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Large-scale ontology management is one of the main issues when using ontology data practically. Although many approaches have been proposed in relational database management systems (RDBMSs) or object-oriented DBMSs (OODBMSs) to develop large-scale ontology management systems, they have several limitations because ontology data structures are intrinsically different from traditional data structures in RDBMSs or OODBMSs. In addition, users have difficulty using ontology data because many terminologies (ontology nodes) in large-scale ontology data match with a given string keyword. Therefore, in this study, we propose a (graph database-based ontology management system (GOMS) to efficiently manage large-scale ontology data. GOMS uses a graph DBMS and provides new query templates to help users find key concepts or instances. Furthermore, to run queries with multiple joins and path conditions efficiently, we propose GOMS encoding as a filtering tool and develop hash-based join processing algorithms in the graph DBMS. Finally, we experimentally show that GOMS can process various types of queries efficiently.

Sensors and smart equipment are frequently used in biomechanical systems and applications in sports and rehabilitation to measure various physical quantities. Various sensors, measuring different parameters, can produce a large amount of data at high speeds and volumes that must be stored for real-time or post-processing and analysis. In addition to sensor data, metadata is an important component and can vary between biomechanical applications. Currently however, each application typically has its own unique data flow and storage solution. In this research, we present a universal data model solution that can be applied to any sensor-based biomechanical application in sport and physical rehabilitation. Our proposed cloud platform architecture allows for the manipulation of sensor data and metadata using a combination of Big Data and conventional techniques. The main idea of this research is to develop a platform that allows a universal way for any biomechanical application to handle its data regardless of the type of data and metadata. This is achieved by creating a universal data model, and implementing this data model in a generalized architecture using a graph database. We demonstrate the benefits of this approach using examples from existing biomechanical systems and describe the development of the cloud platform architecture and the underlying data model. We also provide an example of the use of this platform in a sport shooting application. This approach is unique in that it allows data from different sources and applications to be stored and processed using the same procedures and techniques, facilitating data analysis and application development. We envision this system will expand to multiple different biomechanical applications in the future. We expect that in time, the ability to compare various data and store different biomechanical datasets will become necessity. With the advantages of modern recommender systems and utilization of artificial intelligence, huge amounts of relevant and well-prepared data with useful metadata are required thus having such system is an important advantage for future biomechanical systems development. With the increase of people's awareness and usage of devices that increase well-being and quality of life, presented platform and similar systems will play a pivotal role in shaping the future lifestyle. [ABSTRACT FROM AUTHOR]

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Purpose: In this work, we present a subsystem of a robotic circulating nurse, that produces recommendations for the next supplied sterile item based on incomplete requests from the sterile OR staff, the current situation, predefined knowledge and experience from previous surgeries. We describe a structure to store and query the underlying information in terms of entities and their relationships of varying strength. Methods: For the implementation, the graph database Neo4j is used as a core component together with its querying language Cypher. We outline a specific structure of nodes and relationships, i.e., a graph. Primarily, it allows to represent entities like surgeons, surgery types and items, as well as their complex interconnectivity. In addition, it enables to match given situations and partial requests in the OR with corresponding subgraphs. The subgraphs provide suitable sterile items and allow to prioritize them according to their utilization frequency. Results: The graph database was populated with existing data from 854 surgeries describing the intraoperative use of sterile items. A test scenario is evaluated in which a request for "Prolene" is made during a cholecystectomy. The software identifies a specific "Prolene" suture material as the most probable requested sterile item, because of its utilization frequency from over 95%. Other "Prolene" suture materials were used in less than 15% of the cholecystectomies. Conclusion: We have proposed a graph database for the selection of sterile items in the operating room. The example shows how the partial information from different sources can be easily integrated in a query, leading to an unique result. Eventually, we propose possible enhancements to further improve the quality of the recommendations. In the next step, the recommendations of the software will be evaluated in real time during surgeries. [ABSTRACT FROM AUTHOR]

