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In order to solve the problem of ''potpourri'' of safety risk prevention and control measures, which is caused by the unclear mechanism of the spatial effect of coal mine safety level heterogeneity and its influencing factors. This paper discriminates the dominant factors of spatio-temporal heterogeneity of coal mine safety production level in China and their spatial effect types by means of GeoDetector and the Spatial Dubin Model (SDM), specifies the categories and degrees of acts on local and surrounding areas by changes in indicators, and provides further visualization of the detection outcomes with the assistance of the Neo4j graph database, and the findings indicate that:(1) All 15 indicators selected in the study have a certain influence on the generation of spatial heterogeneity in China's coal mine safety production level, and all of them show an enhancement relationship after the interaction of indicators. Especially, the combination of excavated environment and other indicators basically has a non-linear enhancement relationship. (2) In terms of spatial effects, the influence of the 5 effects on the spatio-temporal heterogeneity of coal mine safety level is, in descending order, industrial development effect > capital allocation effect > production environment effect > government supervision effect > enterprise management effect, which indicates that macroeconomic and market conditions have a much stronger influence on the generation of spatio-temporal heterogeneity in coal mine production safety status. (3) From the single indicator perspective, the average annual temperature, average annual wind speed, coal consumption and monitoring efficiency primarily affect the dependent variable through direct effects; GDP per capita, average labor compensation as well as railroad operating mileage have positive spatial spillover on the changes of coal mine safety production level in surrounding areas; the evaluation of the spatial effect for average labor compensation exhibits a positive indirect effect with low influence; for the two indicators of production efficiency and ex-factory price, not only do they have negative effects on the local coal mine safety level, but also have significant spillover effects on surrounding areas.

This paper presents a holistic approach to biological and agricultural research focused on the use of interconnected technologies in the context of climate change. Researchers from different countries have analyzed how smart technologies can help agriculture adapt to these changes. The most representative works in the field are analyzed. Among these tech-nologies are graph database systems such as Neo4j, which have demonstrated success in predicting the studied phenomena. The paper describes the development of a soybean crop productivity prediction model using monthly and annual data of meteorological phenomena such as precipitation, air temperature, hydrothermal coefficient, soil moisture and others. Some of the results of this promising research are also presented.

With the rapid advancement of technology and the vast volume of unstructured data available on the Internet, there is a pressing need to extract information from diverse data formats effectively. This is essential as valuable pieces of information may be lost. To address this issue, researchers are using Machine Learning (ML) and Natural Language Processing (NLP) techniques to extract information from unstructured text, including the utilization of Knowledge Graphs (KGs). This paper demonstrates end-to-end experimental studies of KG construction from unstructured text using open-source techniques and concrete real-world examples in different problem domains. The unstructured data underwent a text processing pipeline consisting of coreference resolution, named entity linking, and relationship extraction. The pipeline is designed to support automatic data storage in a graph database known as Neo4j. This storage includes the extracted entities and their relationships. Experiments were conducted on a real-world unstructured BBC News Dataset to analyze the outcome obtained from the pipeline. The experience can facilitate the adoption of KG creation for practitioners to capture valuable information from a large volume of unstructured text. The results from the relationship extraction step using two techniques were evaluated, including extracted entities, relationship types, accuracies of 61.4% with OpenNRE and 87% with REBEL, and processing time. Further, the data processing pipeline was applied to analyze the unstructured dataset from the Transportation Safety Board’s (TSB) Findings for aviation safety analysis. The results showed that structured relationships identified through the pipeline provided valuable indicators, as they captured critical aviation safety information, such as the flight, aircraft type, event, etc. This pipeline can be fine-tuned with a domain-specific knowledge base to provide higher accuracy and better entity detection.

Knowledge Graph Question Answering (KGQA) plays a crucial role in extracting valuable insights from interconnected information. Existing methods, while commendable, face challenges such as contextual ambiguity and limited adaptability to diverse knowledge domains. This paper introduces TransKGQA, a novel approach addressing these challenges. Leveraging Sentence Transformers, TransKGQA enhances contextual understanding, making it adaptable to various knowledge domains. The model employs question-answer pair augmentation for robustness and introduces a threshold mechanism for reliable answer retrieval. TransKGQA overcomes limitations in existing works by offering a versatile solution for diverse question types. Experimental results, notably with the sentence-transformers/all-MiniLM-L12-v2 model, showcase remarkable performance with an F1 score of 78%. This work advances KGQA systems, contributing to knowledge graph construction, enhanced question answering, and automated Cypher query execution.

Databases are a fundamental element of contemporary software applications. The most widely used and recognized type in practice is the relational database, valued for its ability to store and organize data in tabular structures, its emphasis on data consistency and integrity, and its use of a standardized query language, SQL. However, with the rapid increase in both the volume and complexity of data, relational databases have recently encountered challenges in effectively modeling this expanding information. To address performance challenges, new database systems have emerged, offering alternative approaches to data modeling—these are known as NoSQL databases. In this paper, we present an indoor navigation application designed to operate on both a relational database, Microsoft SQL Server, and a graph-based NoSQL database, Neo4j. We describe the algorithms implemented for testing and the performance metrics analyzed to draw our conclusions. The results revealed Neo4j’s strength in managing data with complex relationships but also exposed its limitations in handling concurrent access, where SQL Server demonstrated significantly greater stability.

