Relational Model Research Articles

MMKP: A mind mapping knowledgebase prototyping tool for precision medicine.

BackgroundWith significant advancements in the area of precision medicine, the breadth and complexity of the relevant knowledge in the field has increased significantly. However, the difficulty associated with dynamic modelling and the disorganization of such knowledge hinders its rapid development potential.ResultsTo overcome the difficulty in using the relational database model for dynamic modelling, and to aid in the organization of precision medicine knowledge, we developed the Mind Mapping Knowledgebase Prototyping (MMKP) tool. The MMKP implements a novel design that we call a “polymorphic foreign key”, which allows the establishment of a logical linkage between a single table field and a record from any table. This design has advantages in supporting dynamic changes to the structural relationships in precision medicine knowledge. Knowledge stored in MMKP is presented as a mind map to facilitate human interaction. When using this tool, medical experts may curate the structure and content of the precision knowledge in a flow that is similar to the human thinking process.ConclusionsThe design of polymorphic foreign keys natively supports knowledge modelling in the form of mind mapping, which avoids the hard-coding of medical logic into a rigid database schema and significantly reduces the workload that is required for adapting a relational data model to future changes to the medical logic. The MMKP tool provides a graphical user interface for both data management and knowledgebase prototyping. It supports the flexible customization of the data field constraints and annotations. MMKP is available as open-source code on GitHub: https://github.com/ZjuLiangsl/mmkp.

Creating the Conceptual and Logical Model of a Journal Database

This article describes the process of creating the conceptual and logical model of a journal database. To efficiently extract the information from the articles published in the journal so far, the idea of creating a database has emerged. To create a database, it is recommended to design a high-level conceptual model and convert that into a logical data model. The benefit of the thoughtful design is that it shows the structure of the database in an easily comprehensible form. The entity-relationship model is a fast and efficient way to create the conceptual model and it can be easily converted to a relational database model, which is a logical model. The initial version of the entity-relationship model of the journal database had one entity type, 25 attributes, and no relationship. The final version contained three entity types, 39 attributes, and three relationships. This final conceptual model was converted to a logical model, the relational model. The result was ten tables to store entity data with 22 different fields and another three tables to ensure the relationships between the tables. The developed model can be created in a relational database manager and is suitable for serving information needs related to the journal.

DCSO: towards an ontology for machine-actionable data management plans

The concept of Data Management Plan (DMP) has emerged as a fundamental tool to help researchers through the systematical management of data. The Research Data Alliance DMP Common Standard (DCS) working group developed a set of universal concepts characterising a DMP so it can be represented as a machine-actionable artefact, i.e., machine-actionable Data Management Plan (maDMP). The technology-agnostic approach of the current maDMP specification: (i) does not explicitly link to related data models or ontologies, (ii) has no standardised way to describe controlled vocabularies, and (iii) is extensible but has no clear mechanism to distinguish between the core specification and its extensions.This paper reports on a community effort to create the DMP Common Standard Ontology (DCSO) as a serialisation of the DCS core concepts, with a particular focus on a detailed description of the components of the ontology. Our initial result shows that the proposed DCSO can become a suitable candidate for a reference serialisation of the DMP Common Standard.

Why Capital Always Win — A Case Study for Gamestop

A stock market bubble is an economic bubble that occurs in the stock market when market participants push stock prices above their value in some systematic stock valuations. Financial professionals and academics have long been puzzled by the ubiquity and repetition of market bubbles and crashes. In real life, it is common to see mistakes in the reasonable pricing of assets traded in the stock market. This paper aims to explain the behaviors of the stock market by taking GameStop's dramatic stock price change in early 2021 as an example. Firstly, the paper reviews the development history of GameStop and its weaknesses of the time. Secondly, this article focuses on the stock market institutions and retail, the game between the use of the relevant theories of game theory and related data model. Then the article reviewed the why retail investors in the short-term game victory, the victory of the retail is the media, government. Finally, the reasons for the different stock markets under different social forms are discussed. In the current era of economic globalization, changes in the US stock market will lead to changes in the global economy. Taking the 2008 financial crisis as an example, it is very important for the national economy to understand the causes of market changes and stop losses in time.

