Database Management System Research Articles

Kalman filtering for estimation of closed-die forging load based on shop floor data

Estimation of forging load in a closed-die forging process is important for process planners and designers. Physics-based models of the process require very high computational time as well as accurate input data. This work estimates the forging load by extracting the information from similar products in the shop floor. Accuracy of the estimate is optimized by means of a Kalman filter. A novel feature of this work is that instead of one deterministic estimate, three estimates, viz. lower, upper and most likely, are obtained. For demonstrating the efficacy of the proposed methodology, finite element method simulations using ABAQUS are used in lieu of real shop floor data. Six different products each having eight models are considered. Forging is supposed to be carried out with as well as without lubrication. In different cases, Kalman filtered most-likely estimate came very close to actual (FEM simulated) forging load and in no case deviation was more than 9%. The estimation error keeps on reducing with availability of data in an exponential manner. In an ideal case of no fluctuation in the measured actual (FEM simulated) forging load, error reduced from 30 to 5 kN after 7 data; further 13 data reduced the error to 1.7 kN. The interval of estimation (i.e. the difference between upper and lower estimates) also keeps on reducing with the availability of more data. For example, for an axisymmetric product, availability of 6 more data reduced the range of estimation from 62 to 25 kN. This establishes the efficacy of Kalman filter. In the proposed procedure the data is stored in an open source relational database management system, the MySQL, which can be retrieved easily. In Industry 4.0, shop floor data is easily available. Hence, the proposed method can be applied readily in real production shops.

Bedrock Geologic Database of the Vincennes 30- x 60-Minute Quadrangle, Indiana

Located in the eastern Illinois Basin, the Vincennes 1:100,000-scale quadrangle comprises portions of Daviess, Knox, Sullivan, and Greene Counties in Indiana. It is situated in the rolling topography of the Wabash Lowland and dominated by Pennsylvanian-age coal-bearing strata. Pennsylvanian siliciclastics in the Vincennes quadrangle host economically significant coal seams. The most widespread coal beds occur in the Carbondale Group, but the Raccoon Creek Group hosts superior coal quality (low sulfur, low ash) in Indiana. Both surface and underground mining of these coals have been extensive, with expansive impacts on the landscape and subsequent reclamation. Bedrock outcrop is largely obscured by multiple cycles of glacial sediments, and extensive fluvial activity of the Wabash River has formed deep channels (paleo and active) filled with outwash and alluvium. The geological map of Gray and others (1970) is the fundamental compilation of previous geological knowledge from this area. Additional geological information for the Vincennes quadrangle comes from previous 1:24k-scale mapping through the STATEMAP program. Specifically, Fraser and others (1998) conducted bedrock mapping in the 7.5-minute Wheatland and Washington quadrangles, and Fraser and Zlotin (1997) did some mapping in the 7.5-minute Montgomery quadrangle. Conolly and others (1996) performed a coal availability study in the 7.5-minute Wheatland quadrangle. However, the most information for the 1:100k Vincennes quadrangle and, more broadly, the western corridor, comes from the Indiana-scale coal seam mapping conducted between 2011 and 2017 based on the Indiana Coal Stratigraphic Database (NCRDS) and Indiana Coal Quality Database (ICQD), and geophysical logs from petroleum wells archived in the Petroleum Database Management System (PDMS). These data were previously presented in Esri-based Indiana Coal Atlas StoryMaps for the entire western corridor (Drobniak and others, 2017a, 2017b, 2017c, 2017d). This geologic map was funded in part through the STATEMAP Program supported by the U.S. Geological Survey under Cooperative Agreement No. G21AC10646. The Bedrock Geologic Database of the Vincennes 30- x 60- Minute Quadrangle is a composite geodata set that conforms to "GeMS (Geologic Map Schema)--a standard format for the digital publication of geologic maps," available at http://ngmdb.usgs.gov/Info/standards/GeMS/. Metadata records associated with each element within the geodata set contain more detailed descriptions of their purposes, constituent entities, and attributes. An OPEN shapefile version of the data set is also available. It consists of shapefiles, DBF files, and delimited text files and retains all information in the native geodatabase, but some programming will likely be necessary to assemble these components into usable formats.

Managing andmonitoring a pandemic: showcasing a practical approach forthe genomic surveillance ofSARS-CoV-2.

