Neo4j graph database realizes efficient storage performance of oilfield ontology

Abstract

The integration of oilfield multidisciplinary ontology is increasingly important for the growth of the Semantic Web. However, current methods encounter performance bottlenecks either in storing data and searching for information when processing large amounts of data. To overcome these challenges, we propose a domain-ontology process based on the Neo4j graph database. In this paper, we focus on data storage and information retrieval of oilfield ontology. We have designed mapping rules from ontology files to regulate the Neo4j database, which can greatly reduce the required storage space. A two-tier index architecture, including object and triad indexing, is used to keep loading times low and match with different patterns for accurate retrieval. Therefore, we propose a retrieval method based on this architecture. Based on our evaluation, the retrieval method can save 13.04% of the storage space and improve retrieval efficiency by more than 30 times compared with the methods of relational databases.

Mesenchymal stem cells of Systemic Sclerosis patients, derived from different sources, show a profibrotic microRNA profiling

“…The results obtained were included in a Neo4J graph database 28 to provide a graphical representation of miRNAs-targets interactions (Fig. 3).…”

A C-RFBS model for the efficient construction and reuse of interpretable design knowledge records across knowledge networks

“…For instance, Percuku et al (2017) used the Neo4j database to store the measurement data of substation, which make up for the defects of memory resource waste and poor relational adaptability of relational database when the data structure is complex. In the field of petroleum exploration and development, research showed that Neo4j database can save more than 13% memory space and promote retrieval efficiency by more than 30 times compared to relational database (Gong et al, 2018). Knowledge reuse, such as knowledge retrieval, knowledge-assisted decision-making, knowledge reasoning and so on, realizes the interaction between human and data through IT system.…”

Analysis of the Effect of the Reliability of the NB-Iot Network on the Intelligent System

“…Experimental results indicate that an increase in temperature can sufficiently convert a perfect LoRa link into an almost useless link, and that different Port Physical Layer (PHY) settings of LoRa exert different effects on temperature fluctuations [29]. Cattani et al reduced the size of packet fragments to improve the reliability of communications in duty-limited networks [30]. The reliability of communication is reflected by the energy consumption, throughput, and end-to-end delay.…”