Object-relational Database Research Articles

Інформаційна технологія оцінювання теплоізоляції багатошарових стін

In today's conditions, the problem of insulation of surfaces is very relevant for wide circles of the population of Ukraine. Everyone knows that surface insulation allows to reduce heat transfer due to the use of materials with low thermal conductivity. In the absence of permanent heating, the issue of thermal insulation of cylindrical pipes is very relevant. The issue of insulation selection and structural design requires a complex and professional approach. To calculate the problem of thermal insulation, a specialist needs to have a toolkit for calculating the temperature field and saving the obtained results for further analysis. The work presents the developed information technology that allows a specialist to calculate the temperature field for multilayer cylindrical pipes. The object-oriented programming language C# and the Microsoft Visual Studio integrated development environment were used to build the IT toolkit. It should be noted that when making calculations, there is no need to specify the characteristics of pipe and insulation materials, since all data is stored in the IT database. The object-relational database management system - PostgreSQL was used to build the data module. After the calculations, all the built calculation models and the received numerical models of the results are also stored in the database. This allows you not to recalculate if necessary, but to use the data that is already available. To analyze the results, the developed toolkit allows you to plot temperature changes of a multilayer wall. At the same time, the user selects the necessary calculation models independently from IT database data. The DirectX three-dimensional graphics development tool package is used for visual representation of numerical model calculations. The authors developed an algorithm for constructing a color temperature field according to the calculations. Red is chosen for the highest field temperature, and blue for the lowest temperature. A feature of the proposed information technology is that its functionality allows three users with different levels of access to work with the toolkit: a database administrator, a mathematical model construction engineer, and a numerical model analysis specialist.

RDF(S) Store in Object-Relational Databases

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) provide a flexible model for semantically representing data on the web. With the widespread acceptance of RDF(S) (RDF and RDFS for short), a large number of RDF(S) is available. Databases play an important role in managing RDF(S). However, there are few studies on using object-relational databases to store RDF(S). In this paper, the authors propose the formal definitions of RDF(S) model and object-relational databases model. Then they introduce the approach for storing RDF(S) in object-relational databases based on the formal definitions. They implement a prototype system to demonstrate the feasibility of the approach and test the performance and semantic retention ability of this prototype system with the benchmark dataset.

Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors.

Polymer-based semiconductors and organic electronics encapsulate a significant research thrust for informatics-driven materials development. However, device measurements are described by a complex array of design and parameter choices, many of which are sparsely reported. For example, the mobility of a polymer-based organic field-effect transistor (OFET) may vary by several orders of magnitude for a given polymer as a plethora of parameters related to solution processing, interface design/surface treatment, thin-film deposition, postprocessing, and measurement settings have a profound effect on the value of the final measurement. Incomplete contextual, experimental details hamper the availability of reusable data applicable for data-driven optimization, modeling (e.g., machine learning), and analysis of new organic devices. To curate organic device databases that contain reproducible and findable, accessible, interoperable, and reusable (FAIR) experimental data records, data ontologies that fully describe sample provenance and process history are required. However, standards for generating such process ontologies are not widely adopted for experimental materials domains. In this work, we design and implement an object-relational database for storing experimental records of OFETs. A data structure is generated by drawing on an international standard for batch process control (ISA-88) to facilitate the design. We then mobilize these representative data records, curated from the literature and laboratory experiments, to enable data-driven learning of process-structure-property relationships. The work presented herein opens the door for the broader adoption of data management practices and design standards for both the organic electronics and the wider materials community.

Technology of using deep learning algorithms

Deep learning is a branch of machine learning (machine learning-ML). Deep learning methods utilize high-level model abstraction of nonlinear transformations in large databases. In other areas, the implementation of deep learning architectures has contributed significantly to the development of artificial intelligence. This paper presents recent research on newly applied deep learning algorithms. Convolutional Neural Networks are used in deep learning. Database Management System PostgreSQL object-relational database. The implementation resulted in achieving the set goals and objectives. The method of analyzing the input data is described, the differences between machine learning and deep learning are explained, and an example of classifying an image representing a sign language image using logistic regression, one of the deep learning algorithms, is presented. Deep neural networks can work with the full set of available data better than alternative approaches. During the learning process, the neural network itself determines which features in the data are important and which are not. Artificial neural networks can predict symptoms that humans cannot. Thus, with the help of deep neural networks, we can solve problems that traditional machine learning algorithms cannot perform.