With the rapidly growing amount of biological data, powerful but also flexible data management and visualization systems are of increasingly crucial importance. The COVID-19 pandemic has more than highlighted this need and the challenges scientists are facing. Here, we provide an example and a step-by-step template for non-IT personnel to easily implement an intuitive, interactive data management solution to manage and visualize the high influx of biological samples and associated metadata in a laboratory setting. Our approach is illustrated with the genomic surveillance for SARS-CoV-2 in Germany, covering over 11 600 internal and 130 000 external samples from multiple datasets. We compare three data management options used in laboratories: (i) simple, yet error-prone and inefficient spreadsheets, (ii) complex and long-to-implement laboratory information management systems and (iii) high-performance database management systems. We highlight the advantages and pitfalls of each option and outline why a document-oriented NoSQL option via MongoDB Atlas can be a suitable solution for many labs. Our example can be treated as a template and easily adapted to allow scientists to focus on their core work and not on complex data administration.

Application of machine learning algorithm in electronic book database management system

Intelligent algorithms have excellent performance in managing large databases and mining effective knowledge, and many researchers have introduced these algorithms into research to improve the performance of book management systems. This article introduced the application of machine learning algorithms in electronic book database management systems. A linear classifier (linear regression algorithm is one of machine learning algorithms) was used to analyze the empirical loss value. This paper studied the application of machine learning algorithm in the database management of library special collection resources, aiming to optimize the management of library special collection resources database through machine learning algorithm, and provided students with better experience of using special collection resources. This paper verified the effectiveness of the machine learning algorithm from three aspects: the retrieval time before and after the special resource database, the number of crashes in a month and the number of library staff. The average time of the five sets using the machine learning algorithm was about 150% faster than the average search time of the five sets of special resource databases. The number of crashes after using machine learning algorithm was 2, 1, 1, 2, 1, which was much lower than that of special resource database. After using the machine learning algorithm, the five groups of workers were 7, 5, 9, 6, and 4, respectively, far fewer than the five groups of special collectors. Finally, by comparing the linear regression model, Support vector machine model, and random forest model, it was found that the accuracy of the linear regression model reached 98.2%, an increase of 8.2% compared to the random forest model, the precision rate reached 96.7%, and the recall rate reached 98.2%. 97.8%, F1 value reached 97.5%.It can be seen from the experimental data that the machine learning algorithm plays a good role in the database management of the library special collection resources.

KVMod—A Novel Approach to Design Key-Value NoSQL Databases

The growth of structured, semi-structured, and unstructured data produced by the new applications is a result of the development and expansion of social networks, the Internet of Things, web technology, mobile devices, and other technologies. However, as traditional databases became less suitable to manage the rapidly growing quantity of data and variety of data structures, a new class of database management systems named NoSQL was required to satisfy the new requirements. Although NoSQL databases are generally schema-less, significant research has been conducted on their design. A literature review presented in this paper lets us claim the need to create modeling techniques to describe how to structure data in NoSQL databases. Key-value is one of the NoSQL families that has received too little attention, especially in terms of its design methodology. Most studies have focused on the other families, like column-oriented and document-oriented. This paper aims to present a design approach named KVMod (key-value modeling) specific to key-value databases. The purpose is to provide to the scientific community and engineers with a methodology for the design of key-value stores using the maximum automation and therefore the minimum human intervention, which equals the minimum number of errors. A software tool called KVDesign has been implemented to automate the proposed methodology and, thus, the most time-consuming database modeling tasks. The complexity is also discussed to assess the efficiency of our proposed algorithms.

Aplikasi Absensi Non ASN Berbasis Web

Presence is crucial in a government institution. With an effective attendance system, it is hoped that it can assist in controlling the task completion process. The Attendance Recording System used in the Public Section applies fingerprint technology. Fingerprint is one of the information technology innovations that makes it easier to record employee activities every day. At this time employees do not take attendance using a fingerprint machine and return to using a manual system. The system is done manually, namely by signing the attendance book that has been provided by the staffing department. The purpose of developing this application is to facilitate, and speed up, employees in taking attendance and managing attendance outside the office. The method used in developing web-based non-ASN attendance applications is the waterfall model. as a structured software approach, starting from the stages of analysis, design, coding, testing, to the support stage. The PHP programming language, Laravel Framework and Database Management System (DBMS) use MySQL. The result of this research is the development of a web-based non-asn attendance application for non-asn employees in the general section of the regional secretariat of Bekasi district

Knowledge, awareness and attitude towards cancer: An intervention-based study among the students of the University of Delhi, India

