Relational Model Research Articles

A 1NF Data Model for Representing Time-Varying Data in Relational Framework

Attaching Date and Time to varying data plays a definite role in representing a dynamic domain and resources on the database systems. The conventional database stores current data and can only represent the knowledge in static sense, whereas Time-varying database represents the knowledge in dynamic sense. This paper focuses on incorporating interval-based timestamping in First Normal Form (1NF) data model. 1NF approach has been chosen for the easily implementation in relational framework as well as to provide the temporal data representation with the modeling and querying power of relational data model. Simulation results revealed that the proposed approach substantially improved the performance of temporal data representation in terms of required memory storage and queries processing time.

Research on armored equipment IETM data

This thesis studies armored equipment IETM data concerning its huge volume, various forms and the difficulty to collect, process, store and manage. Special focuses are on data module requirements list and the establishment of relation data model after the analysis of data features and the creation of relation data model by S1000D, which provide data basis for system development.

A Relational Database Model and Tools for Environmental Sound Recognition

A Relational Database Model and Tools for Environmental Sound Recognition

Declarative data analysis

The relational database model constituted a major breakthrough in database technology. It provided a conceptual model for data storage and retrieval that made querying databases much easier. A crucial aspect of this was the introduction of declarative query languages, such as SQL. Today, databases are not only used for retrieving data, but also for analyzing them. While the science and technology of data analysis have made tremendous progress in the last decades, they have not made the leap that database technology has made. This observation was made already in the nineties. Two decades later, the situation has hardly improved. Yet, progress has been made in several relevant areas. This article discusses the concept of declarative data analysis, the scientific fields that are relevant for this, the state of the art, and some future directions.

Mapping of Description Logic to the Relational Data Model

General issues of the data integration problem are discussed, including ontology-based data integration in the semantic web. The importance of establishing mappings between ontologies and relational databases is shown, and a brief analytical review of studies in this field is given. The binary relational data model is defined and the mapping of the description logic ALC and its extensions to the binary relational data model is described.

Domain model for cadastral systems with land use component

Well-structured and organised cadastral records and cadastral maps are a prerequisite for improving land administration services. In recent years, numerous problems and issues associated with cadastral data have been encountered in Serbia and attempts to overcome these problems have been made. The integration of land registry data with cadastral data containing land use component usually results in inconsistencies in land administration databases. To address this problem, an appropriate domain model has been developed using the Unified Process methodology and considering the land administration domain model and other ISO 19000 standards. Examples of verifying land administration data integrity in relational and object-oriented data models are presented.

Seam: provably safe local edits on graphs

Algorithms that create and mutate graph data structures are challenging to implement correctly. However, verifying even basic properties of low-level implementations, such as referential integrity and memory safety, remains non-trivial. Furthermore, any extension to such a data structure multiplies the complexity of its implementation, while compounding the challenges in reasoning about correctness. We take a language design approach to this problem. We propose Seam, a language for expressing local edits to graph-like data structures, based on a relational data model, and such that data integrity can be verified automatically. We present a verification method that leverages an SMT solver, and prove it sound and precise (complete modulo termination of the SMT solver). We evaluate the verification capabilities of Seam empirically, and demonstrate its applicability to a variety of examples, most notably a new class of verification tasks derived from geometric remeshing operations used in scientific simulation and computer graphics. We describe our prototype implementation of a Seam compiler that generates low-level code, which can then be integrated into larger applications. We evaluate our compiler on a sample application, and demonstrate competitive execution time, compared to hand-written implementations.

Relational model of temporal data based on 6th normal form

Relational model of temporal data based on 6th normal form

An Efficient Approach for Modeling Temporal Database with Interval-Based Timestamping in Conventional Database Management Systems

Attaching Date and Time to varying data plays a definite role in representing a dynamic domain and resources on the database systems. The conventional database stores current data and can only represent the knowledge in static sense. Time-varying data representations in relational framework are well described in literature. In this paper we propose an approach for modeling First Normal Form (1NF) and interval-based temporal database over conventional Database Management Systems (DBMS). We choose 1NF approach with interval-based temporal data model for the easily implementation in relational framework as well as to provide the proposed temporal data representation with the modeling and querying power of 1NF relational data model.

Relational similarity-based model of data part 2: dependencies in data

This paper is a sequel to our previous paper on relational similarity-based model of data and its fundamental query systems. The present paper elaborates on the dependency theory in the similarity-based model, focusing mainly on similarity-based functional dependencies, their semantic entailment, model-theoretic properties, complete axiomatizations, characterization of nonredundant bases, computational issues, and related algorithms. The paper shows that various aspects of dependencies in ranked data tables over domains with similarities can be properly formalized using complete residuated lattices as structures for similarities and ranks. In addition to their theoretical importance, the results can be directly applied in the areas of similarity-based constraints, query result analysis, and knowledge discovery from relational data which involves similarity-based reasoning. We assume that readers are acquainted with the prequel of this paper.

