Using a DBMS based on various data models in solving problems of cartography and geoinformatics

A database management system (DBMS) should provide capabilities for defining and enforcing certain integrity constraints that must hold for the data recorded in the database. These constraints are derived from the semantics of the data and from the rules of the system under study (SuS) that database represents. Every DBMS is based on a data model that provides the means to achieve data abstraction and to express database schema. Each data model has a specific set of integrity constraints types. Some of constraint types are common for several data models and some of them are typical for a data model. Beyond well-known constraint types there are more complex ones that are difficult to express and enforce within the data model. In this paper we deal with the extended tuple constraint (ETC) type. It is the extension of tuple constraint that is one of constraint types of relational data model. In this paper we explain the usage of extended tuple constraint in relational data model and its implementation in relational DBMSs. XML data model does not recognize ETC type. We specify the extended tuple constraint in XML data model and propose two techniques for implementation of ETC in XML DBMSs. Each of them depends on the characteristics of a selected XML DBMS. We use XQuery functions in eXist DBMS, and triggers in Sedna DBMS.

One can distinguish between two ways of incorporating database management system (DBMS) in geographic information systems (GIS). The first way is that GIS vendors use DBMS mainly to store thematic data. The second way to integrate DBMS within GIS is DBMS offering support of designers spatial data types. Most GIS and computer aided design system users would be concerned if there was no multi-user access possible on data during design processes. There is no question that the results of their work being constructed on shared data should be automatically stored within the database periodically. Many DBMSs have evolved to offer spatial functionality, which brings further benefits for GIS and construction applications. The spatial functionality concerns the support of spatial data types in the data model, the implementation of spatial access methods, and the execution of spatial database queries. Geometry and topology are often used in Geo-DBMS terminology as synonyms to denote the different ways of describing spatial data types.

Small-scale application development has evolved rapidly due to increasing demands for flexibility, scalability, and faster deployment cycles. Data management plays a central role in these applications, influencing performance, maintainability, and long-term adaptability. Traditionally, relational database management systems have dominated application development because of their structured schemas, strong consistency guarantees, and mature query capabilities. However, the emergence of NoSQL data models has introduced alternative approaches designed to handle unstructured data, horizontal scalability, and agile development requirements. This review examines the comparative strengths and limitations of relational and NoSQL data models in the context of small application development. Key aspects such as data modeling flexibility, query expressiveness, scalability mechanisms, consistency models, development complexity, and operational overhead are critically analyzed. The study highlights how relational models remain effective for applications requiring complex relationships, transactional integrity, and standardized query support, while NoSQL models provide advantages in schema flexibility, rapid iteration, and scalability for data-intensive or evolving workloads. Particular emphasis is placed on decision-making factors relevant to small development teams, including resource constraints, ease of maintenance, and future growth considerations. By synthesizing findings from prior empirical and conceptual studies, this review aims to clarify common misconceptions surrounding database selection and to provide a balanced perspective on model suitability. The analysis suggests that neither approach is universally superior; instead, the choice depends on application requirements, data characteristics, and expected evolution. The review concludes that informed database selection, aligned with project scale and objectives, can significantly enhance development efficiency and application reliability in small-scale software projects while reducing technical debt and long-term operational risks.

Many geographical applications have to deal with spatial objects that reveal an intrinsically vague or fuzzy nature. A spatial object is fuzzy if locations exist that cannot be assigned completely to the object or to its complement. Spatial database systems and Geographical Information Systems are currently unable to cope with this kind of data. Based on an available abstract data model of fuzzy spatial data types for fuzzy points, fuzzy lines, and fuzzy regions, this paper proposes the Spatial Plateau Algebra that provides spatial plateau data types as an executable type system for fuzzy spatial data types. Each spatial plateau object consists of a finite number of crisp counterparts that are all adjacent or disjoint to each other, are associated with different membership values, and hence form different plateaus. The formal framework and the implementation are based on well known, exact models and available implementations of crisp spatial data types. Spatial plateau set and metric operations as well as spatial plateau topological predicates on spatial plateau objects are expressed as a combination of geometric methods on the underlying crisp spatial objects. This paper offers a conceptually clean specification for implementing a database extension for fuzzy spatial objects and their operations and predicates. Further, we demonstrate the embedding of these new data types as attribute data types in a database schema as well as the incorporation of fuzzy spatial operations and predicates into a database query language.