Test model for database architectures: an assessment for job search engine systems

Data is a fundamental part of information and management systems; thus, controlling its complexity is an essential step nowadays. NoSQL databases adopt new approaches to data management differing from relational structures. For this study, two database systems are considered, MongoDB as a NoSQL data storage model and PostgreSQL as a relational data model, they are compared and evaluated on a job search system. For this purpose, a dataset was defined, and its representation was constructed in databases based on each technology. This process allowed the data modeling in terms of the best practices, then, the development of a test plan prepared the environment for the determination of the comparison metrics of both databases under the methodology specified by the International Software Testing Qualifications Board (ISTQB) and the types of database testing. This study determined that the SQL schema provides greater functionality, that ensures the support of transactions and data integrity, the opposite happened with the NoSQL schemas, resulting in more efficient but lacking functionalities that are characteristic and required for data representation of a consistent system.

ON DATABASE QUERIES OVER A WEAKLY O-MINIMAL DOMAIN WITH A SMALL COUNTABLE SPECTRUM

In the relational database model introduced by E.F. Codd, the database state is understood as a finite set of relationships between elements. The names of the relationships and their arnts (locations) are fixed and called the database schema. The individual information stored in the relationships of this schema is called the state of the database. Although relational databases were devised for finite sets of data, it is often convenient to assume that there is an infinite domain. We consider relational databases organized over an ordered domain with some additional relations – a typical example is the set of rational numbers together with the relation of linear order and binary operation of addition. If the first-order predicate logic language is used as the query language, then queries can use both database relationships and domain relationships, with variables changing throughout the domain. In the focus of our study are the first-order (FO) queries that are invariant under order-preserving permutations – such queries are called order-generic. It was discovered that for some domains order-generic FO queries fail to express more than pure order queries. Here we prove the collapse result theorem over a weakly o-minimal domain having convexity rank 1 and a small countable spectrum.

RESEARCH OF METHODS TO SUPPORT DATA MIGRATION BETWEEN RELATIONAL AND DOCUMENT DATA STORAGE MODELS

The subject matter of the article is heterogeneous model-inhomogeneous data migration between relational and document-oriented data storage models, existing strategies and methods to support such migrations, the use of relational algebra and set theory in the context of databases in building a new data migration algorithm. The goal of the work is to consider the features and procedure of data migration, explore methods to support data migration between relational and documentary data models, build a mathematical model and algorithm for data migration. The following methods were used: analysis and comparison of existing approaches to data migration, choice of strategy for further use in compiling the migration algorithm, mathematical modeling of the algorithm of heterogeneous model-inhomogeneous data migration, formalization of the data migration algorithm. The following tasks were solved in the article: consideration of the concept and types of data migration, justification for choosing a document-oriented data model as a target for data migration, analysis of existing literature sources on methods and strategies of heterogeneous model data migration from relational to document-oriented data model, highlighting advantages and disadvantages existing methods, choosing an approach to the formation of the data migration algorithm, compiling and describing a mathematical model of data migration using relational algebra and set theory, presentation of the data migration algorithm, which is based on the focus on data queries. The following results were obtained: the possibilities of relational algebra and set theory in the context of data models and queries are used, as well as in model redesign, the strategy of migration of data models is chosen, which provides relational and document-oriented data models, the algorithm of application of this method is described. Conclusions: because of the work, the main methods of migration support for different data storage models are analyzed, with the help of relational algebra, set theory a mathematical model is built, and an algorithm for transforming a relational data model into a document-oriented data model is taken into account. The obtained algorithm is suitable for use in real examples, and is the subject of further research and possible improvements, analysis of efficiency in comparison with other methods.

The Programming of Algebra

We present module theory and linear maps as a powerful generalised and computationally efficient framework for the relational data model, which underpins today's relational database systems. Based on universal constructions of modules we obtain compact and computationally efficient data structures for data collections corresponding to union and deletion, repeated union, Cartesian product and key-indexed data. Free modules naturally give rise to polysets, which generalise multisets and facilitate expressing database queries as multilinear maps with asymptotically efficient evaluation on polyset constructors. We introduce compact maps as a way of representing infinite (poly)sets constructible from an infinite base set and its elements by addition and subtraction. We show how natural joins generalise to algebraic joins, while intersection is implemented by a novel algorithm on nested compact maps that carefully avoids visiting parts of the input that do not contribute to the eventual output. Our algebraic framework leads to a worst-case optimal evaluation of cyclic relational queries, which is known to be impossible using textbook query optimisers that operate on lists of records only.