Особливості інтеграції геопросторових даних автомобільних доріг із застосуванням операції з’єднання (JOIN)

The purpose of the article is to research the integration of attributes of the set of geospatial data “Roads”, which was created on the basis of the topographic database of the Main State Topographic Map, and the geoinformation system of roads of the State Agency of Roads of Ukraine (hereinafter – Ukravtodor) for the creation and maintenance of core reference data of roads of the national spatial data infrastructure (hereinafter – NSDI) using an operation Theta-JOIN in object-relational database management systems (hereinafter referred to as DBMS). Method. The basis of the research is the analysis of the possibilities of applying the theory of spatial databases and knowledge bases, international and national harmonized standards in the field of Geographic Information/Geomatics to solve the issue of integration of spatial data using the Theta-JOIN operation in object-relational database management systems. The results. The article analyzes the geo-information model of the road, based on the data of which it is possible to make management decisions during planning, design, operation, management, organization of road safety, etc. The requirements for the presentation of this model are defined in the GDF 5.1 standard, which is used for modeling road infrastructure in a geographic information environment using the Unified Modeling Language (UML) and Model Management Architecture (MDA) technologies. This allows for data interoperability at different levels, for example, national and pan-European. The geoinformation model of a public road of state importance in the topographic database is a continuous feature that is an aggregation of the features of the road and transport network through which this model passes. Also, on the basis of regulatory and technical support, the geographic identifier of the highway was established – the road index, the structure and principles of which are defined in the draft of the national standard DSTU xxx:20xx “Geoinformation system of public highways. Part 1. Composition, content and requirements for the database”. Two sets of spatial data TDB of the Main state topographic map and GIS roads of Ukravtodor were combined using the theta-JOIN operation in the PostgreSQL/PostGIS. The scientific novelty and practical significance is in the fact that the integration of sets of core reference and thematic spatial data of roads was investigated using the theta-join operation of relational algebra, which is implemented in modern GIS and DBMS for the development of the national spatial data infrastructure. This will make it possible to expand the attributes of road features contained in TDB, due to the official data of Ukravtodor, joining these sets with a geographic identifier, road index.

Development of a geodatabase for retrieval of geochemical data from oil wells: A case study from the Gachsaran oilfield; SW Iran

Managing geochemical data is crucial in the petroleum industry, as the exploration and development of oilfields typically require a large volume of data from geochemical analyses. Specifically, geochemical data relevant to oil wells, source rocks, and reservoir rocks are often related to location dimensions. Therefore, the geodatabase can be a powerful tool in collecting, storing, retrieving, processing, and analysis of these data. In this study, a novel geodatabase approach was utilized to store, display, and present geochemical data of petroleum samples from oil wells. The geodatabase scheme comprised two types of data layers: spatial data (e.g., oil well, oilfield boundary, formations including source rocks/reservoir rocks and sector) and non-spatial data (e.g., oil samples and geochemical information). The PostgreSQL/PostGIS object-relational database management system was used to design and implement the geodatabase, while QGIS was employed to connect to the database and visualize the geometrical data of the Gachsaran oilfield. The QGIS Data Plotly plugin was then used to interpret geochemical data. According to the findings of this study, the PostgreSQL/PostGIS open-source database is a suitable method for spatial data management of oil well geometrical data. In addition, the geochemical evaluation of possible source rocks and determination of geochemical characteristics of reservoir oil were analyzed using this geodatabase. Moreover, integrating oil geochemical data into a geodatabase prevents the scattering of geochemical data and facilitates data interpretation.

