Relevant bibliographies by topics / Transactional data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Transactional data'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Transactional data"

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Abdurashitova, Muniskhon. "DATA MODELS AND ARCHITECTURES IN DATA WAREHOUSING." RESEARCH AND EDUCATION 3, no. 3 (2024): 21–25. https://doi.org/10.5281/zenodo.10897094.

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<em>The separation between transactional computing and data analysis ensures that the transactional systems responsible for processing and recording business transactions can operate efficiently and without interruption. Meanwhile, data analysis systems can focus on processing and analyzing large amounts of data to extract valuable insights that can inform business decisions. By employing different data warehousing architectures and models, organizations can separate these two functions, preventing the resource-intensive data analysis processes from interfering with the smooth functioning of t
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Chaubey, Suraj, Abhishek Ram, Deepak Gupta, Abdul Raheman, and Vivek Pandey. "Business Mapping in Context of Web 3.0 using Blockchain Technology." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1189–96. http://dx.doi.org/10.22214/ijraset.2023.50277.

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Abstract: The foundation of e-commerce is based on conducting transactions and transferring goods online without the need for physical travel. However, cyber threats can pose a risk when it comes to transferring transactional data. Our primary goal is to develop a system that can protect against such mishaps during the process of transmitting transactional data. Additionally, we aim to implement an automated system that ensures error-free transactions. To achieve these objectives, we will leverage the latest technologies such as blockchain and smart contracts. Blockchain technology provides a
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Khan, Mohammad Monirujjaman, Nesat Tasneem RoJa, Faris A. Almalki, and Maha Aljohani. "Revolutionizing E-Commerce Using Blockchain Technology and Implementing Smart Contract." Security and Communication Networks 2022 (May 31, 2022): 1–8. http://dx.doi.org/10.1155/2022/2213336.

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The days of storing data manually are behind us. We are opting for the online form of data storage and transfer. The new era of data digitization comes with its own perks and detriments. Cybersecurity is still a crucial concern today. As more data transfer occurs through an online medium, the risks of a breach and cyberattacks are inevitable. The whole foundation of e-commerce is based on the online transfer of goods and transactions without the need to travel. Transferring transactional data and transactions in e-commerce are prone to cyber threats. Our research’s major objective is to develo
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Kashi, Tejasvi, Kenneth Salem, Jaemyung Kim, and Khuzaima Daudjee. "Eventual Durability." Proceedings of the VLDB Endowment 17, no. 13 (2024): 4733–45. https://doi.org/10.14778/3704965.3704979.

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For latency-critical transactional applications, durability is often what limits performance. That is, executing transactions is fast, but guaranteeing that they are durable is slow. As a result, most of each transaction's latency is attributable to durability. To address this problem, some database systems allow applications to sacrifice durability guarantees in exchange for lower transaction latencies. These ad hoc techniques are effective, but they can make it difficult for applications to understand and manage the risks associated with failures. In this paper, our goal is to offer a more p
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Thomson, Sara Day. "Preserving Transactional Data." International Journal of Digital Curation 11, no. 2 (2017): 126–37. http://dx.doi.org/10.2218/ijdc.v11i2.419.

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This paper is an adaptation of a longer report commissioned by the UK Data Service. The longer report contributes to on-going support for the Big Data Network – a programme funded by the Economic and Social Research Council (ESRC). The longer report can be found at doi:10.7207/twr16-02. This paper discusses requirements for preserving transactional data and the accompanying challenges facing the companies and institutions who aim to re-use these data for analysis or research. It presents a range of use cases – examples of transactional data – in order to describe the characteristics and diffic
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Yousif, Firas Abdulrahman. "Securing Cloud Data Storage through Blockchain-Enhanced Encryption." Journal of Internet Services and Information Security 15, no. 1 (2025): 130–52. https://doi.org/10.58346/jisis.2025.i1.009.

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Blockchain technology has brought a new change in many sectors mainly through provision of decentralized transactional solutions and it is an open, continuously growing list of records, which is distributed and not controlled by centralized authorities. The current regulation is the first one of such kind because it encompasses payment, communication, registration, and monitoring of transactions between members, without the need to have a central authority to manage the needs of the organization. There are however, challenges, which persist for example in data storage and scalability. This pap
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Izadpanah, Ramin, Christina Peterson, Yan Solihin, and Damian Dechev. "PETRA." ACM Transactions on Architecture and Code Optimization 18, no. 2 (2021): 1–26. http://dx.doi.org/10.1145/3446391.

