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Relevant bibliographies by topics / Sequential transaction data

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Academic literature on the topic 'Sequential transaction data'

Author: Grafiati

Published: 11 December 2022

Last updated: 25 January 2023

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Journal articles on the topic "Sequential transaction data"

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Astuti, Tri, and Lisdya Anggraini. "Analysis of Sequential Book Loan Data Pattern Using Generalized Sequential Pattern (GSP) Algorithm." IJIIS: International Journal of Informatics and Information Systems 2, no. 1 (2019): 17–23. http://dx.doi.org/10.47738/ijiis.v2i1.10.

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As a center for learning and information services, STMIK Amikom Purwokerto Library is a source of learning and a source of intellectual activity that is very important for the entire academic community in supporting the achievement of the college Tridharma program. Book lending transaction data, can produce information that is important as supporting decision making when further analyzed. One useful information is that it can provide information in the form of user behavior patterns in borrowing books that are used to maintain the availability of related book stocks to be balanced. This study

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Saputra, Diki Andika, Eneng Tita Tosida, and Fajar Delli W. "PENENTUAN POLA SEKUENSIAL DATA TRANSAKSI PENJUALAN MENGGUNAKAN ALGORITMA SEQUENTIAL PATTREN DISCOVERY USING EQUIVALENT CLASSES (SPADE)." Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 16, no. 2 (2019): 271–82. http://dx.doi.org/10.33751/komputasi.v16i2.1621.

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Transaction data is customer or customer data at a commercial or non-commercial institution that contains the consumer id, transaction time, and transaction items. From transaction data such as supermarket transactions, sequential patterns can be found to determine the interrelationship between items or items. Data if further processed or analyzed will produce information or knowledge that is important and valuable as a support in decision making. This study aims to determine the consumption patterns owned by customers and provide information that can be used in determining the layout of new s

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Ouyang, Wei Min, and Qin Hua Huang. "Mining Indirect Temporal Sequential Patterns in Large Transaction Databases." Applied Mechanics and Materials 385-386 (August 2013): 1362–65. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1362.

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Sequential pattern is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining sequential patterns focus on the frequent sequences, which do not consider the infrequent sequences and lifespan of each sequence. On the one hand, some infrequent patterns can provide very useful insight view into the data set, on the other hand, without taking lifespan of each sequence into account, not only some discovered patterns may be invalid, but also some useful patterns may not be discovered. So, we extend the sequential patterns to the indirect temporal sequent

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Ramadhan, Dayan Ramly, and Nur Rokhman. "Segmentation-Based Sequential Rules For Product Promotion Recommendations As Sales Strategy (Case Study: Dayra Store)." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14, no. 3 (2020): 243. http://dx.doi.org/10.22146/ijccs.58107.

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One of the problems in the promotion is the high cost. Identifying the customer segments that have made transactions, sellers can promote better products to potential consumers. The segmentation of potential consumers can be integrated with the products that consumers tend to buy. The relationship can be found using pattern analysis using the Association Rule Mining (ARM) method. ARM will generate rule patterns from the old transaction data, and the rules can be used for recommendations. This study uses a segmented-based sequential rule method that generates sequential rules from each customer

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Somya, Ramos, Edi Winarko, and Sigit Priyanta. "A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3422–32. http://dx.doi.org/10.11591/eei.v11i6.4021.

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In the future, the quality of product suggestions in online retailers will influence client purchasing decisions. Unqualified product suggestions can result in two sorts of errors: false negatives and false positives. Customers may not return to the online store as a result of this. By merging sales transaction data and consumer behavior data in clickstream data format, this work offers a hybrid recommender system in an online store utilizing sequential pattern mining (SPM). Based on the clickstream data components, the product data whose status is only observed by consumers is assessed using

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Daraghmi, Eman, Cheng-Pu Zhang, and Shyan-Ming Yuan. "Enhancing Saga Pattern for Distributed Transactions within a Microservices Architecture." Applied Sciences 12, no. 12 (2022): 6242. http://dx.doi.org/10.3390/app12126242.

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The saga pattern manages transactions and maintains data consistency across distributed microservices via utilizing local sequential transactions that update each service and publish messages to trigger the next ones. Failure by one transaction causes the execution of compensating transactions that counteract the preceding one. However, saga lacks isolation, meaning that reading and writing data from an incomplete transaction is allowed. Therefore, this research proposes an enhanced saga pattern that resolves the lack of isolation issue via the use of the quota cache and the commit-sync servic

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Yu, Li, Zaifang Zhang, and Jin Shen. "Dynamic customer preference analysis for product portfolio identification using sequential pattern mining." Industrial Management & Data Systems 117, no. 2 (2017): 365–81. http://dx.doi.org/10.1108/imds-12-2015-0496.

