Relational Model Research Articles

Implementación Asistida de Bases de Datos Relacionales en Firebird/Interbase

Un universo de discurso puede representarse a través de un diseño conceptual traducible en un modelo de datos lógico relacional y puede implementarse utilizando un sistema de gestor de bases de datos relacionales como Firebird/Interbase (FB). FB tiene una serie de tecnologías tales como: disparadores, procedimientos almacenados, tablas derivadas a partir de procedimientos; que permiten la resolución de los siguientes casos de implementación genéricos: atributos derivados, cardinalidad máxima, tipo - subtipo mutuamente excluyentes, entidad fuerte – entidad débil con discriminante ascendente consecutivo. Estos casos típicos se enmarcan en dos modelos de implementación posibles y extremos: interacción directa (modelo A) e interacción indirecta (modelo B) sobre las tablas de la base de datos. Se desarrollan cada una de estas implementaciones posibles en ambos modelos; se automatiza todo el desarrollo utilizando un software desarrollado como producto de este trabajo, que facilita la correcta implementación de bases de datos relacionales.

이기종 스마트워치의 라이프로그 수집을 위한 관계형 데이터베이스 모델

최근 웨어러블 디바이스 시장이 커지며 스마트워치를 이용하여 라이프로그를 수집하는 연구들도 많이 진행되었다. 하지만 Fitbit, 애플, 삼성, 샤오미 등 다양한 스마트워치 제조사에서 각기 다른 플랫폼에서 서비스함에 따라 각각의 라이프로그를 수집하는 것이 어렵다. 따라서 이 논문은 이기종 스마트워치에서 생성되는 라이프로그를 수집하는 시스템과 라이프로그를 통합적으로 관리할 수 있는 데이터베이스 모델을 제안한다. 제안 모델은 관계형 데이터베이스에 기반하며 이기종 스마트워치를 위한 확장성에 초점을 맞춘다. 또한 이 논문은 각 스마트워치 플랫폼에서의 라이프로그 수집기를 구현하고 제안 데이터베이스 모델을 구축한다. 평가를 위하여 라이프로그를 저장할 수 있는 두 가지 유형의 데이터베이스 모델을 정의하고 확장성을 평가한다. 또한 각 스마트워치 별 라이프로그 수집기의 비교를 통하여 제안 데이터베이스 모델의 우수성을 보인다.

Дослідження методів і технологій інтеграції онтологічної моделі з реляційними даними

A study of methods and technological solutions on Data Mapping concerning the integration of an ontological and relational data models has been carried out.The main objective of the investigation is to accelerate and reduce the cost of constructing some ontological models for systems that process distributed data in a heterogeneous environment.At present, the theoretical basis for the integration of various data models (Data Mapping) is a di-rection that has been defined as Ontology-based data integration. The theoretical and practical develop-ment of this area is the Ontology-Based Data Access (OBDA) approach, which integrates ontological models presented in the form of RDF graphs with relational data.The methodology of Data Mapping application for distributed data processing systems has been developed. As an example the process of data models consolidation for Ukrainian State Budget monitor-ing system is given. The database of Ukrainian State Budget monitoring system consists of many rela-tional tables, which contain reports of the State Treasury on the implementation of revenue and expendi-ture parts of both state and municipal budgets, as well as the regional section. In addition, the database contains data on lending and arrears of budget institutions.The ontology model connects to data sources through a declarative specification provided in terms of display, including classes and their properties.The given application converts a SPARQL queries into a SQL query to the relational database. The generated SQL query may be execute by the Oracle 11g database driver, which returns the result as a data snapshot. Then, to improve the performance of SPARQL queries, it should be used the semantic query optimization method.Indicative application of the methodology on the example of the construction of an ontological model of the system of monitoring the state budget of Ukraine in Protege 5. It has been demonstrated the results of the execution of the SPARQL-queries to the relational data of the budget process under the Oracle 11g Database. It has been shown the directions semantic optimization for SPARQL-queries, which allow improving obtained data quality.The proposed methodology allows: to integrate data presented by different models — ontological and relational knowledge acquisition; to overcome constraints when databases based on outdated data models are being merged with modern ontological-oriented systems; to eliminate data redundancy in designing knowledge-based systems.

