Closing the gap between early and detailed ship design models

In this paper, a Graph-based semantic Data Model (GDM) is proposed with the primary objective of bridging the gap between the human perception of an enterprise and the needs of computing infrastructure to organize information in some particular manner for efficient storage and retrieval. The Graph Data Model (GDM) has been proposed as an alternative data model to combine the advantages of the relational model with the positive features of semantic data models. The proposed GDM offers a structural representation for interacting to the designer, making it always easy to comprehend the complex relations amongst basic data items. GDM allows an entire database to be viewed as a Graph (V, E) in a layered organization. Here, a graph is created in a bottom up fashion where V represents the basic instances of data or a functionally abstracted module, called primary semantic group (PSG) and secondary semantic group (SSG). An edge in the model implies the relationship among the secondary semantic groups. The contents of the lowest layer are the semantically grouped data values in the form of primary semantic groups. The SSGs are nothing but the higher-level abstraction and are created by the method of encapsulation of various PSGs, SSGs and basic data elements. This encapsulation methodology to provide a higher-level abstraction continues generating various secondary semantic groups until the designer thinks that it is sufficient to declare the actual problem domain. GDM, thus, uses standard abstractions available in a semantic data model with a structural representation in terms of a graph. The operations on the data model are formalized in the proposed graph algebra. A Graph Query Language (GQL) is also developed, maintaining similarity with the widely accepted user-friendly SQL. Finally, the paper also presents the methodology to make this GDM compatible with the distributed environment, and a corresponding query processing technique for distributed environment is also suggested for the sake of completeness.

Abstract. Over the recent years, the usage of semantic web technologies and Resources Description Framework (RDF) data models have been notably increased in many fields. Multiple systems are using RDF data to describe information resources and semantic associations. RDF data plays a very important role in advanced information retrieval, and graphs are efficient ways to visualize and represent real world data by providing solutions to many real-time scenarios that can be simulated and implemented using graph databases, and efficiently query graphs with multiple attributes representing different domains of knowledge. Given that graph databases are schema less with efficient storage for semi-structured data, they can provide fast and deep traversals instead of slow RDBMS SQL based joins allowing Atomicity, Consistency, Isolation and durability (ACID) transactions with rollback support, and by utilizing mathematics of graph they can enormous potential for fast data extraction and storage of information in the form of nodes and relationships. In this paper, we are presenting an architectural design with complete implementation of BIM-GIS integrated RDF graph database. The proposed integration approach is composed of four main phases: ontological BIM and GIS model’s construction, mapping and semantic integration using interoperable data formats, then an import into a graph database with querying and filtering capabilities. The workflows and transformations of IFC and CityGML schemas into object graph databases model are developed and applied to an intelligent urban mobility web application on a game engine platform validate the integration methodology.

Graph theory, which is a branch of discrete mathematics, has emerged as a powerful tool in various domains, including database management. This abstract investigates the ways in which ideas and methods from graph theory which can be applied to database systems, offering a thorough synopsis of their benefits. Complex interactions within data can be well-modeled by using the basic concepts of graph theory, such as nodes, edges, and relationships. Because of its capacity to represent and query complex relationships, graph databases have become more and more popular in the field of database administration. Graph databases are well-suited for situations such as social networks, recommendation systems, and interconnected data domains because they are excellent at representing and traversing relationships, in contrast to standard relational databases, which are excellent at managing structured data. The abstract delves into the key graph-based data models, such as property graphs, RDF (Resource Description Framework), explaining how they facilitate the representation of diverse relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets. The document highlights specific use cases where graph theory contributes to database management, including fraud detection, social network analysis, and recommendation systems. Additionally, it discusses the challenges associated with integrating graph databases into existing infrastructures and proposes solutions to address scalability and performance concerns. The abstract also touches upon the advancements in graph database query languages (Cypher) and SPARQL, showcasing their expressive power in querying complex relationships. The inclusion of graph-based indexing and optimization techniques demonstrates how database systems can efficiently handle queries involving large-scale graph data. As graph databases continue to evolve, this abstract concludes by outlining potential future directions in the intersection of graph theory and database management. It emphasizes the importance of ongoing research in developing scalable and efficient solutions for managing interconnected data, ultimately paving the way for more sophisticated and context-aware database systems relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets.

This paper discusses a systematic approach of how to link hypertext, databases and a data modelling tool together to support users' complex application definitions and maintenance. The salient features of this approach is that a user can model an application environment by using his/her preference data modelling method, maintain application data by databases and browse document structure by using hypertext facilities. In this paper, I will first summarize those main features of two systems, GORML and InterSect in order to discuss why and how we can link them together. On the basis of this, the data model of the integrated system is introduced and essential data structure will be analysed. Finally, the design strategies of the prototype implementation will be discussed.