A Type-2 Fuzzy Relational Database Model

This paper introduces a type-2 fuzzy relational database model (T-2FRDB) as an extension of type-1 fuzzy relational database models with a full set of basic fuzzy relational algebraic operations that can represent and query uncertain and imprecise information in real world applications. In this model, the membership degree of tuples in a fuzzy relation is represented by fuzzy numbers on [0,1], and fuzzy relational algebraic operations are defined by using the extension principle for computing minimum and maximum values of such fuzzy numbers. Some properties of the type-2 fuzzy relational algebraic operations in T-2FRDB are also formulated and proven as extensions of their counterpart in the type-1 fuzzy relational database models. DOI: 10.32913/rd-ict.vol3.no14.352

Application of GIS to Oil and Gas Pipeline Management (A Case Study of South-South Nigeria)

Over the years, the oil and gas industries have been engaging in traditional practices and procedures in their mode of operation in pipeline management. This has brought some draw backs in terms of planning and mapping of pipeline facilities. Several challenges in terms of natural disaster, equipment failure, and human error could not be averted because the traditional methods could not produce survey maps and plans required for “real time “applications. The advent of Geographic Information System (GIS) technology in the oil and gas industries has brought about rapid changes and improvements in the activities of the oil and gas industries in terms of exploration, production and transmission of products from the field to the end users. In this research, digital maps showing oil and gas pipelines as well as other relevant features such as terrain, vegetation and settlements within the study area were generated. A relational database model was developed using pipeline attributes such as; the pipeline diameter, date of installation, pressure, coating and soil type. Queries on the database provided the required information such as; the required date of maintenance of pipes, areas prone to sabotage, areas with specific pipe diameters in various sections, previous spillages and the affected areas.

Reengineering Probabilistic Relational Databases with Fuzzy Probability Measures into XML Model

This paper concentrates on modeling probabilistic events with fuzzy probability measures in relational databases and XML (Extensible Markup Language). Instead of crisp probability degrees or interval probability degrees, fuzzy sets are applied to represent imprecise probability degrees in relational databases and XML. A probabilistic XML model with fuzzy probability measures is introduced, which incorporates fuzzy probability measures to handle imprecision and uncertainty. In particular, the formal approach to reengineering the relational database model with fuzzy probability measures into the DTD (document type definition) model with fuzzy probability measures is developed in the paper.

A fuzzy ontology modeling for case base knowledge in diabetes mellitus domain

Knowledge-Intensive Case-Based Reasoning Systems (KI-CBR) mainly depend on ontologies. Ontology can play the role of case-base knowledge. The combination of ontology and fuzzy logic reasoning is critical in the medical domain. Case-base representation based on fuzzy ontology is expected to enhance the semantic and storage of CBR knowledge-base. This paper provides an advancement to the research of diabetes diagnosis CBR by proposing a novel case-base fuzzy OWL2 ontology (CBRDiabOnto). This ontology can be considered as the first fuzzy case-base ontology in the medical domain. It is based on a case-base fuzzy Extended Entity Relation (EER) data model. It contains 63 (fuzzy) classes, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, and 105 fuzzy datatypes. We populated the ontology with 60 cases and used SPARQL-DL for its query. The evaluation of CBRDiabOnto shows that it is accurate, consistent, and cover terminologies and logic of diabetes mellitus diagnosis.

A novel method for providing relational databases with rich semantics and natural language processing

PurposeWith the development of systems and applications, the number of users interacting with databases has increased considerably. The relational database model is still considered as the most used model for data storage and manipulation. However, it does not offer any semantic support for the stored data which can facilitate data access for the users. Indeed, a large number of users are intimidated when retrieving data because they are non-technical or have little technical knowledge. To overcome this problem, researchers are continuously developing new techniques for Natural Language Interfaces to Databases (NLIDB). Nowadays, the usage of existing NLIDBs is not widespread due to their deficiencies in understanding natural language (NL) queries. In this sense, the purpose of this paper is to propose a novel method for an intelligent understanding of NL queries using semantically enriched database sources.Design/methodology/approachFirst a reverse engineering process is applied to extract relational database hidden semantics. In the second step, the extracted semantics are enriched further using a domain ontology. After this, all semantics are stored in the same relational database. The phase of processing NL queries uses the stored semantics to generate a semantic tree.FindingsThe evaluation part of the work shows the advantages of using a semantically enriched database source to understand NL queries. Additionally, enriching a relational database has given more flexibility to understand contextual and synonymous words that may be used in a NL query.Originality/valueExisting NLIDBs are not yet a standard option for interfacing a relational database due to their lack for understanding NL queries. Indeed, the techniques used in the literature have their limits. This paper handles those limits by identifying the NL elements by their semantic nature in order to generate a semantic tree. This last is a key solution towards an intelligent understanding of NL queries to relational databases.

