Relational Model Research Articles

Modeling of Linguistic Reference Schemes

When using natural language, people typically refer to individual things by using proper names or definite descriptions. Data modeling languages differ considerably in their support for such linguistic reference schemes. Understanding these differences is important for modeling reference schemes within such languages and for transforming models from one language to another. This article provides a comparative review of reference scheme modeling within the Unified Modeling Language (version 2.5), the Barker dialect of Entity Relationship modeling, Object-Role Modeling (version 2), relational database modeling, and the Web Ontology Language (version 2.0). The author identifies which kinds of reference schemes can be captured within these languages as well as those reference schemes that cannot be. The author's analysis covers simple reference schemes, compound reference schemes, disjunctive reference and context-dependent reference schemes.

Concepts and evaluation of the extended entity-relationship approach to database design in a multi-paradigm information system modeling tool

Different approaches to information system (IS) development are based on different data models. The selection of a data model for conceptual design, among other things, depends on the problem domain, the knowledge, and the personal preferences of an IS designer. In some situations, a simultaneous usage of different approaches to the conceptual database design and IS development may lead to the most appropriate solutions. In our previous research we have developed a tool that provides an evolutive and incremental approach to IS development, which is based on the form type data model. The approaches based on the Extended Entity-Relationship (EER) and class data models are broadly accepted throughout the community of IS designers. In order to support the simultaneous usage of approaches based on the form type, EER and class data models, we have developed the Multi-Paradigm Information System Modeling Tool (MIST). In this paper, we present a part of our MIST tool that supports EER approach to a database design. MIST components currently provide a formal specification of an EER database schema specification and its transformation into the relational data model, or the class model. Also, MIST allows generation of Structured Query Language code for a database creation and procedural code for implementing database constraints. In addition, Java code that stores and processes data from the database, may be generated from the class model. In this paper, we present the evaluation study of the MIST EER domain-specific language. Users' perceptions of language quality characteristics are used for the evaluation.

On some algorithms for the division in the relational data models

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Multivalued Dependencies in Fuzzy Multivalued Relational Databases Using Fuzzy Functions

In this paper we discuss an important integrity constraint called multivalued dependency (mvd), which occurs as a result of the first normal form, in the framework of a newly proposed model called fuzzy multivalued relational data model. The fuzzy multivalued relational data model proposed in this paper accommodates a wider class of ambiguities by representing the domain of attributes as a “set of fuzzy subsets”. We show that our model is able to represent multiple types of impreciseness occurring in the real world. To compute the equality of two fuzzy sets/values (which occur as tuple-values), we use the concept of fuzzy functions. So the main objective of this paper is to extend the mvds in context of fuzzy multivalued relational model so that a wider class of impreciseness can be captured. Since the mvds may not exist in isolation, a complete axiomatization for a set of fuzzy functional dependencies (ffds) and mvds in fuzzy multivalued relational schema is provided and the role of fmvds in obtaining the lossless join decomposition is discussed. We also provide a set of sound Inference Rules for the fmvds and derive the conditions for these Inference Rules to be complete. We also derive the conditions for obtaining the lossless join decomposition of a fuzzy multivalued relational schema in the presence of the fmvds. Finally we extend the ABU's Algorithm to find the lossless join decomposition in context of fuzzy multivalued relational databases. We apply all of the concepts of fmvds developed by us to a real world application of “Technical Institute” and demonstrate that how the concepts fit well to capture the multiple types of impreciseness.

On the computability of affordances as relations

AbstractOne of the principal advantages of affordance-based design is that Gibson's theory of affordances is a relational theory, akin to other relational approaches such as relational biology and relational computer science. The relationships between artifacts and their designers and users are of such primary importance that only a theory that is able to deal with those relationships directly appears to be sufficient for describing the wide breadth of problems in engineering design. However, there is no precise definition for what qualifies as a relational theory. In mathematics, we do find something approaching a theory of relations, dating back at least to Charles Peirce'sLogic of Relativesaround 1870. While rather general, Peirce's ideas on the subject laid the foundation for advances in the 20th century, including the relational model of databases. This paper is a first attempt at applying the mathematics of relations to affordances, with the aim of more precisely characterizing affordances, which heretofore have been difficult to define and, lacking appropriate mathematics, nearly impossible to subject to computation. Meanwhile, the implicit computability of affordances as relations is demonstrated by examples drawn from engineering, physics, computer science, biology, and architecture.