Method of designing the system of classes of the subject area model on the basis of the architecture of integrated objects

The article presents a method of designing the system of software classes, which includes the stages of conceptual, logical and physical design of classes with the implementation of a software system in the architecture of integrated objects. The input data for design is the model of the subject area, which is obtained as the result of object-oriented analysis, which is represented by conceptual, mathematical and semantic models. The conceptual model defines the entities and attributes of entities and is considered as the basis for designing the properties of classes. Mathematical model, as a dynamic model of entity behavior, is formally or informally defined by mathematical formulas, algorithms, diagrams or descriptions of the implementation, which is the basis for designing class methods. The semantic model defines the concepts of subject area in relation to the elements of its model and includes identifiers, meaningful names and descriptions used for the design, programming and implementation of system functionality in terms of subject area. The proposed design method is based on the following principles: normalization of classes (reduction of data to atomic values, reduction of class methods arguments and removal of redundant class methods), replacement of inheritance by logical composition and physical integration, decapsulation (separation of physical functional model from logical data model), virtualization of classes (logical model of classes are considered as a set of relational data model and physical functional model of class methods), interrelation of models (end-to-end mapping of conceptual model on logical and physical model, and class implementation). The essence of the method is to perform successive stages of design: normalization of conceptual model, logical design of classes (transformation of mathematical model into normalized functional model, design and analysis of class characteristics), physical design of classes in architecture of integrated objects (creation of classes, mapping of logical classes on physical classes, implementation of classes in programming language). Actually, the developed design method leads to the creation of a model focused on solving problems, when instances of entities are not created entirely and only the properties and methods that are needed to calculate the desired results for given input values are used. An example of application of the method for a certain subject area with demonstration of design results is considered, and also the approach on the basis of query language of formulation of problems in terms of subject area with generation of source code in programming language is shown. The application of the method allows to increase the efficiency of creating software systems due to the non-redundancy of the system of classes and parameters of class methods, reuse of code and selection of the minimum required set of properties and class methods when solving specific problems.

SIMURG: A new model for the integrated assessment of sustainability

Sustainability assessment is one of the basic issues in the agenda of public authorities and it requires practical tools to measure performance in terms of sustainable development goals. Most studies in literature deal with only one dimension of the problem of environmental components of sustainability. These studies discuss entities at only one level (cities, buildings, etc.), and one layer (green, smart, etc.) in selected dimensions. The literature includes no models that claim to provide an integrated assessment of entities’ performance in the 3D Cartesian system. The presently available models do not offer solutions that would be applicable in practice. SIMURG (A performance-based and Sustainability-Oriented Integration Model Using Relational database architecture to increase Global competitiveness of construction industry) proposes using layers and their KPI sets in the assessment process. In addition to philosophical, organisational, integrational, and computational models, this study aims to develop a lean architecture of a relational database model by eliminating ineffective solutions in the practical dimension, i.e. in the computer model. The model can be used by individuals to help them choose built environment whose characteristics match their expectations. Public authorities can utilise the model to increase the level of accountability, transparency, and legitimacy in their decision-making processes.