A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution

With an ever-increasing volume and complexity of data collected from a variety of sources, the efficient management of geospatial information becomes a key topic in disaster risk management. For example, the representation of assets exposed to natural disasters is subjected to changes throughout the different phases of risk management reaching from pre-disaster mitigation to the response after an event and the long-term recovery of affected assets. Spatio-temporal changes need to be integrated into a sound conceptual and technological framework able to deal with data coming from different sources, at varying scales, and changing in space and time. Especially managing the information life-cycle, the integration of heterogeneous information and the distributed versioning and release of geospatial information are important topics that need to become essential parts of modern exposure modelling solutions. The main purpose of this study is to provide a conceptual and technological framework to tackle the requirements implied by disaster risk management for describing exposed assets in space and time. An information life-cycle management solution is proposed, based on a relational spatio-temporal database model coupled with Git and GeoGig repositories for distributed versioning. Two application scenarios focusing on the modelling of residential building stocks are presented to show the capabilities of the implemented solution. A prototype database model is shared on GitHub along with the necessary scenario data.

Bilingual Education Research of Database System

System and Application is a core course of Information Management and Information Systems speciality at the Beijing Institute of Graphic Communication. This course included these contents: Relational data model theory, Database analysis and design, SQL, Database recovery and Database security. The course is basic knowledge of Information system. So, the education quality of this course is related to the education quality of professional courses. Instructor not only focused on knowledge but professional ability train. This paper mainly presented the bilingual education experiences of this course. The paper firstly introduced the challenges bilingual Education of this course. The paper mainly presented practicing bilingual education from three aspects: Selecting textbooks, Reforming Examination Style and Teaching methods. Thirdly, the paper evaluated teaching works of Database System. Finally, the paper summarized the problems in teaching practice and introduced future work.

Interview with Paul De Bra

Prof. dr. Paul De Bra is Full Professor in the Department of Computer Science at the Eindhoven University of Technology (TU/e). He holds a doctorate in mathematics and computer science from the University of Antwerp, Belgium (1987), with a thesis on Horizontal Decomposition in the Relational Database Model. During 1988 and 1989 he was a post-doctoral researcher at AT&T Bell Laboratories in Murray Hill, New Jersey, studying principles and technology for WYSIYWYG interfaces for document processing. Since December 1989 he has been working at the TU/e, first as an associate professor, and since August 1996 as a full professor and chair of the Information Systems group. Since he started working at the TU/e the research has shifted to the area of hypertext and hypermedia, with research in adaptive Web-based systems. He initiated and led the AHAM (reference model), AHA! and GRAPPLE projects on adaptive Technology-Enhanced Learning, the NWO CHIP project on adaptation and personalization in Cultural Heritage, and numerous other activities to stimulate research and development of adaptive hypermedia technology and applications. He is scientific director of the Dutch Research School for Information and Knowledge Systems and is president of User Modeling Inc. (the non-profit organization for User Modeling and Adaptive Systems). In "real" life Paul De Bra plays, repairs, composes and arranges for the accordion (and takes pictures at conferences too). "Paul De Bra" and "accordion" currently yields 76 hits on YouTube whereas "Paul De Bra" and "hypertext" yields 0. (On Google the hypertext query is more successful.)

Implementation of scalable fuzzy relational operations in MapReduce

One of the main restrictions of relational database models is their lack of support for flexible, imprecise and vague information in data representation and querying. The imprecision is pervasive in human language; hence, modeling imprecision is crucial for any system that stores and processes linguistic data. Fuzzy set theory provides an effective solution to model the imprecision inherent in the meaning of words and propositions drawn from natural language (Zadeh, Inf Control 8(3):338–353, doi:10.1016/S0019-9958(65)90241-X, 1965; IGI Global, https://books.google.com/books?id=nt-WBQAAQBAJ, 2013). Several works in the last 20 years have used fuzzy set theory to extend relational database models to permit representation and retrieval of imprecise data. However, to our knowledge, such approaches have not been designed to scale-up to very large datasets. In this paper, the MapReduce framework is used to implement flexible fuzzy queries on a large-scale dataset. We develop MapReduce algorithms to enhance the standard relational operations with fuzzy conditional predicates expressed in natural language.

Database of Physical Protection of Potentially Hazardous Objects

Эффективность построения системы физической защиты потенциально опасных объектов во многом зависит от качественного подбора комплектующих. Ввиду того что на сегодняшний день не существует единой базы технических средств физической защиты, предлагается разработанная с помощью MS SQL Server Express база данных «Средства физической защиты потенциально опасных объектов». Для проектирования базы данных был выбран Entity-Attribute-Value (EAV) - паттерн. Особенностью EAV-подхода к проектированию является представление данных в виде разреженной матрицы, где сохраняются только непустые значения. Представлена концептуальная модель предметной области с учетом особенностей EAV-подхода проектирования. В отличие от реляционной модели данных, где каждая сущность хранится в виде отдельной таблицы в базе данных, в модели EAV каждый экземпляр сущности - это объект. База данных «Средства физической защиты потенциально опасных объектов» согласно ER-диаграмме состоит из таблиц: «objtype», «objects», «strdesc», «propdesc», «strings», «properties», «units», что позволяет хранить информацию о множестве разнообразных средств физической защиты с различными атрибутами.