This paper presents a method to store information about megalithic monument-building components as graph nodes in a knowledge graph (KG). As a case study, we analyse the dolmens from the region of Pavia (Portugal). To build the KG, information has been extracted from unstructured data to populate a schema model based on the International Committee for Documentation – Conceptual Reference Model (CIDOC-CRM). In order to prepare the archaeological monument’s information for bulk loading, it was transformed into semi-structured data. While the semi-structured file was used to populate the classes with their respective properties and instances, the KG labels and types were defined using some of the entities and relations defined by the CIDOC-CRM. The knowledge-driven model was built to represent dolmens in a formal and structured manner using Neo4j, a property-graph database. Modelling a labelled property graph based on predefined labels as a KG enables to transform textual semantic data into instances and properties. Thus, we show that it is possible to represent at a granular level all the information about the structural components of monuments since heterogeneities, granularities, and large amounts of data can be handled by a KG. Therefore, a KG implemented using a native graph database can improve data storage and processing, making it interoperable between humans, humans and machines and machine to machine.

Abstract This paper presents a model-based approach for converting Common Grid Model Exchange Specification (CGMES) power system models into operational networks. The approach utilizes the Model-Driven Architecture (MDA), borrowed from software engineering, to create a Platform-Independent Model (PIM) saved in a graph database, neo4j. The PIM is then transformed into Platform-Specific Model (PSM) using cypher queries. Finally, from the PSM, a Pandapower network is generated that is used later for running power flow simulations. The Common Information Model (CIM) ontology classes, which form the metamodel behind CGMES, are loaded into the same database, connecting them to the grid instances, allowing for automatic validation and inference. The proposed approach has been successfully applied to large-scale real-world grids that include renewable energy sources.

With the proliferation of data, relational database management systems face the challenge of managing big volumes of data because of the absence of dynamic data models, high functioning, high scalability, high availability, and low cost. The NoSQL technology has the features which RDBMS lacks. Therefore, many organizations are migrating from RDBMS to NoSQL databases. The migration from RDBMS to NoSQL is not always effective if the data is lost, if the data is replicated while transmission, and if the performance is degraded. For this reason, enhancement of transformation algorithm proposed by Shabana Ramzan et al. has been performed to implement the methodology which is used to successfully migrate data from structured and normalized SQL database MySQL to NoSQL graph database Neo4j. SchemaCrawler engine has been used to metamodeling from source to destination database and execute the transformation rules. This study has also evaluated and validated the performance of the enhanced algorithm after successful migration of data. The NoSQL graph database Neo4j handles the most common form of data that are unstructured, semi-structured, and structured as well as they support the ACID property of relational databases. This feature is not supported by other NoSQL databases such as Oracle NoSQL, MongoDB, and HBase databases.

Conversational AI systems have emerged as key enablers of human-like interactions across diverse sectors. Nevertheless, the balance between linguistic nuance and factual accuracy has proven elusive. In this paper, we first introduce LLMXplorer, a comprehensive tool that provides an in-depth review of over 205 large language models (LLMs), elucidating their practical implications, ranging from social and ethical to regulatory, as well as their applicability across industries. Building on this foundation, we propose a novel functional architecture that seamlessly integrates the structured dynamics of knowledge graphs with the linguistic capabilities of LLMs. Validated using real-world AI news data, our architecture adeptly blends linguistic sophistication with factual rigor and further strengthens data security through role-based access control. This research provides insights into the evolving landscape of conversational AI, emphasizing the imperative for systems that are efficient, transparent, and trustworthy.

Using the CNN network model and attention mechanism, this paper completes the construction of the cognitive graph pattern layer based on Protégé programming, crawls the relevant data from the dimensions of language ability, learning ability, thinking, and cultural character of multicultural literacy in English teaching, extracts the entity-relationship of multicultural literacy in English teaching, stores it in the Neo4j corpus, and ultimately completes the construction of the cognitive graph for the cultivation of multicultural literacy in English teaching. Following simulated experiments to evaluate the algorithm’s performance, a case study was carried out to foster English multicultural literacy from the standpoint of the cognitive graph. The information demonstrates that the convolution technique and an attention mechanism work well together to increase relationship extraction efficiency. Further evidence that the cultivation of multicultural literacy in English teaching focuses on language ability, learning ability, thinking character, and cultural character in order to quickly improve students’ English multicultural literacy comes from the keywords with high word frequency in English multicultural literacy: “language ability (388, 0.899)”, “learning ability (185, 0.449)”, “thinking character” (114, 0.38), and “cultural character” (105, 0.335).

