Relational Model Research Articles

Underutilised crops database for supporting agricultural diversification

Digital agriculture is driven by the interrelated needs to increase crop production, develop sustainable food systems and cope with the global change. Databases exist for several major crops but a barrier to increasing agrobiodiversity is the lack, and dispersed nature, of information for variously termed minor, orphan or underutilised crops. In this article, we describe an attempt to build a globally accessible database that can be used to store information for underutilised crops. A relational data model was adopted due to its robustness in terms of prototyping and building user interfaces for data governance and dissemination. We have reviewed the design of related agricultural databases, data standards and crop diversification priorities, to build a data model that encompasses major elements of the value chain of crops in the food system. Due to the importance of data accuracy, we added a metadata table that stores information about the sources of all data recorded in the database. We also built a web-based user interface for data management and access. The open-access user interface allows simple data sorting and filtering operations based on the user's needs. As an example of potential use, data were used to build an automated crop selection tool that could shortlist suitable crops based on location at global scale. Other use-cases are discussed including the development of metrics, indicators for the United Nations Sustainable Development Goals and developing bespoke diversification solutions.

Implementation of mappings between the description logic and the binary relational data model on the RDF level

This paper is dedicated to the data integration problem. In article the task of practical implementation of mappings between description logic and a binary relational data model is discussed. This method was formulated earlier at a theoretical level. A practical technique to test mapping engines using RDF is provided in the current paper. To transform the constructs of the description logic ALC and its main extensions into RDF triplets the OWL 2-to-RDF mappings are used. To convert RDB to RDF graph, the R2R Mapping Language (R2R ML) was chosen. The mappings DL ALC and its main extensions to the RDF triplets are described in the publication. The mapping of the DL axioms into an RDF triplet also is considered in the publication. The main difficulties in describing DL-to-RDF transformations are given in the corresponding section. For each constructor of concepts and roles a corresponding expression in OWL 2 and its mapping into the RDF triplet. A schematic representation of the resulting RDF graph for each mapping is created. The paper also provides an overview of existing methods that relate to the use of RDF when mapping RDB to ontology and vice versa.

Объектно-ориентированный подход к разработке моделей данных

The modern understanding of the object-oriented approach focuses on one of the main problems of programming – its automation. Many important and practically useful results have been achieved in this direction. Methods for creating object libraries and programming technologies have been developed, which greatly facilitate software development. This is especially evident in the development of WEB-applications. MVC technology has improved the efficiency of developing programs that link remote databases to client applications. However, this and similar technologies have one significant drawback, which is manifested in the fact that the time of obtaining data from their database often does not meet the requirements of both the developer and the user. For a long time, there have been ongoing discussions on this topic on the Internet. Various methods are proposed to speed up database calls, which, as a rule, complicate programming and are artificial methods that go beyond the basic technology, which are applicable to solving particular problems and require significant processing in each specific case. The article assumes that the reason for these problems lies in the fact that in the programming community there is a solidified opinion, the essence of which is that the relational data model, which serves as the basis for most modern DBMS, has nothing to do with the object-oriented paradigm. Therefore, developers of programming technologies use an intermediate technology called object-relational mapping (ORM), which, in their opinion, should connect the non-object database and the object program that provides the client application with the database. In fact, any way you organize your database systems is in the object-oriented paradigm. Based on these premises, the article discusses the object method of developing data models. For this purpose, the properties of data models are formulated, which are necessary for them to be object and to ensure high performance of programs that implement data processing on their basis. The concept of an abstract algebraic machine is introduced, which is a two-base algebraic system. Examples are given that illustrate the use of an abstract algebraic machine for solving various problems. The possibility is considered and a method is proposed for constructing algebraic systems for complex data structures, namely, tuples, which can be considered as elements of such data aggregates as multidimensional matrices, relations, files. In conclusion, it is concluded that the proposed approach allows one to prove the correspondence of various data models, which, in turn, makes it possible to effectively implement parallel processing of various databases on software and hardware complexes, the architecture of which is based on a multidimensional matrix computation model.

Leader Humor and Employee Upward Voice: The Role of Employee Relationship Quality and Traditionality

Despite the continuous increase in empirical research on leader humor, the important issue of how and when leader humor affects employees’ interpersonal, proactive behaviors in the form of upward voice has largely been overlooked. Drawing on relational process model of humor and data from one multiwave, multisource field study and one experimental field study, we find that the positive effects of leader humor on upward voice behavior can be accounted for by both supervisor–subordinate nonwork ties (i.e., supervisor–subordinate guanxi) and supervisor–subordinate work ties (i.e., leader–member exchange). The indirect effects of both supervisor–subordinate guanxi and leader–member exchange on the relationship between leader humor and upward voice behavior are stronger when employees score low on traditionality. These results shed light on the role of leader humor in promoting the bottom–up flow of potentially critical information in organizations through high-quality relationships with followers and provide insights into who will benefit more from humor in leadership.