Microtools Based on the Relational Data Model – Conformity between Foreign and Primary Keys

Abstract Tools which support and automate the execution of information processes may be created in a spreadsheet by following the author’s signature concept of “Microtools Based on the Relational Data Model”. While developing them, one must apply adequate solutions enabling representation of identified entities and ensuring data integrity as well as acquisition of the reports and collations assumed to be generated. This article addresses the problem of conformity between values of foreign keys in child entities and values of primary keys in parent entities. Four cases of relationships between a parent entity and a child entity have been discussed. One of the criteria that differentiates between these cases is the occurrence of a simple key or a composite key. Another applicable criterion is the sequence in which data are entered using the tool. With regard to all the identified cases, solutions that support data integrity have been presented and discussed. In accordance with the concept in question, the solutions proposed in the paper make use of standard spreadsheet features and components, without involving any additional code created in any programming language.

Hybrid approach for big data localization and semantic annotation

SummaryMost of the data concerning business‐oriented systems are still based on either NoSQL or the relational data model. On the other hand, Semantic Web data model Resource Description Framework (RDF) has become the new standard for data modeling and analysis. Due to this situation integration of NoSQL, Relational Database (RDB) and RDF data models are becoming a required feature of the systems. Many solutions like tools and languages are provided in the shape of the transformation of data from RDB to RDF. This research is aimed to compare and map data models used for transformation between NoSQL, RDB, and Semantic Web. This study will help in achieving much better and enhanced technology‐based systems for retrieval and storage of data among Big‐data annotation using Semantic Web. It is aimed to reduce the response time of queries and offer compatibility with the web and semantically enriched data format. A drugs dataset is being used and transformed to have semantical meaning embedded and linked to support big data localization. At the end of this paper, RDF graph and bar chart are used to represent transformed data after passing through the proposed model. Big data localization helps in gaining fast and accurate results.

Descriptive conversion of performance indicators in rugby union

ObjectivesThe primary aim of this study was to examine whether accuracy of rugby union match prediction outcomes differed dependent on the method of data analysis (i.e., isolated vs. descriptively converted or relative data). A secondary aim was to then use the most appropriate method to investigate the performance indicators (PI’s) most relevant to match outcome. MethodsData was 16 PI’s from 127 matches across the 2016–17 English Premiership rugby season. Given the binary outcome (win/lose), a random forest classification model was built using these data sets. Predictive ability of the models was further assessed by predicting outcomes from data sets of 72 matches across the 2017–18 season. ResultsThe relative data model attained a balanced prediction rate of 80% (95% CI – 75–85%) for 2016–17 data, whereas the isolated data model only achieved 64% (95% CI – 58–70%). In addition, the relative data model correctly predicted 76% (95% CI – 68–84%) of the 2017–18 data, compared with 70% (95% CI – 63–77%) for the isolated data model. From the relative data model, 10 PI’s had significant relationships with game outcome; kicks from hand, clean breaks, average carry distance, penalties conceded when the opposition have the ball, turnovers conceded, total metres carried, defenders beaten, ratio of tackles missed to tackles made, total missed tackles, and turnovers won. ConclusionsOutcomes of Premiership rugby matches are better predicted when relative data sets are utilised. Basic open-field abilities based around an effective kicking game, ball carrying abilities, and not conceding penalties when the opposition are in possession are the most relevant predictors of success.