In order to promote the integrated development of culture and the tourism industry, this paper explores the integration of traditional culture and the rural tourism industry. Firstly, a bottom-up knowledge graph construction method is designed based on the DGCN relational extraction model, Muhead-CU-FL-BE entity extraction model and Neo4j high-performance graph database. Then, based on the domain and characteristics of traditional culture and rural tourism industry integration, the knowledge graph of rural traditional culture and tourism industry integration is constructed from three aspects: line results, ontology model and description of this paper. Finally, the performance of the knowledge graph constructed in this paper is tested on the relevant dataset, and the visualization analysis of industrial integration is carried out through the constructed knowledge graph. The results show that the overall performance of the relationship extraction algorithm in this paper is around 0.7, the entity extraction algorithm has the best performance, and the overall performance is around 0.8, and the ratio of the public ancestor nodes of 5 times linking in the 5th and 6th layers is greater than 0.65. The centrality of culture and tourism industry, cultural and tourism fusion, high-quality development, culture and tourism, and industry fusion are 0.78, 0.60, 0.58, and 0.80. The centrality of the keywords is 0.78, 0.60, 0.58, and 0.80, respectively, 1.00, and the strength of the salient values of each keyword is concentrated around 1~3.

This paper proposes a personalized teaching method based on knowledge mapping and the commonly used personalized learning methods. A web crawler is used to crawl English exercises data on the Internet and classify them according to the knowledge categories, establish the connection between topics according to their cosine similarity, and build an English knowledge graph using NEO4J. After creating the knowledge map, a select few nodes are categorized with labels that reflect the characteristics of various pupils, and the node categories are divided into two categories: mastered and unmastered, after which the whole knowledge map is labeled using a graph convolutional neural network to generate a personalized knowledge map. After testing, the model works best when the threshold value is increased to 0.8 when generating the English knowledge map. And the overall English score of the experimental class taught with this paper’s method improved by 5.27 points, among which the average scores of completion, writing, and reading increased by 2.58, 1.32, and 1.17 points, while the overall English level of the control class did not change significantly. As a result, the approach used in this research to create tailored learning routes based on personalized knowledge mapping has excellent applicability.

Systems Biology Markup Language (SBML) has emerged as a standard for representing biological models, facilitating model sharing and interoperability. It stores many types of data and complex relationships, complicating data management and analysis. Traditional database management systems struggle to effectively capture these complex networks of interactions within biological systems. Graph-oriented databases perform well in managing interactions between different entities. We present neo4jsbml, a new solution that bridges the gap between the Systems Biology Markup Language data and the Neo4j database, for storing, querying and analyzing data. The Systems Biology Markup Language organizes biological entities in a hierarchical structure, reflecting their interdependencies. The inherent graphical structure represents these hierarchical relationships, offering a natural and efficient means of navigating and exploring the model’s components. Neo4j is an excellent solution for handling this type of data. By representing entities as nodes and their relationships as edges, Cypher, Neo4j’s query language, efficiently traverses this type of graph representing complex biological networks. We have developed neo4jsbml, a Python library for importing Systems Biology Markup Language data into a Neo4j database using a user-defined schema. By leveraging Neo4j’s graphical database technology, exploration of complex biological networks becomes intuitive and information retrieval efficient. Neo4jsbml is a tool designed to import Systems Biology Markup Language data into a Neo4j database. Only the desired data is loaded into the Neo4j database. neo4jsbml is user-friendly and can become a useful new companion for visualizing and analyzing metabolic models through the Neo4j graphical database. neo4jsbml is open source software and available at https://github.com/brsynth/neo4jsbml.

Abstract Background Graph databases enable efficient storage of heterogeneous, highly-interlinked data, such as clinical data. Subsequently, researchers can extract relevant features from these datasets and apply machine learning for diagnosis, biomarker discovery, or understanding pathogenesis. Methods To facilitate machine learning and save time for extracting data from the graph database, we developed and optimized Decision Tree Plug-in (DTP) containing 24 procedures to generate and evaluate decision trees directly in the graph database Neo4j on homogeneous and unconnected nodes. Results Creation of the decision tree for three clinical datasets directly in the graph database from the nodes required between 0.059 and 0.099 s, while calculating the decision tree with the same algorithm in Java from CSV files took 0.085–0.112 s. Furthermore, our approach was faster than the standard decision tree implementations in R (0.62 s) and equal to Python (0.08 s), also using CSV files as input for small datasets. In addition, we have explored the strengths of DTP by evaluating a large dataset (approx. 250,000 instances) to predict patients with diabetes and compared the performance against algorithms generated by state-of-the-art packages in R and Python. By doing so, we have been able to show competitive results on the performance of Neo4j, in terms of quality of predictions as well as time efficiency. Furthermore, we could show that high body-mass index and high blood pressure are the main risk factors for diabetes. Conclusion Overall, our work shows that integrating machine learning into graph databases saves time for additional processes as well as external memory, and could be applied to a variety of use cases, including clinical applications. This provides user with the advantages of high scalability, visualization and complex querying.

Our objective was to transform the text data in migraine diagnosis and treatment guidelines into a visualized knowledge graph by using knowledge graph technology, visually display the clinical diagnosis and treatment process, and provide assistance for the standardization of diagnosis and treatment of migraine with integrative medicine.

