Relational Database Management System Research Articles

A novel approach to exploring the dark genome and its application to mapping of the vertebrate virus fossil record

BackgroundGenomic regions that remain poorly understood, often referred to as the dark genome, contain a variety of functionally relevant and biologically informative features. These include endogenous viral elements (EVEs)—virus-derived sequences that can dramatically impact host biology and serve as a virus fossil record. In this study, we introduce a database-integrated genome screening (DIGS) approach to investigate the dark genome in silico, focusing on EVEs found within vertebrate genomes.ResultsUsing DIGS on 874 vertebrate genomes, we uncover approximately 1.1 million EVE sequences, with over 99% originating from endogenous retroviruses or transposable elements that contain EVE DNA. We show that the remaining 6038 sequences represent over a thousand distinct horizontal gene transfer events across 10 virus families, including some that have not previously been reported as EVEs. We explore the genomic and phylogenetic characteristics of non-retroviral EVEs and determine their rates of acquisition during vertebrate evolution. Our study uncovers novel virus diversity, broadens knowledge of virus distribution among vertebrate hosts, and provides new insights into the ecology and evolution of vertebrate viruses.ConclusionsWe comprehensively catalog and analyze EVEs within 874 vertebrate genomes, shedding light on the distribution, diversity, and long-term evolution of viruses and reveal their extensive impact on vertebrate genome evolution. Our results demonstrate the power of linking a relational database management system to a similarity search-based screening pipeline for in silico exploration of the dark genome.

Research on the effectiveness of data compression methods in relational and NoSQL DBMS

Research on the effectiveness of data compression methods in relational and NoSQL DBMS

Application-Level Caching Approach Based on Enhanced Aging Factor and Pearson Correlation Coefficient

Relational database management systems (RDBMS) have long served as the fundamental infrastructure for web applications. Relatively slow access speeds characterize an RDBMS because its data is stored on a disk. This RDBMS weakness can be overcome using an in-memory database (IMDB). Each query result can be stored in the IMDB to accelerate future access. However, due to the limited capacity of the server cache in the IMDB, an appropriate data priority assessment mechanism needs to be developed. This paper presents a similar cache framework that considers four data vectors, namely the data size, timestamp, aging factor, and controller access statistics for each web page, which serve as the foundation elements for determining the replacement policy whenever there is a change in the content of the server cache. The proposed similarCache employs the Pearson correlation coefficient to quantify the similarity levels among the cached data in the server cache. The lowest Pearson correlation coefficients cached data are the first to be evicted from the memory. The proposed similarCache was empirically evaluated based on simulations conducted on four IRcache datasets. The simulation outcomes revealed that the data access patterns, and the configuration of the allocated memory cache significantly influenced the hit ratio performance. In particular, the simulations on the SV dataset with the most minor memory space configuration exhibited a 2.33% and 1% superiority over the SIZE and FIFO algorithms, respectively. Future tasks include building a cache that can adapt to data access patterns by determining the standard deviation. The proposed similarCache should raise the Pearson coefficient for often available data to the same level as most accessed data in exceptional cases.

Migration of a Vehicle Tracking System Running on Relational Database to Big Data Environment

Building a high-performance and scalable system has always been a challenge in tracking systems. At the root of this problem lies the excessive and real-time data overload. This paper aims to replace traditional approaches with big data approaches. In this study, a new big data ecosystem design for vehicle tracking system architecture is presented. The aim is to process real-time and extremely fast-generated location/tracking data very fast and increase the overall system performance. The process speed performance of the newly developed big data ecosystem is compared with the table query speed performance of a relational database. As a result of the comparison, the query speed of the big data ecosystem was found to be much faster than that of the relational database management system.

