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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. [ABSTRACT FROM AUTHOR]

Asset management is a process that deals with numerous types of data, including spatial and temporal data. Such an occurrence is attributed to the proliferation of information sources. However, the lack of a comprehensive asset data model that encompasses the management of both spatial and temporal data remains a challenge. Therefore, this paper proposes a graph-based spatio-temporal data model to integrate spatial and temporal information into asset management. In the spatial layer, we provide a graph-based method that uses topological containment and connectivity relationships to model the interior building space using data from 3D city models. In the temporal layer, we proposed the Aggregated Directly-Follows Multigraph (ADFM), a novel process model based on a directly-follows graph (DFG), to show the chronological flow of events in asset management by taking into consideration the repetitive nature of events in asset management. The integration of both layers allows spatial, temporal, and spatio-temporal queries to be made regarding information about events in asset management. This method offers a more straightforward query, which helps to eliminate duplicate and false query results when assessed and compared with a flattened graph event log. Finally, this paper provides information for the management of 3D spaces using a NoSQL graph database and the management of events and their temporal information through graph modelling. [ABSTRACT FROM AUTHOR]

This article presents mechanisms to support the formation and execution of ontological queries for an orgraphic coherent two-level model of information units (knowledge and data bases). The application of the proposed approaches in the processing of knowledge bases should make it possible to make such queries without intermediaries (programmers). The implementation of the proposed solutions on a platform designed for the creation and use of cloud-based artificial intelligence systems is also described. [ABSTRACT FROM AUTHOR]

Graph databases have become essential for managing and analyzing complex data relationships, with Neo4j emerging as a leading player in this domain. Neo4j, a high-performance NoSQL graph database, excels in efficiently handling connected data, offering powerful querying capabilities through its Cypher query language. However, due to Cypher's complexities, making it more accessible for nonexpert users requires translating natural language queries into Cypher. Thus, in this paper, we propose a text-to-Cypher model to effectively translate natural language queries into Cypher. In our proposed model, we combine several methods to enable nonexpert users to interact with graph databases using the English language. Our approach includes three modules: key-value extraction, relation–properties prediction, and Cypher query generation. For key-value extraction and relation–properties prediction, we leverage BERT and GraphSAGE to extract features from natural language. Finally, we use a Transformer model to generate the Cypher query from these features. Additionally, due to the lack of text-to-Cypher datasets, we introduced a new dataset that contains English questions querying information within a graph database, paired with corresponding Cypher query ground truths. This dataset aids future model learning, validation, and comparison on text-to-Cypher task. Through experiments and evaluations, we demonstrate that our model achieves high accuracy and efficiency when comparing with some well-known seq2seq model such as T5 and GPT2, with an 87.1% exact match score on the dataset. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This research leverages Memgraph, an open-source graph database, to analyze graph-based network data and apply Graph Neural Networks (GNNs) for a detailed classification of cyberattack tactics categorized by the MITRE ATT&CK framework. As part of graph characterization, the page rank, degree centrality, betweenness centrality, and Katz centrality are presented. Node classification is utilized to categorize network entities based on their role in the traffic. Graph-theoretic features such as in-degree, out-degree, PageRank, and Katz centrality were used in node classification to ensure that the model captures the structure of the graph. The study utilizes the UWF-ZeekDataFall22 dataset, a newly created dataset which consists of labeled network logs from the University of West Florida's Cyber Range. The uniqueness of this study is that it uses the power of combining graph-based characterization or analysis with machine learning to enhance the understanding and visualization of cyber threats, thereby improving the network security measures. [ABSTRACT FROM AUTHOR]

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) concept focuses on providing air‐taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT‐4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user's question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human‐readable answers for the user. Our team implemented and tested both solutions to analyze requirements within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches. [ABSTRACT FROM AUTHOR]

