Spatial data interoperability among multi-clients based on shared database

The article explores current challenges associated with the methodology adopted in Ukraine in the year two thousand twenty-one for determining the normative monetary value of land. A central issue identified is a methodological inconsistency: although the approach is presented as unified, it uses two distinct methods to determine the coefficient that accounts for location – one based on simplified formulas and another based on spatial modeling. This contradiction undermines the principle of methodological consistency and creates disparities in valuation results. As spatial analysis technologies become more widely used in land management and valuation processes, there is a growing need for unified standards, clear legal frameworks, and technical guidelines. In particular, the broader use of computerized spatial analysis systems in land registry activities calls for stronger institutional and methodological support. This article argues that the application of European experience and best practices can offer effective solutions to these challenges. The primary aim of this study is to examine contemporary approaches to determining the monetary value of land in Ukraine, with a focus on the integration of spatial analysis systems into the valuation process. The article also evaluates the experiences of Germany, Poland, and France concerning legal, technical, and professional mechanisms that support the use of spatial data and technologies in land valuation procedures. This comparative analysis is intended to inform practical recommendations for Ukraine. Recent studies highlight the urgent need to revise and improve Ukraine's land valuation methodology, particularly through the greater application of spatial data technologies. Prior research emphasizes the advantages of using spatial analysis systems for land valuation purposes. These systems contribute to improved accuracy, transparency, and efficiency, and support the revision of valuation zones and the standardization of procedures. A detailed review of the existing valuation methodology reveals significant weaknesses, many of which are related to the varied roles and functions of different land categories. For example, the current approach to urban land often relies on outdated data and previous assessments that do not reflect current urban conditions. In the case of agricultural land, the methodology fails to distinguish between different types of land use, leading to generalized and sometimes misleading valuation results. European countries offer valuable models. Ukraine’s legal environment already permits the use of digital tools in land valuation, and land registry data can serve as a foundational layer for integrating spatial analysis technologies. In the European Union, legal instruments such as the directive establishing a shared spatial information framework and the regulation on data specifications ensure consistency and interoperability of spatial data across member states. Additionally, data protection is addressed through legislation that safeguards personal information in digital systems. Other legal instruments promoting open access to data and mortgage financing further support the use of spatial technologies in land-related decision-making. Germany has established a highly integrated system where spatial technologies are regularly used to determine standard land values, ensuring transparency and reliability. Poland has built a robust framework that incorporates spatial data into mass valuation processes and relies on professional standards that mandate the use of digital technologies. France has embedded spatial analysis into its urban planning codes, allowing for detailed land valuation and efficient access to land registry data. Reforms such as the Law on the Evolution of Housing, Planning, and Digital Technologies have advanced the digitization of the property valuation system in France, offering open access to transaction records to support market-based analysis. In conclusion, countries such as Germany, Poland, and France provide practical, tested models of how spatial technologies can be fully integrated into land valuation systems. For Ukraine, the adoption and adaptation of such models could significantly enhance the accuracy, consistency, and transparency of its land valuation framework, supporting broader goals of land reform and sustainable territorial development.

IntroductionInteroperability of collected dataset has been distress for researchers. Introduction of database management software made some improvements, however, still we need many of craftworks to convert and clean up original...

The Human Genome Project (HGP) plans to sequence the human genome by the beginning of the next century. It will generate DNA sequences of more than 10 billion bases and complex marker sequences (maps) of more than 100 million markers. All of these information will be stored in database management systems (DBMSs). However, existing data models do not have the abstraction mechanism for modelling sequences and existing DBMS`s do not have operations for complex sequences. This work addresses the problem of sequence modelling in the context of the HGP and the more general problem of an extensible object data model that can incorporate the sequence model as well as existing and future data constructs and operators. First, we proposed a general sequence model that is application and implementation independent. This model is used to capture the sequence information found in the HGP at the conceptual level. In addition, abstract and biological sequence operators are defined for manipulating the modelled sequences. Second, we combined many features of semantic and object oriented data models into an extensible framework, which we called the ``Extensible Object Model``, to address the need of a modelling framework for incorporating the sequence data model with other types of data constructs and operators. This framework is based on the conceptual separation between constructors and constraints. We then used this modelling framework to integrate the constructs for the conceptual sequence model. The Extensible Object Model is also defined with a graphical representation, which is useful as a tool for database designers. Finally, we defined a query language to support this model and implement the query processor to demonstrate the feasibility of the extensible framework and the usefulness of the conceptual sequence model.

