Examinando por Materia "BASES DE DATOS ORIENTADAS A GRAFOS"
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- Artículo de Publicación PeriódicaAnalytical queries on semantic trajectories using graph databases(2019-10) Gómez, Leticia Irene; Kuijpers, Bart; Vaisman, Alejandro Ariel"This article studies the analysis of moving object data collected by location-aware devices, such as GPS, using graph databases. Such raw trajectories can be transformed into so-called semantic trajectories, which are sequences of stops that occur at “places of interest.” Trajectory data analysis can be enriched if spatial and non-spatial contextual data associated with the moving objects are taken into account, and aggregation of trajectory data can reveal hidden patterns within such data. When trajectory data are stored in relational databases, there is an “impedance mismatch” between the representation and storage models. Graphs in which the nodes and edges are annotated with properties are gaining increasing interest to model a variety of networks. Therefore, this article proposes the use of graph databases (Neo4j in this case) to represent and store trajectory data, which can thus be analyzed at different aggregation levels using graph query languages (Cypher, for Neo4j). Through a real-world public data case study, the article shows that trajectory queries are expressed more naturally on the graph-based representation than over the relational alternative, and perform better in many typical cases."
- Artículo de Publicación PeriódicaA model and query language for temporal graph databases(2021-09) Debrouvier, Ariel; Parodi, Eliseo; Perazzo, Matías; Soliani, Valeria; Vaisman, Alejandro Ariel"Graph databases are becoming increasingly popular for modeling different kinds of networks for data analysis. They are built over the property graph data model, where nodes and edges are annotated with property-value pairs. Most existing work in the field is based on graphs were the temporal dimension is not considered. However, time is present in most real world problems. Many different kinds of changes may occur in a graph as the world it represents evolves across time. For instance, edges, nodes, and properties can be added and/or deleted, and property values can be updated. This paper addresses the problem of modeling, storing, and querying temporal property graphs, allowing keeping the history of a graph database. This paper introduces a temporal graph data model, where nodes and relationships contain attributes (properties) timestamped with a validity interval. Graphs in this model can be heterogeneous, that is, relationships may be of different kinds. Associated with the model, a high-level graph query language, denoted T-GQL, is presented, together with a collection of algorithms for computing different kinds of temporal paths in a graph, capturing different temporal path semantics. T-GQL can express queries like “Give me the friends of the friends of Mary, who lived in Brussels at the same time than her, and also give me the periods when this happened”. As a proof-of-concept, a Neo4j-based implementation of the above is also presented, and a client-side interface allows submitting queries in T-GQL to a Neo4j server. Finally, experiments were carried out over synthetic and real-world data sets, with a twofold goal: on the one hand, to show the plausibility of the approach; on the other hand, to analyze the factors that affect performance, like the length of the paths mentioned in the query, and the size of the graph."
- Proyecto final de GradoModelado y consulta de data warehouses usando bases de datos de grafo(2019-06) Besteiro, María Florencia; Valverde Melito, Maximiliano Javier; Vaisman, Alejandro Ariel"El objetivo de este proyecto es poder modelar y consultar un data warehouse usando bases de datos de grafos, de forma de poder comparar la performance del mismo en contraste con su alternativa relacional. Para ello se definirán distintos tipos de consultas que luego serán ejecutadas en ambos modelos, con el fin de determinar que situaciones se destaca cada uno de ellos en términos de performance."
- Ponencia en CongresoModeling and querying sensor networks using temporal graph databases(2022) Kuijpers, Bart; Soliani, Valeria; Vaisman, Alejandro Ariel"Transportation networks (e.g., river systems or road net works) equipped with sensors that collect data for several different pur poses can be naturally modeled using graph databases. However, since networks can change over time, to represent these changes appropriately, a temporal graph data model is required. In this paper, we show that sensor-equipped transportation networks can be represented and queried using temporal graph databases and query languages. For this, we extend a recently introduced temporal graph data model and its high-level query language T-GQL to support time series in the nodes of the graph. We redefine temporal paths and study and implement a new kind of path, called Flow path. We take the Flanders’ river system as a use case."
