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The paper is focused on a functional querying in graph databases. We consider labelled property graph model and mention also the graph model behind XML databases. An attention is devoted to functional modelling of graph databases both at a conceptual and data level. The notions of graph conceptual schema and graph database schema are considered. The notion of a typed attribute is used as a basic structure both on the conceptual and database level. As a formal approach to declarative graph database querying a version of typed lambda calculus is used. This approach allows to use a logic necessary for querying, arithmetic as well as aggregation function. Another advantage is the ability to deal with relations and graphs in one integrated environment.

Without geospatial data management, today’s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis.

Tomato leaf pests and diseases pose a significant threat to the yield and quality of Q6 tomatoes, highlighting the necessity for comprehensive studies on effective control methods. Current control measures predominantly rely on experience and manual observation, hindering the integration of multi-source data. To address this, we integrated information resources related to tomato leaf pests and diseases from agricultural standards documents, knowledge websites, and relevant literature. Guided by domain experts, we preprocessed this data to construct a sample set. We utilized the Named Entity Recognition (NER) model ALBERT-BiLSTM-CRF to conduct end-to-end knowledge extraction experiments, which outperformed traditional models such as 1DCNN-CRF and BiLSTM-CRF, achieving a recall rate of 95.03%. The extracted knowledge was then stored in the Neo4j graph database, effectively visualizing the internal structure of the knowledge graph. We developed a digital diagnostic system for tomato leaf pests and diseases based on the knowledge graph, enabling graphical management and visualization of pest and disease knowledge. The constructed knowledge graph offers insights for controlling tomato leaf pests and diseases and provides new research directions for pest control in other crops.

Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for machines, and even for humans. Knowledge graphs have become prevalent in both of industry and academic circles these years, to be one of the most efficient and effective knowledge integration approaches. Techniques for knowledge graph construction can mine information from either structured, semi-structured, or even unstructured data sources, and finally integrate the information into knowledge, represented in a graph. Furthermore, knowledge graph is able to organize information in an easy-to-maintain, easy-to-understand and easy-to-use manner. In this paper, we give a summarization of techniques for constructing knowledge graphs. We review the existing knowledge graph systems developed by both academia and industry. We discuss in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via logical inferring and reasoning. We also review the issues of graph data management by introducing the knowledge data models and graph databases, especially from a NoSQL point of view. Finally, we overview current knowledge graph systems and discuss the future research directions.

Heterogeneous graphs are a data format for graphs that could define complicated and diverse real-world interactions by accommodating distinct sorts of nodes and edge types. Heterogeneous graphs organize varied medical data to help patients, therapies, drugs, and healthcare practitioners make informed decisions. Medical recommendation systems use them to represent and analyze complicated connections between healthcare data items. Heterogeneous graphs can potentially be constructed and analyzed using the Graph Attention Network (GAT). The purpose of this research is to tackle the issue of implementing a complicated and extremely diverse dataset, which consists of: Using the GATNet (Graph Attention Network) method, we will show how to perform two things: (1) Construct a model with several attributes and relationships using EMR (electronic medical record), and (2) Use that model in a disease prognostic prediction challenge. The initial graph database utilizes a graphical depiction of a patient's progression, showcasing a query of a predictive network that produces analytical findings of AUROC–0.75 and AUPRC–0.17 which is 0.03% & 0.02% higher compared to the existing models.