In this paper we discuss two data models for spatial database systems: the linear data model and the topological data model. Both can be used to model a wide range of applications. The linear data model is particularly suited to model spatial database applications in which exact geometrical information is required and in which this information can be approximated by linear geometrical spatial objects. The topological model on the other hand is suitable for applications in which rather than exact geometrical information the relative position of spatial objects is of importance.We will specify in each case which types of spatial data and spatial databases are under consideration. A semantics for both data models is formally defined in terms of finite representations of spatial databases in the data models. We also present languages to query spatial databases in both models and briefly investigate their expressiveness.KeywordsSpatial DataAffine TransformationSpatial DatabaseSpatial ObjectLinear FigureThese 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.

Two spatial data sets are considered to be similar if they originate from the same stochastic process in terms of their spatial structure. Many tests have been developed over recent years to test the similarity of certain types of spatial data, such as spatial point patterns, geostatistical data and images. This research proposes a generic spatial similarity test able to handle various types of spatial data, for example images (modelled spatially), point patterns, marked point patterns, geostatistical data and lattice patterns. A simulation study is done in order to test the method for each spatial data set. After the simulation study, it was concluded that the proposed spatial similarity test is not sensitive to the user-defined resolution of the pixel image representation. From the simulation study, the proposed spatial similarity test performs well on lattice data, some of the unmarked point patterns and the marked point patterns with discrete marks. We illustrate this test on property prices in the City of Cape Town and the City of Johannesburg, South Africa.

A data model for moving objects is a blueprint that describes data of interest related to movements of objects and a set of operations represented in an information or database management system. Designing an appropriate data model fosters conceptual understanding of the dynamic phenomena of moving objects, yields efficient data storage and retrieval, and further facilitates spatiotemporal analytical processes. Various data models have been proposed to efficiently and effectively store, manage, query, and analyze data about moving objects. This entry starts with the description of fundamental elements of moving objects and their abstract data types. Then it reviews data modeling approaches for representing moving objects, including conventional spatiotemporal data model, constraint model, moving object data model, semantic trajectory data model, event‐based data model, and models for handling big moving object data.

BackgroundRobust, extensible and distributed databases integrating clinical, imaging and molecular data represent a substantial challenge for modern neuroscience. It is even more difficult to provide extensible software environments able to effectively target the rapidly changing data requirements and structures of research experiments. There is an increasing request from the neuroscience community for software tools addressing technical challenges about: (i) supporting researchers in the medical field to carry out data analysis using integrated bioinformatics services and tools; (ii) handling multimodal/multiscale data and metadata, enabling the injection of several different data types according to structured schemas; (iii) providing high extensibility, in order to address different requirements deriving from a large variety of applications simply through a user runtime configuration.MethodsA dynamically extensible data structure supporting collaborative multidisciplinary research projects in neuroscience has been defined and implemented. We have considered extensibility issues from two different points of view. First, the improvement of data flexibility has been taken into account. This has been done through the development of a methodology for the dynamic creation and use of data types and related metadata, based on the definition of “meta” data model. This way, users are not constrainted to a set of predefined data and the model can be easily extensible and applicable to different contexts. Second, users have been enabled to easily customize and extend the experimental procedures in order to track each step of acquisition or analysis. This has been achieved through a process-event data structure, a multipurpose taxonomic schema composed by two generic main objects: events and processes. Then, a repository has been built based on such data model and structure, and deployed on distributed resources thanks to a Grid-based approach. Finally, data integration aspects have been addressed by providing the repository application with an efficient dynamic interface designed to enable the user to both easily query the data depending on defined datatypes and view all the data of every patient in an integrated and simple way.ResultsThe results of our work have been twofold. First, a dynamically extensible data model has been implemented and tested based on a “meta” data-model enabling users to define their own data types independently from the application context. This data model has allowed users to dynamically include additional data types without the need of rebuilding the underlying database. Then a complex process-event data structure has been built, based on this data model, describing patient-centered diagnostic processes and merging information from data and metadata. Second, a repository implementing such a data structure has been deployed on a distributed Data Grid in order to provide scalability both in terms of data input and data storage and to exploit distributed data and computational approaches in order to share resources more efficiently. Moreover, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.ConclusionsBased on such repository, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.

BackgroundHigh-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data.ResultsIn this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google's BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB.ConclusionsThe performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data model as a basis for migrating tranSMART's implementation to a more scalable solution for Big Data.

