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Relevant bibliographies by topics / Online transactional processing

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Academic literature on the topic 'Online transactional processing'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "Online transactional processing"

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Mohammed Omer Shakeel Ahmed. "Enhancing CRM Decision-Making with HTAP: Leveraging Real-Time Analytics for Competitive Advantage." Journal of Information Systems Engineering and Management 10, no. 23s (2025): 50–57. https://doi.org/10.52783/jisem.v10i23s.3675.

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Customer Relationship Management (CRM) systems traditionally separate transactional and analytical data into distinct layers, with transactional databases optimized for fast writes and analytical data warehouses for read-heavy queries. This separation introduces delays in analytics, hindering real-time insights and timely decision-making. Hybrid Transactional/Analytical Processing (HTAP) offers a unified solution by integrating Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) in a single system. This paper explores the feasibility of applying HTAP in CRM systems, highlighting its potential to enable real-time analytics, improve decision-making, and enhance business agility, while addressing associated challenges

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Abu, Sarwar Zamani, and Kumar Gupta Pankaj. "SOA-Based Distributed System in Online Transaction Processing." Journal of Information Sciences and Computing Technologies 4, no. 1 (2015): 258–64. https://doi.org/10.5281/zenodo.3968720.

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Service Oriented Architecture (SOA) with transactional workflow support is a state- of- the- art architectural style for constructing enterprise application. In this research, rate of progress activities raise distributed service in a coordinated manner, using transaction context propagating message, coordination protocol and compensation logic. We reviewed the past, present and future of transaction processing and transaction integrity. Most of the challenges and requirement that led to the development and evolution of transaction processing system are still applicable today and recently, we have some intriguing developments. We take an explorative approach to probe the theoretical and implementational feasibility of managing transaction in the web service world.

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Anagha, Bhunje, and Ahirrao Swati. "Workload Aware Incremental Repartitioning of NoSQL for Online Transactional Processing Applications." International Journal of Advances in Applied Sciences (IJAAS) 7, no. 1 (2018): 54–65. https://doi.org/10.11591/ijaas.v7.i1.pp54-65.

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Numerous applications are deployed on the web with the increasing popularity of internet. The applications include, 1) Banking applications, 2) Gaming applications, 3) E-commerce web applications. Different applications reply on OLTP (Online Transaction Processing) systems. OLTP systems need to be scalable and require fast response. Today modern web applications generate huge amount of the data which one particular machine and Relational databases cannot handle. The E-Commerce applications are facing the challenge of improving the scalability of the system. Data partitioning technique is used to improve the scalability of the system. The data is distributed among the different machines which results in increasing number of transactions. The work-load aware incremental repartitioning approach is used to balance the load among the partitions and to reduce the number of transactions that are distributed in nature. Hyper Graph Representation technique is used to represent the entire transactional workload in graph form. In this technique, frequently used items are collected and Grouped by using Fuzzy C-means Clustering Algorithm. Tuple Classification and Migration Algorithm is used for mapping clusters to partitions and after that tuples are migrated efficiently.

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Bhunje, Anagha, and Swati Ahirrao. "Workload Aware Incremental Repartitioning of NoSQL for Online Transactional Processing Applications." International Journal of Advances in Applied Sciences 7, no. 1 (2018): 54. http://dx.doi.org/10.11591/ijaas.v7.i1.pp54-65.

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<p><span lang="EN-US">Numerous applications are deployed on the web with the increasing popularity of internet. The applications include, 1) Banking applications,<br /> 2) Gaming applications, 3) E-commerce web applications. Different applications reply on OLTP (Online Transaction Processing) systems. OLTP systems need to be scalable and require fast response. Today modern web applications generate huge amount of the data which one particular machine and Relational databases cannot handle. The E-Commerce applications are facing the challenge of improving the scalability of the system. Data partitioning technique is used to improve the scalability of the system. The data is distributed among the different machines which results in increasing number of transactions. The work-load aware incremental repartitioning approach is used to balance the load among the partitions and to reduce the number of transactions that are distributed in nature. Hyper Graph Representation technique is used to represent the entire transactional workload in graph form. In this technique, frequently used items are collected and Grouped by using Fuzzy C-means Clustering Algorithm. Tuple Classification and Migration Algorithm is used for mapping clusters to partitions and after that tuples are migrated efficiently.</span></p>

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Iqbal Alamsyah, Muhammad, Ferry Kosadi, and Ridwan Ridwan. "TRAINING AND MENTORING FOR ONLINE-BASED TRANSACTION RECORDING (ONLINE TRANSACTION PROCESSING SYSTEMS-OLTP)." PICS-J Pasundan International of Community Service Journal, Issue Volume 5 No, 1 june 2023 (June 30, 2023): 1–10. http://dx.doi.org/10.23969/picsj.v5i1.6744.

