Relational Database Implementation Research Articles

Implementation of Relational Database in the STEAM-Problem Based Learning Model in Algorithm and Programming

Currently, digital technology is developing in all fields. The development of this technology certainly has a significant impact on the world of education. A suitable learning model is needed, especially in the Algorithm and Programming course, to face global challenges in the Industrial Revolution 4.0. Students are expected to have skills that include critical and creative thinking in solving problems, communication, and collaboration with the support of technology. The STEAM-Problem Based Learning model can be used in the Algorithm and Programming learning process with seven syntaxes: preparation and knowledge identification; problem identification; plan solution; create and test products; communicate; evaluation and feedback and giving rewards. All activities carried out in the learning process by lecturers and students can be stored in a database. This research attempts to determine the validity of the STEAM-Problem Based Learning database design, which will be implemented in the Algorithm and Programming course. The data analysis technique used is validity analysis, which is based on assessing the data obtained through a questionnaire or a questionnaire using a Likert scale. Data was processed using Aiken’s V validity coefficient formula to test the expert’s judgment. Assessment indicators include correctness, consistency, relevance, completeness, and minimality. The results of the study show that the validity test on the STEAM-Problem Based Learning database design is valid, so it is feasible to implement it in algorithm and programming learning.

Eris: efficiently measuring discord in multidimensional sources

AbstractData integration is a classical problem in databases, typically decomposed into schema matching, entity matching and data fusion. To solve the latter, it is mostly assumed that ground truth can be determined. However, in general, the data gathering processes in the different sources are imperfect and cannot provide an accurate merging of values. Thus, in the absence of ways to determine ground truth, it is important to at least quantify how far from being internally consistent a dataset is. Hence, we propose definitions of concordant data and define a discordance metric as a way of measuring disagreement to improve decision-making based on trustworthiness. We define the discord measurement problem of numerical attributes in which given a set of uncertain raw observations or aggregate results (such as case/hospitalization/death data relevant to COVID-19) and information on the alignment of different conceptualizations of the same reality (e.g., granularities or units), we wish to assess whether the different sources are concordant, or if not, use the discordance metric to quantify how discordant they are. We also define a set of algebraic operators to describe the alignments of different data sources with correctness guarantees, together with two alternative relational database implementations that reduce the problem to linear or quadratic programming. These are evaluated against both COVID-19 and synthetic data, and our experimental results show that discordance measurement can be performed efficiently in realistic situations.

Data Structure Comparison Between MySql Relational Database and Firebase Database NoSql on Mobile Based Tourist Tracking Application

Tracking application is widely used to help outdoor activities. One of them is used to help tourists who do tourist destinations in group activities. Tourist tracking application requires data on the position of tourists on earth. This coordinate data can be obtained using latitude and longitude data from GPS (Global Positioning System). Coordinate latitude and longitude position of tourists from time to time are stored in a database. Database Selection for media storage will determines the level of speed and ease in accessing data. There are some differences in designing the structure of the MySql and Firebase databases. Relational Database begins with identification of entities involved in the system. One entity with another entity is related and has a cardinality level of the relationship. Relational database implementation in the form of tables in normal forms that are interconnected. Data will be stored in columns and rows. Normalization of tables causes high query computation when calling data that involves many tables. Firebase, which is one of the NoSql databases, in building data structures is stored in JSON objects. JSON tree structures usually consider the display of information that will be presented to users. So that the generated JSON tree has high efficiency and speed in processing data. In this study a comparison between the design of the relational database structure, namely MySql and NoSql Firebase database structure was developed. Firebase is more power full for the development of applications which update data in realtime and and handle a lot of coordinate data such as tourist tracking applications. Applications that do not have complex data relations such as tourist tracking applications are more suitable to use firebase than using MySQL.

Benchmarking top-[formula omitted] keyword and top-[formula omitted] document processing with T[formula omitted]K[formula omitted] and T[formula omitted]K[formula omitted]D[formula omitted

Top-k keyword and top-k document extraction are very popular text analysis techniques. Top-k keywords and documents are often computed on-the-fly, but they exploit weighted vocabularies that are costly to build. To compare competing weighting schemes and database implementations, benchmarking is customary. To the best of our knowledge, no benchmark currently addresses these problems. Hence, in this paper, we present T2K2, a top-k keywords and documents benchmark, and its decision support-oriented evolution T2K2D2. Both benchmarks feature a real tweet dataset and queries with various complexities and selectivities. They help evaluate weighting schemes and database implementations in terms of computing performance. To illustrate our benchmarks’ relevance and genericity, we successfully ran performance tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand, and on different relational (Oracle, PostgreSQL) and document-oriented (MongoDB) database implementations, on the other hand.