There are limited studies evaluating the knowledge of the student communities in India about cancer. The present study assesses the knowledge, awareness and attitude of the students of the University of Delhi, India towards cancer. Two sets of structured questionnaires (set 1& 2) were used for the survey. Set 1 was used for the survey of 1000 students, comprising 500 male and 500 female students. The data was collected using a self-designed Relational Database Management System (RDBMS) on a Likert scale (1-5) and was subsequently converted to scores. The significance of the scores was evaluated by one-way Analysis of Variance (ANOVA). A Set 2 questionnaire was used for the follow-up survey. The results of the survey based on the Set 1 questionnaire indicated that the students had some knowledge about “cancer risk factors, types of cancer, and sources of cancer awareness”. However, students lacked knowledge about “cancer symptoms and age susceptibility to cancer”. Consequent to this survey, awareness programs were conducted among these students spread over 5 months. The awareness gained was assessed by the follow-up surveys using the Set 2 questionnaire (split into pre- and post-follow-up studies). The awareness campaigns significantly increased the knowledge and changed the attitude of the students towards cancer. This study thus highlights the need and effectiveness of awareness campaigns with special reference to student communities in countries such as India.

Big Data in Bioinformatics and Computational Biology: Basic Insights.

The human genome was first sequenced in 1994. It took 10years of cooperation between numerous international research organizations to reveal a preliminary human DNA sequence. Genomics labs can now sequence an entire genome in only a few days. Here, we talk about how the advent of high-performance sequencing platforms has paved the way for Big Data in biology and contributed to the development of modern bioinformatics, which in turn has helped to expand the scope of biology and allied sciences. New technologies and methodologies for the storage, management, analysis, and visualization of big data have been shown to be necessary. Not only does modern bioinformatics have to deal with the challenge of processing massive amounts of heterogeneous data, but it also has to deal with different ways of interpreting and presenting those results, as well as the use of different software programs and file formats. Solutions to these problems are tried to present in this chapter. In order to store massive amounts of data and provide a reasonable period for completing search queries, new database management systems other than relational ones will be necessary. Emerging advance programing approaches, such as machine learning, Hadoop, and MapReduce, aim to provide the capacity to easily construct one's own scripts for data processing and address the issue of the diversity of genomic and proteomic data formats in bioinformatics.

What a Rich BIOTROP: Development Database Framework of Biodiversity Heritage Collections

The need for biodiversity science with the availability and interoperability of data on the internet reinforces each other. This is driven by the increasing literacy of the world community regarding the impact of loss of biodiversity on the sustainability of human life. High-quality biodiversity data is needed to demonstrate that spatio-temporal loss of biodiversity has led to a reduction in quality of life. This facts have underpinned SEAMEO BIOTROP to increase the visibility of managed biodiversity collection data. SEAMEO BIOTROP maintains thousands of valuable herbarium collections of weeds, trees and invasive plants; fungi, insects and pests for research and testing purposes. These historic and valuable collections of biodiversity need to be preserved and better managed in digital format so that the information is available to botanists and the general public around the world. A study regarding the development of an integrated biodiversity collection database framework has been carried out by SEAMEO BIOTROP. This study aims to formulate and develop an initial framework for the BIOTROP biodiversity database that is relevant to national, regional and global needs. This study succeeded in developing an integrated database framework that brings together all digital data from the SEAMEO BIOTROP biodiversity collection into a database management system. This database management system also adopts Darwin Core metadata to ensure easy exchange and sharing of data with existing biodiversity data management systems in the world.

The study of performance and methods of storage of JSON objects in PostgreSQL database management system

Currently there are several methods of data storage, such as table and object, as JSON objects. National and foreign scientists conducted studies on the comparison of performance of relational database management systems and NoSQL database management system, but there were no studies that compared the performance in data insertion and storage using ordinary records and JSON objects within the relational database management system. An experiment was carried out in terms of the real examples of corporate use of the database management system; the results show the relevance of the use of JSON objects in storage of large volumes of data.

A system of environmentally important decision-making for the sustainable use of marine estuaries in the conditions of anthropogenic and climatic changes