This work presents a threat modelling approach to represent changes to the attack paths through an Internet of Things (IoT) environment when the environment changes dynamically, that is, when new devices are added or removed from the system or when whole sub-systems join or leave. The proposed approach investigates the propagation of threats using attack graphs, a popular attack modelling method. However, traditional attack-graph approaches have been applied in static environments that do not continuously change, such as enterprise networks, leading to static and usually very large attack graphs. In contrast, IoT environments are often characterised by dynamic change and interconnections; different topologies for different systems may interconnect with each other dynamically and outside the operator's control. Such new interconnections lead to changes in the reachability amongst devices according to which their corresponding attack graphs change. This requires dynamic topology and attack graphs for threat and risk analysis. This article introduces an example scenario based on healthcare systems to motivate the work and illustrate the proposed approach. The proposed approach is implemented using a graph database management tool (GDBM), Neo4j, which is a popular tool for mapping, visualising, and querying the graphs of highly connected data. It is efficient in providing a rapid threat modelling mechanism, making it suitable for capturing security changes in the dynamic IoT environment. Our results show that our developed threat modelling approach copes with dynamic system changes that may occur in IoT environments and enables identifying attack paths, whilst allowing for system dynamics. The developed dynamic topology and attack graphs can cope with the changes in the IoT environment efficiently and rapidly by maintaining their associated graphs. [ABSTRACT FROM AUTHOR]

Influence analysis, derived from Social Network Analysis (SNA), is extremely useful in academic literature analytic. Different Academic Social Network Sites (ASNS) have been widely examined for influence analysis in terms of co-authorship and co-citation networks. The impact of other network-based features, such as followers and followings, provided by ASNS such as ResearchGate (RG) and Academia is yet to be anatomised. As proven in ingrained social theories, the followers and followings have significant impact in influence prorogation. This research aims at examining the same in one of the widely adopted ASNS, RG. The rendering process is developed to render real-time RG information, which is modelled into graph. Standard centrality measures are implemented to identify influential users from the constructed RG graph. Each centrality measure gives a list of top- k influential RG users. The results are compared with RGScore and Total Research Interest (TRI) to discover the most effective centrality measure. Betweenness and closeness centrality measures have shown the outperforming results compared with others. A procedure is established to discover influential RG users that are commonly present in all top- k centrality results to identify dominant skills, affiliations, departments and locations from the rendered data. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) can store a large amount of civil engineering information and has become a high-value economic and social resource. To improve BIM data interoperability, Industry Foundation Class (IFC) was proposed. However, the IFC instance data usually exceeds megabytes, and the logical relationship of the data is complex and changeable. Therefore, how to further enhance the usability of IFC instance data will become very important. To this end, this paper proposes a data deconstruction model 'Topological Induction Model (TIM)', which deconstructs the IFC instance data into a network structure 'Topological Induction Network (TIN)'. Through the analysis and operation of TIN, more effective utilization of IFC instance data can be realized. Based on TIN, the skeleton structure, citation structure and corresponding acquisition algorithm of IFC instance data are discussed, and the hierarchical characteristic, internal aggregation characteristic and data similarity of data topology are found. Subsequently, the working performance of TIM (TIN) is evaluated and compared with existing data deconstruction strategies. The results show that the overall performance of TIM (TIN) is better than traditional methods. Finally, we show how TIM (TIN) can be used in a graph database to enhance the usability of IFC instance data. [ABSTRACT FROM AUTHOR]

With the continuous development of the power grid, its structure is becoming increasingly complex. The occurrence of faults in transmission lines may lead to cascading failures in the power grid, ultimately resulting in widespread power outages. The transmission of equipment information and the sending of fault reports in the power grid rely on the power communication network. This network is crucial for ensuring the safe, stable, and economical operation of the power grid. As the number of devices in the power grid increases and sensor technology becomes more widespread, the volume of data generated by both the power grid and the power communication network has increased sharply. However, relational databases have limited scalability and struggle to meet the growing volume of data and user demands. This paper proposes a graph mapping method based on the power grid and communication network, utilizing data from both networks to construct a unified data plane in a graph database. Taking power transfer operations as an example, a unified standard data model and monitoring indicator system are established for both networks, enabling faster response and power restoration to blackout areas in the event of power grid faults. Simulation results demonstrate that compared to traditional relational databases, graph databases exhibit significantly improved efficiency in handling large-scale, highly connected data, making them more suitable for future power grids. [ABSTRACT FROM AUTHOR]