Analytics Driven Data Model in Digital Services

Data models are generally applied to construct consolidated abstraction of various rich and different domains of data. In this paper, we focus on the digital services domain in particularly customer related data model and its structure in helping to shape the analytics capabilities. The traditional Entity Relationship Diagram (ERD) is used as the cornerstone of the strategy and further elaboration is made through abstraction to encompass areas in the digital services. A data model is developed to cover both static aspects (customers’ profile) and dynamic aspects (customers’ behaviour). The foundation of the customer aspect is constructed in classes that represent different types of customer touch points represented as digital footprint which analogize physical activities. The customer dynamic aspects of digital service are modeled with a group of classes where priority is embodied in different associations involving creation and termination of the identified interaction. The suggested data model can be deployed in development of frameworks for customer related applications and enhancement of analytics capabilities. Keywords: Data model, Analytics, Digital services

A case-base fuzzification process: diabetes diagnosis case study

Medical case-based reasoning (CBR) systems require the handling of vague or imprecise data. The fuzzy set theory is particularly suitable for this purpose. This paper proposes a case-base preparation framework for CBR systems, which converts the electronic health record medical data into fuzzy CBR knowledge. It generates fuzzy case-base knowledge by suggesting a standard crisp entity–relationship data model for CBR case-base. The resulting data model is fuzzified using a proposed relational data model fuzzification methodology. The performances of this methodology and its resulting fuzzy case-base structure are evaluated. Diabetes diagnosis is used as a case study. A set of 60 real diabetic cases is used in the study. A fuzzy CBR system is implemented to check the diagnoses accuracy. It combines the resulting fuzzy case-base with a proposed fuzzy similarity measure. Experimental results indicate that the proposed fuzzy CBR method is superior to traditional CBR and other machine-learning methods. Our fuzzy CBR achieves an accuracy of 95%, a precision of 96%, a recall 97.96%, an f-measure of 96.97%, a specificity of 81.82%, and good robustness for dealing with vagueness. The resulting fuzzy case-base relational database enhances the representation of case-base knowledge, the performance of retrieval algorithms, and the querying capabilities of CBR systems.

Development of relational river data model based on river network for multi-dimensional river information system

Development of relational river data model based on river network for multi-dimensional river information system

Modeling fuzzy data with RDF and fuzzy relational database models

Modeling fuzzy data with RDF and fuzzy relational database models

Centralised Bitcoin: A Secure and High Performance Electronic Cash System

The initial iterations of the Bitcoin peer-to-peer electronic cash system have demonstrated its benefits as payment system and the superiority of fixed supply, cryptographically secured forms currency over conventional fiat currencies. The operation of the mining process over the last nine years had demonstrated the natural tendency of mining to become a commercially and geographically centralised process, resulting from economies of scale and regional variations in electricity prices. Bottlenecks in transaction processing and increasing transaction costs demonstrate we have now reached the point where Bitcoin should evolve to its next iteration. This iteration will produce vast improvements of performance in terms of transactions processing times, transaction processing costs, transaction process per second and reduction in the creation of greenhouse gases. It will also create a dramatic improvement in security, interoperability with existing financial infrastructure and compliance with international regulatory standards. These improvements will be monumental compared to those that could be achieved through the implementation of Bitcoin Cash or Segregated Witness (SegWit). The proposal is to covert the existing Blockchain data structure to a relational data model and migrate from the current proof of work model to a centralised transaction validation service (TVS).

A knowledge base with modularized ontologies for eco-labeling: Application for laundry detergents

Along with the rising concern of environmental performance, eco-labeling is becoming more and more popular. However, the complex process of eco-labeling is demotivating manufacturers and service providers to be certificated. The knowledge contained in eco-labeling criteria documents is not semantically exploitable to computers. Traditional knowledge base in relational data model is not inter-operable, lacks inference support and is difficult to be reused. In our research, we propose a comprehensive knowledge base composed of interconnected OWL (Ontology Web Language) ontologies. This ontology based knowledge base allows reasoning and semantic query. In this paper, a modularization scheme about ontology development is introduced and it has been applied to EU Eco-label (European Union Eco-label) laundry detergent product criteria. This scheme separates entity knowledge and rule knowledge so that the ontology modules can be reused easily in other domains. Reasoning and inference based on SWRL (Semantic Web Rule Language) rules in favor of eco-labeling process is also presented.