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Emerging byte-addressable Non-Volatile Memories (NVMs) enable persistent memory where process state can be recovered after crashes. To enable applications to rely on persistent data, durable data structures with failure-atomic operations have been proposed. However, they lack the ability to allow users to execute a sequence of operations as transactions. Meanwhile, persistent transactional memory (PTM) has been proposed by adding durability to Software Transactional Memory (STM). However, PTM suffers from high performance overheads and low scalability due to false aborts, logging, and ordering
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Kraft, Peter, Qian Li, Xinjing Zhou, et al. "Epoxy: ACID Transactions across Diverse Data Stores." Proceedings of the VLDB Endowment 16, no. 11 (2023): 2742–54. http://dx.doi.org/10.14778/3611479.3611484.

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Developers are increasingly building applications that incorporate multiple data stores, for example to manage heterogeneous data. Often, these require transactional safety for operations across stores, but few systems support such guarantees. To solve this problem, we introduce Epoxy, a protocol for providing transactions across heterogeneous data stores. We make two contributions. First, we adapt multi-version concurrency control to a cross-data store setting, storing versioning information in record metadata and filtering reads with predicates on metadata so they only see record versions in
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Wang, Wennan, Guoxin Chen, Chiawei Chu, and Wusong Lan. "A Blockchain-Based Continuous Micropayment Scheme Using Lockable Signature." Mathematics 11, no. 16 (2023): 3472. http://dx.doi.org/10.3390/math11163472.

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A blockchain-oriented continuous micropayment system forms an integral element of the digital economy, enabling seamless transactions and encouraging minor purchases. However, due to the inherent throughput constraints of blockchain, payment channels (PCs) are customarily deployed for managing high-volume transactions in an off-chain mode. Despite this, the integration of a PC necessitates a trusted intermediary to safeguard transactional security, thereby imposing an extra security assumption as only the initial and concluding transactions are visible to other system participants. To circumve
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Zhou, Libin, Lu Xing, Yeasir Rayhan, and Walid G. Aref. "GTX: A Write-Optimized Latch-free Graph Data System with Transactional Support." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–27. https://doi.org/10.1145/3725305.

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This paper introduces GTX, a standalone main-memory write-optimized graph data system that specializes in structural and graph property updates while enabling concurrent reads and graph analytics through ACID transactions. Recent graph systems target concurrent read and write support while guaranteeing transaction semantics. However, their performance suffers from updates with real-world temporal locality over the same vertices and edges due to vertex-centric lock contentions. GTX has an adaptive delta-chain locking protocol on top of a carefully designed latch-free graph storage. It eliminate
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Dissertations / Theses on the topic "Transactional data"

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Jarvis, Kimberley James. "Transactional data structures." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/transactional-data-structures(7060eaec-7dbd-4d5a-be1a-a753d9aa32d5).html.

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Concurrent programming is difficult and the effort is rarely rewarded by faster execution. The concurrency problem arises because information cannot pass instantly between processors resulting in temporal uncertainty. This thesis explores the idea that immutable data and distributed concurrency control can be combined to allow scalable concurrent execution and make concurrent programming easier. A concurrent system that does not impose a global ordering on events lends itself to a scalable distributed implementation. A concurrent programming environment in which the ordering of events affectin
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Hassan, Ahmed Mohamed Elsayed. "Designing, Modeling, and Optimizing Transactional Data Structures." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/56656.

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Transactional memory (TM) has emerged as a promising synchronization abstraction for multi-core architectures. Unlike traditional lock-based approaches, TM shifts the burden of implementing threads synchronization from the programmer to an underlying framework using hardware (HTM) and/or software (STM) components. Although TM can be leveraged to implement transactional data structures (i.e., those where multiple operations are allowed to execute atomically, all-or-nothing, according to the transaction paradigm), its intensive speculation may result in significantly lower performance than the
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Topsholm, Max. "Transactional Data Encryption View." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-147885.