Sustainable use of marine water areas is one of the priorities of preserving biodiversity and the environment as a whole. The problem of a clear plan of rehabilitation measures, which would be based on complex and balanced solutions arises for their effective restoration and further functioning. These decisions should take both environmental and economic aspects into account. Estuaries, unlike marine water areas, are more sensitive to anthropogenic and climatic changes. They are characterized by shallow water level, functioning in the conditions of limited water exchange, dependence on river flow and greater anthropogenic load compared to marine water areas. These features are the cause of more intensive changes in the structure of biocenoses, rise in emergency situations, loss of productivity and sustainability of ecosystems as a whole. Thus, there is a need to introduce environmental management for these reservoirs, which should be based on a multi-component decision support system. This system will make it possible to develop clear plans for the restoration of marine ecosystems and prevent negative changes and degradation in them. The proposed scheme for developing plans for the restoration of the ecosystems of estuaries is represented by three blocks. The first “Block of data collection” involves the implementation of 3 stages: assessment of the current state of the reservoir; definition of problems and their ranking; assessment of ecosystem services. The second block “Search for a solution” is based on 4 components: Component 1. Database management system; Component 2. Geoinformational system; Component 3. Mathematical and predictive models; Component 4. Informational and management system for choosing strategic and operational decisions. The third block “Implementation of measures” involves the implementation of 4 stages: environmental assessment of decisions and projects, implementation of environmental measures, evaluation of effectiveness, search for new solutions and their improvement. The proposed scheme will enable a comprehensive approach to the restoration of estuarine ecosystems and ensure their sustainable use.

An Empirical Evaluation of Columnar Storage Formats

Columnar storage is a core component of a modern data analytics system. Although many database management systems (DBMSs) have proprietary storage formats, most provide extensive support to open-source storage formats such as Parquet and ORC to facilitate cross-platform data sharing. But these formats were developed over a decade ago, in the early 2010s, for the Hadoop ecosystem. Since then, both the hardware and workload landscapes have changed. In this paper, we revisit the most widely adopted open-source columnar storage formats (Parquet and ORC) with a deep dive into their internals. We designed a benchmark to stress-test the formats' performance and space efficiency under different workload configurations. From our comprehensive evaluation of Parquet and ORC, we identify design decisions advantageous with modern hardware and real-world data distributions. These include using dictionary encoding by default, favoring decoding speed over compression ratio for integer encoding algorithms, making block compression optional, and embedding finer-grained auxiliary data structures. We also point out the inefficiencies in the format designs when handling common machine learning workloads and using GPUs for decoding. Our analysis identified important considerations that may guide future formats to better fit modern technology trends.

Forensic analysis of SQL server transaction log in unallocated area of file system

The importance of database forensics is increasing day by day as the use of databases to store sensitive corporate and personal data increases. Database forensics is a field of digital forensics that deals with database-related incidents such as data corruption, breaches, and leaks. One of the key functions of database forensics is information reconstruction, which is the tracing of actions from the time of an event to the present based on various information stored in the database. This feature allows investigators to identify unauthorized user actions and data deletion or manipulation when an incident occurs. Database log data is primarily used to reconstruct information. Database logs include transaction logs, error logs, event logs, and trace logs. Among them, we focus on the transaction log of Microsoft SQL Server (MSSQL), one of the most popular database management systems in the world. Raw-level studies have been conducted on the transaction logs of Oracle and MySQL, other databases used at the enterprise level. However, there is very little research on MSSQL transaction logs. For this reason, we analyze the internal structure of the MSSQL transaction log. Based on these finding, we present an empirical method to identify and extract transaction log records in unallocated area.

Simulation-Omitted Replan with Cone Beam Computed Tomography based Adaptive Online Radiotherapy System ― Transferring Adapted Plan to Non-Adaptive Ring Gantry Linear Accelerator for Image Guided Radiotherapy

Artificial intelligence powered cone beam computed tomography (CBCT) based online adaptive radiotherapy (oART) system offers a streamlined and efficient process for daily ART as the default. In our prior work, we developed a workflow to utilize this oART system as simulation-omitted replan platform and treat the adapted plan on the oART system with image guided radiotherapy (IGRT) until next adaptation. However, the IGRT fractions will occupy the treatment slots of the machine. In this work, we aim to develop a semi-automatic workflow to allow the adapted plan to be treat on the non-adaptive ring gantry linear accelerator (non-ART Linac) and dedicate the oART system for adaptive treatments. The oART system and the non-ART Linac were machine-matched to the same representative beam data. In the oART system, the initial plan is setup as 'adaptive' treatment and patients are only treated on the oART system for adaptive replan. The IGRT fractions are all treated on the non-ART Linac. An API script was developed to automatically (1) grab the adapted DICOM plan files from the secondary calculation system and write directly back to the database of the treatment management system (TMS), (2) change the DICOM tags to make the files compatible in the TMS system, (3) insert the kV-CBCT field to make the plan deliverable in the non-ART Linac. There are minimum remaining manual steps to setup the number of fractions to the intended number of IGRT fractions and to link plan to the prescription in TMS. We compare the required resources and the percentage of ART treatments on the oART system before and after the implementation of the proposed workflow to quantify the improvement of service. The proposed workflow and automation eliminated the need to convert between IGRT/ART fractions in the Ethos system and reduced manual work by 25 minutes each adapted plan transfer. Table 1 summarizes the number of physics tasks and the percentage of ART fractions in oART system per month before and after the proposed workflow. This workflow reduced the physics IGRT/ART tasks from 107±31 to 65±21 tasks per month (p<0.05). Percentage of ART treatments on oART system increase from 30%±3% to 57%±13% (p<0.05). We also observed increased utilization of ART from 46% in the 1st month to 71% in the 6th month since it is easier to find a feasible time slot for the clinical team. The majority of the remaining IGRT on oART system are lung SBRT where the first fraction is not adapted due to being within a week of the simulation. Leveraging CBCT based ART system as replan platform and non-ART Linac as IGRT platform is clinically feasible. This process significantly improved the turnaround time for replan, reduced the required resource and promotes the utilization of oART.