Class dependency of fuzzy relational database using relational calculus and conditional probability

In this paper, we propose a design of fuzzy relational database to deal with a conditional probability relation using fuzzy relational calculus. In the previous, there are several researches about equivalence class in fuzzy database using similarity or approximate relation. It is an interesting topic to investigate the fuzzy dependency using equivalence classes. Our goal is to introduce a formulation of a fuzzy relational database model using the relational calculus on the category of fuzzy relations. We also introduce general formulas of the relational calculus for the notion of database operations such as ’projection’, ’selection’, ’injection’ and ’natural join’. Using the fuzzy relational calculus and conditional probabilities, we introduce notions of equivalence class, redundant, and dependency in the theory fuzzy relational database.

Model driven development of hybrid databases using lightweight metamodel extensions

ABSTRACTThe transition from traditional to Big Data technologies has put new challenges in front of existing database modelling techniques. The ‘Design then implement’ approach to relational database modelling, could not be fully applied to ‘Discover and analyze’ databases with rapid data growth. In the context of Big Data, models have preserved a descriptive role: they are used to analyze and understand the architecture of hybrid data structures. From the Model-Driven Engineering point of view, several questions emerge: Could Big Data and hybrid database models represent a formal specification for a solution, which can be transformed to an executable specification for a specific target platform? Does the refining mechanism have its place in the process of hybrid data structure development? An approach to modelling hybrid databases, based on empirically validated concepts of Model-Driven Engineering, is presented in this paper. The approach was applied during the implementation of Precision Agriculture Based Information Service (PAIS) application – a web-based service for the collection and archiving of diverse data on crops, and their presentation to farmers.

A data infrastructure for the assessment of health care performance: lessons from the BRIDGE-health project

The integration of different administrative data sources from a number of European countries has been shown useful in the assessment of unwarranted variations in health care performance. This essay describes the procedures used to set up a data infrastructure (e.g., data access and exchange, definition of the minimum common wealth of data required, and the development of the relational logic data model) and, the methods to produce trustworthy healthcare performance measurements (e.g., ontologies standardisation and quality assurance analysis). The paper ends providing some hints on how to use these lessons in an eventual European infrastructure on public health research and monitoring. Although the relational data infrastructure developed has been proven accurate, effective to compare health system performance across different countries, and efficient enough to deal with hundred of millions of episodes, the logic data model might not be responsive if the European infrastructure aims at including electronic health records and carrying out multi-cohort multi-intervention comparative effectiveness research. The deployment of a distributed infrastructure based on semantic interoperability, where individual data remain in-country and open-access scripts for data management and analysis travel around the hubs composing the infrastructure, might be a sensible way forward.

QPortal: A platform for data-driven biomedical research

Modern biomedical research aims at drawing biological conclusions from large, highly complex biological datasets. It has become common practice to make extensive use of high-throughput technologies that produce big amounts of heterogeneous data. In addition to the ever-improving accuracy, methods are getting faster and cheaper, resulting in a steadily increasing need for scalable data management and easily accessible means of analysis.We present qPortal, a platform providing users with an intuitive way to manage and analyze quantitative biological data. The backend leverages a variety of concepts and technologies, such as relational databases, data stores, data models and means of data transfer, as well as front-end solutions to give users access to data management and easy-to-use analysis options. Users are empowered to conduct their experiments from the experimental design to the visualization of their results through the platform. Here, we illustrate the feature-rich portal by simulating a biomedical study based on publically available data. We demonstrate the software’s strength in supporting the entire project life cycle. The software supports the project design and registration, empowers users to do all-digital project management and finally provides means to perform analysis. We compare our approach to Galaxy, one of the most widely used scientific workflow and analysis platforms in computational biology. Application of both systems to a small case study shows the differences between a data-driven approach (qPortal) and a workflow-driven approach (Galaxy).qPortal, a one-stop-shop solution for biomedical projects offers up-to-date analysis pipelines, quality control workflows, and visualization tools. Through intensive user interactions, appropriate data models have been developed. These models build the foundation of our biological data management system and provide possibilities to annotate data, query metadata for statistics and future re-analysis on high-performance computing systems via coupling of workflow management systems. Integration of project and data management as well as workflow resources in one place present clear advantages over existing solutions.