\nBased on the characteristics of the video database and the basic structure of the video database and several typical video data models, the segmentation-based multi-level data model is used to describe the landscape information video database, the network database model and the road network management database system. Landscape information management system detailed design and implementation of a detailed preparation.\n

Although many temporal extensions of the relational data model have been proposed, there is no comparable amount of work in the context of object-oriented data models. Moreover, extensions to the relational model have been proposed in the framework of SQL standards, whereas no attempts have been made to extend the standard for objectoriented databases, defined by ODMG. This paper presents T_ODMG, a temporal extension of the ODMG-93 standard data model. The main contributions of this work are, thus, the formalization of the ODMG standard data model and its extension with time. Another contribution of this work is the investigation, on a formal basis, of the main issues arising from the introduction of time in an object-oriented model.KeywordsObject TypeTemporal TypeTemporal ExtensionObject IdentifierTemporal DatabaseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Information imprecision and uncertainty exist in many real-world applications, and for this reason fuzzy data modeling has been extensively investigated in various database models. In particular, Zadeh’s fuzzy set theory has been identified as a successful technique for modeling imprecise and uncertain information in various database models. This has resulted in numerous contributions, mainly with respect to the popular fuzzy conceptual data models (fuzzy ER/EER model, fuzzy UML data model, and etc.) and fuzzy logical database models (fuzzy relational database model, fuzzy object-oriented database model, and etc.). Also, it is shown that fuzzy set theory is very useful in Web-based business intelligence. Therefore, topics related to the modeling of fuzzy data are considered very interesting in XML since it is the current standard data representation and exchange format over the Web. In particular, to manage fuzzy XML data, it is necessary to integrate fuzzy XML and various fuzzy databases, and various fuzzy database models (fuzzy relational database model and fuzzy object-oriented database model) need to be used for mapping to and from the fuzzy XML models. Therefore, in this chapter, we mainly introduce several fuzzy database models, including fuzzy UML data model, fuzzy relational database model, and fuzzy object-oriented database model. Before that, we briefly introduce some notions of fuzzy set theory.

In the last few decades, the execution of various scientific experiments aimed at a more comprehensive understanding of one's environment, has shown a tremendous increase in data production. Database models provide a more or less adequate mechanism for mapping real-world applications into a computer-bound reality. Since scientific knowledge can be modelled a priori only to some extent, the question arises of how able a database schema is to evolve. On the other hand, knowledge can be provided by the underlying scientific data on which data mining algorithms are applied. The main question which arises is how to provide a suitable environment in order to accommodate the results coming out from data analysis tasks and how these tasks can be supported by a database model.

The development of an Electronic Health Record (EHR) system requires a well-defined data set and a robust data model to support complex clinical workflows, interoperability, and data quality. Many existing EHR implementations lack standardized and integrated data models, resulting in redundancy, semantic inconsistency, and limited support for secondary data use. This study aims to develop a comprehensive EHR data model based on a Minimum Data Set (MDS) that systematically represents medical service processes across emergency, outpatient, inpatient, and surgical care. The proposed approach involves the design of a conceptual, logical, and physical data model. Business processes were first analysed to identify relationships among clinical and administrative data classes, which were then transformed into a conceptual data model. The logical data model was developed through database normalization and schema definition, while the physical data model was constructed to represent the actual database structure for implementation. The results demonstrate a structured EHR data model comprising 21 interrelated data classes that reflect real-world medical service workflows. The conceptual data model establishes clear entity relationships, supporting data consistency and interoperability. The logical data model provides a normalized SQL-based schema that minimizes redundancy and enhances data integrity, while the physical data model enables efficient data storage and access. A prototype EHR system was implemented to evaluate the feasibility of the proposed data model, showing stable performance, usability, and alignment with functional requirements. This study concludes that a standardized EHR data model derived from an MDS can effectively support comprehensive clinical documentation, interoperability, and future scalability. The proposed model provides a strong foundation for developing national or institutional EHR systems and can serve as a reference for further EHR data standardization efforts.