Purpose Data protection requirements heavily increased due to the rising awareness of data security, legal requirements and technological developments. Today, NoSQL databases are increasingly used in security-critical domains. Current survey works on databases and data security only consider authorization and access control in a very general way and do not regard most of today’s sophisticated requirements. Accordingly, the purpose of this paper is to discuss authorization and access control for relational and NoSQL database models in detail with respect to requirements and current state of the art. Design/methodology/approach This paper follows a systematic literature review approach to study authorization and access control for different database models. Starting with a research on survey works on authorization and access control in databases, the study continues with the identification and definition of advanced authorization and access control requirements, which are generally applicable to any database model. This paper then discusses and compares current database models based on these requirements. Findings As no survey works consider requirements for authorization and access control in different database models so far, the authors define their requirements. Furthermore, the authors discuss the current state of the art for the relational, key-value, column-oriented, document-based and graph database models in comparison to the defined requirements. Originality/value This paper focuses on authorization and access control for various database models, not concrete products. This paper identifies today’s sophisticated – yet general – requirements from the literature and compares them with research results and access control features of current products for the relational and NoSQL database models.

Digital Twins as virtual representations of industrial assets are being used to assimilate varied sources of data for improved awareness and decision making in operations and process optimisation. This paper explores the integration of IoT sensors into a spatial digital twin called Fuse that Woodside Energy has been building for the assets it operates. We describe the Fuse platform and its knowledge graph data core that is used to organise and inter-relate data for presentation within 3D visualisations, domain-specific contexts and immersive augmented reality presentations. The key contribution here is the use of a knowledge graph to link diverse data sources so as to contextualise sensor data for actionable insights. One area of significant innovation has been the development and use of new Internet of Things (IoT) devices which have been enabled by advances in sensor technology, connectivity, and cloud computing. These new tailored data sources are complimenting existing plant and resource planning data for improved asset monitoring, predictive maintenance and process automation.

Semantic interoperability establishes intercommunications and enables data sharing across disparate systems. In this study, we propose an ostensive information architecture for healthcare information systems to decrease ambiguity caused by using signs in different contexts for different purposes. The ostensive information architecture adopts a consensus-based approach initiated from the perspective of information systems re-design and can be applied to other domains where information exchange is required between heterogeneous systems. Driven by the issues in FHIR (Fast Health Interoperability Resources) implementation, an ostensive approach that supplements the current lexical approach in semantic exchange is proposed. A Semantic Engine with an FHIR knowledge graph as the core is constructed using Neo4j to provide semantic interpretation and examples. The MIMIC III (Medical Information Mart for Intensive Care) datasets and diabetes datasets have been employed to demonstrate the effectiveness of the proposed information architecture. We further discuss the benefits of the separation of semantic interpretation and data storage from the perspective of information system design, and the semantic reasoning towards patient-centric care underpinned by the Semantic Engine.

Our overall research aim is to statically derive execution cost and other metrics from program code written in the P4 programming language. For this purpose, we extract a detailed control flow graph (CFG) from the code, that can be turned into a full, formal model of execution, to extract properties -- such as execution cost -- from the model. While CFG extraction and analysis is well researched area, details are dependent on code representation and therefore application of textbook algorithms (often defined over unstructured code listings) to real programming languages is often non-trivial. Our aim is to present an algorithm for CFG extraction over P4 abstract syntax trees (AST). During the extraction we create direct links between nodes of the CFG and the P4 AST: this way we can access all information in the P4 AST during CFG traversal. We are utilizing Gremlin, a graph query language to take advantage of graph databases, but also for compactness and to formally prove algorithm correctness.

Over decades, relational database management systems (RDBMSs) have been the first choice to manage data. Recently, due to the variety properties of big data, graph database management systems (GDBMSs) have emerged as an important complement to RDBMSs. As pointed out in the existing literature, both RDBMSs and GDBMSs are capable of managing graph data and relational data; however, the boundaries of them still remain unclear. For this reason, in this paper, we first extend a unified benchmark for RDBMSs and GDBMSs over the same datasets using the same query workload under the same metrics. We then conduct extensive experiments to evaluate them and make the following findings: (1) RDBMSs outperform GDMBSs by a substantial margin under the workloads which mainly consist of group by, sort, and aggregation operations, and their combinations; (2) GDMBSs show their superiority under the workloads that mainly consist of multi-table join, pattern match, path identification, and their combinations.