Why so Humorous? The Roles of Traditionality and Gender (Dis)Similarity in Leader Humor Behavior and Employee Job Performance

Although the consequences of leader humor have been well documented, limited research attention has been devoted to its antecedents. The current research addresses this important issue by exploring whether and when an individual characteristic (i.e., traditionality) influences leader humor behavior. Based on the relational process model of humor and data from one multiwave, multisource field study, we find that leaders scoring low on traditionality are more likely to express humor with their followers, which in turn affects followers’ job performance. Moreover, the mediating effect of leader humor behavior on the link between leader traditionality and employee job performance is stronger when the genders of the leader and subordinates differ. We discuss the implications of the findings and future directions for research concerning the antecedents of leader humor.

CREATION FOR A CADASTRAL INFORMATION SYSTEM FOR GONIN GORA LAYOUT IN CHIKUN LOCAL GOVERNMENT AREA OF KADUNA STATE, NIGERIA

This study was carried out to determine the geographic information system (GIS) as a tool to examine spatial patterns of Gonin Gora layout in Chikun Local Government Area of Kaduna state. The study involves acquiring spatial and attribute data of the study area, creating relevant spatial layers containing vital information about the parcel within the area of study, designing an appropriate database using relational database model (RDBM) cadastral information system and demonstrating the usability of the cadastral information system (CIS) that was created by carrying out simple queries. The research procedure also involves data acquisition using Promark 3 GPS instrument, database design and spatial queries using ARCGIS (10.1). Three(3) single criteria queries were generated for parcel earmarked for religion worship, parcel set for educational purposes, parcels for commercial purposes were identified and multi- criteria seven (7) queries were generated. The database was created and it was successfully test run for cadastral information system as effective tool for quick retrieval of information

On Detecting and Enforcing the Non-Relational Constraints Associated to Dyadic Relations in MatBase

MatBase is a prototype data and knowledge base management expert intelligent system based on the Relational, Entity-Relationship, and (Elementary) Mathematical Data Models. Dyadic relationships are quite common in data modeling. Besides their relational-type constraints, they often exhibit mathematical properties that are not covered by the Relational Data Model. This paper presents and discusses the MatBase algorithm that assists database designers in discovering all non-relational constraints associated to them, as well as its algorithm for enforcing them, thus providing a significantly higher degree of data quality.

Normalization of Inconsistent Neutrosophic Data in a Model View of Relational Database

Database of neutrosophic relational model simplify the relational classical database model by accepting inaccurate data in the neutrosophic form. Here, authors are focused on extensive view of different normalizing neutrosophic forms of classical relational model using neutrosophic set. Initially, in this work authors have introduced the concept of neutrosophic closure of attribute set and neutrosophic key which are essential to develop the normalization concepts of neutrosophic relational database. An algorithm has been developed by the authors for neutrosophic closure based on attributes .These attributes are used to locate the neutrosophic key easily. Then, we have used the  -nfd, partial  -nfd concepts and neutrosophic key as focused in [1] different forms of normalization for the database of neutrosophic relational. Finally, this neutrosophic normalization technique is demonstrated on some real life neutrosophic relation.

Building a trust-based doctor recommendation system on top of multilayer graph database.

In healthcare applications, developing a data model for storing patient-doctor relationships is important. Though relational models are popular for many commercial and business applications, they may not be appropriate for modeling patient-doctor relationships due to their inherent irregular nature and complexities. In this paper, as a case study, we propose to build a doctor recommendation system for the patients. The recommendation system is built on top of a multilayer graph data model. Contemporary research papers have already shown that multilayer graph data models can be efficiently used in many applications where large, heterogeneous data are to be modeled. As part of the recommendation system, the paper also introduces a concept of trust which is one important ingredient of any kind of recommendation. The trust factor introduced in the paper exploits certain characteristics of the multilayer graph model. The paper also presents some analysis to demonstrate the efficiency of the graph data model in comparison with relational data model.

A MULTI-PURPOSE CULTURAL HERITAGE DATA PLATFORM FOR 4D VISUALIZATION AND INTERACTIVE INFORMATION SERVICES

Abstract. The already arduous task of collecting, processing and managing heterogeneous cultural heritage data is getting more intense in terms of indexing, interaction and dissemination. This paper presents the creation of a 4D web-based platform as a centralized data hub, moving beyond advanced photogrammetric techniques for 3D capture and multi-dimensional documentation. Precise metric data, generated by a combination of image-based, range and surveying techniques, are spatially, logically and temporally correlated with cultural and historical resources, in order to form a critical knowledge base for multiple purposes and user types. Unlike conventional information systems, the presented platform, which adopts a relational database model, has the following front-end functionalities: (i) visualization of high-resolution 3D models based on distance dependent Level of Detail (LoD) techniques; (ii) web Augmented Reality; and (iii) interactive access and retrieval services. Information deduced from the developed services is tailored to different target audiences: scientific community, private sector, public sector and general public. The case study site is the UNESCO world heritage site of Meteora, Greece, and particularly, two inaccessible huge rocks, the rock of St. Modestos, known as Modi, and the Alyssos rock.