Firstly, the basic principles of graph database and graph algorithms are introduced, including the data model of graph database, query language, and common graph algorithms.Then, the method of constructing the graph model of the power system is elaborated, where system components are represented as nodes and component relationships are represented as edges.Finally, the graph algorithm process of carbon flow calculation is designed, using the AtlasGraph graph database and graph computing components to perform carbon flow iterative calculation.This method makes full use of the advantages of graph database and graph algorithms, achieving accurate and efficient calculation of power system carbon flow.This research provides strong support for monitoring, analyzing, and optimizing carbon emissions in power systems, and is of great significance for promoting the green and low-carbon development of power systems.

Graph databases have gained widespread adoption in various industries and have been utilized in a range of applications, including financial risk assessment, commodity recommendation, and data lineage tracking. While the principles and design of these databases have been the subject of some investigation, there remains a lack of comprehensive examination of aspects such as storage layout, query language, and deployment. The present study focuses on the design and implementation of graph storage layout, with a particular emphasis on tree-structured key-value stores. We also examine different design choices in the graph storage layer and present our findings through the development of TuGraph, a highly efficient single-machine graph database that significantly outperforms well-known Graph DataBase Management System (GDBMS). Additionally, TuGraph demonstrates superior performance in the Linked Data Benchmark Council (LDBC) Social Network Benchmark (SNB) interactive benchmark.

Increasingly technical advancements have created new hurdles for industrial software development, this article proposes the validation of an architecture for the visualization of information through complex networks, accomplished by the alignment of the practices of the Capability Maturity Model Integration (CMMI) with the OpenUp disciplines. Relying on ontology methods, CMMI and OpenUP data were crossed on Neo4j graph database management system, following the proposed architecture. This demonstration case started with tabled datasets that have low complexity at the data level, therefore, as defined, we followed for the graph database (Neo4j), then to the user interface finalizing at information visualization. The main contribution of this study is certainly the validation of the technological architecture, through the cross mapping of CMMI and OpenUP in graph database, allowing future contributions to the matter. For future work, we propose the alignment of the model in a study case applied in the real environment, through the development of an adequate replicable framework. [ABSTRACT FROM AUTHOR]

With a high percentage of distributed new energy sources connected to the power system, the power grid needs to reserve a larger margin to deal with the uncertainty of renewable energy outputs, leading to an increase in the cost of controlling the margins for the safe operation of the power grid. In order to reduce costs and increase efficiency, a quantitative assessment of new energy output uncertainty is needed. In this paper, a quantitative assessment method of new energy output uncertainty based on the prediction error is proposed, which makes use of a graph database to efficiently obtain massive new energy historical data, uses the clustering in quest (CLIQUE) algorithm to cluster the new energy historical data, and calculates the renewable energy real power confidence interval based on a given new energy power prediction, taking account of the impact of prediction errors caused by the new energy uncertainty and realizing the quantitative description of new energy output uncertainty. Finally, the method is calculated and analyzed together with the actual example data to verify the practical effect of the method. [ABSTRACT FROM AUTHOR]

In this paper, we present and discuss a promising research avenue, that is the use of graph-based models and software for prosopographical data sets. Our case study will be constituted by Romans 1by1 (http://romans1by1.com/), a digital-born prosopography focusing on people attested in classical era inscriptions; it presently hosts approximately 18,000 open access persons files. The project aimed at employing new techniques and methodologies that come from other fields (i.e. computer science), in order to approach the study of ancient population in an innovative way, to ease the research, and to create an open-access tool, available for the academic community. In the scope of this paper, we use Romans1by1 as an example to explore the perspectives of ingesting the information from a prosopographical relational database into a graph database. Graph based modelling was employed to reveal data and details on the lives of the 'ordinary' people who lived in the Roman Empire. [ABSTRACT FROM AUTHOR]