Extended tuple constraint type as a complex integrity constraint type in XML data model - definition and enforcement

A database management system (DBMS) is based on a data model whose concepts are used to express a database schema. Each data model has a specific set of integrity constraint types. There are integrity constraint types, such as key constraint, unique constraint and foreign key constraint that are supported by most DBMSs. Other, more complex constraint types are difficult to express and enforce and are mostly completely disregarded by actual DBMSs. The users have to manage those using custom procedures or triggers. eXtended Markup Language (XML) has become the universal format for representing and exchanging data. Very often XML data are generated from relational databases and exported to a target application or another database. In this context, integrity constraints play the essential role in preserving the original semantics of data. Integrity constraints have been extensively studied in the relational data model. Mechanisms provided by XML schema languages rely on a simple form of constraints that is sufficient neither for expressing semantic constraints commonly found in databases nor for expressing more complex constraints induced by the business rules of the system under study. In this paper we present a classification of constraint types in relational data model, discuss possible declarative mechanisms for their specification and enforcement in the XML data model, and illustrate our approach to the definition and enforcement of complex constraint types in the XML data model on the example of extended tuple constraint type.

MetaHCR: a web-enabled metagenome data management system for hydrocarbon resources.

The ever-increasing metagenomic data necessitate appropriate cataloguing in a way that facilitates the comparison and better contextualization of the underlying investigations. To this extent, information associated with the sequencing data as well as the original sample and the environment where it was obtained from is crucial. To date, there are not any publicly available repositories able to capture environmental metadata pertaining to hydrocarbon-rich environments. As such, contextualization and comparative analysis among sequencing datasets derived from these environments is to a certain degree hindered or cannot be fully evaluated. The metagenomics data management system for hydrocarbon resources (MetaHCRs) enables the capturing of marker gene and whole metagenome sequencing data as well as over 300 contextual attributes associated with samples, organisms, environments and geological properties, among others. Moreover, MetaHCR implements the Minimum Information about any Sequence–hydrocarbon resource specification from the Genomic Standards Consortium; it integrates a user-friendly web interface and relational database model, and it enables the generation of complex custom search. MetaHCR has been tested with 36 publicly available metagenomic studies, and its modular architecture can be easily customized for other types of environmental and metagenomics studies.

MetaNIRS: A Relational Database for the Categorization, Organization and Meta-Analysis of Optical Functional Neuroimaging Research

MetaNIRS: A Relational Database for the Categorization, Organization and Meta-Analysis of Optical Functional Neuroimaging Research

Experimental study on the effectiveness of a teaching approach using barker or Bachman notation for conceptual database design

Relational databases are one of the pillars of information systems and as a consequence conceptual and logical design can be seen as the foundation of almost every application regardless of the platform. Nevertheless, little research has been done so far on the effectiveness of teaching approaches to database basic concepts and design. This paper introduces a multi-level experimental study developed to examine the effectiveness of learning database fundamentals, depending on the notation used for conceptual design. The main experiment was carried out at a higher education institution. We developed different experimental instruments to evaluate the effectiveness of a teaching approach using Barker or Bachman notation for conceptual database design. In contrast to Barker notation, Bachman notation incorporates elements of logical design (i.e. foreign keys) in the conceptual design level. Students? achievements were examined with regard to influencing factors throughout the learning process. Introducing the Bachman notation and a manual transformation from a conceptual into a logical data model, could increase students? understanding of conceptual, logical and relational data model concepts. We found that the notation used during the learning process has an impact on students? achievements. Moreover, students achieved better results when solving tasks presented with a notation that incorporates elements of logical design into the conceptual level. Finally, we required the use of manual transformation from a conceptual into a logical data model and the results confirmed an increase in students? understanding.