Graph databases are becoming a topic of interest in the research community because of the possibilities that offers in the era of big data. Recently, different types of graph databases have been proposed, however, majority of graph databases are under fifteen years of age and have constant improvements. This article presents a review of graph databases and introduces an architecture which represents the basis for most of the graph databases. Besides, according to data storage type and the data model, a graph database taxonomy is proposed. Such taxonomy allows a categorization from graph databases studied in this research. In the last part we present a survey that describe deeply some popular graph databases. This survey has the aim to provide a guideline to the selection of one of these databases.KeywordsGraph databasesData modelsGraph data management

The article presents an approach to modeling domain ontologies based on graph databases. Ontology is traditionally considered as a means of studying and formalizing the subject area. Based on ontologies, knowledge bases of information systems are formed, which can later be replenished and used to study certain applied aspects of the subject area. At the same time, with the development of NoSQL technologies and graph databases aimed at optimizing work with related data, it becomes possible to design a data warehouse without a strict pre-established domain model. Due to the obvious graph nature of ontologies, graph databases are a promising solution for storing and working with ontologies. However, with all the similarities, the ontology and data models used in graph DBMSs are not so easy to combine. The paper proposes an approach to the integration of ontological and graph models of knowledge representation and considers its application in creating a database for a prototype of a web ontology editor. Visual tools for editing ontologies are described, an algorithm for layer-by-layer rendering of a directed graph is given, a mechanism for generating dynamic forms for editing classes and ontology instances is described, and basic support for ontological design patterns is implemented.

Current knowledge graphs encompass diverse data formats, including images, text, tables, audio files, and videos. Additionally, the graph database ecosystem is required to support multiple co-existing data models. Addressing these challenges is essential for promoting interoperability between data sources. This demo introduces MillenniumDB, a high-performing, open-source graph database handling this diversity of data formats and models.

Abstract. Disaster management aims to reduce catastrophic losses of disasters. Geographic information technologies support disaster management activities for effective and collaborative data management considering the complex nature of disasters. This study with an original conceptual approach aims to develop interoperable geographic data model and analysis tools to manage geographic data sets coming from different sources. For landslide disaster, 39 scenario-based activities were analysed with the required data according to user needs in a cycle of activities at mitigation, preparedness, response, and recovery phases. An interoperable geographic data model for disaster management (ADYS), enabling up-to-date exchange of geographic data, was designed, compliant with the standards of ISO/TC211 Geographic Information/Geomatics, Open Geospatial Consortium (OGC), and the Turkish National GIS (TUCBS). An open source and free analysis toolbox was developed and tested in the case study of activities such as landslide hazard analysis and a disaster warning system to support the Provincial Disaster Management Centres of Turkey. Open data models and analysis tools make effective activity management and data sharing possible. However, transforming data sets into data exchange formats is laborious.

In order to combat global climate change and reduce harmful emissions, various countries and International Maritime Organization (IMO) have issued and updated regulations that limit ship gas emissions. From a long-term perspective, more and more relevant regulations will be introduced for the development of green shipping. However, the uncertainty of policies poses a large challenge to ship design, especially early ship design. Without considering the uncertainty of policy, the ship design may have been excessive eco-friendly or under-eco-friendly in the long term, affecting the vessels in delivering value to key stakeholders throughout the lifecycle of the system. This paper proposes a model to optimize the main design indicators of the ship to deal with the uncertainty associated with future environmental regulations and to ensure that the economic performance of the ship can still maintain market competitiveness. The optimization model is mainly composed of three parts, namely, the uncertainty prediction model of environmental regulation trends, the evaluation of the economic performance of the ship design and the modeling of the ship. Among them, the regulation uncertainty prediction model proposes a new ship emission indicator based on the current regulations, considering that the current regulations are facing unforeseen changes. The regulations trend simulation is loosely based on a Jump-diffusion process and then add bounds to create an uncertain environmental regulations scenario by using extrapolation. The proposed method is used to simulate the future regulation trends in a range to provide advice for designers when design freedom is still high. This framework optimizes the comprehensive economic performance of the ship that includes ship cost and total annual cost including the fuel cost of main engines, in-transit cargo inventory cost and non-fuel vessel operating cost etc.The early stage design of a container ship is used as a verification case to prove that the model can deal with the impact of regulation uncertainty effectively.