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This thesis is about the theory behind and software development of TDE View. The application produces a graphical presentation containing graphical objects, such as charts and tables, which display real time data derived from online and currently available services in Swedbank. TDE View is a long term replacement of the previous solution, TDE System Monitor, the purpose of TDE View is to provide the ability to preview the aforementioned features online instead of local deployment. Moreover, the thesis will cover aspects regarding concepts and ideas in transferring data from different hosts in
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Larson, Richard C. 1943. "Deducing Queue Statistics from Transactional Data." Massachusetts Institute of Technology, Operations Research Center, 1987. http://hdl.handle.net/1721.1/5071.

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The transactional data of a queueing system are the recorded times of service commencement and service completion for each customer served. With increasing use of computers to aid or even perform service one often has machine readable transactional data, but virtually no information about the queue itself. In this paper we propose a way to deduce the queueing behavior of Poisson arrival queueing systems from only the transactional data and the Poisson assumption. For each congestion period in which queues may form, the key quantities obtained are mean wait in queue, time-dependent mean number
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Annerwall, Staffan. "Exploring Short Text Clustering for Transactional Data." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447520.

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The digital revolution has led to an increase in digitization of transactional information. Due to the large amount of data, the transactions must be categorized such that an overview of spending can be obtained. To aid the process of manually classifying transactions, we consider a process of clustering short text transactional data as a pre-processing step. If clusters have high homogeneity, then entire clusters, and hence multiple transactions, can be classified at once. We explore two short text clustering methods, and evaluate them on real-world data in terms of execution time and cluster
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Goehring, David (David G. ). "Decibel : transactional branched versioning for relational data systems." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106022.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 151-159).<br>As scientific endeavors and data analysis become increasingly collaborative, there is a need for data management systems that natively support the versioning or branching of datasets to enable concurrent analysi
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Apeh, Edward Tersoo. "Adaptive algorithms for real-world transactional data mining." Thesis, Bournemouth University, 2012. http://eprints.bournemouth.ac.uk/20989/.

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The accurate identification of the right customer to target with the right product at the right time, through the right channel, to satisfy the customer’s evolving needs, is a key performance driver and enhancer for businesses. Data mining is an analytic process designed to explore usually large amounts of data (typically business or market related) in search of consistent patterns and/or systematic relationships between variables for the purpose of generating explanatory/predictive data models from the detected patterns. It provides an effective and established mechanism for accurate identifica
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Davidzon, Guido Alejandro. "Using EMR transactional data for personalize clinical decision support." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/57687.

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Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 41-44).<br>Collective intelligence techniques have been used to predict stock prices, customer purchasing habits, movies and books preferences for years, yet they remain unused in the medical profession. With the increasing adoption of electronic medical records, patients' medical data has grown exponentially and thus constitutes an untapped field where similar techniques could be applied. If data were collectively farmed and intellige
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Bertsimas, Dimitris J., and Les D. Servi. "Deducing Queueing From Transactional Data: The Queue Inference Engine, Revisited." Massachusetts Institute of Technology, Operations Research Center, 1990. http://hdl.handle.net/1721.1/5190.

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Larson [1] proposed a method to statistically infer the expected transient queue length during a busy period in 0(n 5 ) solely from the n starting and stopping times of each customer's service during the busy period and assuming the arrival distribution is Poisson. We develop a new O(n3 ) algorithm which uses this data to deduce transient queue lengths as well as the waiting times of each customer in the busy period. We also develop an O(n) on line algorithm to dynamically update the current estimates for queue lengths after each departure. Moreover we generalize our algorithms for the case of
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Reiman, Minna. "Generating targeted campaigns based on transactional data using Uplift Modeling." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279949.

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Uplift modeling is a technique to model the incremental effect of a campaign or marketing offer. The incremental effect of a campaign is measured by com- paring two customer groups - one group receiving a campaign and another group not receiving any. The goal of this technique is to reduce the cost of a campaign by identifying the most optimal targets. This study presents results from applying uplift models on customer data at Klarna, with the goal of understanding how to target campaigns effectively. To understand how targeting should be performed, a dataset has been compiled based on eight t
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Books on the topic "Transactional data"

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Maurice, Sebastian. Transactional Machine Learning with Data Streams and AutoML. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7023-3.

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Teal, Roger, Niels Larsen, David King, Candace Brakewood, and Charlotte Frei. Development of Transactional Data Specifications for Demand-Responsive Transportation. Transportation Research Board, 2020. http://dx.doi.org/10.17226/25800.