USING THE ANALYTIC HIERARCHY PROCESS WITH FUZZY LOGIC ELEMENTS TO OPTIMIZE THE DATABASE STRUCTURE

Context. Informational systems are very common and use databases to store information that users need. Many different data models can be used but the relational model is still relevant. The last decade show tendency of using distributed databases while working with relational data model and this approach requires a specially designed module to synchronize data of all separate databases. Considering optimizing the database structure, researchers didn’t pay much attention to the potential of users’ SQL-queries history. The optimal structure of all the distributed nodes could reduce the necessity of synchronization while the data access speed and its actuality would remain stable. The object of the research is the process of optimizing the structure of the distributed database of corporate information systems, which are based on the relational database’s model. Objective. The research aims at improving the accuracy of the data representation marker’s value on the distributed corporate information system’s (DCIS) node, obtained using the analytic hierarchy process by applying the fuzzy logic elements while processing the alternatives’ global priority vector. Method. The research’s authors in the set of their previous works emphasize the potential of using the collected history of users’ SQL queries. Firstly presented technology of users’ queries parsing. Then, the idea of using the multidimensional database for analyzing users’ queries by slices of workstation type, application, user, and his/her position was considered. Finally, the authors gave the full-scaled mathematical model for formalizing database and query models, and criteria of database structure’s optimality.The current research continues the given sequence and tries to increase the efficiency of the decision support system, by introducing elements of fuzzy logic to the analytic hierarchy process algorithm. The approach’s main idea is in presenting the global priorities vector in the form of a series of fuzzy sets of one variable with subsequent transformation to the exact value. This approach made it possible to maintain the accuracy of the obtained result while decreasing the number of solution alternatives. For new tuples added to the database’s tables after all calculations had been performed, the problem was formalized. After obtaining the probability of a tuple belonging to the class “needed” and performing the normalization of the value, it is taken as the level of the representation marker. Accordingly, the data is loaded onto the node if this value is greater than the optimal level of the representation marker for the DCIS node. Results. After calculating and obtaining the alternatives global priorities’ vector in order to improve the accuracy of the obtained result, the apparatus of fuzzy sets was used. The obtained vector of global priorities was presented as a vector of fuzzy digits for the data representation marker with subsequent transformation to the exact value. This approach made it possible to maintain the accuracy of the obtained result while decreasing the number of solution alternatives. Conclusions. While working on the research, the concept of a data representation marker on the DCIS node for the elements of the SQL query model was introduced. An aggregation function has been developed that allows determining the level of need for attributes and tuples in the database’s relation for the DCIS node based on the statistics of SQL queries. A model of the dependence of the database structure’s optimality criteria on the value of the data representation marker is built. Received further development method of analytic hierarchy process. The initialization of the alternatives’ pairwise comparisons matrix can be performed automatically according to the obtained mathematical models. Representation of the obtained result in the form of the vector of fuzzy numbers with the reduction to the exact value allows increasing the accuracy of the obtained results.

Making Learned Query Optimization Practical

Query optimization has been a challenging problem ever since the relational data model had been proposed. The role of the query optimizer in a database system is to compute an execution plan for a (relational) query expression comprised of physical operators whose implementations correspond to the operations of the (relational) algebra. There are many degrees of freedom for selecting a physical plan, in particular due to the laws of associativity, commutativity, and distributivity among the operators in the (relational) algebra, which necessitates our taking the order of operations into consideration. In addition, there are many alternative access paths to a dataset and a multitude of physical implementations for operations, such as relational joins (e.g., merge-join, nestedloop join, hash-join). Thus, when seeking to determine the best (or even a sufficiently good) execution plan there is a huge search space.

DEVELOPMENT OF A PROTOTYPE DATABASE OF INFORMATION TECHNOLOGY FOR MONITORING WATER CONSUMPTION FROM THE WATER SUPPLY NETWORK OF SETTLEMENTS

The availability of reliable and systematic information on water consumption modes is the basis for optimizing the operation modes of water supply facilities. The necessity of water consumption monitoring is substantiated in order to detect changes in the operation conditions of water supply system facilities due to the influence of seasonal and social factors. The daily water consumption graph was used as an indicator of the operation mode of water supply facilities. The procedure of analysis of actual water consumption graphs is the methodological basis of the proposed information technology. It involves the search for hidden regularities in the formation of water consumption and is based on the description of irregularities and the form of daily water consumption graphs. The design of the information technology prototype is performed using a unified modeling language. The functionality and behavior of information monitoring technology is described using a use case diagram. A structured database is the information basis of information technology. It contains a structured set of initial data on daily water consumption graphs, the results of the description of their irregularities and shapes, the results of the classification of daily water consumption graphs and a formalized description of the analysis results. A relational data model was used to construct the database. The developed database contains twelve tables. Relationship between tables is defined as «one to many». Implementation of monitoring on a regular basis will ensure the accumulation of new data. The integration of the developed prototype database and information technology into the information system for monitoring the functioning of the water supply system will help to identify new knowledge about the peculiarities of the formation of the water consumption mode. This will identify changes in the actual operation conditions of water supply facilities.