- Ponencia en CongresoModelling and querying star and snowflake warehouses using graph databases(2019) Vaisman, Alejandro Ariel; Besteiro, María Florencia; Valverde Melito, Maximiliano Javier"In current “Big Data” scenarios, graph databases are increasingly being used. Online Analytical Processing (OLAP) operations can expand the possibilities of graph analysis beyond the traditional graphbased computation. This paper studies graph databases as an alternative to implement star and snowflake schemas, the typical choices for data warehouse design. For this, the MusicBrainz database is used. A data warehouse for this database is designed, and implemented over a Postgres relational database. This warehouse is also represented as a graph, and implemented over the Neo4j graph database. A collection of typical OLAP queries is used to compare both implementations. The results reported here show that in ten out of thirteen queries tested, the graph implementation outperforms the relational one, in ratios that go from 1.3 to 26 times faster, and performs similarly to the relational implementation in the three remaining cases."
- Proyecto final de GradoTemporal graph visualizer(2020-11-17) Orlando, Diego; Ormachea, Joaquín; Vaisman, Alejandro Ariel; Aizemberg, Diego Ariel"Real world scenarios are increasingly being represented with graph databases. Networks in general, and social networks in particular, can be represented as node in a graph, linked through edges. When relationships and node include temporal information, the graphs are called temporal. Temporal graphs are then, graphs that keep track of the history of their nodes and edges. Although these scenarios are normally found in real-world scenarios, there is no tool in the market that can handle appropriately the temporal dimension in graphs. The present work introduces a platform to address this problem. The framework presented here allows displaying temporal graphs and navigating them across time. The result of queries expressed in a high level temporal query language can also be captured and navigated using this tool."
- Proyecto final de GradoTemporal index: optimizaciones para el cálculo de caminos continuos en grafos temporales(2021-12-16) Ribas, Ignacio; Soliani, Valeria"La adopción de bases de datos de grafos es cada vez mayor para diversas aplicaciones. Un concepto no muy extendido pero con mucho potencial, en especial en el ámbito de las redes sociales, es el de las bases de datos de grafos temporales, es decir, aquellas en las cuáles se almacena un historial de los nodos y las relaciones. En el presente trabajo se estudian algunas alternativas para la optimización de consultas por caminos continuos en bases de datos de grafos temporales. Estas optimizaciones involucran no sólo el uso de un índice estructural en el grafo cuya subestructura es el mismo camino continuo, sino también estrategias sin índice que aprovechan los algoritmos de cálculo de caminos built-in de Neo4j, el motor de base de datos en el que se desarrolla el sistema. También se presenta una extensión del lenguaje TGQL, permitiendo realizar operaciones sobre aristas que consideran sus consecuentes actualizaciones a los índices creados, así como operaciones propias para la creación de índices y la consulta a estos antes de realizar una consulta de cálculo de caminos continuos."
- Ponencia en CongresoTowards temporal graph database(2016) Campos, Alexander; Mozzino, Jorge; Vaisman, Alejandro Ariel"In spite of the extensive literature on graph databases (GDBs), temporal GDBs have not received too much attention so far. Tempo ral GBDs can capture, for example, the evolution of social networks across time, a relevant topic in data analysis nowadays. We propose a data model and query language (denoted TEG-QL) for temporal GDBs, based on the notion of attribute graphs. This allows a straightforward translation to Neo4J, a well-known GBD."
- Artículo de Publicación PeriódicaTowards the Internet of water: Using graph databases for hydrological analysis on the Flemish river system(2021-07) Bollen, Erik; Hendrix, Rik; Kuijpers, Bart; Vaisman, Alejandro Ariel"The “Internet of Water” project will deploy 2,500 sensors along the Flemish river system, in Belgium. These sensors will be part of a monitoring system. This will produce anenormous amount of data, on which prediction and analysis tasks can be performed. To represent, store, and query river data, relational databases are normally used. However, this choice introduces an “impedance mismatch” between the conceptual representation (typically a graph) and the storage model (relational tables). To solve this problem, this article proposes to use graph databases. The Flemish river system is presented as a use case and the Neo4j graph database and its high-level query language, Cypher, are used for storing and querying the data, respectively. A relational alternative is implemented over the PostgreSQL database. A collection of representative queries of interest for hydrologists is defined over both database implementations."