Most database models are extended to fuzzy database models for managing and retrieving imperfect information. In recent years, considerable attention was focused on the relational data model but more recently greater attention is being turned to the investigation of object-oriented data models. The incorporation of these research results into commercial relational databases management systems (RDBMS) has not as yet a reality. The main reason for this has to do with the rigidity/inflexibility of actual RDBMS. New generation object-oriented databases management systems (OODBMS) offer greater possibilities for incorporating imperfect information thanks to the reusability and extensibility properties of OODBMS. Strategic information systems of the 1990’s should be able to deal with nuanced information, i.e. imprecision, uncertainty, subjective characteristics of economic and sociological data. In this paper, we propose a solution for the representation of fuzzy information in an object-oriented environment. The solution offers facilities for defining membership functions necessary for managing fuzzy information and for retrieving objects close to an established profile in a complex objects base. The economic sciences serve as testing ground for these concepts.

Database model for hierarchical control of manufacturing cell systems in a manufacturing shop

The development and increased use of geographic information systems (GIS) in policing has intensified the demand for public access to digital spatial information. For example, those in the academic arena require access to spatial information to augment their learning, teaching and research endeavours, while the general public need spatially related crime information related to their local area or region to inform decisions that impact upon their everyday living (for example, safety precautions, choice of residential suburb). Much of this information exists, such as the 2001 Census from Australian Bureau of Statistics (ABS, 2003), crime data available on the Queensland Police Service (QPS) website (http://www.police.qld.gov.au/), and the Atlas of Crime in Australia 2000 (AIC, 2000), yet often the information is not in a form that is easily accessible or comprehendable, requiring an understanding of and access to relatively complex commercial GIS packages. In recent years, police and researchers have worked closely together to develop publicly accessible web-based GIS applications for crime mapping, crime analysis, community mapping and information sharing (see, e.g., Police Foundation, 2001; Shyy et al., 2005; Chainey and Smith, 2006). Dragicevic (2004) highlights how web-based GIS has enhanced the open use of GIS in three main directions including: spatial data access and dissemination; spatial data exploration and geovisualization; and spatial data processing, analysis and modelling.

Data model research is a fundamental part of the field of big data, and strongly supports the higher levels of database construction, data access, data analysis and data mining. This paper considers the shortcomings of traditional databases such as limited support for data types, low concurrent query performance and lack of horizontal scalability. And based on this, it proposes an Open Scalable Relational Data Model (OSRDM), which provides open support for varieties of data types and enables complete horizontal scalability on the basis of keeping and extending the relational descriptive power of traditional relational data models. It caters for the characteristics of big data such as volume, variety and velocity. Finally, the paper analyzes and evaluates OSRDM by comparing it with popular data models from two perspectives: functional features and performance indicators. The results show the superiority of OSRDM.

The amount of data has increased exponentially as a consequence of the availability of new data sources and the advances in data collection and storage. This data explosion was accompanied by the popularization of the Big Data term, addressing large volumes of data, with several degrees of complexity, often without structure and organization, which cannot be processed or analyzed using traditional processes or tools. Moving towards Big Data Warehouses (BDWs) brings new problems and implies the adoption of new logical data models and tools to query them. Hive is a DW system for Big Data contexts that organizes the data into tables, partitions and buckets. Several studies have been conducted to understand ways of optimizing its performance in data storage and processing, but few of them explore whether the way data is structured has any influence on how quickly Hive responds to queries. This paper investigates the role of data organization and modelling in the processing times of BDWs implemented in Hive, benchmarking multidimensional star schemas and fully denormalized tables with different Scale Factors (SFs), and analyzing the impact of adequate data partitioning in these two data modelling strategies.

With the rapid development of technology, especially in the industrial sector and the widespread use of the internet in Indonesia, effective data storage media is needed. Like MySQL, database management systems (DBMS) have become an effective solution for managing and processing data. As a type of DBMS, MySQL has demonstrated the ability to support application development by both experienced and novice users. On the other hand, in the ever-evolving digital era, web and mobile applications have become important, and Node.js and REST APIs have become the basis for creating effective web applications. Technological advances have also produced an alternative to relationless data storage, known as NoSQL. MongoDB, for example, offers flexibility and fast performance when storing data in JSON document format. Load testing performance analysis between MySQL and MongoDB on Node.js applications is very important because they have different data models and workload types. This is an important technique for measuring and evaluating application performance. Search speed, data storage, and query complexity are influenced by the MySQL and MongoDB NoSQL relational data models. The goal of this load testing performance analysis is to gain an understanding of how MySQL and MongoDB perform when faced with different workloads, particularly in the context of Node.js applications. The results of this analysis are expected to provide a clear picture of the advantages and disadvantages of each database when used with Node.js applications, so that developers and system architects can make wise, data-driven decisions when they use Node.js applications.