A Fuzzy Query Method Oriented to Knowledge Graph

With the widespread application of Knowledge Graphs, Knowledge Graph query technology has received widespread attention. The daily use of natural language usually contains fuzzy terms. The use of fuzzy terms in queries is more in line with natural language habits. Therefore, fuzzy questions have been widely discussed and applied in relational data models, and their use in NoSQL data models has also begun to be affected in recent years. RDF (Resource Description Framework) is a representative model of the knowledge graph, and SPARQL is the standard query language of RDF. To realize the fuzzy query of the knowledge graph, this paper introduces the unclear expansion syntax of aggregate function based on summarizing and analyzing the existing SPARQL fuzzy expansion. Supplements the qualitative preference analysis method then proposes and SPARQL1. 1 Corresponding fuzzy SPARQL1.1 (f-SPARQL1.1) simultaneously provides strategies and techniques for rewriting ambiguous queries into classic SPARQL queries. In addition, this article also provides a visual interface for defining the membership functions of fuzzy terms and provides three interactive methods for setting fuzzy membership functions. Compared with the previous fuzzy query methods for RDF models, the method proposed in this paper has more vital fuzzy query ability and more flexible universality. The experimental results show the usability of the method proposed in this paper.

Transforming OpenAPI Specification 3.0 documents into RDF-based semantic web services

Web services are provided with documents that at the very least specify the endpoint, input parameters, and output or response of each operation to expose their capabilities. This should be considered through an understandable format for humans and/or machines. In the Representational State Transfer (REST) architectural style, the OpenAPI Specification (OAS) is used as a reference to create web service descriptions. However, it only supports syntactic interoperability, leading to the incapability of supporting the automated selection process. To overcome this, OAS documents must be enhanced by including semantics to each resource to provide “understandable” services. Therefore, this study aims to develop a system capable of transforming resources in OAS documents into RDF-based semantic web services. To begin, a relational database schema based on the OAS structure is created to store all objects in the OAS document. The published open-linked vocabulary was then used to create the ontology, which maps resources and their relationships on the RDF data model. To build RDF-based semantic web services, R2RML was used to generate the relational database model into triple RDF. The proposed system was also tested through prototyping and using a dataset of 106 OAS documents, which were downloaded from APIs.guru between 5–10 May 2021. The number of triple RDFs generated per document varied with resource rate. An OAS document generates 36 to 16,505 triple RDF in a dataset. The end product was a triple RDF knowledge base maintained by a graph management database. It is now possible to find service operations, input and output parameters, and service composition requirements utilizing the repository semantic web services using SPARQL. On the other hand, the use of relational databases to store OAS resources increased reuse efficiency by approximately 48%, owing to service developers designing interoperability between uniform parameter services, which were then used as input and output.

Information system of the university editorial-publishing complex

This article presents the results of designing the structure of the data models necessary for the proper functioning of a database supporting the editorial, proofreading, printing, and publishing activities of an educational organization. The goal of the study is to create a coherent, objective, holistic, and non-redundant data set appropriate to the subject area to support the process of digital transformation of the publishing and publication sphere within an educational organization. The resulting data models reflect the logical structure of databases independent of the choice of a particular database management system. As part of the design, JavaScript Object Notation models and relational data models are developed to create a relational database. The results allow identifying and estimating the resources needed to implement each process, determining all participants affecting the implementation of the relevant processes, and defining the access to resources needed by certain participants in the process.

Collaborative Development of Polymer-Based Collection Survey Methodology and Relational Data Model

ABSTRACT Plastic-focused collection surveys are a fundamental starting point for locating, identifying and assessing the conservation needs of polymer-based objects, and are increasingly conducted internationally. As surveys are both time- and people-intensive, it is critical to ensure the optimal usefulness of the data for the institution and wider conservation community through consistent documentation, using flexible robust data structures, and making results accessible. However, only summaries of survey results have been published in the conservation literature, resulting in a lack of continuity and information dissemination. This is problematic for institutions without the necessary resources to conduct extensive surveys and to identify polymer types. This paper outlines the collaborative development of a plastic-focused collection survey methodology, highlighting the importance of developing and testing standardised procedures, engaging with expert collaborators, and ensuring results are accessible.