A relational database model and prototype for storing diverse discrete linguistic data

A relational database model and prototype for storing diverse discrete linguistic data

Một mô hình cơ sở dữ liệu quan hệ mờ

This paper introduces a fuzzy relational data base model (FRDB) that extends the conventional relational data base model with two key features: (1) the relations represent the set of data tuples to be the fuzzy relations; (2) selection conditions are associated with fuzzy set values to be able to query the fuzzy, imprecise information of objects in relations. An interpretation of the membership degree of tuples for fuzzy relations is proposed on the basis of the fuzzy set theory to develop the data model and data manipulating model of FRDB that consist of schemas, fuzzy relations and algebraic operations. Some properties of the fuzzy relational algebraic operations are also formulated and proven as extensions of the properties of relational algebraic operations in the conventional relational data base model.

Extracting Entity Relationship Diagram (ERD) From Relational Database Schem

Database Reverse Engineering (DBRE) is an operation used to extract requirements from any system. The operation is implemented to facilitate the understanding of the system that has a little documentation about design and architecture. DBRE is a very important process used when database designers would like to expand the system or transition to the latest technology in DBRE fields. In the relational database model DBRE try to extract Entity Relationship Diagram (ERD) from relational database schema. Database designers find content of the data for a lot of attributes are not related with their names. In this paper, proposed methodology used to extract ERD from relational database schema with the attributes related with their names, both types of entities regular and weak entity, relationships and keys, which are found in the table that has extracted the relational database schema. The basic inputs of this approach are relational database schema that generated from database. The relational database schema used to extract the information about ERD. After that, obtain information that contain keywords help database designers to extract entities and their attributes semantics related with their names from relational database schemas. Then, Determine primary keys, foreign keys and constraints of the database system. In the final step, the ERD is successfully extracted

Fuzziness in database management systems: Half a century of developments and future prospects

This comprehensive, bird's view research note combines the state of the art, a brief presentation of the history and some original solutions, and position like views of some prospective future developments of one of the most relevant and interesting areas related to the use of fuzzy logic in database management systems, notably in its querying component, and – to some extent – in a broader issue of data and information management. We briefly summarize the roots of those new applications of fuzzy logic, more relevant proposals and development in the context of fuzzification of the basic relational database model, and then some of its further generalizations. We particularly focus on fuzzy querying as a human consistent and friendly way of retrieving information due to real human intentions and preferences expressed in natural language represented via fuzzy logic and possibility theory. We mention some extensions, notably fuzzy queries with linguistic quantifiers, and point their close relation to linguistic summaries. As for newer, prospective developments, we mainly focus on bipolar queries that can accomodate the users' intentions and preferences involving some sort of a required and desired, mandatory and optional, etc. conditions. We show various ways of handling such queries. We conclude with some brief position statements of our view on relevant and promising directions, and challenges.

Integrate inconsistent and heterogeneous data based on user feedback

Purpose – Data integration is to combine data residing at different sources and to provide the users with a unified interface of these data. An important issue on data integration is the existence of conflicts among the different data sources. Data sources may conflict with each other at data level, which is defined as data inconsistency. The purpose of this paper is to aim at this problem and propose a solution for data inconsistency in data integration. Design/methodology/approach – A relational data model extended with data source quality criteria is first defined. Then based on the proposed data model, a data inconsistency solution strategy is provided. To accomplish the strategy, fuzzy multi-attribute decision-making (MADM) approach based on data source quality criteria is applied to obtain the results. Finally, users feedbacks strategies are proposed to optimize the result of fuzzy MADM approach as the final data inconsistent solution. Findings – To evaluate the proposed method, the data obtained from the sensors are extracted. Some experiments are designed and performed to explain the effectiveness of the proposed strategy. The results substantiate that the solution has a better performance than the other methods on correctness, time cost and stability indicators. Practical implications – Since the inconsistent data collected from the sensors are pervasive, the proposed method can solve this problem and correct the wrong choice to some extent. Originality/value – In this paper, for the first time the authors study the effect of users feedbacks on integration results aiming at the inconsistent data.