Graph databases are nowadays considered the most appropriate solution for highly connected domains. Nevertheless, the lack of a fixed schema perplexes the implementation of business rules and inhibits the usage of graph database technology in practical use cases. To tackle this challenge, we study cardinality constraints in graph databases, as their focus is on the essential component of the property graph data model - relationships between entities. This paper presents an abstract cardinality constraints model for enforcing cardinality constraints in graph databases, which represent complex graph patterns. We extend our initial k -vertex cardinality constraints model to allow the representation of cardinality constraints between a node and a subgraph with given node and/or edge properties. Second, we implement the proposed model as procedures deployed in the Neo4j Graph Database Management System (GDBMS) to prevent adding new edges that violate k -vertex cardinality constraints to the graph database. Finally, we study the performance of the implemented approach on synthetic and real datasets and analyze its performance compared to the initial model. Overall, the query execution time (QET) of the procedure increases exponentially on larger datasets. Still, the added node/edge property-level evaluation does not show a significant performance effect on the edge insertion process. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The query optimizer uses cost-based optimization to create an execution plan with the least cost, which also consumes the least amount of resources. The challenge of query optimization for relational database systems is a combinatorial optimization problem, which renders exhaustive search impossible as query sizes rise. Increases in CPU performance have surpassed main memory, and disk access speeds in recent decades, allowing data compression to be used--strategies for improving database performance systems. For performance enhancement, compression and query optimization are the two most factors. Compression reduces the volume of data, whereas query optimization minimizes execution time. Compressing the database reduces memory requirement, data takes less time to load into memory, fewer buffer missing occur, and the size of intermediate results is more diminutive. This paper performed query optimization on the graph database in a cloud dew environment by considering, which requires less time to execute a query. The factors compression and query optimization improve the performance of the databases. This research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers. [ABSTRACT FROM AUTHOR]

The complexity and age of industrial plants have prompted a rapid increase in equipment maintenance and replacement activities in recent years. Consequently, plant owners are challenged to reduce the process and review time of equipment purchase order (PO) documents. Currently, traditional keyword-based document search technology generates unintentional errors and omissions, which results in inaccurate search results when processing PO documents of equipment suppliers. In this study, a purchase order knowledge retrieval model (POKREM) was designed to apply knowledge graph (KG) technology to PO documents of steel plant equipment. Four data domains were defined and developed in the POKREM: (1) factory hierarchy, (2) document hierarchy, (3) equipment classification hierarchy, and (4) PO data. The information for each domain was created in a graph database through three subprocesses: (a) defined in a hierarchical structure, (b) classified into nodes and relationships, and (c) written in triples. Ten comma-separated value (CSV) files were created and imported into the graph database for data preprocessing to create multiple nodes. Finally, rule-based reasoning technology was applied to enhance the model's contextual search performance. The POKREM was developed and implemented by converting the Neo4j open-source graph DB into a cloud platform on the web. The accuracy, precision, recall, and F1 score of the POKREM were 99.7%, 91.7%, 100%, and 95.7%, respectively. A validation study showed that the POKREM could retrieve accurate answers to fact-related queries in most cases; some incorrect answers were retrieved for reasoning-related queries. An expert survey of PO practitioners indicated that the PO document review time with the POKREM was reduced by approximately 40% compared with that of the previous manual process. The proposed model can contribute to the work efficiency of engineers by improving document search time and accuracy; moreover, it may be expandable to other plant engineering documents, such as contracts and drawings. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

