Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Planning, developing, and updating software cannot be separated from the role of the database. From various types of databases, graph databases are considered to have various advantages over their predecessor, relational databases. Graph databases then become the latest trend in the software and data science industry, apart from the development of graph theory itself. The proliferation of research on GDB in the last decade raises questions about what topics are associated with GDB, what industries use GDB in its data processing, what the GDB models are, and what types of GDB have been used most frequently by users in the last few years. This article aims to answer these questions through a Literature Review, which is carried out by determining objectives, determining the limits of review coverage, determining inclusion and exclusion criteria for data retrieval, data extraction, and quality assessment. Based on a review of 60 studies, several research topics related to GDB are Semantic Web, Big Data, and Parallel computing. A total of 19 (30%) studies used Neo4j as their database. Apart from Social Networks, the industries that implement GDB the most are the Transportation sector, Scientific Article Networks, and general sectors such as Enterprise Data, Biological data, and History data. This Literature Review concludes that research on the topic of the Graph Database is still developing in the future. This is shown by the breadth of application and the variety of new derivatives of GDB products offered by researchers to address existing problems

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Planning, developing, and updating software cannot be separated from the role of the database. From various types of databases, graph databases are considered to have various advantages over their predecessor, relational databases. Graph databases then become the latest trend in the software and data science industry, apart from the development of graph theory itself. The proliferation of research on GDB in the last decade raises questions about what topics are associated with GDB, what industries use GDB in its data processing, what the GDB models are, and what types of GDB have been used most frequently by users in the last few years. This article aims to answer these questions through a Literature Review, which is carried out by determining objectives, determining the limits of review coverage, determining inclusion and exclusion criteria for data retrieval, data extraction, and quality assessment. Based on a review of 60 studies, several research topics related to GDB are Semantic Web, Big Data, and Parallel computing. A total of 19 (30%) studies used Neo4j as their database. Apart from Social Networks, the industries that implement GDB the most are the Transportation sector, Scientific Article Networks, and general sectors such as Enterprise Data, Biological data, and History data. This Literature Review concludes that research on the topic of the Graph Database is still developing in the future. This is shown by the breadth of application and the variety of new derivatives of GDB products offered by researchers to address existing problems

Graph indexing and retrieval based on graph prototypes

[ANGLÈS] Taking a query from a high number of data stored into a database, as fast as possible, is a recurrent problem in the field of computer sciences practically since its origins. At the existence of this problem, it’s necessary to add, moreover, the fact that actually databases contains data types of more diverse and unexpected character possible. Now we are not talking about originating databases which only contained sets of numbers or characters strings. (...) All that I want to make into the present work and I think that was achieved as far as possible, has been to develop and to present a methodology to carry out this process. The Metric Trees of prototypes are based on a well-known strategy, which is based on grouping the data stored in database at the smartest possible way. But also we has added the concept of a graph prototype. A structure that contains information of a set of instances represented by graphs, used until now for classification and recognition. In this thesis we have used graphs as representatives of elements that have to be queried in databases. Note that graphs have the capacity to represent complex objects, for this reason the number of graph databases is increasing. Due to in the literature appears different ways to build a prototype, the work presented here shows a comparative study between the main methods. Combining these two concepts, the Metric Tree and the graph prototype, we propose the construction of metric trees where the graph prototypes are routing nodes to help to decide the way to explore when we make a search in the tree. We have used Metric Trees to make classification and to find all instances that are lower than a maximum distance. (...)[CATALÀ] El trobar-nos davant una gran quantitat de dades i tenir que fer cerques d’aquestes el més ràpid possible és un problema recurrent en el camp de les ciències de la computació pràcticament des dels seus orígens. A l'existència d'aquest problema, se li ha d’afegir, a més a més, el fet de que actualment les bases de dades emmagatzemen tipus de dades de la naturalesa més diversa i molts cops inesperada possible. Ja no parlem de les bases de dades originaries que únicament contenien números o cadenes caràcters. (...) El que he volgut en aquest treball i penso que en la mesura del que era possible s'ha aconseguit, és desenvolupar i presentar una metodologia per portar a terme aquest procés. Els Metric Trees de prototips, que es basen en la ja coneguda estratègia d'agrupar les dades que anem guardant a una base de dades de la forma més intel·ligent possible per no haver d’explorar totes les instàncies que tenim quan volem fer una cerca, però a més a més s'ha afegit el concepte de prototip. Una estructura, que agrupa la informació d'un conjunt d'instàncies, utilitzada fins ara per a fer classificació i reconeixement. Conjugant aquests dos conceptes, el de Metric Tree i el de prototip, plantejem la construcció d'arbres de cerca on els prototips siguin els nodes intermedis, que ens ajudin a decidir quin camí explorar quan volem fer una cerca sobre l'arbre. I utilitzant, aquests tant per a fer classificació com per a buscar totes les instàncies que estiguin una distància més petita d’una distància máxima. Tot això tenint present, que les dades amb que treballem són grafs, és a dir que la metodologia presentada, té la versatilitat de poder-se aplicar, a qualsevol tipus d'informació que es pugui representar d'aquesta manera. (...

