a high demand by businesses as it offers a convenient way to store data and is significantly different from our traditional relational databases. It is easy to process unstructured data, offers a cloud-friendly ap- proach and grows through the distribution of data over lots of commodity computers. Most of these NoSQL databases are distributed in several different locations, spanning countries and are known as geo-distributed cloud datastores. We work to customize one of these known as Cassandra. Given the size of the database and the size of applications accessing the data stored, it has been challenging to customize it to meet existing application Service Level Agreement (SLAs). We live in an era of data breaches and even though some types of information are stripped of all sensitive data, there are ways to easily identify and link it to data of real persons or government. Data saved in different countries are subject to the rules and regulations of that specific country and security measures employed to safeguard consumer data. In this thesis, we describe mechanisms for selectively replicating data in a large scale NoSQL datastore in respect of privacy and legal regulations. We introduce an easily extensible constraint language to implement these policy constraints through the creation of a pluggable topology provider in the configuration files of Cassandra. Experiments using the modified Cassandra trunk demonstrate that our techniques work well, respect response times and improves read and write latencies.

Object oriented databases have been gaining popularity over the years. Their ease of use and the advantages that they offer over relational databases have made them a popular choice amongst database administrators. Their use in previous years was restricted to business and administrative applications, but improvements in technology and the emergence of new, data-intensive applications has led to the increase in the use of object databases. This study investigates four Open Source object-oriented databases on their ability to carry out the standard database operations of storing, querying, updating and deleting database objects. Each of these databases will be timed in order to measure which is capable of performing a particular function faster than the other.

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Orientador: Claudia Bauzer Medeiros / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-22T03:52:51Z (GMT). No. of bitstreams: 1 Senra_RodrigoDiasArruda_D.pdf: 1421466 bytes, checksum: 10286dd7415a632d012984fc38bcca53 (MD5) Previous issue date: 2012 / Resumo: Compartilhamento de informação sempre foi um aspecto chave em qualquer tipo de esforço conjunto. Paradoxalmente, com o dilúvio de dados, a incremental disponibilização de informação tem dificultado o projeto e implementação de soluções que efetivamente estimulam o compartilhamento. Esta tese analisa aspectos distintos do compartilhamento - desde ambientes relacionados à eScience até informação pessoal. Como resultado desta análise, ela provê respostas para alguns dos problemas encontrados, ao longo de três eixos. O primeiro, SciFrame, é um arcabouço específico para descrição de sistemas ou processos envolvendo manipulação de dados científicos no formato digital, servindo como um padrão que auxilia a comparação de sistemas. A adoção do SciFrame para descrição de ambientes científicos virtuais permite a identificação de pontos em comum e oportunidades de interoperabilidade. O segundo eixo de contribuição contempla o problema da comunicação entre sistemas arbitrários e serviços oferecidos por bancos de dados, através do uso dos então chamados descritores de bancos de dados ou DBDs. Estes descritores contribuem para desacoplar aplicações dos serviços, melhorando, portanto o compartilhamento entre aplicações e bancos de dados. A terceira contribuição, Organografos, provê meios para a organização de informação multifacetada. Ela contempla problemas de compartilhamento de informação pessoal por intermédio da exploração da forma como organizamos tais informações. Neste caso, ao invés de tentarmos prover meios para o compartilhamento da informação propriamente dita, a unidade de compartilhamento é a própria organização da informação. Através do projeto e compartilhamento de organografos, grupos distintos trocam entre si visões reconfiguráveis de como a informação está organizada, promovendo assim interoperabilidade e reuso. Organografos são umas abordagens inovadoras para o gerenciamento de dados hierárquicos. Essas três contribuições estão centradas nas idéias básicas de construção e compartilhamento de informação organizada hierarquicamente. Parte destas contribuições foi validada por estudos de caso e, no caso de organografos, por uma implementação de fato / Abstract: Information sharing has always been a key issue in any kind of joint effort. Paradoxically, with the data deluge, the more information available, the harder it is to design and implement solutions that effectively foster such sharing. This thesis analyzes distinct aspects of sharing - from eScience-related environments to personal information. As a result of this analysis, it provides answers to some of the problems encountered, along three axes. The first, SciFrame, is a specific framework that describes systems or processes involving scientific digital data manipulation, serving as a descriptive pattern to help system comparison. The adoption of SciFrame to describe distinct scientific virtual environments allows identifying commonalities and points for interoperation. The second axe contribution addresses the specific problem of communication between arbitrary systems and services provided by distinct database platforms, via the use of the so-called database descriptors or DBDs. These descriptors contribute to provide independence between applications and the services, thereby enhancing sharing across applications and databases. The third contribution, Organographs, provides means to deal with multifaceted information organization. It addresses problems of sharing personal information by means of exploiting the way we organize such information. Here, rather than trying to provide means to share the information itself, the unit of sharing is the organization of the information. By designing and sharing organographs, distinct groups provide each other dynamic, reconfigurable views of how information is organized, thereby promoting interoperability and reuse. Organographs are an innovative approach to hierarchical data management. These three contributions are centered on the basic idea of building and sharing hierarchical organizations. Part of these contributions was validated by case studies and, in the case of organographs, an actual implementation / Doutorado / Ciência da Computação / Doutor em Ciência da Computação