While the continuing decline in genotyping and sequencing costs has largely benefited plant research, some key species for meeting the challenges of agriculture remain mostly understudied. As a result, heterogeneous datasets for different traits are available for a significant number of these species. As gene structures and functions are to some extent conserved through evolution, comparative genomics can be used to transfer available knowledge from one species to another. However, such a translational research approach is complex due to the multiplicity of data sources and the non-harmonized description of the data. Here, we provide two pipelines, referred to as structural and functional pipelines, to create a framework for a NoSQL graph-database (Neo4j) to integrate and query heterogeneous data from multiple species. We call this framework Orthology-driven knowledge base framework for translational research (Ortho_KB). The structural pipeline builds bridges across species based on orthology. The functional pipeline integrates biological information, including QTL, and RNA-sequencing datasets, and uses the backbone from the structural pipeline to connect orthologs in the database. Queries can be written using the Neo4j Cypher language and can, for instance, lead to identify genes controlling a common trait across species. To explore the possibilities offered by such a framework, we populated Ortho_KB to obtain OrthoLegKB, an instance dedicated to legumes. The proposed model was evaluated by studying the conservation of a flowering-promoting gene. Through a series of queries, we have demonstrated that our knowledge graph base provides an intuitive and powerful platform to support research and development programmes.

This paper presents an overview of the history, conceptualization, and development of the Database of Byzantine Book Epigrams, an ongoing research project hosted at Ghent University. It also offers a glimpse into current and future research threads carried out within the project, with an eye on long-term sustainability. The first part of the paper pinpoints the position of DBBE within the broad field of Digital Humanities and addresses the question of how and why Byzantine metrical paratexts have been collected in an open-access online database. In the second part of the article, we describe the main features of the relational database currently available, both from the perspective of its users and from a technical point of view. The third section of the paper includes the description of four subprojects connected to DBBE, which at present involve the development of a graph database complementary to the relational one, the implementation of natural language pre-processing applied to the DBBE corpus, the linguistic analysis of formulaicity in book epigrams, and the exploration of the broad implications of the study of book epigrams for a better understanding of Byzantine book culture.

Navigating through a real-world map can be represented in a bi-directed graph with a group of nodes representing the intersections and edges representing the roads between them. In cycling, we can plan training as a group of nodes and edges the athlete must cover. Optimizing routes using artificial intelligence is a well-studied phenomenon. Much work has been done on finding the quickest and shortest paths between two points. In cycling, the solution is not necessarily the shortest and quickest path. However, the optimum path is the one where a cyclist covers the suitable distance, ascent, and descent based on his/her training parameters. This paper presents a Neo4j graph-based dataset of cycling routes in Slovenia. It consists of 152,659 nodes representing individual road intersections and 410,922 edges representing the roads between them. The dataset allows the researchers to develop and optimize cycling training generation algorithms, where distance, ascent, descent, and road type are considered.

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.

Ontology building can greatly influence the development cycle of an information system and enhance interoperability among its constituent elements. Throughout the projects we have been developing we have detected, by studying the current literature, a need to develop an agile method to conceive and mapping ontologies, which allows a quick and effective response to R&D projects. Designing a method for building an ontology, which is integrated and aligned with a systematic development approach, represents a crucial challenge in new approaches to system design and exploitation. Extant proposed methods for building an ontology, especially following agile approaches, have achieved interesting results but lack integration and alignment with a wider-view development framework. Thus, we have defined the first version of a semantic model allowing the alignment with the previously defined information model. Following the best practices for ontology building and based on our previous work on software system development, we now propose a method for designing an ontology, the 4SRS Method for Ontological Design based on the V-Model 4SRS, aligning it with a proven development method. We further demonstrate this approach by applying the proposed method in a real case, to develop an ontology for a choen restricted scope within the domain problem. [ABSTRACT FROM AUTHOR]

Over the last few decades, graphs have become increasingly important in many applications and domains for managing Big data. Big data analysis in a graph database is described as an analysis of exponentially increasing massive interconnected data concerning time. However, analyzing big connected data in social networks and synthetic identity detection is challenging. In previous approaches, fraud detection has been done on the complete graph data, which is a time-consuming process and will create bottlenecks while query execution. To overcome the issue, this paper proposes a new fraud detection technique to unveil synthetic identities involved in the Panama Paper leak dataset (unprecedented leak of 11.5 m data from the database of the world's fourth-biggest offshore law arm, Mossack Fonseca) using a Node rank-based fraud detection algorithm by integrating distributed data profiling techniques on a minimized graph by minimizing the least influential nodes. The proposed model is verified on the three nodes cluster to improve data scalability, reduce the query execution time by an average of 30–36% and finally reduce the fraud detection time by 18.2%. [ABSTRACT FROM AUTHOR]