This work uses Memgraph, an open-source graph data platform, to analyze, visualize, and apply graph machine learning techniques to detect cybersecurity attack tactics in a newly created Zeek Conn log dataset, UWF-ZeekData22, generated in The University of West Florida's cyber simulation environment. The dataset is transformed to a representative graph, and the graph's properties studied in this paper are PageRank, degree, bridge, weakly connected components, node and edge cardinality, and path length. Node classification is used to predict the connection between IP addresses and ports as a form of attack tactic or non-attack tactic in the MITRE framework, implemented using Memgraph's graph neural networks. Multi-classification is performed using the attack tactics, and three different graph neural network models are compared. Using only three graph features, in-degree, out-degree, and PageRank, Memgraph's GATJK model performs the best, with source node classification accuracy of 98.51% and destination node classification accuracy of 97.85%. [ABSTRACT FROM AUTHOR]

Firstly, the basic principles of graph database and graph algorithms are introduced, including the data model of graph database, query language, and common graph algorithms. Then, the method of constructing the graph model of the power system is elaborated, where system components are represented as nodes and component relationships are represented as edges. Finally, the graph algorithm process of carbon flow calculation is designed, using the AtlasGraph graph database and graph computing components to perform carbon flow iterative calculation. This method makes full use of the advantages of graph database and graph algorithms, achieving accurate and efficient calculation of power system carbon flow. This research provides strong support for monitoring, analyzing, and optimizing carbon emissions in power systems, and is of great significance for promoting the green and low-carbon development of power systems. [ABSTRACT FROM AUTHOR]

Flood risks are increasing in Europe, and the EU Floods Directive is one of the main efforts to implement and harmonize effective flood risk governance and management among member states. At the same time, the ongoing shift from mere flood protection to multi‐functional flood risk governance calls for increased collaboration among a diversity of actors, typically with varying interests and responsibilities. In this study, we analyze the actor networks unfolding from flood risk management plans to visualize the diversity of roles and connections between actors participating in flood risk management measures. We introduce a new framework for analyzing flood risk management plans and related actor networks using Social Network Analysis and test it in the context of Finland. The framework helps to visualize the diversity of networks for different flood risk management strategies. The analysis also reveals the central roles that different types of actors—including those outside the public sector—occupy in the network. The findings suggest that Social Network Analysis can be used in the context of EU flood risk governance and that flood risk management plans offer an excellent opportunity for comparative governance studies in the EU. [ABSTRACT FROM AUTHOR]

Inconsistent disease coding standards in medicine create hurdles in data exchange and analysis. This paper proposes a machine learning system to address this challenge. The system automatically matches unstructured medical text (doctor notes, complaints) to ICD-10 codes. It leverages a unique architecture featuring a training layer for model development and a knowledge base that captures relationships between symptoms and diseases. Experiments using data from a large medical research center demonstrated the system's effectiveness in disease classification prediction. Logistic regression emerged as the optimal model due to its superior processing speed, achieving an accuracy of 81.07% with acceptable error rates during high-load testing. This approach offers a promising solution to improve healthcare informatics by overcoming coding standard incompatibility and automating code prediction from unstructured medical text. [ABSTRACT FROM AUTHOR]