Over the past years, flight delay has been a critical concern in the aviation sector due to the increased air traffic congestion worldwide. Moreover, it also prolongs the other flights, which can discourage users from traveling with the particular airline. As a result, we proposed a model to predict the overall flight delay using a random forest and path-finding algorithm. The proposed model focuses on searching flights (can be nonstop or connecting) between the source and destination at the earliest. The proposed model identifies the fastest flights between source and destination based on the input by the user using some open source/public Application Programming Interface (APIs), which are further inserted into Neo4j to convert it into a JavaScript Object Notation (JSON) format. Finally, the experimental results on the real-time data set show the proposed model’s effectiveness compared to the state-of-the-art models. The results and analysis yield an accuracy of 98.2% for delay prediction on historical data using the random forest algorithm.

Graph databases have recently gained a lot of attention in areas where the relationships between data and the data itself are equally important, like the semantic web, social networks, and biological networks. A graph database is simply a database designed to store, query, and modify graphs. Recently, several graph database models have been developed. The goal of this research is to evaluate the performance of the two most popular graph databases, Neo4j and TigerGraph, for network centrality metrics including degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and PageRank. We applied those metrics to a set of real-world networks in both graph databases to see their performance. Experimental results show Neo4j outperforms TigerGraph for computing the centrality metrics used in this study, but TigerGraph performs better during the data loading phase.

It is difficult to judge the root cause of equipment accident from the appearance of coal mine electromechanical equipment accident and part of monitoring data, and there is a lack of effective methods to improve the efficiency of equipment accident treatment by using historical data and experience knowledge. In order to solve the above problems, the mine electromechanical equipment accident knowledge graph is constructed. Firstly, the data relationships of the four-group ontology model are designed, and the ontology and the relationship types between the ontologies are determined. Secondly, according to the designed data relationships, a combination method of machine learning and rule templates is used to extract entities, relationships and attributes from databases and texts. Finally, based on the Python language, through the py2neo library, the entities, relationships and attributes are created and stored in the Neo4j graph database with Cypher statements, so as to realize the construction and update of the knowledge graph. The application of mine electromechanical equipment accident knowledge graph in mine electromechanical equipment accident diagnosis, risk management and intelligent question and answer can enable users to effectively use related knowledge of mine electromechanical equipment accident, help equipment maintenance personnel to quickly find the accident chain, locate the cause of the accident and put forward maintenance schemes, so as to achieve the purpose of reducing the accident rate and the accident handling time.

Abstract Scientometrics is the field of study and evaluation of scientific measures such as the impact of research papers and academic journals. It is an important field because nowadays different rankings use key indicators for university rankings and universities themselves use them as Key Performance Indicators (KPI). The purpose of this work is to propose a semantic modeling of scientometric indicators using the ontology Statistical Data and Metadata Exchange (SDMX). We develop a case study at Tecnologico de Monterrey following the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. We evaluate the benefits of storing and querying scientometric indicators using linked data as a mean for providing flexible and quick access knowledge representation that supports indicator discovery, enquiring and composition. The semi-automatic generation and further storage of this linked data in the Neo4j graph database enabled an updatable and quick access model.

Abstract Objective Overconsumption of processed foods has led to an increase in chronic diet-related diseases such obesity and type 2 diabetes. Although diets high in fresh fruits and vegetables are linked with healthier outcomes, the specific mechanisms for these relationships are poorly understood. Experiments examining plant phytochemical production and breeding programs, or separately on the health effects of nutritional supplements have yielded results that are sparse, siloed, and difficult to integrate between the domains of human health and agriculture. To connect plant products to health outcomes through their molecular mechanism an integrated computational resource is necessary. Results We created the Aliment to Bodily Condition Knowledgebase (ABCkb) to connect plants to human health by creating a stepwise path from plant $$\rightarrow$$ → plant product $$\rightarrow$$ → human gene $$\rightarrow$$ → pathways $$\rightarrow$$ → indication. ABCkb integrates 11 curated sources as well as relationships mined from Medline abstracts by loading into a graph database which is deployed via a Docker container. This new resource, provided in a queryable container with a user-friendly interface connects plant products with human health outcomes for generating nutritive hypotheses. All scripts used are available on github ( https://github.com/atrautm1/ABCkb ) along with basic directions for building the knowledgebase and a browsable interface is available ( https://abckb.charlotte.edu ).

Jiajing Wu1,2 1School of Information Science and Engineering, Ocean University of China, Qingdao, 265100,People’s Republic of China; 2School of Mathematics and Computer Science, Chifeng University, Chifeng City, 024000, Inner Mongolia, People’s Republic of ChinaCorrespondence: Jiajing Wu Email wujiajing@stu.ouc.edu.cnAbstract: Since first outbreak of respiratory disease in China, the Coronavirus epidemic (COVID-19) spread on a large scale, causing huge losses to individuals, families, communities and society in the country. The conventional research on the transmission process is basically to study the law or trend of the transmission of infectious diseases from a macro perspective. For in-depth study of the critical data of the newly confirmed patients, one effective way to improve the social isolation measures requires the formation of an organized tracking knowledge system for the confirmed patients and the personnel who have been removed, and the deep data mining and application. Knowledge graph (KG) is one of the irreplaceable techniques to quickly gather patient contact information and outbreak event, which reflecting the relationship between knowledge evolution and structure of novel Coronavirus. Therefore, this paper proposes a method for the analysis of COVID-19 epidemic situation using knowledge graph combined with interactive visual analysis. Firstly, based on the key factors of novel Coronavirus disease, the entity model of the patient, the relationship type of the patient and the expression of knowledge modeling were proposed, and the knowledge graph of the action track of the COVID-19 patient was deeply explored and comparative summarized. Secondly, in the process of constructing knowledge graph, conditional random field (CRF) algorithm is used to extract entity and knowledge. Meanwhile, to better analyze the disease relationship between patients, the semantic relationship of knowledge graph was combined with the visualization of knowledge graph, and the semantic model was verified by deep learning calculation and node attribute similarity. To discover the community detection of patients in the patient knowledge graph, this paper uses PageRank combined with Label propagation algorithms to discover community propagation in the network. Finally, COVID-19 epidemic situation was analyzed from confirmed patient data and multi-view collaborative interactions, such as map distribution visualization, knowledge graph visualization, and track visualization. The results show that the construction of a knowledge graph of COVID-19 patient activity is feasible for the transmission process, analysis of key nodes and tracing of activity tracks.Keywords: COVID-19, knowledge graph, Neo4j, CRF, tracking