In order to realize the knowledge extraction, knowledge fusion and structured storage f unstructured high-speed trains multi-domain data, the construction method of high-speed trains maintainability design knowledge graph was studied based on multi-domain data fusion. The ontology concept elements of model layer, the processes of the construction of the data layer and the knowledge fusion of high-speed trains maintainability design knowledge graph were analyzed closely, the bogie knowledge graph platform and the knowledge retrieval system were displayed based on Neo4j graph database. The knowledge graph construction technology studied may extract and integrate knowledge from multi-source and multi-domain data in the field of high-speed trains, assist designers in product maintainability design and improve product quality. [ABSTRACT FROM AUTHOR]

Background: Medical databases normally contain large amounts of data in a variety of forms. Although they grant significant insights into diagnosis and treatment, implementing data exploration into current medical databases is challenging since these are often based on a relational schema and cannot be used to easily extract information for cohort analysis and visualization. As a consequence, valuable information regarding cohort distribution or patient similarity may be missed. With the rapid advancement of biomedical technologies, new forms of data from methods such as Next Generation Sequencing (NGS) or chromosome microarray (array CGH) are constantly being generated; hence it can be expected that the amount and complexity of medical data will rise and bring relational database systems to a limit. Description: We present Graph4Med, a web application that relies on a graph database obtained by transforming a relational database. Graph4Med provides a straightforward visualization and analysis of a selected patient cohort. Our use case is a database of pediatric Acute Lymphoblastic Leukemia (ALL). Along routine patients' health records it also contains results of latest technologies such as NGS data. We developed a suitable graph data schema to convert the relational data into a graph data structure and store it in Neo4j. We used NeoDash to build a dashboard for querying and displaying patients' cohort analysis. This way our tool (1) quickly displays the overview of patients' cohort information such as distributions of gender, age, mutations (fusions), diagnosis; (2) provides mutation (fusion) based similarity search and display in a maneuverable graph; (3) generates an interactive graph of any selected patient and facilitates the identification of interesting patterns among patients. Conclusion: We demonstrate the feasibility and advantages of a graph database for storing and querying medical databases. Our dashboard allows a fast and interactive analysis and visualization of complex medical data. It is especially useful for patients similarity search based on mutations (fusions), of which vast amounts of data have been generated by NGS in recent years. It can discover relationships and patterns in patients cohorts that are normally hard to grasp. Expanding Graph4Med to more medical databases will bring novel insights into diagnostic and research. [ABSTRACT FROM AUTHOR]

Abstract The big graph database provides strong modeling capabilities and efficient querying for complex applications. Subgraph isomorphism which finds exact matches of a query graph in the database efficiently, is a challenging problem. Current subgraph isomorphism approaches mostly are based on the pruning strategy proposed by Ullmann. These techniques have two significant drawbacks- first, they are unable to efficiently handle complex queries, and second, their implementations need the large indexes that require large memory resources. In this paper, we describe a new subgraph isomorphism approach, the HyGraph algorithm, that is efficient both in querying and with memory requirements for index creation. We compare the HyGraph algorithm with two popular existing approaches, GraphQL and Cypher using complexity measures and experimentally using three big graph data sets—(1) a country-level population database, (2) a simulated bank database, and (3) a publicly available World Cup big graph database. It is shown that the HyGraph solution performs significantly better (or equally) than competing algorithms for the query operations on these big databases, making it an excellent candidate for subgraph isomorphism queries in real scenarios.