Querying clinical data in HL7 RIM based relational model with morph-RDB

BackgroundSemantic interoperability is essential when carrying out post-genomic clinical trials where several institutions collaborate, since researchers and developers need to have an integrated view and access to heterogeneous data sources. One possible approach to accommodate this need is to use RDB2RDF systems that provide RDF datasets as the unified view. These RDF datasets may be materialized and stored in a triple store, or transformed into RDF in real time, as virtual RDF data sources. Our previous efforts involved materialized RDF datasets, hence losing data freshness.ResultsIn this paper we present a solution that uses an ontology based on the HL7 v3 Reference Information Model and a set of R2RML mappings that relate this ontology to an underlying relational database implementation, and where morph-RDB is used to expose a virtual, non-materialized SPARQL endpoint over the data.ConclusionsBy applying a set of optimization techniques on the SPARQL-to-SQL query translation algorithm, we can now issue SPARQL queries to the underlying relational data with generally acceptable performance.

The Artificial Memory of Mr. Polly: Memory Simulation in Databases and the Emergence of Knowledge

Human memory may be characterized by five dimensions: (1) large capacity; (2) associativity; (3) diversity of memory systems; (4) change over time; and (5) a unified memory experience. The organization and multidimensionality underlying memory can be represented with set theory. This offers a new mathematical perspective, which is the foundation for the cognitive memory architecture Ardemia. The authors present a relational database implementation of Ardemia that supports the creation of the artificial memory of Mr. Polly, the main character in H.G. Wells’s novel The History of Mr. Polly. In addition to the implementation of Mr. Polly’s artificial memory using TimeGlue, his memory is probed with a collection of everyday memory queries that are related to temporal and schema knowledge. The investigation of Mr. Polly’s knowledge suggests an alternative representation of schemas; rather than fixed structures or explicit associations, it is possible to model schemas as the results of the interaction between existing knowledge and remembering.

Precise, dynamic information flow for database-backed applications

We present an approach for dynamic information flow control across the application and database. Our approach reduces the amount of policy code required, yields formal guarantees across the application and database, works with existing relational database implementations, and scales for realistic applications. In this paper, we present a programming model that factors out information flow policies from application code and database queries, a dynamic semantics for the underlying $^JDB$ core language, and proofs of termination-insensitive non-interference and policy compliance for the semantics. We implement these ideas in Jacqueline, a Python web framework, and demonstrate feasibility through three application case studies: a course manager, a health record system, and a conference management system used to run an academic workshop. We show that in comparison to traditional applications with hand-coded policy checks, Jacqueline applications have 1) a smaller trusted computing base, 2) fewer lines of policy code, and 2) reasonable, often negligible, additional overheads.

USRCAT: real-time ultrafast shape recognition with pharmacophoric constraints

BackgroundLigand-based virtual screening using molecular shape is an important tool for researchers who wish to find novel chemical scaffolds in compound libraries. The Ultrafast Shape Recognition (USR) algorithm is capable of screening millions of compounds and is therefore suitable for usage in a web service. The algorithm however is agnostic of atom types and cannot discriminate compounds with similar shape but distinct pharmacophoric features. To solve this problem, an extension of USR called USRCAT, has been developed that includes pharmacophoric information whilst retaining the performance benefits of the original method.ResultsThe USRCAT extension is shown to outperform the traditional USR method in a retrospective virtual screening benchmark. Also, a relational database implementation is described that is capable of screening a million conformers in milliseconds and allows the inclusion of complex query parameters.ConclusionsUSRCAT provides a solution to the lack of atom type information in the USR algorithm. Researchers, particularly those with only limited resources, who wish to use ligand-based virtual screening in order to discover new hits, will benefit the most. Online chemical databases that offer a shape-based similarity method might also find advantage in using USRCAT due to its accuracy and performance. The source code is freely available and can easily be modified to fit specific needs.