Multiple graph matching and applications

En aplicaciones de reconocimiento de patrones, los grafos con atributos son en gran medida apropiados. Normalmente, los vértices de los grafos representan partes locales de los objetos i las aristas relaciones entre estas partes locales. No obstante, estas ventajas vienen juntas con un severo inconveniente, la distancia entre dos grafos no puede ser calculada en un tiempo polinómico. Considerando estas características especiales el uso de los prototipos de grafos es necesariamente omnipresente. Las aplicaciones de los prototipos de grafos son extensas, siendo las más habituales clustering, clasificación, reconocimiento de objetos, caracterización de objetos i bases de datos de grafos entre otras. A pesar de la diversidad de aplicaciones de los prototipos de grafos, el objetivo del mismo es equivalente en todas ellas, la representación de un conjunto de grafos. Para construir un prototipo de un grafo todos los elementos del conjunto de enteramiento tienen que ser etiquetados comúnmente. Este etiquetado común consiste en identificar que nodos de que grafos representan el mismo tipo de información en el conjunto de entrenamiento. Una vez este etiquetaje común esta hecho, los atributos locales pueden ser combinados i el prototipo construido. Hasta ahora los algoritmos del estado del arte para calcular este etiquetaje común mancan de efectividad o bases teóricas. En esta tesis, describimos formalmente el problema del etiquetaje global i mostramos una taxonomía de los tipos de algoritmos existentes. Además, proponemos seis nuevos algoritmos para calcular soluciones aproximadas al problema del etiquetaje común. La eficiencia de los algoritmos propuestos es evaluada en diversas bases de datos reales i sintéticas. En la mayoría de experimentos realizados los algoritmos propuestos dan mejores resultados que los existentes en el estado del arte.In pattern recognition, the use of graphs is, to a great extend, appropriate and advantageous. Usually, vertices of the graph represent local parts of an object while edges represent relations between these local parts. However, its advantages come together with a sever drawback, the distance between two graph cannot be optimally computed in polynomial time. Taking into account this special characteristic the use of graph prototypes becomes ubiquitous. The applicability of graphs prototypes is extensive, being the most common applications clustering, classification, object characterization and graph databases to name some. However, the objective of a graph prototype is equivalent to all applications, the representation of a set of graph. To synthesize a prototype all elements of the set must be mutually labeled. This mutual labeling consists in identifying which nodes of which graphs represent the same information in the training set. Once this mutual labeling is done the set can be characterized and combined to create a graph prototype. We call this initial labeling a common labeling. Up to now, all state of the art algorithms to compute a common labeling lack on either performance or theoretical basis. In this thesis, we formally describe the common labeling problem and we give a clear taxonomy of the types of algorithms. Six new algorithms that rely on different techniques are described to compute a suboptimal solution to the common labeling problem. The performance of the proposed algorithms is evaluated using an artificial and several real datasets. In addition, the algorithms have been evaluated on several real applications. These applications include graph databases and group-wise image registration. In most of the tests and applications evaluated the presented algorithms have showed a great improvement in comparison to state of the art applications