BigData Oriented Open Scalable Relational Data Model

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

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.

Spatial data model is a key factor in GIS research. Traditional spatial data models based on Euclidean geometry frameworks are limited in terms of unifying space-time and different objects of different dimensions owing to the complexity associated with calculating topological relationships. Thus, a new spatial data model based on geometric algebra (GA-based spatial data model) is developed, which introduces GA into the spatial data model by using multidimensional vectors to unify space-time and multidimensional objects. To link traditional spatial data models and GA-based spatial data models, in this paper, a GA-based spatial data description language (GASDL) is proposed. GASDL is based on the Extensible Markup Language (XML), and it mainly describes data in terms of geometry, topological relationships and properties. GASDL considers the characteristics of both the traditional spatial data model and the GA-based spatial data model. By using GASDL, data can be imported and exported between traditional spatial data models and GA-based spatial data models. Thus, GIS software applications that use traditional spatial data models and GA-based data models can be connected.

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.

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 data stream management systems are based on extensions of the relational data model and query languages, but rigorous analyses of the problems and limitations of this approach, and how to overcome them, are still wanting. In this article, we elucidate the interaction between stream-oriented extensions of the relational model and continuous query language constructs, and show that the resulting expressive power problems are even more serious for data streams than for databases. In particular, we study the loss of expressive power caused by the loss of blocking query operators, and characterize nonblocking queries as monotonic functions on the database. Thus we introduce the notion of NB -completeness to assure that a query language is as suitable for continuous queries as it is for traditional database queries. We show that neither RA nor SQL are NB -complete on unordered sets of tuples, and the problem is even more serious when the data model is extended to support order—a sine-qua-non in data stream applications. The new limitations of SQL, compounded with well-known problems in applications such as sequence queries and data mining, motivate our proposal of extending the language with user-defined aggregates (UDAs). These can be natively coded in SQL, according to simple syntactic rules that set nonblocking aggregates apart from blocking ones. We first prove that SQL with UDAs is Turing complete. We then prove that SQL with monotonic UDAs and union operators can express all monotonic set functions computable by a Turing machine ( NB -completeness) and finally extend this result to queries on sequences ordered by their timestamps. The proposed approach supports data stream models that are more sophisticated than append-only relations, along with data mining queries, and other complex applications.

Visualization is one of the most important activities involved in modern exploratory data analysis. Traditional database data models, in their current forms, are inadequate to satisfy the data modeling need of exploratory data analysis in general and visualization in particular. A comprehensive scientific data model is required for seamless integration of various components of a scientific database system which includes visualization, data analysis, and data management.This paper identifies the criteria of a comprehensive scientific data model and discusses the implementation aspect of such a model based on existing DBMS. Recent research in scientific data modeling and various issues raised in the workshop subgroup are also presented.KeywordsData ModelDatabase SystemScientific DataData VisualizationExploratory Data AnalysisThese 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.

An increasing number of database management systems are expanding their functionality to work with various types of spatial data. This is true for both relational and NoSQL data models. The article describes the main features of those data models for which the functions of storing and processing spatial data are implemented. A comparative analysis of the performance of typical spatial queries for database management systems based on various data models, including multi-model ones, is carried out. The dataset on which the comparison is performed is presented in the form of three blocks of OpenStreetMap vector data for the territory of the Novosibirsk region. Based on the results of the study, recommendations are made on the use of certain data models, depending on the available data and the tasks to be solved.

Data model for warehousing historical Web information

The data structure of GIS is complex and it is difficult for the traditional GIS data model to express the existence of unstructured data. Meanwhile, traditional GIS data model can not express the complex relationship such as inheritance and aggregation which widely used in GIS. This article will propose a new way of establishing mineral geography data model based on Geodatabase. Through the analysis minerals geography data, the division feature classes and the definition of feature classes and relationship classes, the characteristics of mineral dynamic data structures will be analyzed on the base of Geodatabase data structure. The objects, elements and the corresponding relationships between spatial entities will be established by the use of UML and CASE tools. The mineral geography database will be automatically generated and the accuracy of mapping relationships between data will be ensured by the use of this method.