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Morton, Nigel. Transactional VSAM overview and planning guide. IBM International Technical Support Organization, 2002.

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International Business Machines Corporation. International Technical Support Organization, ed. Co-locating transactional and data warehouse workloads on System z. IBM, International Technical Support Organization, 2010.

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Lenchner, Erez. Mining Transactional Student-Level Data to Predict Community College Student Outcomes. [publisher not identified], 2017.

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N, Chorafas Dimitris. Transaction management: Managing complex transactions and sharing distributed databases. St. Martin's Press, 1998.

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Wu, Jiajing, Dan Lin, and Zibin Zheng, eds. Blockchain Transaction Data Analytics. Springer Nature Singapore, 2025. http://dx.doi.org/10.1007/978-981-97-4430-5.

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Stix, Helmut. How do debit cards affect cash demand?: Survey data evidence. Oesterreichische Nationalbank, 2003.

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Kienzle, Jörg. Open Multithreaded Transactions: A Transaction Model for Concurrent Object-Oriented Programming. Springer Netherlands, 2003.

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Atluri, Vijay, and Pierangela Samarati, eds. Security of Data and Transaction Processing. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4461-6.

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Book chapters on the topic "Transactional data"

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Sippu, Seppo, and Eljas Soisalon-Soininen. "Transactional Isolation." In Data-Centric Systems and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12292-2_5.

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Giannotti, Fosca, Cristian Gozzi, and Giuseppe Manco. "Clustering Transactional Data." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45681-3_15.

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Li, Yanrong, and Raj P. Gopalan. "Clustering Transactional Data Streams." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_124.

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Ye, Xinfeng, John Keane, and Guoqing Zhang. "Transactional Multiple Agents." In Intelligent Data Engineering and Automated Learning — IDEAL 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45675-9_39.

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Maurice, Sebastian. "Transactional Machine Learning." In Transactional Machine Learning with Data Streams and AutoML. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7023-3_2.

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Prakash, Naveen, and Deepika Prakash. "Requirements Engineering for Transactional Systems." In Data Warehouse Requirements Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7019-8_1.

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Rodrigues, Melissa, João Gama, and Carlos Abreu Ferreira. "Identifying Relationships in Transactional Data." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34654-5_9.

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Alagar, Vangalur, and Kaiyu Wan. "Contextual Analysis of Transactional Data." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32591-6_115.

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Giceva, Jana, and Mohammad Sadoghi. "Hybrid Transactional and Analytical Processing." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-63962-8_179-2.

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Nofong, Vincent Mwintieru. "Discovering Productive Periodic Frequent Patterns in Transactional Databases." In Data Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24474-7_20.

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Conference papers on the topic "Transactional data"

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Wei, Ethan, and Tommy Dang. "Temporal Dynamics and Anomaly Detection in Transactional Networks." In 2024 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT). IEEE, 2024. https://doi.org/10.1109/bdcat63179.2024.00043.

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Barba-Seara, Oscar, Carolina Cano-Cardona, Martín Molina-Álvarez, and David Díaz-Rodríguez. "Applying Machine Learning to Detect Periodicity in Transactional Banking Data." In 2025 IEEE Symposium on Computational Intelligence in Natural Language Processing and Social Media (CI-NLPSoMe Companion). IEEE, 2025. https://doi.org/10.1109/ci-nlpsomecompanion65206.2025.10977864.

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Perlin, Tiago, Gerson Cavalheiro, and André Rauber Du Bois. "OFG-STM: Transactional Memory for GPUs based on Obstruction-Free STM algorithms." In Simpósio Brasileiro de Linguagens de Programação. Sociedade Brasileira de Computação, 2024. http://dx.doi.org/10.5753/sblp.2024.3465.

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Transactional memory is a high-level programming abstraction for synchronizing concurrent tasks investigated in different architectures, such as multicores, distributed systems and GPUs. Software transactional memory systems (STMs), including those for GPUs, are usually implemented using a lock-based system, where the locks that protect memory locations being accessed by a transaction are acquired, either at commit time or during transaction execution, to guarantee the atomicity of the transaction’s changes to memory. Transactional memory systems can also be lock-free or obstruction-free. Stil
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Popovic, Miroslav, Branislav Kordic, and Ilija Basicevic. "Transaction scheduling for Software Transactional Memory." In 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). IEEE, 2017. http://dx.doi.org/10.1109/icccbda.2017.7951909.