The Pipeline Open Data Standard (PODS) Association develops and advances global pipeline data standards and best practices supporting data management and reporting for the oil and gas industry. This presentation provides an overview of the PODS Association and a detailed overview of the transformed PODS Pipeline Data Model resulting from the PODS Next Generation initiative. The PODS Association’s Next Generation, or Next Gen, initiative is focused on a complete re-design and modernization of the PODS Pipeline Data Model. The re-design of the PODS Pipeline Data Model is driven by PODS Association Strategy objectives as defined in its 2016–2019 Strategic Plan and reflects nearly 20 years of PODS Pipeline Data Model implementation experience and lessons learned. The Next Gen Data Model is designed to be the system of record for pipeline centerlines and pressurized containment assets for the safe transport of product, allowing pipeline operators to: • Achieve greater agility to build and extend the data model, • respond to new business requirements, • interoperate through standard data models and consistent application interface, • share data within and between organizations using well defined data exchange specifications, • optimize performance for management of bulk loading, reroute, inspection data and history. The presentation will introduce the Next Gen Data Model design principles, conceptual, logical and physical structures with a focus on transformational changes from prior versions of the Model. Support for multiple platforms including but not limited to Esri ArcGIS, open source GIS and relational database management systems will be described. Alignment with Esri’s ArcGIS Platform and ArcGIS for Pipeline Referencing (APR) will be a main topic of discussion along with how PODS Next Gen can be leveraged to benefit pipeline integrity, risk assessment, reporting and data maintenance. The end goal of a PODS implementation is a realization of data management efficiency, data transfer and exchange, to make the operation of a pipeline safer and most cost effective.

Abstract. Standard data models are key to enable a set of data integration functionalities, often characterised using the Findability, Accessibility, Interoperabilty and Reusability (FAIR) principles. However, standardisation is a process of trying to meet many requirements, and standard data models are inherently either very abstract or very comprehensive in the details. This results in several ambiguity pitfalls, inconsistent implementation of standard data models, which in turn hinders trust in the interoperability potential of standardised data, and complicates any integration processes. In practice profiling such standards is useful to overcome such issues to create more useful forms of standardised data for specific applications. However defining custom profiles typically requires a great deal of technical expertise in the underlying expression language of the standard. Maintaining access to this level of expertise is a challenge as profiles become outdated through the time and lose connection with the maintenance of the parent standard from which they originate. Therefore, in this paper, a scalable methodology is proposed, built on the OGC Building Blocks Model approach, that uses semantic modelling to support an easier composition of geospatial data models profiles which directly derive from available standards without losing the relevant dependencies that inform stakeholders which components are interoperable with other standards. The approach is tested within a digital building permit project (CHEK), in which data requirements derive from the semantics of city regulations and common geospatial standards (i.e., CityGML and INSPIRE) are used as reference.

The Learning Analytics (LA) discipline analyzes educational data obtained from student interaction with online resources. Most of the data is collected from Learning Management Systems deployed at established educational institutions. In addition, other learning platforms, most notably Massive Open Online Courses such as Udacity and Coursera or other educational initiatives such as Khan Academy, generate large amounts of data. However, there is no generally agreedupon data model for student interactions. Thus, analysis tools must be tailored to each system's particular data structure, reducing their interoperability and increasing development costs. Some e-Learning standards designed for content interoperability include data models for gathering student performance information. In this paper, we describe how well-known LA tools collect data, which we link to how two e-Learning standards - IEEE Standard for Learning Technology and Experience API - define their data models. From this analysis, we identify the advantages of using these e-Learning standards from the point of view of Learning Analytics.