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Virageawie is a company that consists of bamboo craftsmen. We see that there is a need for support for increasing partner empowerment, especially in improving partner management capabilities. The problem is, in its activities/operations the use of digital media for transactions made has not provided convenience and speed in the process of transactional reporting and financial reports. There are obstacles including technical capabilities in the use of information technology, the process of correction and reconciliation of data received from each provider, and conversion to a format that is following the habits of business actors, besides that in terms of understanding the procedures and processes for preparing financial reports by Accounting Standards Finance (SAK). The solution that will be provided is in the form of establishing a transaction recording system and preparing digital-based financial reports, training regarding the use of digital-based transaction recording and financial reporting tools, and assistance/assistance for 3 months until partner HR can be completely released and able to run the new system. Material/content related to formation, training, and mentoring is based on the results of previous research from service members, which had been carried out previously at the binongjati knitting center in Bandung City. The output target in this Community Service is that the partner's management ability (virageawie) increases, with evidence of a description of the results of the management ability test/score before and after the activity.

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Sucharitha, Shetty, Dinesh Rao B., and Prabhu Srikanth. "Growth of relational model: Interdependence and complementary to big data." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1780–95. https://doi.org/10.11591/ijece.v11i2.pp1780-1795.

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A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system.

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Camilleri, Carl, Joseph G. Vella, and Vitezslav Nezval. "HTAP With Reactive Streaming ETL." Journal of Cases on Information Technology 23, no. 4 (2021): 1–19. http://dx.doi.org/10.4018/jcit.20211001.oa10.

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In database management systems (DBMSs), query workloads can be classified as online transactional processing (OLTP) or online analytical processing (OLAP). These often run within separate DBMSs. In hybrid transactional and analytical processing (HTAP), both workloads may execute within the same DBMS. This article shows that it is possible to run separate OLTP and OLAP DBMSs, and still support timely business decisions from analytical queries running off fresh transactional data. Several setups to manage OLTP and OLAP workloads are analysed. Then, benchmarks on two industry standard DBMSs empirically show that, under an OLTP workload, a row-store DBMS sustains a 1000 times higher throughput than a columnar DBMS, whilst OLAP queries are more than 4 times faster on a columnar DBMS. Finally, a reactive streaming ETL pipeline is implemented which connects these two DBMSs. Separate benchmarks show that OLTP events can be streamed to an OLAP database within a few seconds.

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Gaffney, Kevin P., Robert Claus, and Jignesh M. Patel. "Database isolation by scheduling." Proceedings of the VLDB Endowment 14, no. 9 (2021): 1467–80. http://dx.doi.org/10.14778/3461535.3461537.

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Transaction isolation is conventionally achieved by restricting access to the physical items in a database. To maximize performance, isolation functionality is often packaged with recovery, I/O, and data access methods in a monolithic transactional storage manager. While this design has historically afforded high performance in online transaction processing systems, industry trends indicate a growing need for a new approach in which intertwined components of the transactional storage manager are disaggregated into modular services. This paper presents a new method to modularize the isolation component. Our work builds on predicate locking, an isolation mechanism that enables this modularization by locking logical rather than physical items in a database. Predicate locking is rarely used as the core isolation mechanism because of its high theoretical complexity and perceived overhead. However, we show that this overhead can be substantially reduced in practice by optimizing for common predicate structures. We present DIBS, a transaction scheduler that employs our predicate locking optimizations to guarantee isolation as a modular service. We evaluate the performance of DIBS as the sole isolation mechanism in a data processing system. In this setting, DIBS scales up to 10.5 million transactions per second on a TATP workload. We also explore how DIBS can be applied to existing database systems to increase transaction throughput. DIBS reduces per-transaction file system writes by 90% on TATP in SQLite, resulting in a 3X improvement in throughput. Finally, DIBS reduces row contention on YCSB in MySQL, providing serializable isolation with a 1.4X improvement in throughput.

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Lenka, Swagatika, and Ravindra Tiwari. "A Hybrid ML and Data Science Approach to Detect Online Fraud Transaction at Real Time." Journal of Neonatal Surgery 14, no. 1S (2025): 765–75. https://doi.org/10.52783/jns.v14.1601.