Part 1 Data modelling: a relational data model concepts of the relational data model, operations defined on a set of relations, normal operations defined on a set of instances, synthesis - data dictionary, example - production workshop, integrity rules integrity rule, introduction to dependencies, functional and dimensional dependencies, join and decomposition dependencies, inclusion dependencies, relation cycles, other integrity rules, table of ir ranges. Part 2 Transformations: introduction and access paths graph choice of an internal data model, accuracy of an internal structure, access paths graph, relation graph, join of embedded relations, plan of the second part, transformation of a decomposition into a relation graph, relation graph of a decomposition, use of hinges, from a relation graph to an internal data structure choice of an access paths graphs, computing methods of embedding access paths. Part 3 Decomposition of a relation: choosing a relation decomposition decomposing a relation, projection and extension of an integrity rule, qualities of a decomposition, choice crieria for a functional decomposition introduction, problems encountered, structural elements, a solution - mechanisms for completeness of keys and entities, basic decomposition introduction, keys to a relation, minimum fd equivalence classes, block of attributes and groups of fds, invariance of non-redundant bases, homogenous coverings and the basic covering, homogenous decompositions and the basic decomposition of a relation, completely homogenous decomposition, relation graph of a completely homogenous decomposition, FD appendix, Appendix: damping functional cycles damping a functional cycle, validation of a damping. Part 4 Perspectives: perspectives design process for databases, extended relational data model, control mechanisms, Appendix: use of this mechanism.

In order to realize the visualization,spatial analysis,automatic modeling and dynamic updating of model,the mine spatial environment and traditional 3D spatial data models were analyzed.Then an entity-oriented spatio-temporal hybrid data model was designed.It integrated the 3D parameter skeleton model,Topology-concerned and Entity-oriented 3D Vector Data Model(TEVDM) and Octree-Block Model(OBM).The TEVDM and OBM were used to express the boundary and internal property of orebodys.The Parametric Entity-Network Data Model(PENDM) was introduced into the model to describe the skeleton of the roadway engineering and production systems.Particularly,a behavior model was introduced into the model to describe the personnel and devices with behavior characteristics.The model also included the complete spatial feature which could describe the set of semantically related entities.Finally,some samples of using this model were given to describe the mines' spatial phenomenon and carry out spatial analysis.The samples show this model has better practicability in mining than traditional models.

Relational data modeling can often be restrictive as it provides no direct facility for modeling polymorphic types, reified relations, multi-valued attributes, and other common high-level structures in data. This creates many challenges in data modeling and engineering tasks, and has led to the rise of more flexible NoSQL databases, such as graph and document databases. In the absence of structured schemas, however, we can neither express nor validate the intention of data models, making long-term maintenance of databases substantially more difficult. To resolve this dilemma, we argue that, parallel to the role of classical predicate logic for relational algebra, contemporary foundations of mathematics rooted in type theory can guide us in the development of powerful new high-level data models and query languages. To this end, we introduce a new polymorphic entity-relation-attribute (PERA) data model, grounded in type-theoretic principles and accessible through classical conceptual modeling, with a near-natural query language: TypeQL. We illustrate the syntax of TypeQL as well as its denotation in the PERA model, formalize our model as an algebraic theory with dependent types, and describe its stratified semantics.

Computer applications in non-traditional areas have put requirements on conceptual data modeling. Some conceptual data models, being the tool of design databases, have been proposed. However, information in real-world applications is often vague or ambiguous. Currently, less research has been done in modeling imprecision and uncertainty in conceptual data models and the design of databases with imprecision and uncertainty. In this chapter, a different level of fuzziness based on fuzzy set and possibility distribution theory will be introduced into the IFO data model and the corresponding graphical representations will be given. The IFO data model is then extended to a fuzzy IFO data model, denoted IF2O. In particular, we provide the approach to mapping an IF2O model to a fuzzy relational database schema.