In the Islamic domain, Hadiths hold significant importance, standing as crucial texts following the Holy Quran. Each Hadith contains three main parts: the ISNAD (chain of narrators), TARAF (starting part, often from Prophet Muhammad), and MATN (Hadith content). ISNAD, a chain of narrators involved in transmitting that particular MATN. Hadith scholars determine the trustworthiness of the transmitted MATN by the quality of the ISNAD. The ISNAD's data is available in its original Arabic language, with narrator names transliterated into English.This paper presents the Multi-IsnadSet (MIS), that has great potential to be employed by the social scientist and theologist. A multi-directed graph structure is used to represents the complex interactions among the narrators of Hadith. The MIS dataset represent directed graph which consists of 2092 nodes, representing individual narrators, and 77,797 edges represent the Sanad-Hadith connections. The MIS dataset represents multiple ISNAD of the Hadith based on the Sahih Muslim Hadith book. The dataset was carefully extracted from online multiple Hadith sources using data scraping and web crawling techniques tools, providing extensive Hadith details. Each dataset entry provides a complete view of a specific Hadith, including the original book, Hadith number, textual content (MATN), list of narrators, narrator count, sequence of narrators, and ISNAD count. In this paper, four different tools were designed and constructed for modeling and analyzing narrative network such as python library (NetworkX), powerful graph database Neo4j and two different network analysis tools named Gephi and CytoScape. The Neo4j graph database is used to represent the multi-dimensional graph related data for the ease of extraction and establishing new relationships among nodes. Researchers can use MIS to explore Hadith credibility including classification of Hadiths (Sahih=perfection in the Sanad/Dhaif=imperfection in the Sanad), and narrators (trustworthy/not). Traditionally, scholars have focused on identifying the longest and shortest Sanad between two Narrators, but in MIS, the emphasis shifts to determining the optimum/authentic Sanad, considering narrator qualities. The graph representation of the authentic and manually curated dataset will open ways for the development of computational models that could identify the significance of a chain and a narrator. The dataset allows the researchers to provide Hadith narrators and Hadith ISNAD that could be used in a wide variety of future research studies related to Hadith authentication and rules extraction. Moreover, the dataset encourages cross-disciplinary research, bridging the gap between Islamic studies, artificial intelligence (AI), social network analysis (SNA), and Graph Neural Network (GNN).

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.

Graph databases have become essential for managing and analyzing complex data relationships, with Neo4j emerging as a leading player in this domain. Neo4j, a high-performance NoSQL graph database, excels in efficiently handling connected data, offering powerful querying capabilities through its Cypher query language. However, due to Cypher’s complexities, making it more accessible for nonexpert users requires translating natural language queries into Cypher. Thus, in this paper, we propose a text-to-Cypher model to effectively translate natural language queries into Cypher. In our proposed model, we combine several methods to enable nonexpert users to interact with graph databases using the English language. Our approach includes three modules: key-value extraction, relation–properties prediction, and Cypher query generation. For key-value extraction and relation–properties prediction, we leverage BERT and GraphSAGE to extract features from natural language. Finally, we use a Transformer model to generate the Cypher query from these features. Additionally, due to the lack of text-to-Cypher datasets, we introduced a new dataset that contains English questions querying information within a graph database, paired with corresponding Cypher query ground truths. This dataset aids future model learning, validation, and comparison on text-to-Cypher task. Through experiments and evaluations, we demonstrate that our model achieves high accuracy and efficiency when comparing with some well-known seq2seq model such as T5 and GPT2, with an 87.1% exact match score on the dataset.

Summary: The variety of ingredients used in regional cuisine depends on cultural characteristics and the agricultural products available in a region. Native and in‐season foods are frequently used in local cooking. Products like fruits and vegetables may be obtained from nearby farms. The cultural backgrounds of the local population might also have an impact on the cuisine. A balanced combination of four different tastes (bitter, sweet, salty and sour) increases the appeal of a dish. In this study, a flavour network of Turkish cuisine is built and analysed by examining the relationships between recipes, ingredients and components (including vitamins and minerals like iron and calcium). We created a data set containing recipes and ingredients from five different regional cuisines of Turkiye that span a wide range of geographical and climatic areas. Several analyses have been conducted on Turkish cuisine at both the regional and country levels. This data set also contains component information that can be used to perform various analyses, such as calorie information and allergen identification. The results revealed that Turkish cuisine favours cattle meat from animal products, lemon from fruits, yogurt from dairy products, butter from oils and wheat flour from cereal products. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 ow 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Analyzing information diffusion on temporal networks provides invaluable insights into how information spreads throughout a network over time, such as cryptocurrency money flow and virus propagation. Chronoweb is an open-source web information system that analyzes temporal information diffusion. The proposed system embraces existing analytical approaches with a unified RESTful web service. The paper presents the system architecture and its architectural decisions to resolve the challenges of embracing the state-of-the-art approaches: temporal graph traversals and incremental computation. We share the additional in-depth quantitative experiments to evaluate the decisions.

