Pemodelan Graph Database Untuk Moda Transportasi Bus Rapid Transit

Bus Rapid Transit (BRT) merupakan salah satu alternatif transportasi massal. Rute BRT memiliki karakteristik khusus yang dapat dimodelkan dengan graph. Ketika rute dan shelter semakin bertambah, dibutuhkan aplikasi komputer untuk melakukan pencarian rute BRT. Hal tersebut akan memudahkan pencarian dan penjelajahan rute-rute BRT. Namun, ketika rute diimplementasikan menggunakan basis data relasional, performa query dapat menurun karena banyaknya operasi JOIN untuk mencari rute. Artikel ini mengusulkan sebuah model graph database untuk BRT dan implementasinya. Identifikasi kebutuhan data dilakukan, dilanjutkan dengan pemodelan menggunakan entity relationship (ER). Hasil ER tersebut kemudian dipetakan ke dalam property graph untuk kemudian diimplementasikan menggunakan produk graph database Neo4J. Hasil penelitian ini menunjukkan bahwa model yang dibuat bisa diterapkan dalam basis data graph dan graph dapat menunjukkan rute BRT tertentu. Dari sisi performance, basis data graph menunjukkan kinerja perambatan yang lebih baik dibandingkan dengan basis data relasional

PEMODELAN GRAPH DATABASE UNTUK MODA TRANSPORTASI BUS RAPID TRANSIT

Bus Rapid Transit (BRT) merupakan salah satu alternatif transportasi massal. Rute BRT memiliki karakteristik khusus yang dapat dimodelkan dengan graph. Ketika rute dan shelter semakin bertambah, dibutuhkan aplikasi komputer untuk melakukan pencarian rute BRT. Hal tersebut akan memudahkan pencarian dan penjelajahan  rute-rute BRT. Namun, ketika rute diimplementasikan menggunakan basis data relasional, performa query dapat menurun karena banyaknya operasi JOIN untuk mencari rute. Artikel ini mengusulkan sebuah model graph database untuk BRT dan implementasinya. Identifikasi kebutuhan data dilakukan, dilanjutkan dengan pemodelan menggunakan entity relationship (ER). Hasil  ER tersebut kemudian dipetakan ke dalam property graph untuk kemudian diimplementasikan menggunakan produk graph database Neo4J. Hasil penelitian ini menunjukkan bahwa model yang dibuat bisa diterapkan dalam basis data graph dan graph dapat menunjukkan rute BRT tertentu. Dari sisi performance, basis data graph menunjukkan kinerja perambatan yang lebih baik dibandingkan dengan basis data relasional. Keyword : BRT, graphdatabas

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

Cypher: An Evolving Query Language for Property Graphs

International audienceThe Cypher property graph query language is an evolving language, originally designed and implemented as part of the Neo4j graph database, and it is currently used by several commercial database products and researchers. We describe Cypher 9, which is the first version of the language governed by the openCypher Implementers Group. We first introduce the language by example, and describe its uses in industry. We then provide a formal semantic definition of the core read-query features of Cypher, including its variant of the property graph data model, and its " ASCII Art " graph pattern matching mechanism for expressing subgraphs of interest to an application. We compare the features of Cypher to other property graph query languages, and describe extensions, at an advanced stage of development, which will form part of Cypher 10, turning the language into a compositional language which supports graph projections and multiple named graphs

Towards a Graph-based Data Model for Semantics Evolution

Semantic information comes from the things being recognized and understood gradually, and thus it is often in evolution during the modeling process. Existing semantic models usually describe the objects and the relationships in an application-oriented way, which is unsuitable to reuse the schemas during the semantics evolution. In this paper, we propose a new graph-based semantic data model to overcome the limitation. SemGraph adopts a meaning-oriented approach to specifying the subjective view of the things and uses the certain meta-meaning relationships to build a graph-based semantic model. The model is simple but expressive, and is especially fit for the semantics evolution. We introduce the basic concepts and the essential mechanisms of the model, demonstrate its features with examples and typical cases of semantics evolution