As the applications of robots expand across a wide variety of areas, high-level task planning considering human–robot interactions is emerging as a critical issue. Various elements that facilitate flexible responses to humans in an ever-changing environment, such as scene understanding, natural language processing, and task planning, are thus being researched extensively. In this study, a visual question answering (VQA) task was examined in detail from among an array of technologies. By further developing conventional neuro-symbolic approaches, environmental information is stored and utilized in a symmetric graph format, which enables more flexible and complex high-level task planning. We construct a symmetric graph composed of information such as color, size, and position for the objects constituting the environmental scene. VQA, using graphs, largely consists of a part expressing a scene as a graph, a part converting a question into SPARQL, and a part reasoning the answer. The proposed method was verified using a public dataset, CLEVR, with which it successfully performed VQA. We were able to directly confirm the process of inferring answers using SPARQL queries converted from the original queries and environmental symmetric graph information, which is distinct from existing methods that make it difficult to trace the path to finding answers.

With the arrival of the big data era,the relationship that needs to be processed in various industries has increased exponentially,and there is an urgent need for a data model that supports the ability to express massive complex relationship,that is,domain knowledge graph.Although the domain knowledge graph has shown great potential,it is not difficult to find that there is still a lack of mature construction technologies and platforms.It still remains an important challenge to construct domain know-ledge graph rapidly.After the systematic study of domain knowledge graph,a method is proposed to construct domain knowledge graph based on property graph model.Concretely,for structured and semi-structured data stored in a variety of databases,the method completes the construction of the high-quality graph model by graph database data communication protocol,multiple configuration methods of entity and relation schema,etc.Then,the data from the original database is extracted,transformed and loa-ded into the property graph database HugeGraph,completing the construction of domain knowledge graph.Finally,experiments on multiple datasets and test results of Gremlin statement show that the proposed method is complete and reliable.

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.

Conformance checking detects deviations in business process executions. An online detection method is needed to give immediate response to anticipate possible impacts. The state-of-the-art online conformance checking is the Prefix-Alignment (PA) technique. However, this technique has a limitation of maintaining all of the administration data of cases in memory. In an online environment, the last event of a case is never known, whereas a PA requires last event information to release the case from memory to free up space for other cases. Hence, the PA does not meet the requirements of online conformance checking in processing infinite data of event stream without memory constraints. PA also has a complex state space search computation especially for large and complex process model references. In this paper, a Graph-Based Online Token Replay (GO-TR) method is proposed. This method takes benefit from Graph Database to adapts the Token-Based Replay (TBR) technique which has simple replay computation. We propose a Replay Image (RI) to store the case administration and develop a cypher based algorithm to simulate token replay on the RI to handle the event stream. We also propose a cypher-based algorithm to identify and replay invisible paths. The experiment results show that GO-TR has been successful in adapting TBR and solving the problem of wrong-placed tokens in TBR. GO-TR outperforms PA in yielding replay throughputs of relatively small amount of data in online conformance checking. In terms of memory usage, GO-TR shows its superiority over PA because it does not have memory limitations problems.

Many graph-related applications face the challenge of managing excessive and ever-growing graph data in a distributed environment. Therefore, it is necessary to consider a graph partitioning algorithm to distribute graph data onto multiple machines as the data comes in. Balancing data distribution and minimizing edge-cut ratio are two basic pursuits of the graph partitioning problem. While achieving balanced partitions for streaming graphs is easy, existing graph partitioning algorithms either fail to work on streaming workloads, or leave edge-cut ratio to be further improved. Our research aims to provide a better solution that fits the need of streaming graph partitioning in a distributed system, which further reduces the edge-cut ratio while maintaining rough balance among all partitions. We exploit the similarity measure on the degree of vertices to gather structuralrelated vertices in the same partition as much as possible, this reduces the edge-cut ratio even further as compared to the state-of-the-art streaming graph partitioning algorithm-FENNEL. Our evaluation shows that our streaming graph partitioning algorithm is able to achieve better partitioning quality in terms of edge-cut ratio (up to 20% reduction as compared to FENNEL) while maintaining decent balance between all partitions, and such improvement applies to various real-life graphs.