MONITORING OF THE EDUCATIONAL PROCESS USING ARTIFICIAL INTELLIGENCE METHODS

Considered the problem of increasing the effectiveness of the educational process due to the introduction of automatic control of attendance in the classroom using face recognition and additional information for the collection and further analysis of the received data. Algorithms and methods used in modern Facial Recognition Attendance System are studied. An intelligent system for monitoring the educational process and its analysis is proposed. Structural and functional schemes of the system, databases, software were developed, testing was carried out. During attendance monitoring, the webcam captures an image of the face of the participant of the educational process from the video stream, then the computer automatically creates a vector of facial features, which is compared with the vectors of facial features, pre-entered images and recorded in the relevant database. Vectors with 68 features are used for face recognition. In the development of the software, the tools of the OpenCV library and the Python programming language were used. With several successful comparisons, the person's data is identified: name and status (student or teacher), the current date and time are recorded in an Excel file. Operational data of the system is displayed on the monitor screen, which allows you to correct recognition errors if necessary. The system allows you to automatically keep a log of class attendance, create reports, analyze data to provide recommendations for improving the schedule and order of lessons. The object-relational database with open source code PostgreSQL is used for data storage. The grouping of the system's software code is carried out using the Django web application. The system user has the opportunity to create a personal account and create reports according to his requirements. The system was tested on the example of a group of 15 students and showed satisfactory results.

Microservice architecture of the big data stream processing system

The issue of using microservice architecture in big data stream processing systems is considered. Advantages and disadvantages of existing big data analysis architectures are studied. A version of the microservice system for big data processing using the Kafka distributed event streaming platform, the Docker program development and launch platform, the Postgres object-relational database management system, and the FastAPI web platform for creating applications is proposed. Architectural patterns have been developed that can simplify the development of large data applications using microservices, and application concepts using the resulting patterns. Simulation results are presented, which show that a microservice architecture with distributed load on several replicas can provide better scalability and speed compared to a monolithic architecture.

Provenance Framework for Multi-Depth Querying Using Zero-Information Loss Database

Data provenance is a kind of metadata that describes the origin and derivation history of data. It provides the information about various direct and indirect sources of data and different transformations applied on it. Provenance information are beneficial in determining the quality, truthfulness, and authenticity of data. It also explains how, when, why, and by whom this data are created. In a relational database, fine-grained provenance captured at different stages (i.e., multi-layer provenance) is more significant and explanatory as it provides various remarkable information such as immediate and intermediate sources and origin of data. In this paper, we propose a novel multi-layer data provenance framework for Zero-Information Loss Relational Database (ZILRDB). The proposed framework is implemented on top of the relational database using the object relational database concepts to maintain all insert, delete, and update operations efficiently. It has the capability to capture multi-layer provenance for different query sets including historical queries. We also propose Provenance Relational Algebra (PRA) as an extension of traditional relational algebra to capture the provenance for ASPJU (Aggregate, Select, Project, Join, Union) queries in relational database. The framework provides a detailed provenance analysis through multi-depth provenance querying. We store the provenance data in both relational and graph database, and further evaluate the performance of the framework in terms of provenance storage overhead and average execution time for provenance querying. We observe that the graph database offers significant performance gains over relational database for executing multi-depth queries on provenance. We present two use case studies to explain the usefulness of proposed framework in various data-driven systems to increase the understandability of system’s behavior and functionalities.

Metadata-driven Data Migration from Object-relational Database to NoSQL Document-oriented Database

The object-relational databases (ORDB) are powerful for managing complex data, but they suffer from problems of scalability and managing large-scale data. Therefore, the importance of the migration of ORDB to NoSQL derives from the fact that the large volume of data can be handled in the best way with high scalability and availability. This paper reports our metadata-driven approach for the migration of the ORDB to document-oriented NoSQL database. Our data migration approach involves three major stages: a preprocessing stage, to extract the data and the schema's components, a processing stage, to provide the data transformation, and a post-processing stage, to store the migrated data as BSON documents. The approach maintains the benefits of Oracle ORDB in NoSQL MongoDB by supporting integrity constraint checking. To validate our approach, we developed OR2DOD (Object Relational to Document-Oriented Databases) system, and the experimental results confirm the effectiveness of our proposal.