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

Em busca da documentação e otimização de seus processos, a área de Business Process Management (BPM) vem cada vez mais atraindo o interesse do meio empresarial, por ser um importante método no auxílio ao ganho de resultados, como redução de custos e aumento de produtividade. Modelar processos, entretanto, não basta. É preciso que se atente para métodos eficientes de armazená-los, permitindo que as informações sejam manipuladas e utilizadas de maneira prática e inteligente. A presente dissertação propõe duas abordagens para armazenamento de modelos de processo, uma em bancos de dados relacionais e outra em bancos de dados orientados a grafos, comparando-os através de aspectos como desempenho na execução das operações e proximidade da abordagem de cada um deles com os modelos de processos. Enquanto os bancos de dados relacionais são mais populares, sendo utilizados na maior parte das aplicações atuais, os bancos de dados orientados a grafos possuem propriedades e representação gráfica semelhantes aos modelos de processos. Foram realizados testes que visam analisar o desempenho de ambas as abordagens, além da facilidade dos usuários em interagir com os modelos propostos. Os resultados deste estudo podem ser utilizados para a criação de repositórios que compartilhem processos de maneira eficiente, bem como incentivar o estudo de novas maneiras para o armazenamento de processos. / Business Process Management (BPM) area has been increasingly attracted the interest of the business community because users are looking for documentation and optimization. These documents can be an important method in helping to gain results such as reduced costs and increased productivity. However, to model processes is not enough. It is necessary to pay attention to efficient storage methods, allowing information to be handled and used in a practical and intelligent way. The present article compares the use of relational databases and graph databases, considering aspects such as performance in the execution of operations and proximity of the approach of each of them with the process models. While relational databases are more popular, being used in most of the current applications, graph databases have properties and graphical representations similar to process models. The results of this study can be used to create repositories which can both share process efficiently, and encourage the study of new ways of storing processes.

In this research we study what is the theoretical complexity of Neo4J, OrientDB and GraphDB, (three known Graph Databases that can be accessed by a Java instance), and how this complexity is manifested in a real life performance, To study their practical performance, a software was implemented and named as a profiler, which is capable to profile, (to record the time that is needed), each operation, and display the results in an accurate and organized manner. The technical documentation of those 3 databases was reviewed as well, to identify how the databases work, and what are their strong and weak points. By the profiling process, the best performance was displayed by Neo4J, and while OrientDB failed to deliver, GraphDB takes the second place in terms of performance. We can identify a potential in OrientDB’s approach, but its structure is too complex and rigid. Neo4J has a robust structure and an architecture that gives to it a great performance, while the Cypher syntax, which Neo4J uses, minimizes the possibility of human error. GraphDB is optimized for large scale public-data operations but performs well as a stand-alone solution as well. / <p>An important part of this publication is its GitHub Repository</p><p>https://github.com/Exarchias/graph-databases-profiler</p>

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Wang, Yejun. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 97-105). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

This project is a development and tracking system for graduate students in the Department of Computer Science of CSUSB. This project will cover front-end web site development, back-end database design and security. This website provides secure access to information about ideas for projects, status on on-going projects, and reports of finished projects using My SQL and Apache Tomcat.