Modern developers of gaming mobile and Internet applications almost do not imagine themselves without the use of NoSQL databases, if they pursue the goal of creating scalable databases with high-performance and wide functionality. When designing a database for any NoSQL system, the developer needs a clear understanding of the logic of such databases and the capabilities of the tools offered by the corresponding DBMS. However, unfortunately, they do not have unified methods of logical design of such models, as in relational databases. Thus, there is a problem of developing effective methods for the logical design of such databases that would provide the necessary performance when implementing the business logic of the corresponding applications. The subject of the research is approaches to the logical design of NoSQL document and graph databases. The goal of the work is to propose unified logical modeling methods for MongoDB and Neo4j NoSQL systems based on an experimental study of their performance. The following tasks are solved in the work: analysis of current approaches to the logical design of document and graph databases\, the development of logical design methods for them; planning and experimental study of the performance of the proposed methods on the example of models developed with their help. The following methods are used: database design methods, database performance evaluation methods, development methods are based on MongoDB 5.0.5, Neo4j 4.4.3 DBMS, Visual Studio 2022 development environment. The following results are obtained: unified logical design methods for MongoDB and Neo4j NoSQL systems are proposed; on their basis, the corresponding logical models have been developed; experimental measurements of the number of resources required working with the developed models; recommendations on the proposed methods are formed. Conclusions: The proposed modeling methods for MongoDB have their own aspects of their effective use for different types of applications. The strengths and weaknesses of both methods were identified, but a mixed method based on a combination of modeling through normalization and denormalization was recommended. Even though Neo4j lost out to MongoDB in terms of consumed resources in most experiments, both DBMS's' demonstrate good productivity, taking into account the orientation to different tasks.

Diabetic macular edema (DME) is a significant complication of diabetes that impacts the eye and is a primary contributor to vision loss in individuals with diabetes. Early control of the related risk factors is crucial to reduce the incidence of DME. Artificial intelligence (AI) clinical decision-making tools can construct disease prediction models to aid in the clinical screening of the high-risk population for early disease intervention. However, conventional machine learning and data mining techniques have limitations in predicting diseases when dealing with missing feature values. To solve this problem, a knowledge graph displays the connection relationships of multi-source and multi-domain data in the form of a semantic network to enable cross-domain modeling and queries. This approach can facilitate the personalized prediction of diseases using any number of known feature data. In this study, we proposed an improved correlation enhancement algorithm based on knowledge graph reasoning to comprehensively evaluate the factors that influence DME to achieve disease prediction. We constructed a knowledge graph based on Neo4j by preprocessing the collected clinical data and analyzing the statistical rules. Based on reasoning using the statistical rules of the knowledge graph, we used the correlation enhancement coefficient and generalized closeness degree method to enhance the model. Meanwhile, we analyzed and verified these models’ results using link prediction evaluation indicators. The disease prediction model proposed in this study achieved a precision rate of 86.21%, which is more accurate and efficient in predicting DME. Furthermore, the clinical decision support system developed using this model can facilitate personalized disease risk prediction, making it convenient for the clinical screening of a high-risk population and early disease intervention.

Considering that the existing domain knowledge graphs have difficulty in updating data in a timely manner and cannot make use of knowledge sufficiently in the construction process, this paper proposes a legal domain knowledge graph construction approach based on 'China Judgments Online' in order to manage the cases' knowledge contained in it. The construction process is divided into two steps. First, we extract the classification relationships of the cases from structured data. Then, we obtain attribute knowledge of cases from semi-structured data and unstructured data through a relationship extraction model based on an improved cross-entropy loss function. The triples describing knowledge of cases are stored through Neo4j. The accuracy of the proposed approach is verified through experiments and we construct a legal domain knowledge graph which contains more than 4K classification relationships and 12K attribute knowledge to prove its validity.

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 variados los problemas que surgen a la hora de procesar esta información. Uno de estos problemas a solucionar son los de conexión o ruta a través de herramientas graficas como Neo4j, donde, utilizando el método comparativo en el presente trabajo, se realizara una investigación con el objetivo de demostrar las diferencias entre velocidad de ejecución de consultas y eficiencia entre las base de datos orientada a grafo (Neo4j) y las base de datos relacionales (PostgreSQL).