Performance Analysis and Improvement for CRUD Operations in Relational Databases from Java Programs Using JPA, Hibernate, Spring Data JPA

The role of databases is to allow for the persistence of data, no matter if they are of the SQL or NoSQL type. In SQL databases, data are structured in a set of tables in the relational database model, grouped in rows and columns. CRUD operations (create, read, update, and delete) are used to manage the information contained in relational databases. Several dialects of the SQL language exist, as well as frameworks for mapping Java classes (models) to a relational database. The question is what we should choose for our Java application, and why? A comparison of the most frequently used relational database management systems, mixed with the most frequently used frameworks should give us some guidance about when to use what. The evaluation is conducted based on the time taken for each CRUD operation to run, from thousands to hundreds of thousands of entries, using the possible combinations in the relational database system and the framework. Aiming to assess and improve the performance, the experiments included the possibility of warming-up the Java Virtual Machine before the execution of queries. Also, the research investigated the time spent using different methods of code to determine the critical regions (bottlenecks). Thus, the conclusions provide a comprehensive overview of the performances of Java applications accessing databases depending on the suite decisions considering the database type, the framework in use, and the type of operation, with clear comparisons between the alternatives, the key findings of the advantages and drawbacks of each of them, and supporting architects and developers in their technological decisions and improving the speed of their programs.

Methods for Thematic Analysis and Ranking in Information Analysis Systems Based on Relational DBMS

The paper presents the models and algorithms for searching, filtering and ranking data in an information analysis system, based on the ontological description of its structure. The rules for filtering and ranking objects based on their boolean, numeric or enumerable properties are specified by the users, and based on these rules SQL queries are evaluated. Another problem considered is taking subject area into account when searching for objects. Adding this allows one to solve many tasks that occur in current research information systems, such as searching for experts in given subject areas or generating reports. A search query can be specified in the form of a set of keywords, and a statistical approach is used for comparing keyword sets.

Quantum Join Ordering by Splitting the Search Space of QUBO Problems

The join order has a huge impact on the execution time of a query, such that finding an optimal join order plays a crucial role in query optimization. However, join order optimization is known to be NP-hard. Hence, in this paper, we propose an approach for accelerating join order optimization by quantum computers. We extend our previous approach supporting bushy join trees by splitting the search space of possible join orders and solving each of these subspaces on currently available quantum computers to optimize the join of more relations than our previous approach. We have integrated our approach to quantum query optimization in the relational database management system PostgreSQL to conduct studies with real-world queries. In our experiments, we show that we can perform join order optimization up to 7 relations for real-world queries using quantum annealing and up to 8 relations for artificial queries using simulated annealing with a reasonable number of QUBO problems solved by D‑Wave’s Quantum Annealer. Furthermore, we show that our approach can be also used to perform join-order for queries joining five relations on circuit-based quantum computers running the quantum approximate optimization algorithm (QAOA) and variational quantum eigensolver (VQE) approaches.

Improving web-oriented information systems efficiency using Redis caching mechanisms

The responsiveness of a web application with minimum latency time and maximum web pages loading speed is determined by its overall performance. When dealing with a large number of users and amount of data, the performance of web applications is significantly affected by ways of data processing, storage and access. This paper identifies the significance of data caching process to speed up access to relational database. The study examines approaches to improve the performance of web applications through the joint use of MySQL relational database management system (DBMS) and Redis NoSQL DBMS. The practical part of the study presents a description of a web application built based on Java and Spring Boot framework. The paper proposes the implementation of the caching strategies that take into account the principles of aspect-oriented programming. Made experiments on performance testing of the developed web application with and without caching are presented. The presented results of the study allowed us to conclude that it is possible to improve the performance of web applications by the optimal use of caching strategies when performing database queries.

Refactoring a Complex Scientometric Software System to Support PostgreSQL Database Management System

The problem of software import substitution is under a great deal of attention in Russia since 2015. As a part of import substitution for a complex software system, database migration presents a considerable risk. Despite the SQL ISO standardization efforts, different implementations of SQL relational database management system are partially incompatible on the level of source codes for the schema, queries and stored procedures. Incompatibilities can manifest even at the level of query semantics. Some traits of complex scientometric software systems further complicate the migration process. Such traits include the complex data model and, consequently, the presence of non-trivial graph-traversing SQL queries which should complete fast enough to be used interactively for thousands of users simultaneously. In this paper an experience of refactoring a complex scientometric software system to support PostgreSQL database management system is presented. Our approach includes both usage and modification of the existing tools and development of the in-house software to support the refactoring process compounded with an extensive automated testing procedures.