P3 Process for Object-Relational Data Migration to NoSQL Document-Oriented Datastore

The exponential growth of complex data in object-relational databases (ORDB) raises the need for efficient storage with scalability, consistency, and partition tolerance. The migration towards NoSQL (not only structured query language) datastores is the best fit for distributed complex data. Unfortunately, very few studies provide solutions for ORDB migration to NoSQL. This paper reports on how to achieve the migration of complex data from ORDB to a document-oriented NoSQL database. The proposed approach focused on the P3 process that involves three major stages: (P1) the preprocessing stage to access and extract the database features using SQL queries, (P2) the processing stage to provide the data mapping by using a list of mapping rules between the source and target models, and (P3) the post-processing stage to store and request the migrated data within the NoSQL context. A thorough experiments on two real-life databases veriðes the P3 process improves the performance of data migration with complex schema structures.

ГЕОДЕЗІЯ, КАРТОГРАФІЯ І АЕРОФОТОЗНІМАННЯ

The purpose of this work is to study the integration of sets of core reference and thematic geospatial data based on the JOIN operation of relational algebra and its interaction with geocoding of geospatial features, which is implemented in modern geographic information systems (GIS) and database management systems (hereinafter – DBMS) for the development of the national spatial data infrastructure (hereinafter – NSDI). Method. The research is based on the analysis of the possibilities of applying the theory of geospatial databases and knowledge bases, international and national harmonized standards in the field of Geographic Information/ Geomatics to solve the problem of integration of geospatial data using the operation JOIN relational algebra in object-relational database management systems (OR DBMS). Results. The paper examines the models of the Join operation of relational algebra, which underlie the geocoding of features and the creation of electronic gazetteers, and proves its effectiveness: the Join operation integrates of core reference and thematic geospatial datasets. There is a need to define the required geographic identifiers, which must be present among the attributes of the core reference and thematic geospatial datasets to perform the join. The variety of uses of the Join operation covers all possible cases that arise in their practical application. Thus, the use of the Join operation involves identifying these required geographic identifiers at the geospatial database design stage. In particular, it is expedient to determine mandatory geographical identifiers (codes) of features according to the official national systems of features classification (codification) in the relevant sectoral thematic registers, which are responsible for certain holders of thematic data in accordance with Annex 2 of the Decree of Cabinet of Ministers “The order for the functioning of the national spatial data infrastructure” of May 26, 2021, № 532. Scientific novelty and practical significance. The integration of core reference data and thematic geospatial datasets based on JOIN operation models of relational algebra and their interaction with geocoding of geospatial features is researched, which is implemented in modern GIS and DBMS for the development of national spatial data infrastructure. The research was performed on a set of core reference spatial data, namely: information on administrative-territorial units of the Cherkasy region, including their borders; the data from the statistical bulletin of the socio-economic situation of the Cherkasy region for January 2021 of the Main Department of Statistics in Cherkasy region of the State Statistics Service of Ukraine were selected as thematic data. It has been shown that relational algebra join (JOIN) operations can be used to integrate other thematic geospatial data with core reference data using geographic identifiers that contain these datasets.

Estimating search execution time in NoSQL and object-relational databases

Estimating search execution time in NoSQL and object-relational databases

Proposal of a spatial database for indoor navigation

This paper presents an approach to build a spatial database for indoor environments. It will be presented the main requirements for the implementation of an object-relational database for the representation and analysis of indoor environments, which considers the solutions for both type of representations, floor plan and schematic map. These representations consider the high number of information found in an indoor environment and the fact that they are disposed in different floors of the structure. The relationship between objects and their attributes defines the links and restrictions between them. Hence, the model should describe the entities and their interrelationships, as well as the attributes of the elements and their characteristics. After the database was developed, it was implemented an algorithm that calculates routes between points in the indoor environment, considering not only the shortest distance but also the floor change. The model was tested by Antunes and Delazari (2019) using an application and some interviews with users to evaluate the elements included in the database considering a navigating task. Some results pointed out the need to insert new information in the database regarding physical characteristics (color, material) of elements found in the indoor environment to assist users during orientation and navigation tasks. Moreover, it is necessary to include elements from the outdoor environment used as reference points in the cartographic representation.