Data Integration and Interoperability: Towards a Model-Driven and Pattern-Oriented Approach

Data integration is one of the core responsibilities of EDM (enterprise data management) and interoperability. It is essential for almost every digitalization project, e.g., during the migration from a legacy ERP (enterprise resource planning) software to a new system. One challenge is the incompatibility of data models, i.e., different software systems use specific or proprietary terminology, data structures, data formats, and semantics. Data need to be interchanged between software systems, and often complex data conversions or transformations are necessary. This paper presents an approach that allows software engineers or data experts to use models and patterns in order to specify data integration: it is based on data models such as ER (entity-relationship) diagrams or UML (unified modeling language) class models that are well-accepted and widely used in practice. Predefined data integration patterns are combined (applied) on the model level leading to formal, precise, and concise definitions of data transformations and conversions. Data integration definitions can then be executed (via code generation) so that a manual implementation is not necessary. The advantages are that existing data models can be reused, standardized data integration patterns lead to fast results, and data integration specifications are executable and can be easily maintained and extended. An example transformation of elements of a relational data model to object-oriented data structures shows the approach in practice. Its focus is on data mappings and relationships.

Relational Data Model on The University Website with Search Engine Optimization

The visibility of a university’s website on the search engine becomes an essential factor to reach a wider audience. One way to improve the visibility of a website is through Search Engine Optimization (SEO). University’s website development with SEO is inseparable from the data model because SEO supporting factors are parts of the consideration in the components and structure of the data model. This study aims to build a data model for a university website accompanied by SEO. The relational data model is used in this study based on the performance and maturity in defining schema-based design. This study was conducted through four sequential stages: literature review, planning, implementation, and evaluation. The resulting relational data model is one that has accommodated four supporting factors for SEO, namely Meta description, Meta keywords, URL structure, and image description. This study has succeeded in building a relational data model at the abstraction level of conceptual and logical. In the conceptual data model, one entity and 11 attributes are formed. The logical data model was implemented in independent work environments using RelaX and operational requirements can be fulfilled by representing each table or relationship in the schema using relational algebra.

Development of a bacterial regulatory motif database

O b j e c t i v e s . The amount of data generated by modern methods of high-throughput sequencing is such that their analysis is performed mainly in automatic mode. In particular, the use of newly decoded genomic sequences is possible only after the annotation of functional elements of the genome, which, as a rule, is performed by automatic pipelines. Such annotation pipelines do a good job to identify the genes, but none of them annotate regulatory elements. Without these elements it is not possible to understand when and how genes can be expressed. Information on the regulatory elements of bacteria is collected in several specialized databases (RegulonDB, CollecTF, Prodoric2, etc.), however, only a part of this information can be used for annotation of regulatory elements, and only for a very limited range of bacteria. Previously, we proposed a clear formal criterion for applying regulatory information to any bacterial genome. Such a criterion is the CR tag, a sequence of amino acid residues of a transcriptional regulator that specifically contacts the nitrogenous bases of regulatory element in genomic DNA. The mathematical model of a regulatory element (motif) associated with a CR tag can be correctly applied to annotate similar elements in any genomes encoding a transcriptional regulator with an identical CR tag. The accumulation of motifs associated with CR tags raised the question of their ordered storage for the convenience of subsequent use in the annotation of genomic sequences. Since no one of well-known databases uses the concept of CR tags, a new database ought to be developed. Thus, the goal of this work is to create a database with information about bacterial transcription factors and DNA sequences recognized by them, suitable for annotation of regulatory sequences in bacterial genomes.M e t h o d s . Infological modeling of the subject area was carried out using the IDEF1X methodology. The database was developed using the Microsoft SQL Server DBMS. A cross-platform application for importing data into a database is written in C++ using Qt technology.Re s u l t s . As a result of the study of the subject area, a relational data model was developed and implemented in the Microsoft SQL Server DBMS, which allows holistic storage of information about accumulated transcription regulation motifs in bacteria, including information about the publications confirming their correctness. To automate the process of entering accumulated data, a cross-platform application was developed for importing structured data on transcription factors.Co n c l u s i o n . The main difference of the developed database is the use of CR-tag concept. Records of mathematical models of regulatory elements (motifs) in the database are associated with a CR tag and, therefore, can be correctly used to annotate similar elements in any genomes encoding a transcriptional regulator with an identical CR tag. The developed database will provide structured and holistic data storage, as well as their quick search when used in the pipeline for automatic annotation of regulatory elements in bacterial genomic sequences.