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.

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.

This thesis describes the development of the SmartGraph, an AI enabled graph database. The need for such a system has been independently recognized in the isolated fields of graph databases, graph computing, and computational graph deep learning systems, such as TensorFlow. Though prior works have investigated some relationships between these fields, we believe that the SmartGraph is the first system designed from conception to incorporate the most significant and useful characteristics of each. Examples include the ability to store graph structured data, run analytics natively on this data, and run gradient descent algorithms. It is the synergistic aspects of combining these fields that provide the most novel results presented in this dissertation. Key among them is how the notion of “graph querying” as used in graph databases can be used to solve a problem that has plagued deep learning systems since their inception; rather than attempting to embed graph structured datasets into restrictive vector spaces, we instead allow the deep learning functionality of the system to natively perform graph querying in memory during optimization as a way of interpreting (and learning) the graph. This results in a concept of natural and interpretable processing of graph structured data. Graph computing systems have traditionally used distributed computing across multiple compute nodes (e.g. separate machines connected via Ethernet or internet) to deal with large-scale datasets whilst working sequentially on problems over entire datasets. In this dissertation, we outline a distributed graph computing methodology that facilitates all the above capabilities (even in an environment consisting of a single physical machine) while allowing for a workflow more typical of a graph database than a graph computing system; massive concurrent access allowing for arbitrarily asynchronous execution of queries and analytics across the entire system. Further, we demonstrate how this methodology is key to the artificial intelligence capabilities of the system.

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>

In der vorliegenden Masterarbeit werden verschiedene Graphdatenbanksysteme in einer funktionalen und technischen Evaluation hinsichtlich ihrer Eignung für ein aktuelles Forschungsvorhaben der Abteilung Datenbanken der Universität Leipzig untersucht. Ziel des Forschungsprojektes ist die Integration von Unternehmensdaten in ein Informationsnetzwerk und eine darauf aufbauendegraphenorientierte Analyse der Daten.

The user data in social media platforms is an excellent source of information that is beneficial for both commercial and scientific purposes. However, recent times has seen that the user data is not always used for good, which has led to higher demands on user privacy. With accurate statistical research data being just as important as the privacy of the user data, the relevance of differential privacy has increased. Differential privacy allows user data to be accessible under certain privacy conditions at the cost of accuracy in query results, which is caused by noise. The noise is based on a tuneable constant ε and the global sensitivity of a query. The query sensitivity is defined as the greatest possible difference in query result between the queried database and a neighboring database. Where the neighboring database is defined to differ by one record in a tabular database, there are multiple neighborhood notions for edge-labeled graphs. This thesis considers the notions of edge neighborhood, node neighborhood, QL-edge neighborhood and QL-outedges neighborhood. To study these notions, a framework was developed in Java to function as a query mechanism for a graph database. ArangoDB was used as a storage for graphs, which was generated by parsing data sets in the RDF format as well as through a graph synthesizer in the developed framework. Querying a database in the framework is done with Apache TinkerPop, and a Laplace distribution is used when generating noise for the query results. The framework was used to study the privacy and utility trade-off of different histogram queries on a number of data sets, while employing the different notions of neighborhood in edge-labeled graphs. The level of privacy is determined by the value on ε, and the utility is defined as a measurement based on the L1-distance between the true and noisy result. In the general case, the notions of edge neighborhood and QL-edge neighborhood are the better alternatives in terms of privacy and utility. Although, there are indications that node neighborhood and QL-outedges neighborhood are considerable options for larger graphs, where the level of privacy for edge neighborhood and QL-edge neighborhood appears to be negligible based on utility measurements.

Les graphes de données sont naturellement utilisés dans de nombreux contextes incluant par exemple les réseaux sociaux ou le Web sémantique. L'information contenue dans la base de données se trouve alors aussi bien dans les données mêmes que dans la topologie du graphe, c'est-à-dire dans la manière dont les données sont connectées. Cela implique donc de considérer les questions traditionnelles en théorie des bases de données pour des langages de requêtes capables de parler des chemins connectant les nœuds du graphe. Nous nous intéressons en particulier aux problèmes de la déterminabilité et de la réécriture d'une requête à l'aide de vues. Il s'agit alors de décider si une vue de la base de données contient suffisamment d'information pour répondre entièrement à une requête sans consulter la base de données directement, et dans ce cas, d'exprimer explicitement la réponse à la requête à partir de la vue. Ce cadre rencontre de nombreuses applications, notamment pour l'intégration de données et l'optimisation de requêtes. Nous commençons par comparer ces deux questions aux autres problèmes de décision classiques dans ce contexte : calcul des réponses certaines, test de cohérence et mise à jour d'une instance de vue. Nous améliorons ensuite ces résultats dans deux cas spécifiques. Tout d'abord, nous montrons que pour les requêtes régulières de chemin, l'existence d'une réécriture monotone coïncide avec l'existence d'une réécriture dans Datalog. Puis, nous montrons que pour des vues s'intéressant uniquement aux longueurs des chemins du graphe, une notion plus faible de déterminabilité, appelée déterminabilité asymptotique, est décidable et résulte en des réécritures du premier ordre. / Graph databases appear naturally in various scenarios, such as social networks and the semantic Web. In these cases, the information contained in the database lies as much in the data itself as in the topology of the graph, that is, in how the data points are linked together. This leads to considering traditional database theory questions for query languages that return data nodes based on the paths of the graph connecting them. We focus our attention on the view-based query determinacy and rewriting problems. They ask the question whether a view of the database contains enough information to fully answer a query without accessing the database directly. If so, we then want to express the answer to the query directly with regards to the view. This setting occurs in many applications, such as data integration and query optimization. We start by comparing these two tasks to other common task in this setting: computing certain answers, checking consistency of a view instance and updating it. We then build on these results in two specific cases. First, we show that for regular path queries, the existence of a monotone rewriting coincides with the existence of a rewriting expressible in Datalog. Then, we show that for views that only consider the lengths of the path in the graph, we can decide a weaker form of determinacy, called asymptotic determinacy, and produce first-order rewritings for the queries that are asymptotically determined.