A rigorous approach to facilitate and guarantee the correctness of the genetic testing management in human genome information systems

BackgroundRecent medical and biological technology advances have stimulated the development of new testing systems that have been providing huge, varied amounts of molecular and clinical data. Growing data volumes pose significant challenges for information processing systems in research centers. Additionally, the routines of genomics laboratory are typically characterized by high parallelism in testing and constant procedure changes.ResultsThis paper describes a formal approach to address this challenge through the implementation of a genetic testing management system applied to human genome laboratory. We introduced the Human Genome Research Center Information System (CEGH) in Brazil, a system that is able to support constant changes in human genome testing and can provide patients updated results based on the most recent and validated genetic knowledge. Our approach uses a common repository for process planning to ensure reusability, specification, instantiation, monitoring, and execution of processes, which are defined using a relational database and rigorous control flow specifications based on process algebra (ACP). The main difference between our approach and related works is that we were able to join two important aspects: 1) process scalability achieved through relational database implementation, and 2) correctness of processes using process algebra. Furthermore, the software allows end users to define genetic testing without requiring any knowledge about business process notation or process algebra.ConclusionsThis paper presents the CEGH information system that is a Laboratory Information Management System (LIMS) based on a formal framework to support genetic testing management for Mendelian disorder studies. We have proved the feasibility and showed usability benefits of a rigorous approach that is able to specify, validate, and perform genetic testing using easy end user interfaces.

The relational XQuery puzzle: a look-back on the pieces found so far

Given the tremendous versatility of relational database implementations toward a wide range of database problems, it seems only natural to consider them as back-ends for XML data processing. Yet, the assumptions behind the language XQuery are considerably different to those in traditional RDBMSs. The underlying data model is a tree, data and results carry an intrinsic order, queries are described using explicit iteration and, after all, problems are everything else but regular. Solving the relational XQuery puzzle, therefore, has challenged a number of research groups over the past years. The purpose of this article is to summarize and assess some of the results that have been obtained during this period to solve the puzzle. Our main focus is on the Pathfinder XQuery compiler, a full reference implementation of a purely relational XQuery processor. As we dissect its components, we relate them to other work in the field and also point to open problems and limitations in the context of relational XQuery processing.

VIPERdb2: an enhanced and web API enabled relational database for structural virology

VIPERdb (http://viperdb.scripps.edu) is a relational database and a web portal for icosahedral virus capsid structures. Our aim is to provide a comprehensive resource specific to the needs of the virology community, with an emphasis on the description and comparison of derived data from structural and computational analyses of the virus capsids. In the current release, VIPERdb2, we implemented a useful and novel method to represent capsid protein residues in the icosahedral asymmetric unit (IAU) using azimuthal polar orthographic projections, otherwise known as Φ–Ψ (Phi–Psi) diagrams. In conjunction with a new Application Programming Interface (API), these diagrams can be used as a dynamic interface to the database to map residues (categorized as surface, interface and core residues) and identify family wide conserved residues including hotspots at the interfaces. Additionally, we enhanced the interactivity with the database by interfacing with web-based tools. In particular, the applications Jmol and STRAP were implemented to visualize and interact with the virus molecular structures and provide sequence–structure alignment capabilities. Together with extended curation practices that maintain data uniformity, a relational database implementation based on a schema for macromolecular structures and the APIs provided will greatly enhance the ability to do structural bioinformatics analysis of virus capsids.

UB2SQL

UB2SQL is a tool for designing and developing database applications using UML and B formal method. The approach supported by UB2SQL consists of two successive phases. In the first phase, with the design of applications using class, state and collaboration diagrams, B specifications are automatically generated from UML diagrams; the diagrams are then augmented with these B specifications in place. The second phase deals with the refinement of these B specifications into a relational database implementation, for which UML representation is constructed. In both phases, proofs are achieved to ensure correctness of the obtained B specification and correctness of the refinement process. To overcome the lack of rules and tactics in the B prover, UB2SQL defines specific rules and tactics making the proof task seem like a push-button activity. To increase the usability of UB2SQL in both academic and industrial contexts, the tool has been integrated as a plug-in to the Rational Rose CASE tool. Such integration allows users to develop and be able to visualize graphical UML diagrams and formal B notation in a single environment.