A Relational Database Model for Science Mapping Analysis

This paper presents a relational database model for science mapping analysis. It has been specially conceived for use in almost all of the stages of a science mapping workflow (excluding the data acquisition and pre-processing stages). The database model is developed as an entity-relations diagram using information that is typically presented in science mapping studies. Finally, several SQL queries are presented for validation purposes.

EPIARQ: A Spatial Data Infrastructure for research in medieval epigraphy

n this paper, the Polytechnic University of Madrid and the Complutense University of Madrid present EPIARQ. Under this acronym an epigraphic data model SDI (Spatial Data Infrastructure) is described as a first approach to designing and implementing a standard data model to catalogue epigraphic evidence as an aid to research in this scientific field under current European legislation and, in particular, within the framework of the European Directive INSPIRE, OGC standards and ISO 19100 standards. Monumental and architectural epigraphy from Late Antiquity in Hispania (5th to 7th centuries) was chosen for the experimental validation of the new data model database (DB). Once the entire structure of the model has been built, this physical database will be implemented with the aim of analyzing the improvements and innovations introduced in both access to information and maintenance processes, as well as in the scientific analysis of the epigraphic information it contains. In this research both the lexical variables of the pieces and their geographic location are addressed in answer to questions about types, materials, intrinsic elements, people/deities mentioned, etc. The new relational data model will cover the entire lifecycle of an epigraph, including data associated with the epigraphic text itself, the context of its discovery, its physical condition, its location and any associated literature or documents published in relation to it.

Technological issues of storing dynamic data in a relational database on research projects

The paper examines the possibility of storing dynamic data in a relational database. Under dynamic data the paper refers to data with variable structure and variable nature of different periods in terms of research projects. The relational model of databases, which is used everywhere, presents each entity of the system in a separate table. Reasonable, relationships and dependencies between them are described by defining the relationship between the relational tables. Thus, changes in nomenclatures which would lead to changes in the databases of information system will cause the need for new fields in relational tables, or even new tables and relationships that will inevitably lead to changes in software solutions. The paper focuses on how we can reorganize the data in the system, so changes in the database due to changes in database structure to be minimal. The aim of this paper is to examine the way the data for research projects is stored within the database. Specific for the data is that it has a different structure for each calendar year. For this reason, a relational database should often change its structure in order to determine proper functioning of the system. The paper presents a method of storing data on the principle of attribute – value, where the software part cares for the integrity of the data and the database takes care only for the storage. The paper presents a model for such a dynamic storage in a relational database and examines its advantages and disadvantages.

A Comparative Study of Relational and Non-Relational Database Models in a Web- Based Application

The purpose of this paper is to present a comparative study between relational and non-relational database models in a web-based application, by executing various operations on both relational and on non-relational databases thus highlighting the results obtained during performance comparison tests. The study was based on the implementation of a web-based application for population records. For the non-relational database, we used MongoDB and for the relational database, we used MSSQL 2014. We will also present the advantages of using a non-relational database compared to a relational database integrated in a web-based application, which needs to manipulate a big amount of data.

Evaluating the Cassandra NoSQL Database Approach for Genomic Data Persistency.

Rapid advances in high-throughput sequencing techniques have created interesting computational challenges in bioinformatics. One of them refers to management of massive amounts of data generated by automatic sequencers. We need to deal with the persistency of genomic data, particularly storing and analyzing these large-scale processed data. To find an alternative to the frequently considered relational database model becomes a compelling task. Other data models may be more effective when dealing with a very large amount of nonconventional data, especially for writing and retrieving operations. In this paper, we discuss the Cassandra NoSQL database approach for storing genomic data. We perform an analysis of persistency and I/O operations with real data, using the Cassandra database system. We also compare the results obtained with a classical relational database system and another NoSQL database approach, MongoDB.