Kako smo unatrag nekoliko godina dobili određena sredstva (tzv. sveučilišne potpore) kojima smo istraživali određene aspekte grafovskih baza podataka, odlučili smo rezultate istraživanja predstaviti i hrvatskoj akademskoj i stručnoj zajednici. Smatramo kako su istraživanja bila kvalitetna i da rezultati istih imaju potencijal za širu primjenu i implementaciju u dostupnim sustavima za upravljanje grafovskim bazama podataka. Također, postoje i određene mogućnosti za proširenje implementiranih rješenja, kao i prostor za dodatna istraživanja i preslikavanja relacijskih u koncepte grafovskih baza podataka.

With the development of a new generation of information technology, such as big data and cognitive intelligence, we are in the postmodern era of artificial intelligence. Currently, the manufacturing industry is in the critical period of transitioning to smart manufacturing, but the cognitive capabilities of devices in smart factories are still scarce. Knowledge Graph (KG) is one of the key technologies of cognitive intelligence, which opens a new path for the horizontal integration of intelligent manufacturing. Therefore, this paper proposes and builds a manufacturing equipment information query system based on KG. Firstly, a large amount of heterogeneous data that contains vast devices information is obtained from the network. Secondly, the conditional random fields (CRF) algorithm is used to extract the entity name, product place, and company name of the device, and then the relationship between the device entities is identified by calculating the similarity and Chinese syntax analysis. In the validation section, we use to the map of Neo4j graph database, when we input a name of a device in the search box, the system can return a relational graph node. In addition, the shortest path optimization algorithm is used to calculate the similarity between nodes in the search process to achieve the recommendation of similar node information.

With the rapid development of knowledge graph related technologies, domain knowledge graph has become a research hotspot in academia and industry. However, the domain knowledge graph for technical documents is not mature enough, and the semantic information implicit in unstructured technical documents has not been fully tapped. Combining the characteristics of technical documents, the paper proposes a TextCNN-based topic information extraction model and constructs a domain knowledge graph for technical documents. It uses the graph database Neo4j for knowledge storage and visualization. The information extraction model based on TextCNN can automatically extract the subject information of the document and the summary information such as title, ID, status, meeting, organization, etc. Experiments show that the model has high accuracy on the technical document dataset, which can effectively reduce the cost of manual annotation and data collation. At the same time, knowledge graph visualization can facilitate scientific researchers to search, track and update technical documents, which can show the evolution of technology more clearly.

The study concerns the possibility of using the Neo4j database as a graph analysis tool. The analysis was presented on the example of the railway connection network assessment when designing a new version of the railway infrastructure in the Silesian Voivodeship. The authors present the concepts of a laboratory environment built on the basis of the Neo4j database, and an application that allows obtaining data and modifying the railway infrastructure model. Thanks to this, it is possible to simulate many variants of the designed model and its evaluation using the proposed measurement indicators. The study presents methods and tools of analysis that can be successfully used to assess the topology of a graph in many different research areas, such as analysis of management systems, computer networks or biological systems.

Acts of fraud have become much more prevalent in the financial industry with the rise of technology and the continued economic growth in modern society. Fraudsters are evolving their approaches continuously to exploit the vulnerabilities of the current prevention measures in place, many of whom are targeting the financial sector. To overcome and investigate financial frauds, this paper presents STALITA, which is an innovative platform for the analysis of bank transactions. STALITA enables graph-based data analysis using a powerful Neo4j graph database and the Cypher query language. Additionally, a diversity of other supporting tools, such as support for heterogeneous data sources, force-based graph visualisation, pivot tables, and time charts, enable in-depth investigation of the available data. In the Results section, we present the usability of the platform through real-world case scenarios.

Salah satu jenis commonsense knowledge adalah basis pengetahuan, yaitu kumpulan fakta dan informasi umum yang diketahui manusia. Basis pengetahuan yang tersedia sangat banyak, kami memilih Yago karena Yago menggabungkan beberapa sumber data menjadi sebuah ontologi. Fakta fakta ini saling terhubung sehingga representasi data graf adalah representasi data yang paling tepat untuk ini. Kita perlu melakukan ekstraksi basis pengetahuan kedalam basisdata graf supaya bisa digunakan untuk berbagai macam aplikasi lainnya. Tools basisdata graf Neo4j dipilih karena bisa melakukan penyimpanan dan pemrosesan graf dan bersifat opensource. Algoritma GPE digunakan untuk melakukan ekstraksi basis pengetahuan kedalam basisdata graf. Penelitian yang kami lakukan menghasilkan sebuah basisdata graf dengan jumlah node : 6.519.734 dan edge yang dihasilkan 18.724.395. Pengujian menggunakan query dengan berbagai permasalahan graf berhasil dilakukan dan memberikan hasil yang diharapkan