VIPERdb: a relational database for structural virology

VIPERdb () is a database for icosahedral virus capsid structures. Our aim is to provide a comprehensive resource specific to the needs of the structural virology community, with an emphasis on the description and comparison of derived data from structural and energetic analyses of capsids. A relational database implementation based on a schema for macromolecular structure makes the data highly accessible to the user, allowing detailed queries at the atomic level. Together with curation practices that maintain data uniformity, this will facilitate structural bioinformatics studies of virus capsids. User friendly search, visualization and educational tools on the website allow both structural and derived data to be examined easily and extensively. Links to relevant literature, sequence and taxonomy databases are provided for each entry.

Mining customer knowledge for electronic catalog marketing

Abstract Direct marketing is the use of the telephone and non-personal media to communicate product and organizational information to customers, who then can purchase products via mail, telephone, or the Internet. In contrast, catalog marketing is a type of marketing in which an organization provides a catalog from which customers make selections and place orders by mail or telephone. However, most catalogs for retailing firms are presented to customers in the format of paper catalogs without strategic segmentation design and implementation. In this regard, electronic catalog design and marketing could be a method to integrate the Internet and catalog marketing using market segmentation in order to enhance the effectiveness of direct marketing and sales management in retailing. This paper uses data mining based on association rules from relational database design and implementation for mining customer knowledge. As result, marketing knowledge patterns and rules are extracted for the electronic catalog marketing and sales management of a retailing mall in Taiwan.

An integrated temporal data model incorporating time series concept

Most temporal data models have concentrated on describing temporal data based on versioning of objects, tuples or attributes. The concept of time series, which is often needed in temporal applications, does not fit well within these models. The goal of this paper is to propose a generalized temporal database model that integrates the modeling of both version-based and time-series based temporal data into a single conceptual framework. The concept of calendar is also integrated into our proposed model. We also discuss how a conceptual Extended-ER design in our model can be mapped to an object-oriented or relational database implementation.

An information model for a CAM data base to support flexible manufacture of printed circuit boards

Key features from an information model for a computer-aided manufacturing (CAM) database that supports flexible manufacture of printed circuit boards and other selected components and products is presented. This model incorporates information from several sources: shop orders, computer-aided design (CAD), bill of materials, and process setup. The essential activities of manufacturing can be subdivided into several information categories. These categories include products, processes, factory structure, factory operations, and factory inventory. The information model identifies the entities and data elements that are associated with each of these categories, as well as the relationships among the entities. Having provided appropriate levels of abstraction, this model can support a wide variety of products and processes. The resulting relational database implementation is integrated with multiple manufacturing software applications to assist with the operation of a manufacturing facility.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Development and implementation of a cath lab quality assurance relational database

Development and implementation of a cath lab quality assurance relational database

The design and implementation of a road safety relational database

Abstract Road safety can be measured by the rate of road traffic accidents that occur periodically. In developing countries, one major problem confronting researchers in the area of road safety is that the sources of data collection are not centrally controlled. The proliferation of the sources of data collection leads to data incompatibility, incorrectness, inconsistency and redundancy. These, in turn, lead to inaccurate and conflicting statistics generated and published by different research groups. In this paper, a relational database to support road safety system and a technique for its implementation are presented. It is shown how the database can be processed and accurate statistics about road safety generated and analysed to support planning, decision making and control of operations by the authorities involved in road safety.

Matrix for retrieval and sorting

A conception of matrix which is a rectangular cellular array is provided. Application of the matrix for data sorting, retrieval and inverted file implementation are presented. Retrieval time does not depend on the size of the set being searched, and the sorting time is directly proportional to the number of ordered elements. Simulation of the matrix is reported and remarks on possibilities or relational data base implementation in the matrix are included.

Bubbles for relational database

The mechanical disk storage is limited by long initial delay, few inputs/outputs, and serial access, thus necessitating large complex programs in existing database systems in order to map the user's view into the physical storage and to provide different access paths in response to different queries. Bubble devices are capable of set-oriented processing and associative addressing architecture. This paper presents concepts on data structure, storage structure, and access methods for bubble implementation of relational database.