The global volcano monitoring infrastructure database (GVMID)

The global volcano monitoring infrastructure database (GVMID)

The side effect registry immuno-oncology (SERIO) – A tool for systematic analysis of immunotherapy-induced side effects

BackgroundImmunotherapies such as immune checkpoint inhibitors (ICI) are effective in multiple tumor entities but induce a plethora of side effects. Comprehensive real-world analyses are essential to identify new signals, characterize diagnostic features, enable risk assessment, determine pathomechanisms, assess effectiveness of side effect management and compare tumor outcomes. MethodsThe international online `Side-Effect Registry Immuno-Oncology´ (SERIO; www.serio-registry.org) collects rare, complex, and severe immunotherapy-induced side effects across all tumor entities with a strong focus on ICI-induced immune-related adverse events (irAE). The relational database management system (RDMS) contains structured data on patient and tumor characteristics, type of immunotherapy, treatment of side effects, and outcome of tumor and irAE. Data are captured within 25 organ modules including new modules for immune effector cell-associated neurotoxicity syndrome (ICANS) for CAR-T-cell therapies and cytokine release syndrome (CRS) for bispecific antibodies. Information on biological samples is gathered. ResultsA total of 1398 irAE cases have been documented by 58 centers from 13 countries in patients with 17 tumor types. IrAEs were induced by nine different immunotherapies including tebentafusp and CAR-T cell therapies, and resulted, among others, in neurological (7.6%), pulmonary (4.0%), and cardiac toxicities (2.9%). 50.0% of all irAEs were graded severe or life-threatening and 23.0% of patients received second-line therapy for steroid-refractory or steroid-dependent irAE. SERIO has contributed to 44 original publications on topics ranging from irMyocarditis to irEncephalitis to long-term persistent sequelae of immunotherapy. ConclusionsA reliable evidence base is crucial for decision-making in rare, complex or therapy-refractory irAE. SERIO can help optimize side effect management and thereby reduce morbidity and mortality induced by immunotherapy.

RFID Attendance System

Taking traditional manual pen-paper attendance, which is very time-consuming, insecure and usually leads to human errors as well as prone to misconduct, as the valuable time and work gets wasted in organizing and structuring the attendance data in registers. Hence to overcome this major hectic problem, we have used relational database management system in real time with appropriate security measures to access, manipulate and represent the data on the basis of the unique RFID tags, which gets fast and easily scanned on the RFID reader. This system consists of hardware and software with most trending implementation of a lightweight MQTT protocol in IoT technology; designed to take an attendance on the basis of RFID technology with NodeMCU firmware. The main objective of this proposed system is to make the effective and efficient computerized attendance on institutes website in excel sheet format with a particular date and students can also see their particular subject attendance on the webpage.

Performability evaluation of NoSQL-based storage systems

NoSQL database management systems (DBMS) have been commonly adopted in cloud storage applications, as they usually provide better performance and availability than relational DBMSs. Eventual consistency is a remarkable feature of many NoSQL database systems, in which not all redundant nodes have the newest data, but, eventually, such data will be present in all nodes. Distinct consistency levels can be utilized, but they may affect user experience and service level agreements. The assessment of consistency concerning the probability of accessing the newest data is not common, and the jointly evaluation of performance and availability is usually neglected. This work proposes a method based on stochastic Petri nets (SPN) for evaluating the consistency levels of storage systems based on NoSQL DBMS adopting quorum technique. The models take into account distinct consistency levels and redundant nodes for estimating system availability, throughput and the probability of accessing the newest data. Experimental results demonstrate the practical feasibility of our approach.