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To safeguard customers and financial institutions, the swift growth of online transactions calls for strong fraud detection systems. To identify online fraud in real time, this study suggests a mixed machine learning (ML) and data science strategy. Through the integration of many data mining methodologies, such as supervised and unsupervised learning algorithms, the research endeavours to detect trends and anomalies suggestive of fraudulent activity. A comprehensive knowledge of transaction behaviours is made possible by the methodology's emphasis on dynamic feature extraction and selection, which makes use of massive datasets made up of transactional records. Using ensemble learning techniques reduces false positives and improves prediction accuracy. Results from experiments reveal that the hybrid model works well, outperforming conventional techniques in terms of processing speed and detection rates. Furthermore, the model's flexibility facilitates its implementation across multiple internet platforms, guaranteeing efficiency and scalability. The results highlight the value of a multidisciplinary strategy in the fight against online fraud, which will ultimately help create more secure online transaction environments. The foundation for future research targeted at improving fraud detection techniques in an increasingly digital economy is laid by this study.

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Abbassi, Hanae, Saida El Mendili, and Youssef Gahi. "Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion." HighTech and Innovation Journal 5, no. 1 (2024): 185–99. http://dx.doi.org/10.28991/hij-2024-05-01-014.

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Digital trades and payments are becoming increasingly popular, as they typically entail monetary transactions. This not only makes electronic transactions more convenient for the end customer, but it also raises the likelihood of fraud. An adequate fraud detection system with a cutting-edge model is critical to minimizing fraud costs. Identifying fraud at the ideal time entails establishing and setting up ubiquitous systems to consume and analyze massive amounts of streaming data. Recent advances in data analytics methods and introducing open-source technology for big data storage and processing opened new options for detecting fraud. This study aims to tackle this critical issue by providing a newly real-time e-transaction fraud detection schema that consolidates the advantages of both unsupervised learners, including autoencoder and extended isolation forests, with cutting-edge big data gadgets such as Spark streaming and sparkling water. It addresses the shortage of non-fraudulent instances and handles the excessive dimension of the set of features. On two real-world transactional datasets, we assess our suggested technique. Compared with other current fraud identification systems, our methodology delivers an elevated accuracy yield of 99%. Furthermore, it outperforms state-of-the-art approaches in reliably identifying fraudulent samples. Doi: 10.28991/HIJ-2024-05-01-014 Full Text: PDF

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Dissertations / Theses on the topic "Online transactional processing"

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Dixon, Eric Richard. "Developing distributed applications with distributed heterogenous databases." Thesis, Virginia Tech, 1993. http://hdl.handle.net/10919/42748.

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Madron, Lukáš. "Datové sklady a OLAP v prostředí MS SQL Serveru." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235916.

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This paper deals with data warehouses and OLAP. These technologies are defined and described here. Then an introduction of the architecture of product MS SQL Server and its tools for work with data warehouses and OLAP folow. The knowledge gained is used for creation of sample application.

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Books on the topic "Online transactional processing"

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Claybrook, Billy G. OLTP, online transaction processing systems. J. Wiley, 1992.

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Redbooks, IBM. DB2 Udb Ese V8 Non-dpf Performance Guide for High Performance Oltp And Bi. IBM.Com/Redbooks, 2004.

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Mena, Eduardo, and Arantza Illarramendi. Ontology-Based Query Processing for Global Information Systems (The International Series in Engineering and Computer Science). Springer, 2001.

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Book chapters on the topic "Online transactional processing"

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Schill, Alexander. "Online Transaction Processing." In Das OSF Distributed Computing Environment. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60731-8_10.

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Gunther, Neil J. "qcomp: A tool for assessing online transaction processing scalability." In Computer Performance Evaluation Modelling Techniques and Tools. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58021-2_17.

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Spenrath, Yorick, Marwan Hassani, and Boudewijn F. van Dongen. "Online Prediction of Aggregated Retailer Consumer Behaviour." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_16.

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AbstractPredicting the behaviour of consumers provides valuable information for retailers, such as the expected spend of a consumer or the total turnover of the retailer. The ability to make predictions on an individual level is useful, as it allows retailers to accurately perform targeted marketing. However, with the expected large number of consumers and their diverse behaviour, making accurate predictions on an individual consumer level is difficult. In this paper we present a framework that focuses on this trade-off in an online setting. By making predictions on a larger number of consumers at a time, we improve the predictive accuracy but at the cost of usefulness, as we can say less about the individual consumers. The framework is developed in an online setting, where we update the prediction model and make new predictions over time. We show the existence of the trade-off in an experimental evaluation on a real-world dataset consisting of 39 weeks of transaction data.