Monarch

Monarch is a globally-distributed in-memory time series database system in Google. Monarch runs as a multi-tenant service and is used mostly to monitor the availability, correctness, performance, load, and other aspects of billion-user-scale applications and systems at Google. Every second, the system ingests terabytes of time series data into memory and serves millions of queries. Monarch has a regionalized architecture for reliability and scalability, and global query and configuration planes that integrate the regions into a unified system. On top of its distributed architecture, Monarch has flexible configuration, an expressive relational data model, and powerful queries. This paper describes the structure of the system and the novel mechanisms that achieve a reliable and flexible unified system on a regionalized distributed architecture. We also share important lessons learned from a decade's experience of developing and running Monarch as a service in Google.

The Rise of NoSQL Systems

Transaction processing systems are primarily based on the relational model of data and offer the advantages of decades of research and experience in enforcing data quality through integrity constraints, allowing concurrent access and supporting recoverability. From a performance standpoint, they offer joins-based query optimization and data structures to promote fast reads and writes, but are usually vertically scalable from a hardware standpoint. NoSQL (Not Only SQL) systems follow different data representation formats than relations, such as key-value pairs, graphs, documents or column-families. They offer a flexible data representation format as well as horizontal hardware scalability so that Big Data can be processed in real time. In this review article, we review recent research on each type of system, and then discuss how teaching of NoSQL may be incorporated into traditional undergraduate database courses in information systems curricula.

A Hybrid Relational Database Model for Uncertain and Imprecise Information

Recent years, many fuzzy or probabilistic database models have been built for representing and handling imprecise or uncertain information of objects in real-world applications. However, relational database models combining the relevance and strength of both fuzzy set and probability theories have rarely been proposed. This paper introduces a new relational database model, as a hybrid one combining consistently fuzzy set theory and probability theory for modeling and manipulating uncertain and imprecise information, where the uncertainty and imprecision of a relational attribute value are represented by a fuzzy probabilistic triple, the computation and combination of relational attribute values are implemented by using the probabilistic interpretation of binary relations on fuzzy sets, and the elimination of redundant data is dealt with by coalescing e-equivalent tuples. The basic concepts of the classical relational database model are extended in this new model. Then the relational algebraic operations are formally defined accordingly. A set of the properties of the relational algebraic operations is also formulated and proven.

Learning Models over Relational Data Using Sparse Tensors and Functional Dependencies

Integrated solutions for analytics over relational databases are of great practical importance as they avoid the costly repeated loop data scientists have to deal with on a daily basis: select features from data residing in relational databases using feature extraction queries involving joins, projections, and aggregations; export the training dataset defined by such queries; convert this dataset into the format of an external learning tool; and train the desired model using this tool. These integrated solutions are also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models. This article introduces a unified framework for training and evaluating a class of statistical learning models over relational databases. This class includes ridge linear regression, polynomial regression, factorization machines, and principal component analysis. We show that, by synergizing key tools from database theory such as schema information, query structure, functional dependencies, recent advances in query evaluation algorithms, and from linear algebra such as tensor and matrix operations, one can formulate relational analytics problems and design efficient (query and data) structure-aware algorithms to solve them. This theoretical development informed the design and implementation of the AC/DC system for structure-aware learning. We benchmark the performance of AC/DC against R, MADlib, libFM, and TensorFlow. For typical retail forecasting and advertisement planning applications, AC/DC can learn polynomial regression models and factorization machines with at least the same accuracy as its competitors and up to three orders of magnitude faster than its competitors whenever they do not run out of memory, exceed 24-hour timeout, or encounter internal design limitations.

A Simpler and Semantic Multidimensional Database Query Language to Facilitate Access to Information in Decision-making

OLAP and multidimensional database technology have contributed significantly to speed up and build confidence in the effectiveness of methodologies based on the use of management indicators in decision-making, industry, production, and services. Although there are a wide variety of tools related to the OLAP approach, many implementations are performed in relational database systems (R-OLAP). So, all interrogation actions are performed through queries that must be reinterpreted in the SQL language. This translation has several consequences because SQL language is based on a mixture of relational algebra and tuple relational calculus, which conceptually responds to the logic of the relational data model, very different from the needs of the multidimensional databases. This paper presents a multidimensional query language that allows expressing multidimensional queries directly over ROLAP databases. The implementation of the multidimensional query language will be done through a middleware that is responsible for mapping the queries, hiding the translation to a layer of software not noticeable to the end-user. Currently, progress has been made in the definition of a language where through a key statement, called aggregate, it is possible to execute the typical multidimensional operators which represent an important part of the most frequent operations in this type of database.