The investigation of cultural heritage is a continuous process, in which a large scale of metadata is gradually accumulated, i.e., condition data, intervention data, and the comprehension of building techniques. We take the roof tile of Yangxindian Palace as an example. The total number of roof tiles is 17,154. Their positions are various, as they are equally distributed on double-curved irregular surfaces. Their metadata are multisource and heterogeneous. Therefore, the integration between the model and the data can be complex. This paper aims to generate the M1 (metalevel) model with its graph database based on the M3 (metalevel) model through algorithm modeling. This synergy approach consists of three parts. First is the data collection. The unique identifier is used as the index while investigating onsite, which eventually forms the metadata repository. Second is the algorithm modeling. All the roof tiles are rebuilt in Grasshopper with identifiers. Their metadata are merged through Cypher syntax, which can generate the graph database in Neo4j. Third is the delivery. The IFC model and knowledge graph are connected in real time during the whole process. The significance of this paper is to provide a synergy approach and discuss its efficiency for the integration between a model and data of very large quantities. The results show that it is efficient to express the semantic relationships of both conditions and interventions. It is practicable to lightweight the IFC model and to associate metadata into the graph database, enabling online browsing and querying.

In developing NoSQL databases, a major motivation is to achieve better efficient query performance compared with relational databases. The graph database is a NoSQL paradigm where navigation is based on links instead of joining tables. Links can be implemented as pointers, and following a pointer is a constant time operation, whereas joining tables is more complicated and slower, even in the presence of foreign keys. Therefore, link-based navigation has been seen as a more efficient query approach than using join operations on tables. Existing studies strongly support this assumption. However, query complexity has received less attention. For example, in enterprise information systems, queries are usually complex so data need to be collected from several tables or by traversing paths of graph nodes of different types. In the present study, we compared the query performance of a graph-based database system (Neo4j) and relational database systems (MySQL and MariaDB). The effect of different efficiency issues (e.g., indexing and optimization) were included in the comparison in order to investigate the most efficient solutions for different query types. The outcome is that although Neo4j is more efficient for simple queries, MariaDB is essentially more efficient when the complexity of queries increases. The study also highlighted how dramatically the efficiency of relational database has grown during the last decade.

Abstract Keeping an architecture description of a complex system consistent is difficult. View content is specified by viewpoints which draw upon an underlying metamodel. Viewpoints need to define consistency rules. This becomes increasingly hard as the metamodel size and the number of viewpoints increase. Use of an architecture description language (ADL) such as the Unified Modeling Language (UML) complicates this further because its metamodel may not be able to implement the triples in the AD metamodel and therefore one or more views requiring use of an additional ADL. Even for an architecture framework with a small footprint, such as TRAK, it is a time‐consuming and error‐prone process to keep the metamodel, viewpoint definitions, and implementation consistent. To tackle these TRAK has been modeled by creating views against five viewpoints. These viewpoints can be used to define a metamodel, define the viewpoints for an architecture framework, assess the implementation using one or more ADLs, and record model changes over time. The viewpoint definitions and views are wholly graph‐based and provide a mechanism to define allowed and minimum view content. Views and viewpoints can be read as simple assertions requiring no technical knowledge and support query and analysis. The viewpoint definitions have been validated against a model of TRAK held in a Neo4J graph database. This showed that the TRAK metamodel contains c 750 triples not 500 as had been previously thought. It also identified metamodel elements missing from one of the UML profiles. Example views are provided together with CYPHER queries.

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 joining tables. For such kinds of problems, one of the best solutions is to use graph database for storing data. The graph database can be used to store highly connected data. In this article, we are going to put forward a comparison between relational database and graph database with reference to an experiment performed.

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

.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. The effectiveness test consists of measurement the response time of queries that save and load data. This study also take into account the memory increase ratio during data insertion and the level of complexity of the implementation of the test data mappers used in database queries.

У зв'язку зі швидким збільшенням обсягу слабоструктурованих і неструктурованих даних, питання про їх оптимальне збереження є досить актуальним. Важливо зберігати їх в зручному форматі для подальшого оброблення. Проте обробити неструктуровані або слабоструктуровані дані складно через невизначеність у схемі даних. Щоб усунути цю проблему, здійснено аналіз нереляційних баз даних та їх застосування на реальному прикладі для збереження системи і оброблення медичних даних.

The increasing availability of very high-resolution remote sensing imagery (i.e., from satellites, airborne laser scanning, or aerial photography) represents both a blessing and a curse for researchers. The manual classification of these images, or other similar geo-sensor data, is time-consuming and leads to subjective and non-deterministic results. Due to this fact, (semi-) automated classification approaches are in high demand in affected research areas. Ontologies provide a proper way of automated classification for various kinds of sensor data, including remotely sensed data. However, the processing of data entities—so-called individuals—is one of the most cost-intensive computational operations within ontology reasoning. Therefore, an approach based on graph databases is proposed to overcome the issue of a high time consumption regarding the classification task. The introduced approach shifts the classification task from the classical Protégé environment and its common reasoners to the proposed graph-based approaches. For the validation, the authors tested the approach on a simulation scenario based on a real-world example. The results demonstrate a quite promising improvement of classification speed—up to 80,000 times faster than the Protégé-based approach.