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Suharjito and Adrianus B. Kurnadi. "Online Transaction Processing (OLTP) Performance Improvement Using File-Systems Layer Transparent Compression." In Intelligent Information and Database Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54472-4_29.

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Hayamizu, Yuto, Kazuo Goda, Miyuki Nakano, and Masaru Kitsuregawa. "Application-Aware Power Saving for Online Transaction Processing Using Dynamic Voltage and Frequency Scaling in a Multicore Environment." In Architecture of Computing Systems - ARCS 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19137-4_5.

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Fu, Lixin, and Wen-Chen Hu. "Online Analytical Processing and Data-Cube Technologies." In Handbook of Research on Public Information Technology. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-857-4.ch057.

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Since the late ’80s and early ’90s, database technologies have evolved to a new level of applications: online analytical processing (OLAP), where executive management can make quick and effective strategic decisions based on knowledge in terms of queries against large amounts of stored data. Some OLAP systems are also regarded as decision support systems (DSSs) or executive information systems (EIS). The traditional, well-established online transactional processing (OLTP) systems such as relational database management systems (RDBMS) mainly deal with mission-critical daily transactions. Typically, there are a large number of short, simple queries such as lookups, insertions, and deletions. The main focus is transaction throughput, consistency, concurrency, and failure recovery issues. OLAP systems, on the other hand, are mainly analytical and informational. OLAP systems are usually closely coupled with data warehouses, which can contain very large data sets that may include historical data as well as data integrated from different departments and geographical locations. So the sizes of data warehouses are usually significantly larger than common OLTP systems. In addition, the workloads of OLAP are quite different from those of traditional transaction systems: The queries are unpredictable and much more complicated. For example, an OLAP query could be, “For each type of car and each manufacturer, list market share change in terms of car sales between the first quarter of 2005 and the first quarter of 2006.” The purpose of these queries is not for the daily operational maintenance of data; instead, it is for deeper knowledge from data used for decision support.

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Cavero Barca, José María, Esperanza Marcos Martinez, Mario G. Piattini, and Adolfo Sánchez de Miguel. "Data Warehouse Development." In Encyclopedia of Information Science and Technology, First Edition. IGI Global, 2005. http://dx.doi.org/10.4018/978-1-59140-553-5.ch127.

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The concept of data warehouse first appeared in Inmon (1993) to describe a “subject oriented, integrated, non-volatile, and time variant collection of data in support of management’s decisions” (31). It is a concept related to the OLAP (online analytical processing) technology, first introduced by Codd et al. (1993) to characterize the requirements of aggregation, consolidation, view production, formulae application, and data synthesis in many dimensions. A data warehouse is a repository of information that mainly comes from online transactional processing (OLTP) systems that provide data for analytical processing and decision support.

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Hutson, James, and Daniel Plate. "Leveraging AI to Personalize and Humanize Online Learning." In Humanizing Online Teaching and Learning in Higher Education. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-0762-5.ch011.

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This chapter explores the potential of artificial intelligence (AI) to personalize and humanize online education, transcending transactional learning. It emphasizes how AI, particularly generative large language models (LLM) utilizing natural language processing (NLP), can foster deeper, human-like interactions between students and educators. Moving away from traditional keyword-based searches, the chapter highlights how AI integrates with tools like search engines and research databases, transforming the way students and teachers think, research, and converse. Despite concerns over the potential dehumanizing impact of AI, the chapter argues that its capabilities can, in fact, actually facilitate continuous and creative engagement, while privileging understanding and creativity over rote memorization. These new generative tools can enhance, not challenge, the human element in online education and promote more authentic assessments in the process.

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Weippl, Edgar R. "Database Security and Statistical Database Security." In Database Technologies. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-058-5.ch126.

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In this article we will present an introduction to issues relevant to database security and statistical database security. We will briefly cover various security models, elaborate on how data analysis in data warehouses (DWH) might compromise an individual’s privacy, and explain which safeguards can be used to prevent attacks. In most companies, databases are an essential part of IT infrastructure since they store critical business data. In the last two decades, databases have been used to process increasing amounts of transactional data, such as, a complete account of a person’s purchases from a retailer or connection data from calls made on a cell phone. As soon as this data became available from transactional databases and online transactional processing (OLTP) became well established, the next logical step was to use the knowledge contained in the vast amounts of data. Today, data warehouses (DWH) store aggregated data in an optimal way to serve queries related to business analysis.

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Röhm, Uwe. "OLAP with a Database Cluster." In Database Technologies. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-058-5.ch047.