Reasons of the transformed toward NOSQL Databases and its data models

The relational database model is the main focus of the database for the previous time. It provides robust data storage and structure that supports transaction characteristics and data retrieval with structured query language- SQL. The emergence of web technologies and clustering technology in large servers called for the need to store unstructured data and move away from the pattern of tables and static fields and not topic to transaction conditions, especially in distributed systems. Instead, it used CAP theory. New data models are emergence including: Document Data model, Key-Value model, Column-Family model, and Graph model, And new programming languages that deal with these models. An asset inventory system is a model of systems that are not topic to a fixed structure. Each location in the organization has fairly different assets and assets in the same location have different specifications that are difficult to organize in the form of tables and columns. For this reason using of NOSQL CouchDB database system was adopted and the use of MAP-REDUCE technology to organize data display and Cloudant Query in Mango-view method to retrieve data from the inventory database.

SIMURG_CITIES: Meta-Analysis for KPI's of Layer-Based Approach in Sustainability Assessment

SIMURG_CITIES, is the research and development project that is developed under the main project named as SIMURG: “A performance-based and Sustainability-oriented Integration Model Using Relational database architecture to increase Global competitiveness of Turkish construction industry in industry 5.0 era”, is the relational database model that is currently being developed in a dissertation for performance-based development and assessment of sustainable and sophisticated solutions for the built environment. This study aims to analyze the key performance indicators (KPIs) at «Cities Level» for the smart city concept that is referred to as «Layers» in the master project. KPIs for the concept of a smart city is determined by using the meta-analysis technique. Hence, the three most reputable urban journals issued from 2017 through 2020 are reviewed in this study. In addition to this, models of smart city frameworks/assessment tools/KPIs are reviewed within the context of this paper; environment, economy and governance were found to have domain themes on the urban sustainability according to the literature review. Consequently, efficient and integrated urban management, environmental monitoring and management, public and social services of urban development and sustainability are found to be the most important dimensions in urban and regional planning. SIMURG_CITIES evaluation models for urban projects can use the findings of this paper.

Cloud Based IoT Solution for Fault Detection and Localization in Power Distribution Systems

Power restoring time in power distribution systems (PDS) can be minimized by using efficient fault localization techniques. This paper proposes a novel, robust and scalable cloud based internet of things (IoT) solution for identification and localization of faults in PDS. For this purpose, a new algorithm is developed that can detect single and multiple simultaneous faults in the presence of single and multiple device or sensor failures. The algorithm has utilized a zone based approach that divides a PDS into different zones. A current sensing device (CSD) was deployed at the boundary of a zone. The function of CSD is to provide time synchronized current measurements and communicate with a cloud server through an edge device (ED). Another contribution of this research work is the unique implementation of context aware policy (CAP) in ED. Due to CAP, only those measurements are transmitted to cloud server that differ from the previously transmitted measurements. The cloud server performed calculations at regular intervals to detect faults in PDS. A relational database model was utilized to log various fault events that occur in PDS. An IEEE 37 node test feeder was selected as PDS to observe the performance of our solution. Two test cases were designed to simulate individual and multiple simultaneous faults in PDS. A third test case was implemented to demonstrate the robustness and scalability of proposed solution to detect multiple simultaneous faults in PDS when single and multiple sensor failures were encountered. It was observed that the new algorithm successfully localized the faults for all the three cases. Consequently, significant reductions were noticed in the amount of data that was sent to the cloud server. In the end, a comparison study of a proposed solution was performed with existing methods to further highlight the benefits of our technique.

Integrating memory-mapping and N-dimensional hash function for fast and efficient grid-based climate data query

ABSTRACT Database systems are pervasive components in the current big data era. However, efficiently managing and querying grid-based or array-based multidimensional climate data are still beyond the capabilities of most databases. The mismatch between the array data model and relational data model limited the performance to query multidimensional data in a traditional database when data volume hits a cap. Even a trivial data retrieval on large multidimensional datasets in a relational database is time-consuming and requires enormous storage space. Given the scientific interests and application demands on time-sensitive spatiotemporal data query and analysis, there is an urgent need for efficient data storage and fast data retrieval solutions on large multidimensional datasets. In this paper, we introduce a method for multidimensional data storing and accessing, which includes a new hash function algorithm that works on a unified data storage structure and couples with the memory-mapping technology. A prototype database library, LotDB developed as an implementation, is described in this paper, which shows promising results on data query performance compared with SciDB, MongoDB, and PostgreSQL.

Анализ программных средств для построения моделей реляционных баз данных

Анализ программных средств для построения моделей реляционных баз данных