Online Transaction Processing Database Research Articles

Hybrid Graph Partitioning with OLB Approach in Distributed Transactions

Online Transaction Processing (OLTP) gets support from data partitioning to achieve better performance and scalability. The primary objective of database and application developers is to provide scalable and reliable database systems. This research presents a novel method for data partitioning and load balancing for scalable transactions. Data is efficiently partitioned using the hybrid graph partitioning method. Optimized load balancing (OLB) approach is applied to calculate the weight factor, average workload, and partition efficiency. The presented approach is appropriate for various online data transaction applications. The quality of the proposed approach is examined using OLTP database benchmark. The performance of the proposed methodology significantly outperformed with respect to metrics like throughput, response time, and CPU utilization.

Web Portal for Online Agri-Auction

Abstract: In this project, an online auction system is presented. It has a very large potential market of sellers and buyers. An On Line Transaction Processing (OLTP) database model structure is, therefore, desirable. The project involves the design and implementation of an online auction system. The analysis stage is performed first for the case study.Project begins by analyzing and presenting the OLTP database model for the online auction house. Besides, the intention to establish what goes on operationally within the online auction house. It also defines the layout in android application. In which many number of bidders bids the auction and final the auction won will store the result of the winning bidder details. And finally the shipment process will carried out

Health-care databases and its role in transformation of medicine

Health-care database is a collection of health-care data organized for storage, accessibility, and retrieval. Health-care database serves to replace the paper documents, filing cabinets of old, and file folders, which will be more convenient and immediate. At present, health-care system is in a transition from a patient care perspective toward information-based medicine requiring data from various sources such as laboratory tests, genetic tests, medical images, family histories, and patient medical records, which need to be easily stored, integrated, and analyzed. The most commonly used database in health care is the online transaction processing (OLTP) database among different types of databases available. OLTP database is the one that is a single computer application runs on, and the main strength of it is quick and real-time transaction processing. Electronic health record is a prime example of the OLTP, and practitioners enter routine clinical and laboratory data into them during usual practice as a record of the patient's care. Data can be stored externally in a secure place and can be backed up to prevent data loss with these health-care databases. The back-end data can therefore become more accurate and standardized because of the front-end software providing tip text and enforce data. Furthermore, the health-care database allows the rapid processing of classical transactions such as laboratory results and payment claims as the data are computerized.

Development of Online Egg Grading Information Management System with Data Warehouse Technique

Highlights Digital data collection and management system is developed for the USDA-AMS’s shell-egg grading program. Database system consisting of OLTP, data warehouse and OLAP databases enables online data entry and trend reporting. Data and information management is done through web application servers. Users access the databases via web browsers. Abstract . This paper is concerned with development of web-based online data entry and reporting system, capable of centralized data storage and analytics of egg grading records produced by USDA egg graders. The USDA egg grading records are currently managed in paper form. While there is useful information for data-driven knowledge discovery and decision making, the paper-based egg grading record system has fundamental limitations in effective and timely management of such information. Thus, there has been a demand to electronically and digitally store and manage the egg grading records in a database for data analytics and mining, such that the quality trends of eggs observed at various levels (e.g., nation or state) are readily available to decision makers. In this study, we report the design and implementation of a web-based online data entry and reporting information system (called USDA Egg Grading Information Management System, EGIMS), based on a data warehouse framework. The developed information system consisted of web applications for data entry and reporting, and internal databases for data storage, aggregation, and query processing. The internal databases consisted of online transaction processing (OLTP) database for data entry and retrieval, data warehouse (DW) for centralized data storage and online analytical processing (OLAP) database for multidimensional analytical queries. Thus, the key design goal of the system was to build a system platform that could provide the web-based data entry and reporting capabilities while rapidly updating the OLTP, DW and OLAP databases. The developed system was evaluated by a simulation study with statistically-modeled egg grading records of one hypothetical year. The study found that the EGIMS could handle approximately up to 600 concurrent users, 32 data entries per second and 164 report requests per second, on average. The study demonstrated the feasibility of an enterprise-level data warehouse system for the USDA and a potential to provide data analytics and data mining capabilities such that the queries about historical and current trends can be reported. Once fully implemented and tested in the field, the EGIMS is expected to provide a solution to modernize the egg grading practice of the USDA and produce the useful information for timely decisions and new knowledge discovery. Keywords: Data warehouse, Database, OLTP, OLAP, Egg grading, Information management, Web application, Information system, Data.

OLTP PERFORMANCE IMPROVEMENT USING FILE-SYSTEMS LAYER COMPRESSION

Database for Online Transaction Processing (OLTP) application is used by almost every corporations that has adopted computerisation to support their operational day to day business. Compression in the storage or file-systems layer has not been widely adopted for OLTP database because of the concern that it might decrease database performance. OLTP compression in the database layer is available commercially but it has a significant licence cost that reduces the cost saving of compression. In this research, transparent file-system compression with LZ4, LZJB and ZLE algorithm have been tested to improve performance of OLTP application. Using Swing-bench as the benchmark tool and Oracle database 12c, The result indicated that on OLTP workload, LZJB was the most optimal compression algorithm with performance improvement up to 49% and consistent reduction of maximum response time and CPU utilisation overhead, while LZ4 was the compression with the highest compression ratio and ZLE was the compression with the lowest CPU utilisation overhead. In terms of compression ratio, LZ4 can deliver the highest compression ratio which is 5.32, followed by LZJB, 4.92; and ZLE, 1.76. Furthermore, it is found that there is indeed a risk of reduced performance and/or an increase of maximum response time.

Next Generation Data Warehouse Design with OLTP and OLAP Systems Sharing same Database

Online Transaction Processing (OLTP) systems have been using the traditional relational Database management system for many years. Online Analytical Processing (OLAP) system for analytical reporting is optimized to aggregate many records which involve more read operations. Hence to enhance the performance in OLAP systems various modeling options like star schema, extended star schema was implemented. But with the latest innovations in hardware technologies like availability of multi core dual processors, availability of 64 bit processors, decreasing cost of main memory and software technology innovations like In-Memory computing, Columnar storage and other with various other new features now there is less concern towards the performance degradation and performance optimization in a data warehouse system. If there is an option to use same database for the OLTP and OLAP systems then the business intelligence system can get the real time data for the analytical query reporting. This document presents the overview of the next generation data warehouse architecture which shares the database along with OLTP and discuss how it is going to use the in memory capability to apply real time analytics.

Compacting transactional data in hybrid OLTP&OLAP databases

Growing main memory sizes have facilitated database management systems that keep the entire database in main memory. The drastic performance improvements that came along with these in-memory systems have made it possible to reunite the two areas of online transaction processing (OLTP) and online analytical processing (OLAP): An emerging class of hybrid OLTP and OLAP database systems allows to process analytical queries directly on the transactional data. By offering arbitrarily current snapshots of the transactional data for OLAP, these systems enable real-time business intelligence. Despite memory sizes of several Terabytes in a single commodity server, RAM is still a precious resource: Since free memory can be used for intermediate results in query processing, the amount of memory determines query performance to a large extent. Consequently, we propose the compaction of memory-resident databases. Compaction consists of two tasks: First, separating the mutable working set from the immutable "frozen" data. Second, compressing the immutable data and optimizing it for efficient, memory-consumption-friendly snapshotting. Our approach reorganizes and compresses transactional data online and yet hardly affects the mission-critical OLTP throughput. This is achieved by unburdening the OLTP threads from all additional processing and performing these tasks asynchronously.