Data acquired from site surveys provide an essential resource to various exploration and production activities, such as field development planning, well positioning, and subsea equipment positioning. For many years, site survey data has been delivered as a Computer-aided Design (CAD) product. As operators move into a Geographic Information Systems (GIS) environment, shortfalls from CAD-based workflows have become obvious. This paper presents a method of managing site survey data based on geo-information management principles and practices. The methodology presented in this paper defines a GIS data model for seabed survey data interpretation that satisfies GIS query, presentation, and data exchange purposes. This paper also defines a standard for GIS seabed survey data deliverables from contractors to operators, including methods and processes to move the model to/from CAD-based applications. The implementation of the data model has proven to be a relative success and allows for a better integration of subsurface applications internally. The results have helped define an efficient contractor-operator data exchange process by eliminating data ambiguity and enabling seamless GIS data transfer. Results have also produced a more intelligent seabed survey GIS dataset with rules and behaviors capability offered by an ArcGIS Geodatabase embedded into the data model. Use of a standard GIS data model and a standard list of GIS data deliverables required from contractors to operators will allow project teams to fully embrace the power of GIS data integration and allow nearly seamless and lossless movement of CAD/GIS representations of the model. Introduction In order to reach maximum potential benefit of data provided from seabed site surveys throughout the E & P lifecylcle (Figure 1), there is a need to ensure that data delivered from survey contractors to operators is done so in a structured, efficient, and consistent manner.

Most work on database integration has considered only support for data retrieval, not support for updates, and often the use of a special semantically rich data model has been required. In this paper we present an approach to database integration which supports updates and which uses only the standard relational data model. Many of the ideas used in this approach are applicable to database integration in the context of other data models as well.

Moving objects database systems are specialized to store and process data with spatiotemporal attributes. A simple object type in this context is a moving point, which can represent the motion of a pedestrian, car or airplane. Another, more complex, type of object is a moving region, which is a region that changes position and shape over time. This thesis discusses various aspects for handling moving regions. One topic in this thesis is the region interpolation problem, where snapshots of regions are interpolated to moving regions. This procedure can be performed in several ways with varying results. The goal is to achieve a high quality result, which means primarily that the database model should match the corresponding real world objects as accurately as possible. Another chapter presents a new, polyhedra based, model for moving regions. This has two advantages compared to the prevailing unit based models: Complex moving regions, especially the results of binary set operations as, for instance, “union” can be represented in a much more compact way. Furthermore, operations on moving regions can be mapped into geometrical operations on polyhedra, for which robust and efficient algorithms are available. Based on this new model, operations for spatiotemporal coverage analysis of moving regions are developed. This is a set of operations to determine which regions are covered for which time intervals by a given moving region. In the light of large forest fires in the world, a method for creating moving regions from earth satellite scan data is discussed, that has several advantages over the prevailing methods. Another topic is the decomposition of moving regions into smaller and less complex entities, to improve their manageability in moving objects databases. Finally, an alternative data model for moving regions is introduced, which is optimized for rigid objects. The class of moving regions supported by that model is unable to change shape, but in return rotational movement is modeled accurately. For this new data type, useful operations like “moving point inside”, “traversed area” and more are introduced. As a part of this work, reference implementations for all these topics are distributed in form of freely available C++ libraries, which can be easily integrated into existing moving objects database systems, as, for example, the DBMS Secondo developed at the FernUniversitat in Hagen.

• BIM-to-BRICK bridges BIM and BRICK schemas, enhancing building data interoperability. • Graph modeling reduces manual intervention in BIM-to-BRICK integration workflows. • Converts 932 BIM instances into 458 BRICK objects with 1219 relationships in 17 seconds. • Tool supports improved environmental comfort via efficient multi-source data integration. • Streamlined approach aids building lifecycle management and sustainable operations. The holistic management of a building requires data from heterogeneous sources such as building management systems (BMS), Internet-of-Things (IoT) sensor networks, and building information models (BIM), all aimed at environmental well-being. Data interoperability is a key component to eliminate silos of information, and using semantic web technologies like the BRICK schema, an effort to standardize semantic descriptions of the physical, logical, and virtual assets in buildings and the relationships between them, is a suitable approach. However, current data integration processes can involve significant manual interventions. This paper presents a methodology to automatically collect, assemble, and integrate information from a building information model into a knowledge graph. The resulting application, BIM-to-BRICK, is implemented on a higher education case study building in a tropical climate. BIM-to-BRICK generated a bidirectional link between a BIM model of 932 instances and experimental data collected for 17 subjects into 458 BRICK objects and 1219 relationships in 17 seconds. The automation of this approach can be compared to traditional manual mapping of data types. This scientific innovation incentivizes the convergence of disparate data types required for achieving and enhancing environmental comfort conditions and other strategic built-environment applications.