Results 61 to 70 of about 4,333 (216)
RELATIONAL DATABASE AND GRAPH DATABASE: A COMPARATIVE ANALYSIS [PDF]
Since 1970, relational database models have been in use for storing, manipulating and retrieving data. The importance of relational databases is going to decrease due to the exponential growth of data as it is difficult to work with large number of ...
Surajit Medhi, Hemanta K. Baruah
doaj +1 more source
Proteome Analysis of Corynebacterium diphtheriae–Macrophage Interaction
ABSTRACT Contact of Corynebacterium diphtheriae with macrophages induces adaptations on both bacterial and cellular sides. The study presented here was aiming to shed light on the simultaneous intracellular adaptation of the bacteria and changes in the proteome of the phagocytes in response to the internalization of C. diphtheriae.
Luca Musella +6 more
wiley +1 more source
IntroductionIn response to the issues of complexity and low efficiency in line loss calculations for actual distribution networks, this paper proposes a fast power flow calculation method for distribution networks based on Neo4j graph models and a ...
Xinrui Wang +4 more
doaj +1 more source
Knowledge graphing in forensics: Modeling forensic case trajectories using news media
Abstract We employ a knowledge graph (KG) approach to aggregate qualitative information to harness and systematically incorporate contextual information into a forensic investigation. We are interested in tracing the spatial distribution and what happens to unidentified human remains after discovery—relationships and successive movements that are not ...
Molly Miranker +3 more
wiley +1 more source
Storage efficiency comparison of UML models in selected database technologies
.The study answers the question which database is the best choice for efficient data storage of UML models. Three products were considered: MongoDB, PostgreSQL and Neo4J.
Andrii Filatov +2 more
doaj +1 more source
The growth experienced by the internet in the past few years as lead to an increased amount of available data and knowledge obtained from said data. However most of this knowledge is lost due to the lack of associated semantics making the task of interpreting data very hard to computers.
Moreira, Ezequiel, Ramalho, José Carlos
openaire +1 more source
Applying knowledge graphs: Migrant decedent case trajectories in South Texas
Abstract The phenomenon of migrant death at the Texas‐Mexico border is well documented, but questions remain as concerns its spatial distribution and what happens to human remains after discovery. To start exploring these questions, we developed a knowledge graph (KG) approach to aggregate qualitative information concerning migrant death and elucidate ...
Molly Miranker +3 more
wiley +1 more source
The metadata ecosystem and AI: Enabling FAIR and AI‐ready data
Abstract Reproducibility is a foundational tenet of science. As artificial intelligence (AI) becomes increasingly embedded across science, the need to accurately document the provenance, structure, and behavior of training data, models, and workflows grows correspondingly. Metadata, understood as explicit and structured knowledge about data and related
Jane Greenberg +5 more
wiley +1 more source
Comparación entre Bases de Datos Orientadas a Grafo y Relacional basado en el rendimiento
Actualmente en cada segundo y minuto en el mundo son generados grandes cantidades de datos que necesitan ser almacenados y procesados, esta información es muy variada en cuanto a la forma de almacenamiento y fuente de procedencia así como son muchos y ...
Royland Rodriguez Reyes
doaj
Abstract The exponential growth of scientific literature—over 2.5 million papers and 3.5 million patents annually—poses critical challenges for knowledge discovery. To address these, we propose SCIMKG (Scientific Knowledge Graph), a framework for constructing scientific knowledge graphs via multi‐source heterogeneous multi‐source.
Erxi Zhu, Yuan Hu
wiley +1 more source