ОСОБЕННОСТИ ВЫБОРА БЕСПИЛОТНОЙ АВИАЦИОННОЙ СИСТЕМЫ

To perform many tasks in the system of EMERCOM of Russia, databases have been developed with the ability to select an aircraft model according to tactical and technical characteristics of helicopter and airplane types. Databases are presented in the form of digital and text invariants, in a certain range of numerical values, which provides search, processing and identification using a computer, on the SQLite3 database management system and for Windows 7 and higher operating systems. The databases in question can be used by employees and specialists of search and rescue teams and other services of EMERCOM of Russia to streamline and differentiate the approach in choosing the necessary unmanned aircraft system of the aircraft type to perform the tasks, taking into account the terrain and conditions of their use. The principle of operation is in the process of filling the database with information about the available models designed to equip the units of EMERCOM of Russia, so that in the future it would be possible to conduct a sample.

Relational Database for Health Care

The development of activities within a company will increase the complexity of business processes with transactional data that will increase from time to time. This is also experienced in health services, which are health service institutions for the community and certainly involve a lot of data in their operations. Starting from the registration process, medical/paramedical examinations, hospitalization, purchasing drugs, laboratories, etc. These data certainly require a container that can be used to manage the data needed in the transaction. So that later these data can be easily integrated and processed into a useful output. Database as one of the media that can be used to accommodate all supporting data. By using a database, and the features of a Database Management System (DBMS) can help companies carry out their operations more effectively and efficiently. The research method used refers to the analysis and design. In the method of analysis, literature studies are carried out from various supporting sources and observations of samples of research objects. While the design method refers to database design using the Database Lifecycle approach

Extract, Transform, Load Monitoring sales Multi Marketplace

This research is made in the design of a database system and online sales files for sales on a medium business scale, Era 5.0 Society many marketplaces make it easy for medium businesses to sell products, So business actors need to see the development of digital business and forecast their merchandise that is sold out online. As for the implementation of 1) database from RDBMS into MYSQL. The research uses an open source database, namely MYSQL, 2) The research method uses Microsoft Access program tools with the link to the Windows 10 operating system odbc in Extract Transform Load (ETL) which is carried out automatically with button 3 process Extract, Transform, Load from the download folder into the relational database management system (RDMS) in ms access and stored in the MySQL database. 3) This research forms raw data facts from several marketplaces and is processed into normalized table dimensions in the data warehouse. This research aims to design a combination of open-source systems and Microsoft access licenses, for business actors who need sales analysis that is sold multichannel (online or offline), to optimize stock and provide products consistently in each sales channel.

Enhancing distribution generator impact mitigation using an adaptive protection scheme based on modified pelican optimization algorithm and active database management system

AbstractThis paper addresses the challenge of protecting electrical networks in the presence of distribution generators (DGs). The use of DGs affects fault currents, leading to miscoordination between protection relays and causing constraints on network reliability. To tackle this issue, the authors propose an adaptive protection scheme (APS) based on a modified pelican optimization algorithm (MPOA) and active database management system (ADBMS). The APS coordinates directional overcurrent relays and distance relays, while the MPOA simulates a pelican mating strategy and includes a modified internal equation. The proposed APS is further upgraded with ADBMS to save system resources by storing relay settings in the database and calling them when the state of DGs changes without running optimization algorithms. The effectiveness of the proposed APS is validated on the Institute of Electrical and Electronics Engineers (IEEE) eight‐bus test system and the IEEE 14‐bus distribution network. Results indicate that the APS can effectively protect electrical networks in the presence of DGs, while the ADBMS upgrade saves system resources.

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.

NBSP: an online centralized database management system for a newborn sickle cell program in India.

NBSP: an online centralized database management system for a newborn sickle cell program in India.