No description available.

Cloud computing has brought availability of services at unprecedented scales but data accessibility considerations become more complex due to involvement of multiple parties in providing the infrastructure. In this thesis, we discuss the problem of enabling cooperative data access in a multi-cloud environment where the data is owned and managed by multiple enterprises. We consider a multi-party collaboration scheme whereby a set of parties collectively decide accessibility to data from individual parties using different data models such as relational databases, and graph databases. In order to implement desired business services, parties need to share a selected portion of information with one another. We consider a model with a set of authorization rules over the joins of basic relations, and such rules are defined by these cooperating parties. The accessible information is constrained by these rules. Specifically, the following critical issues were examined: Combine rule enforcement and query planning and devise an algorithm which simultaneously checks for the enforceability of each rule and generation of minimum cost plan of its execution using a cost metric whenever the enforcement is possible; We also consider other forms of limiting the access to the shared data using safety properties and selection conditions. We proposed algorithms for both forms to remove any conflicts or violations between the limited accesses and model queries; Used graph databases with our authorization rules and query planning model to conduct similarity search between tuples, where we represent the relational database tuples as a graph with weighted edges, which enables queries involving "similarity" across the tuples. We proposed an algorithm to exploit the correlations between attributes to create virtual attributes that can be used to catch much of the data variance, and enhance the speed at which similarity search occurs; Proposed a framework for defining test functionalities their composition, and their access control. We discussed an algorithm to determine the realization of the given test via valid compositions of individual functionalities in a way to minimize the number of parties involved. The research significance resides in solving real-world issues that arise in using cloud services for enterprises After extensive evaluations, results revealed: collaborative data access model improves the security during cooperative data processes; systematic and efficient solving access rules conflict issues minimizes the possible data leakage; and, a systematic approach tackling control failure diagnosis helps reducing troubleshooting times and all that improve availability and resiliency. The study contributes to the knowledge, literature, and practice. This research opens up the space for further studies in various aspects of secure data cooperation in large-scale cyber and cyber-physical infrastructures. / Computer and Information Science

In the last few decades, database management systems became powerful tools for storing large amount of data and executing complex queries over them. In addition to extended functionality, novel types of databases appear like triple stores, distributed databases, etc. Graph databases implementing the property-graph model belong to this development branch and provide a new way for storing and processing data in the form of a graph with nodes representing some entities and edges describing connections between them. This consideration makes them suitable for keeping data without a rigid schema for use cases like social-network processing or data integration. In addition to a flexible storage, graph databases provide new querying possibilities in the form of path queries, detection of connected components, pattern matching, etc. However, the schema flexibility and graph queries come with additional costs. With limited knowledge about data and little experience in constructing the complex queries, users can create such ones, which deliver unexpected results. Forced to debug queries manually and overwhelmed by the amount of query constraints, users can get frustrated by using graph databases. What is really needed, is to improve usability of graph databases by providing debugging and explaining functionality for such situations. We have to assist users in the discovery of what were the reasons of unexpected results and what can be done in order to fix them. The unexpectedness of result sets can be expressed in terms of their size or content. In the first case, users have to solve the empty-answer, too-many-, or too-few-answers problems. In the second case, users care about the result content and miss some expected answers or wonder about presence of some unexpected ones. Considering the typical problems of receiving no or too many results by querying graph databases, in this thesis we focus on investigating the problems of the first group, whose solutions are usually represented by why-empty, why-so-few, and why-so-many queries. Our objective is to extend graph databases with debugging functionality in the form of why-queries for unexpected query results on the example of pattern matching queries, which are one of general graph-query types. We present a comprehensive analysis of existing debugging tools in the state-of-the-art research and identify their common properties. From them, we formulate the following features of why-queries, which we discuss in this thesis, namely: holistic support of different cardinality-based problems, explanation of unexpected results and query reformulation, comprehensive analysis of explanations, and non-intrusive user integration. To support different cardinality-based problems, we develop methods for explaining no, too few, and too many results. To cover different kinds of explanations, we present two types: subgraph- and modification-based explanations. The first type identifies the reasons of unexpectedness in terms of query subgraphs and delivers differential graphs as answers. The second one reformulates queries in such a way that they produce better results. Considering graph queries to be complex structures with multiple constraints, we investigate different ways of generating explanations starting from the most general one that considers only a query topology through coarse-grained rewriting up to fine-grained modification that allows fine changes of predicates and topology. To provide a comprehensive analysis of explanations, we propose to compare them on three levels including a syntactic description, a content, and a size of a result set. In order to deliver user-aware explanations, we discuss two models for non-intrusive user integration in the generation process. With the techniques proposed in this thesis, we are able to provide fundamentals for debugging of pattern-matching queries, which deliver no, too few, or too many results, in graph databases implementing the property-graph model.