An Empirical Comparison of Graph Databases.

Abstract-In recent years, more and more companies provide services that can not be anymore achieved efficiently using relational databases. As such, these companies are forced to use alternative database models such as XML databases, objectoriented databases, document-oriented databases and, more recently graph databases. Graph databases only exist for a few years. Although there have been some comparison attempts, they are mostly focused on certain aspects only. In this paper, we present a distributed graph database comparison framework and the results we obtained by comparing four important players in the graph databases market: Neo4j, OrientDB, Titan and DEX

Estudio y comparación de bases de datos orientadas a grafos

90 p.Un grafo es básicamente un conjunto de puntos (vértices) en el espacio, que están conectados por un conjunto de líneas (aristas). Como una de las formas más generales de modelado de datos, un grafo permite representar fácilmente entidades, sus atributos y sus relaciones.Las Bases de Datos Orientadas a Grafos (BDOG) se caracterizan porque las estructuras de datos para el esquema e instancia se basan en modelos de datos de grafo.Estos modelos se iniciaron en los años ochenta y a principios de los noventa, junto con modelos orientados a objetos. Su influencia decayó poco a poco con la aparición de nuevos modelos de bases de datos. Recientemente, la necesidad de gestionar la informacióon a través de una estructura de grafo y las limitaciones de las bases de datos tradicionales (en particular el modelo relacional), para cubrir las necesidades de las aplicaciones actuales ha llevado al desarrollo de nuevas tecnologías, y por consiguiente ha restablecido la importancia de esta área. El objetivo principal de este estudio es realizar una comparación sistematica de bases de datos de grafo.En este trabajo se presenta una revisión de las bases de datos de grafo actuales y su comparación de acuerdo a algunas caracterìsticas de modelado de datos. Entre las características evaluadas se incluyen: almacenamiento de datos, representación de entidades y relaciones, operación y manipulación de datos (lenguajes de consulta de grafos e interfaces de programación), y restricciones de integridad. Adicionalmente,se presenta una evaluaciçon empírica basada en pruebas de carga y consulta de datos. Este trabajo permite conocer y comparar, de manera teórica y práctica,las capacidades de modelado y ejecución entregadas por cada base de datos de grafo./ABSTRACT: A graph is basically a set of points (vertices) in space, which are connected by a set of lines (edges). As one of the most general forms of data modeling, a graph easily allows the representation of entities, their attributes and their relationships. Graph-oriented Databases (GODB) are characterized because their data structures for the scheme and instance are based on graph data models. These models began in the eighties and early nineties, along with object-oriented models. Their influence gradually faded with the emergence of new models of databases. Recently, the need to manage information through a graph structure and the limitations of traditional databases (in particular the relational model), to meet the needs of current applications has led to the development of new technologies, and therefore restored the importance of this area. The main objective of this study is to perform a systematic comparison of graph databases. This work presents a review of the current graph databases and their comparison according to well-defined data modeling features. Among the evaluated features we include: data storage, representation of entities and relationships, data operation and manipulation (graph query languages and application programming interfaces), and integrity constraints. Additionally, we present an empirical evaluation based on load and query data testing. This work allows to know and compare, from a theoretical and practical point of view, the modeling and execution capabilities provided by each graph database

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Exploiting citation networks for large-scale author name disambiguation

We present a novel algorithm and validation method for disambiguating author names in very large bibliographic data sets and apply it to the full Web of Science (WoS) citation index. Our algorithm relies only upon the author and citation graphs available for the whole period covered by the WoS. A pair-wise publication similarity metric, which is based on common co-authors, self-citations, shared references and citations, is established to perform a two-step agglomerative clustering that first connects individual papers and then merges similar clusters. This parameterized model is optimized using an h-index based recall measure, favoring the correct assignment of well-cited publications, and a name-initials-based precision using WoS metadata and cross-referenced Google Scholar profiles. Despite the use of limited metadata, we reach a recall of 87% and a precision of 88% with a preference for researchers with high h-index values. 47 million articles of WoS can be disambiguated on a single machine in less than a day. We develop an h-index distribution model, confirming that the prediction is in excellent agreement with the empirical data, and yielding insight into the utility of the h-index in real academic ranking scenarios.Comment: 14 pages, 5 figure

Security Economics: A Guide for Data Availability and Needs

The rapid and accelerating development of security economics has generated great demand for more and better data to accommodate the empirical research agenda. The present paper serves as a guide to policy makers and researchers for security-related databases. The paper focuses on two main issues. Firstly, it takes stock of the existing databases, highlighting their main components and also performs a brief statistical comparison. Secondly, it discusses data shortages and needs that are considered essential for enhancing our understanding of the complex phenomenon of terrorism as well as designing and evaluating policy.

Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation

Social inertia in collaboration networks

This work is a study of the properties of collaboration networks employing the formalism of weighted graphs to represent their one-mode projection. The weight of the edges is directly the number of times that a partnership has been repeated. This representation allows us to define the concept of "social inertia" that measures the tendency of authors to keep on collaborating with previous partners. We use a collection of empirical datasets to analyze several aspects of the social inertia: 1) its probability distribution, 2) its correlation with other properties, and 3) the correlations of the inertia between neighbors in the network. We also contrast these empirical results with the predictions of a recently proposed theoretical model for the growth of collaboration networks.Comment: 7 pages, 5 figure