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Ogawa, Yoshiki, Yuki Akiyama, Sekimoto Yoshihide, and Ryosuke Shibasaki. "Estimation of Transactional Network Data Between Branch Offices using Transactional Big Data Throughout Japan." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006229.

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Spiegelman, Alexander, Guy Golan-Gueta, and Idit Keidar. "Transactional data structure libraries." In PLDI '16: ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM, 2016. http://dx.doi.org/10.1145/2908080.2908112.

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Özcan, Fatma, Yuanyuan Tian, and Pinar Tözün. "Hybrid Transactional/Analytical Processing." In SIGMOD/PODS'17: International Conference on Management of Data. ACM, 2017. http://dx.doi.org/10.1145/3035918.3054784.

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Purohit, Sumit, Patrick Mackey, William Smith, et al. "Transactional Knowledge Graph Generation To Model Adversarial Activities." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9672016.

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Herlihy, Maurice. "Fun with hardware transactional memory." In SIGMOD/PODS'14: International Conference on Management of Data. ACM, 2014. http://dx.doi.org/10.1145/2588555.2602132.

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Apeh, Edward T., Bogdan Gabrys, and Amanda Schierz. "Customer profile classification using transactional data." In 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC). IEEE, 2011. http://dx.doi.org/10.1109/nabic.2011.6089414.

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Reports on the topic "Transactional data"

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Thomson, Sara Day. Preserving Transactional Data. Digital Preservation Coalition, 2016. http://dx.doi.org/10.7207/twr16-02.

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León-Rincón, Carlos Eduardo, Jorge Cely, and Carlos Cadena. Identifying interbank loans, rates, and claims networks from transactional data. Banco de la República, 2015. http://dx.doi.org/10.32468/be.881.

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Benedict, Al, Hani Shamat, Nate Seeskin, and Jana Lynott. Connecting Community Transportation: Lessons Learned from Transactional Data Specification Demonstration Projects. Shared-Use Mobility Center, 2024. http://dx.doi.org/10.26419/ppi.00240.001.

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Ray, Brian. Extending the Blockchain: Ensuring Transactional Integrity in Relational Data via Blockchain Technology. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1557484.

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Lynott, Jana. More Mobility, Stronger Connections: It’s Time to Modernize Demand-Responsive Transportation Using the Transactional Data Specification. AARP Public Policy Institute, 2021. http://dx.doi.org/10.26419/ppi.00121.002.

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M'Raihi, D., S. Boeyen, M. Grandcolas, and S. Bajaj. Sharing Transaction Fraud Data. RFC Editor, 2010. http://dx.doi.org/10.17487/rfc5941.

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Baker, Scott, and Lorenz Kueng. Household Financial Transaction Data. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w29027.

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Carreras, Marco, Awa Diouf, and Hannelore Niesten. The Ghana E-Levy: Impact on the Usage of Mobile Money. Institute of Development Studies, 2024. http://dx.doi.org/10.19088/ictd.2024.060.

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Ghana introduced an electronic transaction levy (E-Levy) on digital financial transactions in May 2022, following many other countries in Africa. Our paper looks at the impact of the E-Levy on mobile money usage and user perceptions. We use data from the Ghana Chamber of Telecommunications (GCT) on usage of several mobile money services (sending, receiving, payments, and withdrawals), and nationally representative survey data collected by the ICTD on individuals and businesses.
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Nicholson, Tony. Local Purchase Transaction Data Collection. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada368037.

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Cañón, Carlos, Jorge Florez-Acosta, and Karoll Gómez. The effects of two-way lending between financial conglomerates in the repo market. Banco de la República, 2023. http://dx.doi.org/10.32468/be.1246.

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We examine how market structure, market power, and systemic risk respond to close and intense lending relationships between financial conglomerates (FCs) in non-centrally cleared bilateral repo. Using transaction-level data from Mexico, we document persistent and stable funding relationships between FC-affiliated banks and funds with two distinctive features: first, funding transactions are two-way, that is, a given pair of rival FCs provide lending to one another on the same day; second, two-way transactions are executed at lower average rates than one-way transactions. We show that two-way l
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