Shared Parameter Database of War Gaming Software: Case Study on Commercial Simulation Databases

Background: The current research brings up the perspective of security-critical information systems in shared parameter databases in the context of processing sensitive data at Finnish Naval Warfare Centre. It refers to the environment of isolated military war gaming simulation and modeling systems. The research problem is: How to make an optimal solution for data distribution in different military war gaming simulation and modeling software? Objectives: The objective is to create a single shared database usable with different detail level software, e.g. high-level scenario simulation, technical system-of-system simulations, and system-level physical simulations. Methods: The methods are modeling, simulation and operation analysis. The approach is inductive, the strategy is a qualitative case study and the data collection was implemented by exploring database models and their combinations. The integration was implemented in an object-relational database management system (ORDBMS), PostgreSQL. Results: The shared database led to efficient access to simulation parameters, more straightforward system integration and improved scalability. Conclusions: The results of modeling and simulation indicated that the integration is possible to implement.

Automatic Generation of Scripts for Database Creation from Scenario Descriptions

Aims: Database creation is the most critical component of the design and implementation of any software application. Generally, the process of creating the database from the requirement specification of a software application is believed to be extremely hard. This study presents a method to automatically generate database scripts from a given scenario description of the requirement specification.
 Study Design: The method is developed based on a set of natural language processing (NLP) techniques and a few algorithms. Standard database scenario descriptions presented in popular textbooks on Database Design are used for the validation of the method.
 Place and Duration of Study: Department of Statistics and Computer Science, Faculty of Science, University of Peradeniya, Sri Lanka, Between December 2019 to December 2020.
 Methodology: The description of the problem scenario is processed using NLP operations such as tokenization, complex word handling, basic group handling, complex phrase handling, structure merging, and template construction to extract the necessary information required for the entity relational model. New algorithms are proposed to automatically convert the entity relational model to the logical schema and finally to the database script. The system can generate scripts for relational databases (RDB), object relational databases (ORDB) and Not Only SQL (NoSQL) databases. The proposed method is integrated into a web application where the users can type the scenario in natural or free text. The user can select the type of database (i.e., one of RDB, ORDB, NoSQL) considered in their system and accordingly the application generates the SQL scripts.
 Results: The proposed method was evaluated using 10 scenario descriptions connected to 10 different domains such as company, university, airport, etc. for all three types of databases. The method performed with impressive accuracies of 82.5%, 84.0% and 83.5% for RDB, ORDB and NoSQL scripts, respectively.
 Conclusion: This study is mainly focused on the automatic generation of SQL scripts from scenario descriptions of the requirement specification of a software system. Overall, the developed method helps to speed up the database development process. Further, the developed web application provides a learning environment for people who are novices in database technology.

Data structure set-trie for storing and querying sets: Theoretical and empirical analysis.

Set containment operations form an important tool in various fields such as information retrieval, AI systems, object-relational databases, and Internet applications. In the paper, a set-trie data structure for storing sets is considered, along with the efficient algorithms for the corresponding set containment operations. We present the mathematical and empirical study of the set-trie. In the mathematical study, the relevant upper-bounds on the efficiency of its expected performance are established by utilizing a natural probabilistic model. In the empirical study, we give insight into how different distributions of input data impact the efficiency of set-trie. Using the correct parameters for those randomly generated datasets, we expose the key sources of the input sensitivity of set-trie. Finally, the empirical comparison of set-trie with the inverted index is based on the real-world datasets containing sets of low cardinality. The comparison shows that the running time of set-trie consistently outperforms the inverted index by orders of magnitude.