Design and Implementation of an Online Electronic Platform to Manage Education Activities in the Computer Science Department at Mosul University

In this work, data for electronic activities of lecturers in Computer Science Department was organized using a relational database management system to exploit relations between entities for facilitating access, view, and manipulation of data developed using Microsoft Visual C# programming language to obtain an easy-to-use graphical user interface (GUI). In addition, the Sap Crystal Report program was used to prepare reports for the electronic activities of the lecturers under study. The electronic system would reduce mistakes caused in the manual use of lecturers’ data. Additionally, it would speed up data access in case of viewing, updating or deleting the data without any errors. This project consisted of four phases, namely, system analysis, design, implementation and evaluation. Experiments explained that the suggested database system is more appropriate for lecturers’ information management, as more than 80% of the lecturers studied were satisfied with this project. This type of on-line platform system and its applications can be applied for stakeholders in various ways.

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.

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.

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.

GraphOS: Towards Oblivious Graph Processing

We propose GraphOS, a system that allows a client that owns a graph database to outsource it to an untrusted server for storage and querying. It relies on doubly-oblivious primitives and trusted hardware to achieve a very strong privacy and efficiency notion which we call oblivious graph processing : the server learns nothing besides the number of graph vertexes and edges, and for each query its type and response size. At a technical level, GraphOS stores the graph on a doubly-oblivious data structure , so that all vertex/edge accesses are indistinguishable. For this purpose, we propose Omix++, a novel doubly-oblivious map that outperforms the previous state of the art by up to 34×, and may be of independent interest. Moreover, to avoid any leakage from CPU instruction-fetching during query evaluation, we propose algorithms for four fundamental graph queries (BFS/DFS traversal, minimum spanning tree, and single-source shortest paths) that have a fixed execution trace , i.e., the sequence of executed operations is independent of the input. By combining these techniques, we eliminate all information that a hardware adversary observing the memory access pattern within the protected enclave can infer. We benchmarked GraphOS against the best existing solution, based on oblivious relational DBMS (translating graph queries to relational operators). GraphOS is not only significantly more performant (by up to two orders of magnitude for our tested graphs) but it eliminates leakage related to the graph topology that is practically inherent when a relational DBMS is used unless all operations are "padded" to the worst case.

Impact of Fuzzy Logic in Object-Oriented Database Through Blockchain

In this article, we show that applying fuzzy reasoning to an object-arranged data set produces noticeably better results than applying it to a social data set by applying it to both social and object-situated data sets. A Relational Data Base Management System (RDBMS) product structure offers a practical and efficient way to locate, store, and retrieve accurate data included inside a data collection. In any case, clients typically have to make vague, ambiguous, or fanciful requests. Our work allows clients the freedom to utilise FRDB to examine the database in everyday language, enabling us to provide a range of solutions that would benefit clients in a variety of ways. Given that the degree of attributes in a fuzzy knowledge base goes from 0 to 1, the term "fuzzy" was coined. This is due to the base's fictitious formalization's reliance on fuzzy reasoning. In order to lessen the fuzziness of the fuzzy social data set as a result of the abundance of uncertainty and vulnerabilities in clinical medical services information, a fuzzy article located information base is designed here for the Health-Care space. In order to validate the presentation and sufficiency of the fuzzy logic on both data sets, certain fuzzy questions are thus posed of the fuzzy social data set and the fuzzy item-situated information base..

CACHE DATA REPLACEMENT POLICY BASED ON RECENTLY USED ACCESS DATA AND EUCLIDEAN DISTANCE

Data access management in web-based applications that use relational databases must be well thought out because the data continues to grow every day. The Relational Database Management System (RDBMS) has a relatively slow access speed because the data is stored on disk. This causes problems with decreased database server performance and slow response times. One strategy to overcome this is to implement caching at the application level. This paper proposed SIMGD framework that models Application Level Caching (ALC) to speed up relational data access in web applications. The ALC strategy maps each controller and model that has access to the database into a node-data in the in-Memory Database (IMDB). Not all node-data can be included in IMDB due to limited capacity. Therefore, the SIMGD framework uses the Euclidean distance calculation method for each node-data with its top access data as a cache replacement policy. Node-data with Euclidean distance closer to their top access data have a high priority to be maintained in the caching server. Simulation results show at the 25KB cache configuration, the SIMGD framework excels in achieving hit ratios compared to the LRU algorithm of 6.46% and 6.01%, respectively.