PHYLODB is a modular and extensible framework for large-scale phylogenetic analyses of sequence based typing data, which are essential for understanding epidemics evolution. It relies on the Neo4j graph database for data storage and processing, providing a schema and an API for representing and querying phylogenetic data. Custom algorithms are also supported, allowing users to perform heavy computations directly over the data, and to store results in the database. Multiple computation results are stored as multilayer networks, promoting and facilitating comparative analyses, as well as avoiding unnecessary ab initio computations. The experimental evaluation results showcase that PHYLODB is efficient and scalable with respect to both API operations and algorithms execution.

Abstract Flood risks are increasing in Europe, and the EU Floods Directive is one of the main efforts to implement and harmonize effective flood risk governance and management among member states. At the same time, the ongoing shift from mere flood protection to multi‐functional flood risk governance calls for increased collaboration among a diversity of actors, typically with varying interests and responsibilities. In this study, we analyze the actor networks unfolding from flood risk management plans to visualize the diversity of roles and connections between actors participating in flood risk management measures. We introduce a new framework for analyzing flood risk management plans and related actor networks using Social Network Analysis and test it in the context of Finland. The framework helps to visualize the diversity of networks for different flood risk management strategies. The analysis also reveals the central roles that different types of actors—including those outside the public sector—occupy in the network. The findings suggest that Social Network Analysis can be used in the context of EU flood risk governance and that flood risk management plans offer an excellent opportunity for comparative governance studies in the EU.

Relational database users are switching to non-relational databases because non-relational databases are better able to handle dynamic data storage. One of the institutions that require dynamic data storage is Statistics Indonesia (BPS). Currently, data storage for census and survey activities at BPS is done using a relational database, although there are metadata changes in each activity. Accommodating metadata changes in each activity requires one database, which creates problems when retrieving some raw data. There is an opportunity for convenience if the data collected is stored in a non-relational database, one of which is a graph database. This research discusses the modeling of metadata and data from censuses and surveys at BPS using a graph database. Then we implement the Neo4j DBMS and compare the proposed model with the relational model in the Microsoft SQL Server DBMS. Then, a comparison of the features and characteristics of each DBMS is done, and finally, performance testing is done with Apache JMeter. Modeling has been able to handle dynamic data structure changes, but Neo4j's performance is still lagging behind Microsoft SQL Server.

Asset management is a process that deals with numerous types of data, including spatial and temporal data. Such an occurrence is attributed to the proliferation of information sources. However, the lack of a comprehensive asset data model that encompasses the management of both spatial and temporal data remains a challenge. Therefore, this paper proposes a graph-based spatio-temporal data model to integrate spatial and temporal information into asset management. In the spatial layer, we provide a graph-based method that uses topological containment and connectivity relationships to model the interior building space using data from 3D city models. In the temporal layer, we proposed the Aggregated Directly-Follows Multigraph (ADFM), a novel process model based on a directly-follows graph (DFG), to show the chronological flow of events in asset management by taking into consideration the repetitive nature of events in asset management. The integration of both layers allows spatial, temporal, and spatio-temporal queries to be made regarding information about events in asset management. This method offers a more straightforward query, which helps to eliminate duplicate and false query results when assessed and compared with a flattened graph event log. Finally, this paper provides information for the management of 3D spaces using a NoSQL graph database and the management of events and their temporal information through graph modelling.

This research leverages Memgraph, an open-source graph database, to analyze graph-based network data and apply Graph Neural Networks (GNNs) for a detailed classification of cyberattack tactics categorized by the MITRE ATT&CK framework. As part of graph characterization, the page rank, degree centrality, betweenness centrality, and Katz centrality are presented. Node classification is utilized to categorize network entities based on their role in the traffic. Graph-theoretic features such as in-degree, out-degree, PageRank, and Katz centrality were used in node classification to ensure that the model captures the structure of the graph. The study utilizes the UWF-ZeekDataFall22 dataset, a newly created dataset which consists of labeled network logs from the University of West Florida’s Cyber Range. The uniqueness of this study is that it uses the power of combining graph-based characterization or analysis with machine learning to enhance the understanding and visualization of cyber threats, thereby improving the network security measures.