Geometrical and topological modelling of caves supported by a relational database model

Underground caves and their specific structures are important for geomorphological studies. This paper presents a new tool to identify and map speleothems by surveying cave chamber interiors. The chambers were surveyed using a terrestrial laser scanner to obtain point cloud data sets. For the present work, a point cloud of 45 million points collected inside the main chamber of the Algar do Penico (Portugal) was selected. This point cloud was used to generate four 3D-meshes to represent the surface model of the cave chamber with increasing levels of detail. These models are important for geomorphological studies or virtual visits to the cave. The topological and geometrical data of the four models were implemented using the SQLite relational database management. The topological structure supports the implementation of efficient algorithms to help identifying stalactites and stalagmites from the 3D models even for those big data files of higher detail. In this paper, we also present an octree data structure implemented on this database that enables fast spatial queries. Using relational databases to store 3D models of caves, it is also possible to work with several data models simultaneously, in such a way that it is not limited by computer memory resources.

Combining computational models, semantic annotations and simulation experiments in a graph database.

Model repositories such as the BioModels Database, the CellML Model Repository or JWS Online are frequently accessed to retrieve computational models of biological systems. However, their storage concepts support only restricted types of queries and not all data inside the repositories can be retrieved. In this article we present a storage concept that meets this challenge. It grounds on a graph database, reflects the models’ structure, incorporates semantic annotations and simulation descriptions and ultimately connects different types of model-related data. The connections between heterogeneous model-related data and bio-ontologies enable efficient search via biological facts and grant access to new model features. The introduced concept notably improves the access of computational models and associated simulations in a model repository. This has positive effects on tasks such as model search, retrieval, ranking, matching and filtering. Furthermore, our work for the first time enables CellML- and Systems Biology Markup Language-encoded models to be effectively maintained in one database. We show how these models can be linked via annotations and queried.Database URL: https://sems.uni-rostock.de/projects/masymos/

A CBR system for diabetes mellitus diagnosis: case-base standard data model

Diabetes is the sixth chronic disease causing death all over the world. The early diagnosis of diabetes is a critical step in diabetes care process. Clinical decision support system (CDSS) based on case-based reasoning (CBR) helps in early detection and diagnosis of diabetes. Building CBR's case-base knowledge is the most critical challenge. On the other hand, electronic health record (EHR) is considered as the complete source of patient cases. This paper proposes a standard case-base relational data model for diabetes diagnosis based on HL7 RIM, EHR, and SNOMED CT. This model will collect all patient clinical data from distributed EHRs, and it will formulate it in the form of problem-solution. This case-base knowledge will be used as the knowledge base for a diabetes diagnosis CBR system.

Data Management and License Policy in EEGBase

Data Management and License Policy in EEGBase

A comparison of two Detailed Clinical Model representations: FHIR and CDA

Introduction:In recent years several standardized modeling methods have been proposed that separate health related data models from their underlying technical data model. These methods presuppose representation of information independently of (or uninfluenced by) technical considerations. Among these methods is the Detailed Clinical Model (DCM) paradigm. One of the pillars of this paradigm is that all representations convey the same meaning and are independent of the technical standard that is used and the DCM standard claims to achieve that. In this paper we will challenge that claim by modeling the specific DCMs in two different technical standards (CDA and FHIR) and testing if messages based on these models are interconvertible. Methods: We identified and categorized the problems that may arise when mapping or combining multiple standards creating representations of selected DCMs in both FHIR and CDA to determine possible fundamental problems using a technology independent model (DCM) to represent technical models (FHIR and CDA). To test if the theoretical problems we encountered while creating our example messages also occur during the actual transformation, and to determine any additional problems, we attempted to transform the Clinical Document Architecture (CDA) representations of the DCMs to the FHIR representations using Extensible Style sheet Language Transformations (XSLT). Results: Most aspects of the DCMs could be properly represented in both FHIR and CDA, and can be transformed from CDA to FHIR. However, we identified fundamental issues where information was lost or its meaning was changed. This results in fundamental difficulties during the implementation of the standards and when transforming one standard to another. Conclusion: Our research shows that possible loss and change of meaning and lack of interconvertibility occurs when implementing two separate technical standards based on the same DCMs. This indicates that it does matter which technical standard is used to implement a DCM.