Static Analysis of Graph Database Transformations

We investigate graph transformations, defined using Datalog-like rules based on acyclic conjunctive two-way regular path queries (acyclic C2RPQs), and we study two fundamental static analysis problems: type checking and equivalence of transformations in the presence of graph schemas. Additionally, we investigate the problem of target schema elicitation, which aims to construct a schema that closely captures all outputs of a transformation over graphs conforming to the input schema. We show all these problems are in EXPTIME by reducing them to C2RPQ containment modulo schema; we also provide matching lower bounds. We use cycle reversing to reduce query containment to the problem of unrestricted (finite or infinite) satisfiability of C2RPQs modulo a theory expressed in a description logic

Comparative analysis of PropertyFirst vs. EntityFirst modeling approaches in graph databases

While relational databases still hold the primary position in the database technology domain, and have been for the longest time of any Computer Science technology has since its inception, for the first time the relational databases now have valid and worthy opponent in the NoSQL database movement. NoSQL databases, even though not many people have heard of them, with a significant number of Computer Science people included, have spread rapidly in many shapes and forms and have done so in quite a chaotic fashion. Similarly to the way they appeared and spread, design and modeling for them have been undertaken in an unstructured manner. Currently they are subcategorized in 4 main groups as: Key-value stores, Column Family stores, Document stores and Graph databases. In this thesis, different modeling approaches for graph databases, applied to the same domain are analyzed and compared, especially from a design perspective. The database selected here as the implemented technology is Neo4J by Neo Technologies and is a directed property graph database, which means that relationships between its data entities must have a starting and ending (or source and destination) node. This research provides an overview of two competing modeling approaches and evaluates them in a context of a real world example. The work done here shows that both of these modeling approaches are valid and that it is possible to fully develop a data model based on the same domain data with both approaches and that both can be used later to support application access in a similar fashion. One of the models provides for faster access to data, but at a cost of higher maintenance and increased complexity

Linguaggi di interrogazione per basi di dati semistrutturati a grafo.

Negli ultimi anni, grazie anche all'avvento del web 2.0, si sono rese disponibili grandi sorgenti di dati semistrutturati. Si è quindi sviluppato un certo interesse da parte della comunità di ricerca su alcuni modelli per basi di dati atti ad una migliore rappresentazione e più effciente interrogazione di questo tipo di informazioni: un noto esempio proposto dal W3C è "Resource Description Framework". In questa tesi si analizzano alcuni modelli a grafo sia per dati semistrutturati che non, e si confrontano i relativi linguaggi di interrogazione dal punto di vista del loro potere espressivo

MDG-NoSQL : modelo de dados para bancos NoSQL baseados em grafos

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2015.Os bancos de dados em grafo vêm se tornando populares juntamente com as demais iniciativas NoSQL. Porém, os bancos de dados em grafos não possuem uma notação padrão. Sendo assim, o presente trabalho agrupa diversas notações e propostas de modelagem em grafos, construindo um novo modelo, chamado de Modelo de Dados para Bancos NoSQL Baseados em Grafos (MDG-NoSQL), com recursos para agrupar em uma notação as características dos bancos de dados em grafos. Esse modelo foi validado utilizando a implementação de um banco de dados de vínculos societários de empresas, combinados com os relacionamentos dessas pessoas jurídicas com os processos de compras, também chamadas de licitações, junto ao Governo Federal (Banco de Vínculos Simpli_cado para Licitações e Sociedades). Esse estudo de caso foi direcionado para auxiliar na detecção de fraudes em processos licitatórios que, além de ser diretamente aplicável a vários órgãos de controle, perícia e inteligência em âmbito nacional, permite extrair fundamentos extens íveis a outros problemas com modelagens de vínculos.Currently, Graph Databases are becoming popular along with other NoSQL initiatives. Thus, researchers and companies have been developing data models to manipulate information as graphs However, there is no standard notation involves several features and structures of the graph databases. This model introduces several notations and concepts for building a new graph data model, called Data Model for NoSQL Graph Databases (GDM-NoSQL). The GDM-NoSQL was veri_ed through a database for investigate relationships between companies and people as well as information about the procurements that these companies participated in with the Federal Government. This database was designed to facilitate the process of searching for frauds and to be used on multiple contexts, i.e. several di_erent national agencies. Finally, the designed model was implemented in both a relational and a graph database in order to validate the hypothesis that writing relationships is simpler and more e_cient in graph models than relational databases