Database management system (DBMS) support for expert system knowledge bases requires data-modeling tools matching system needs. To assess its power and fit for this application, the authors applied their logical data structures data-modeling tool, LDS, to an existing rule-based expert system, Galen, and produced a clear logical schema, comprehensible by its producers and other audiences. They believe that this technique could help the design of novel systems and that its use on existing systems will facilitate DBMS support.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cloud Manufacturing (CM) offers the possibility to use and provide production services within an as-a-service concept. The CM paradigm enables more efficient resource utilization in the context of industry 4.0 through fully automated order processing. Existing architecture solutions, however, follow a central platform approach with a global neutral data model. As a consequence, the individuality of companies within the CM is not given any room for expression and symbioses between companies are not established. The result is a disadvantage particularly for SMEs, which generate their competitive advantage from innovative production technologies and symbiotic connections with customers and other SMEs. The focus of Collective Cloud Manufacturing (CCM) is the preservation of these characteristics, in which an over-approximation of costs through the integration of production-related simulations is to be reduced and the data security of the customer is to be enhanced. So in this work, a data model for an existing concept for CCM is presented. The requirements for the data model and the runtime behavior are defined. Subsequently, the state of the art of existing models in the domain of CM is presented. The developed data model is presented with an application evaluation.

Currently, a common way of collaboration between product development and production planning is working with a Bill of Material (BOM) in terms of Concurrent Engineering, which is characterized by a structure as a traditional relational data model. Even though such a BOM can serve as a single point of truth of the product structure, it still leads to information discontinuities from upper- to downstream engineering teams, as they use BOM data for different purposes. Hence, a restructuring work of the product development oriented BOM is needed for tasks in production planning, so that the hierarchy is adapted. The parts structure needs to be rearranged to meet the requirements of the manufacturing and assembly processes. In the circumstances of volatile global market and varying customer needs, the BOM structured in relational data model provides insufficient digital consistency to reach an appropriate time-to-market and quick response on product changes and variations. In order to be able to cope with the increasing amounts of heterogeneous data, graph data model approaches are playing an essential role. Due to its non-centric information representation, it can be used as a neutral data model to dissolve boundaries in engineering. A graph model based BOM structure can fulfill both tasks in product development and production planning directly. Thus, it can reach an optimized consistency concerning information flow through various engineering activities as well. This paper proposes the graph model based BOM to minimize efforts and gaps in collaboration between product development and production planning. In addition, it also shows further potentials of usage in engineering.KeywordsEngineeringProduct developmentProduction planningBill of MaterialGraph data modeNon-relational data model

Mass Engineered Timber products provide a unique opportunity in configuringpanelized building systems that are suitable for both prefabrication and onsitecustomization. The structural nature of these large section elements also bringsabout the need for a coordinated design-fabrication-assembly workflow. Theseproducts can assume different geometric configurations and their behaviour canbe approximated globally by simplifying framing schemas. Current BIMInteroperability standards such as STEP or IFC already acknowledge andsupport the interconnected nature of component properties, yet these DataModels are component focused. Expanding on the relationships betweencomponents and using sets to define part to whole, or exteriority relationshipscould yield a more flexible and agile querying of building information.This wouldbe a framework fit for automated feature derivation and rule based designapplications. To this end Graph structures and Graph Databases, alongsideexisting ontology authoring tools are studied to probe new cognitive possibilitiesin collaborative AEC workflows

Traditional spatio-temporal data model (STDM) is based on relational database, it is hard to convert problem domain model to relational model, which result in complicated query and low expansibility. In this regard, we propose the spatio-temporal data model based on graph database. The data model integrates TGIS's three key elements: space, time and attributes, and expressed spatio-temporal characteristics of TGIS explicitly. Finally, this paper gives a particular description of logistics distribution route optimization. Experimental results show that the model is proved to be appropriate for expressing the spatio-temporal process of world.

In the evolving cyber threat landscape, comprehensive understanding and timely analysis of attack vectors are critical for effective defense. This paper introduces a novel approach to simulate, transform, and integrate synthetic honeypot logs into a graph database using the Structured Threat Information Expression (STIX) data model. Leveraging Neo4j’s graph capabilities, we convert voluminous, complex attack data into interconnected threat intelligence objects, facilitating the visualization and exploration of intricate attack graphs. By employing Python-driven automation for log generation and STIX transformation, challenges concerning data compatibility, nested property flattening, and cybersecurity compliance are addressed. The resultant graph-based threat intelligence framework provides a scalable and standardized platform empowering Security Operations Centers (SOCs) with enhanced situational awareness and decision-making support, paving the way for improved cybersecurity posture and collaborative defense strategies.