Геоинформационное обеспечение изучения размещения зарубежных ТНК в глобальных городах

The article discusses the experience of assessing the location of foreign TNCs in global cities as a significant factor in the field of environmental, economic and social aspects of sustainable development of urban areas. A number of initial theoretical models are proposed from the standpoint of the center-peripheral approach and a conceptual scheme of geoinformation and cartographic support of the study is presented, the main stages of its implementation are revealed. The databases of the European Commission’s Joint Research Center (GHS Urban Center Database, GHS-UCDB) and (GHS Functional Urban Areas, GHS-FUA) were used to get an idea of the real models of the intracity location of large business in the delimitation of the boundaries of agglomerations and their separate zones. To determine the composition of TNC divisions, Forbes 2000 data for 2020 and information from corporate websites on the branch network with geographic reference from the Craft.co reference service were used. In total, 55 global cities of the alpha group and more than 12 thousand foreign branches of TNCs were involved in the analysis. The processing and systematization of the obtained data was carried out using the object-relational database management system (RDBMS) PostgreSQL and the PostGIS extension. The visualization of the research results based on the consolidation of tabular data and spatial relationships using QGIS. Comparative analysis showed the effectiveness of the developed technique. As a result of its application, the overall scale of the presence of big business in the global cities of the alpha group and a significant gap between leaders and outsiders were determined. The absence of a universal model for the zonal distribution of TNCs in the urban space was revealed, the basic invariants of which were visualized using the example of a number of centers. The presented developments can contribute to the development of the direction of microgeographic research in geo-urban studies. In practice, the methodology can be useful for modernizing the assessment of the quality of the urban environment and the conditions for its formation as part of the implementation in the Russian Federation of the national project “Housing and Urban Environment”.

Development of computer-based methodology for remote advanced training of medical doctors

Aim. To formulate the methodology for developing the interactive virtual computer simulations with a rating assessment of trainees’ decisions and the capability of remote access.Material and Methods. The methods of knowledge engineering were used to extract and formalize expert knowledge about the structure, significance, and relevance of clinical diagnostic information. The materials for creating virtual computer simulations were based on texts from the archival medical records, laboratory data, and multimedia results of instrumental methods of study. A three-tier network architecture was applied to provide the capabilities of remote access. It was organizationally represented by three components: a client layer, a business logic layer, and a data layer. Data transfer was provided by the Web protocols while microservices represented the infrastructure.Results. The information was formalized and structured after expert analysis and identification of significant diagnostic and prognostic data. The process included defining the domain model, identifying the aggregates and connections between them, and designing software and user interfaces. Possible solutions for trainees are now presented in the form of interactive reference lists. The artifacts of the user’s work are saved in the storage represented by the module for working with the server file system and the object-relational database management system. Each task module contains static and interactive blocks of information. The purpose of static blocks is to provide trainees with the necessary information for making clinical and diagnostic decisions. The interactive blocks allow selecting one or more solutions from the list. The content and sequence of further information presentation are determined by the trainees’ answers to the questions of an interactive block. Trainees’ decision-making competencies are evaluated using the rating system. The final personal rating is calculated as a multiplication of all coefficients related to the trainees’ decisions. This approach integrates a rating system with the trajectory chosen by the trainee for task completion.Conclusions. The distance learning technologies, developed for clinical disciplines in this study, are quite new and innovative. The repository of virtual computer simulations is under development to improve the methodological support of remote clinical training.

A New Algorithm for Storing and Migrating Data Modelled by Graphs

<span>NoSQL databases have moved from theoretical solutions to exceed relational databases limits to a practical and indisputable application for storing and manipulation big data. In term of variety, NoSQL databases store heterogeneous data without being obliged to respect a predefined schema such as the case of relational and object-relational databases. NoSQL solutions surpass the traditional databases in storage capacity; we consider MongoDB for example, which is a document-oriented database capable of storing unlimited number of documents with a maximal size of 32TB depending on the machine that runs the database and also the operating system. Also, in term of velocity, many researches compared the execution time of different transactions and proved that NoSQL databases are the perfect solution for real-time applications. This paper presents an algorithm to store data modeled by graphs as NoSQL documents, the purpose of this study is to exploit the high amount of data stored in SQL databases and to make such data usable by recent clustering algorithms and other data science tools. This study links relational data to document datastores by defining an effective algorithm for reading relational data, modelling those data as graphs and storing those data as NoSQL documents.</span>