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This chapter presents a new approach to online decision support systems that is scalable, fast, and capable of analysing up-to-date data. It is based on a database cluster: a cluster of commercial off-the-shelf computers as hardware infrastructure and off-the-shelf database management systems as transactional storage managers. We focus on central architectural issues and on the performance implications of such a cluster-based decision support system. In the first half, we present a scalable infrastructure and discuss physical data design alternatives for cluster-based online decision support systems. In the second half of the chapter, we discuss query routing algorithms and freshness-aware scheduling. This protocol enables users to seamlessly decide how fresh the data analysed should be by allowing for different degrees of freshness of the online analytical processing (OLAP) nodes. In particular it becomes then possible to trade freshness of data for query performance.

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Conference papers on the topic "Online transactional processing"

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Zeng, Jingna, Paolo Romano, Joao Barreto, Luis Rodrigues, and Seif Haridi. "Online Tuning of Parallelism Degree in Parallel Nesting Transactional Memory." In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2018. http://dx.doi.org/10.1109/ipdps.2018.00057.

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Jung, Changhun, Jeonil Kang, Aziz Mohaisen, and DaeHun Nyang. "Digitalseal: a Transaction Authentication Tool for Online and Offline Transactions." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462341.

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Setiadi, Tedy, and Syauqi Bima Premapasha. "Scrum Implementation for Online Transaction Processing (OLTP) in Hospital Management." In 2018 12th International Conference on Telecommunication Systems, Services, and Applications (TSSA). IEEE, 2018. http://dx.doi.org/10.1109/tssa.2018.8708797.

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Bog, Anja, Jens Kruger, and Jan Schaffner. "A Composite Benchmark for Online Transaction Processing and Operational Reporting." In 2008 IEEE Symposium on Advanced Management of Information for Globalized Enterprises, AMIGE. IEEE, 2008. http://dx.doi.org/10.1109/amige.2008.ecp.30.

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Kaushik, Sona, and Shalini Puri. "Online transaction processing using enhanced sensitive data transfer security model." In 2012 Students Conference on Engineering and Systems (SCES). IEEE, 2012. http://dx.doi.org/10.1109/sces.2012.6199098.

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Catalan, Maria Luisa, Dennis A. Ludena R., and Hidenori Umeno. "VM-based Benchmark and Analysis System for Testing Online Transaction Processing." In 2007 Second International Conference on Innovative Computing, Information and Control. IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.619.

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Wang, Shuoyao, and Diwei Zhu. "Interpretable Multimodal Learning for Intelligent Regulation in Online Payment Systems." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/645.

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With the explosive growth of transaction activities in online payment systems, effective and real-time regulation becomes a critical problem for payment service providers. Thanks to the rapid development of artificial intelligence (AI), AI-enable regulation emerges as a promising solution. One main challenge of the AI-enabled regulation is how to utilize multimedia information, i.e., multimodal signals, in Financial Technology (FinTech). Inspired by the attention mechanism in nature language processing, we propose a novel cross-modal and intra-modal attention network (CIAN) to investigate the relation between the text and transaction. More specifically, we integrate the text and transaction information to enhance the text-trade joint-embedding learning, which clusters positive pairs and push negative pairs away from each other. Another challenge of intelligent regulation is the interpretability of complicated machine learning models. To sustain the requirements of financial regulation, we design a CIAN-Explainer to interpret how the attention mechanism interacts the original features, which is formulated as a low-rank matrix approximation problem. With the real datasets from the largest online payment system, WeChat Pay of Tencent, we conduct experiments to validate the practical application value of CIAN, where our method outperforms the state-of-the-art methods.

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Honcharenko, Tetyana, Kateryna Kyivska, Mariia Liashchenko, Oleksandr Terentyev, Ievgenii Gorbatyuk, and Elena Dolya. "Mathematical Modeling of Online Transaction Processing System for Design of Building Territory." In 2021 IEEE 3rd Ukraine Conference on Electrical and Computer Engineering (UKRCON). IEEE, 2021. http://dx.doi.org/10.1109/ukrcon53503.2021.9575215.

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Dehbashi, Mehdi, and Gorschwin Fey. "Transaction-Based Online Debug for NoC-Based Multiprocessor SoCs." In 2014 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE, 2014. http://dx.doi.org/10.1109/pdp.2014.72.

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R, Shilpa, and Janhavi V. "Survey on Two-Factor Authentication with Security in Online Transaction." In 3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics. AIJR Publisher, 2018. http://dx.doi.org/10.21467/proceedings.1.66.

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