This paper gives a brief overview about electricity consumption optimization based on consumption profiles of electricity prosumers that are connected to smart grids. The main object of this approach is identification of informatics solutions for electricity consumption optimization in order to decrease electricity bill. In this way, larger scale integration of renewable energy sources is allowed therefore entire society will gain benefits. This paper describes the main objectives of such informatics system and stages for its implementation. The system will analyze the specific profile and behavior of each electricity consumer or prosumer, automatically assist him to make right decisions and offer optimal advice for usage of controllable and non-controllable appliances. It will serve, based on big data transfer from electricity consumers or prosumers, as a powerful tool for grid operators that will be able to better plan their resources.

The aim of this article is to show the importance of Big Data and its growing influence on companies. We can also see how much are the companies willing to invest in big data and how much are they currently gaining from their big data. In this big data era, there is a fiercely competition between the companies and the technologies they use when building their strategies. There are almost no boundaries when it comes to the possibilities and facilities some databases can offer. However, the most challenging part lays in the development of efficient solutions - where and when to take the right decision, which cloud service is the most accurate being given a certain scenario, what database is suitable for the business taking in consideration the data types. These are just a few aspects which will be dealt with in the following chapters as well as exemplifications of the most accurate cloud services (e.g. NoSQL databases) used by business leaders nowadays.

We introduce a new method for storing, modeling, and analyzing power grid data. First, we present an architecture for building the network model for a power grid using the open source graph database Neo4j. Second, we design single- and multi-threading systems for initial energization analysis of the power grid network. We design the shortest path search function and conditional search function based on Neo4j. Finally, we compare the functionality and efficiency of our graph database with a traditional relational database in system initial energization analysis and the shortest path function problems on small to large data sets. The results demonstrate the efficiency and effectiveness of topology modeling and analysis using graph database for a power grid network.

In the field of geoinformation and transportation science, the shortest path is calculated on graph data mostly found in road and transportation networks. This data is often stored in various database systems. Many applications dealing with transportation network require calculation of the shortest path. The objective of this research is to compare the performance of Dijkstra shortest path calculation in PostgreSQL (with pgRouting) and Neo4j graph database for the purpose of determining if there is any difference regarding the speed of the calculation. Benchmarking was done on commodity hardware using OpenStreetMap road network. The first assumption is that Neo4j graph database would be well suited for the shortest path calculation on transportation networks but this does not come without some cost. Memory proved to be an issue in Neo4j setup when dealing with larger transportation networks.

The popularization of big data makes the enterprise need to store more and more data. The data in the enterprise’s database must be accessed as fast as possible, but the Relational Database (RDB) has the speed limitation due to the join operation. Many enterprises have changed to use a NoSQL database, which can meet the requirement of fast data access. However, there are more than hundreds of NoSQL databases. It is important to select a suitable NoSQL database for a certain enterprise because this decision will affect the performance of the enterprise operations. In this paper, fifteen categories of NoSQL databases will be introduced to find out the characteristics of every category. Some principles and examples are proposed to choose an appropriate NoSQL database for different industries.

Internet has become so widespread that most popular websites are accessed by hundreds of millions of people on a daily basis. Monolithic architectures, which were frequently used in the past, were mostly composed of traditional relational database management systems, but quickly have become incapable of sustaining high data traffic very common these days. Meanwhile, NoSQL databases have emerged to provide some missing properties in relational databases like the schema-less design, horizontal scaling, and eventual consistency. This paper analyzes and compares the consistency model implementation on five popular NoSQL databases: Redis, Cassandra, MongoDB, Neo4j, and OrientDB. All of which offer at least eventual consistency, and some have the option of supporting strong consistency. However, imposing strong consistency will result in less availability when subject to network partition events.

Neo4 is a NoSQL graph database that has been emerging in the fields of social networks and web applications with high concurrency. The characteristics of supporting technological transactions and a high scalability have been attracting the attention of the academic world. Therefore, this literature review focuses in analyse four research papers developed in USA, Spain, India and Germany. The authors show the results of benchmarking between Neo4j and other models, including relational databases. The aim of this paper is to illustrate the features of Neo4j, the architecture and advantages. Also, the purpose is to identify if Neo4 is a reliable alternative for replacing the RDBMS (Relational Database Management System) and offer suggestions for carrying out better experiments.

Most of the previous works on relation extraction between named entities are often limited to extracting the pre-defined types; which are inefficient for massive unlabeled text data. Recently; with the appearance of various distributional word representations; unsupervised methods for many natural language processing (NLP) tasks have been widely researched. In this paper; we focus on a new finding of unsupervised relation extraction; which is called distributional relation representation. Without requiring the pre-defined types; distributional relation representation aims to automatically learn entity vectors and further estimate semantic similarity between these entities. We choose global vectors (GloVe) as our original model to train entity vectors because of its excellent balance between local context and global statistics in the whole corpus. In order to train model more efficiently; we improve the traditional GloVe model by using cosine similarity between entity vectors to approximate the entity occurrences instead of dot product. Because cosine similarity can convert vector to unit vector; it is intuitively more reasonable and more easily converge to a local optimum. We call the improved model RGloVe. Experimental results on a massive corpus of Sina News show that our proposed model outperforms the traditional global vectors. Finally; a graph database of Neo4j is introduced to store these relationships between named entities. The most competitive advantage of Neo4j is that it provides a highly accessible way to query the direct and indirect relationships between entities.