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Purpose In the initial stage of product design, product portfolio identification (PPI) aims to translate customer needs (CNs) into product specifications (PSs). This is an essential task, since understanding what customers really want is at the center of product design. However, design information is incomplete and design knowledge is minimal during this stage. Furthermore, PPI is often a confusing and frustrating task, especially when customer preferences are changing rapidly. To facilitate the task, the purpose of this paper is to capture the time-sensitive mapping relationship between CNs a

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Vidyasankar, K. "Unified Theory of Database Serializability12." Fundamenta Informaticae 14, no. 2 (1991): 147–83. http://dx.doi.org/10.3233/fi-1991-14202.

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A database system is a collection of data items, read or written by transactions in a possibly interleaved fashion. An interleaved execution is assumed to be correct if the sequence of the steps of the transactions, called history, is serializable, that is, the effect of the execution is equivalent to that of some serial execution of the same transactions. In this paper we give a new characterization of serializability that brings out the inherent problem of serialization explicitly. We then give a graph-theoretic analogue of serializable histories. We define a new class of graphs, called seri

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Sytsma, Victoria A., and Eric L. Piza. "Script Analysis of Open-air Drug Selling." Journal of Research in Crime and Delinquency 55, no. 1 (2018): 78–102. http://dx.doi.org/10.1177/0022427817709502.

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Objectives: Through the use of closed-circuit television (CCTV) video footage, the current study builds upon the drug transaction work of Piza and Sytsma by developing a crime script for open-air drug selling. Methods: Researchers conducted a systematic social observation of CCTV footage of open-air drug markets in Newark, NJ. The data were used to identify sequential stages of drug transactions. Fisher’s exact tests measured whether buyer and seller activities during specific acts of the drug transaction event were related to activities seen in subsequent stages. Results: This study finds thr

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Lu, Yixin, Alok Gupta, Wolfgang Ketter, and Eric van Heck. "Designing Hybrid Mechanisms to Overcome Congestion in Sequential Dutch Auctions." MIS Quarterly 46, no. 1 (2022): 457–90. http://dx.doi.org/10.25300/misq/2022/16472.

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A common problem in many mature markets is how to deal with congestion—a situation in which transaction requests from market participants cannot be accommodated in an expedited manner. This paper examines the congestion problem in sequential Dutch auction markets. Transactions in these markets typically involve perishable goods, making market clearing speed crucial. Traditionally, sequential Dutch auctions have been implemented with fast-paced auction clocks that process equally attractive bids in the order they arrive and only award the first bidder as the winner of each round, which can lead

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Dissertations / Theses on the topic "Sequential transaction data"

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Wu, SS. "Debt detection and debt recovery with advanced classification techniques." Thesis, 2015. http://hdl.handle.net/10453/39025.

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University of Technology Sydney. Faculty of Engineering and Information Technology.<br>My study is part of an ARC linkage project between University of Technology, Sydney and Centrelink Australia, which aims to applying data mining techniques to optimise the debt detection and debt recovery. A debt indicates an overpayment made by the government to a customer who is not entitled to that payment. In social security, an interaction between a customer and the government department is recorded as an activity. Each customer’s activities happen sequentially along the time, which can be regarded as

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Book chapters on the topic "Sequential transaction data"

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Jing, Chen, Cheng Wang, and Chungang Yan. "Thinking Like a Fraudster: Detecting Fraudulent Transactions via Statistical Sequential Features." In Financial Cryptography and Data Security. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32101-7_34.

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Huynh, Hieu Trung, and Yonggwan Won. "Application of Regularized Online Sequential Learning for Glucose Correction." In Transactions on Large-Scale Data- and Knowledge-Centered Systems XLI. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58808-6_7.

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Zhao, Yanchang, Huaifeng Zhang, Longbing Cao, Chengqi Zhang, and Hans Bohlscheid. "Mining Both Positive and Negative Impact-Oriented Sequential Rules from Transactional Data." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_65.

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Lu, Jing, Weiru Chen, Osei Adjei, and Malcolm Keech. "Sequential Patterns Postprocessing for Structural Relation Patterns Mining." In Business Information Systems. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-969-9.ch049.

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Sequential patterns mining is an important data-mining technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been extensively studied in the literature, and there exists a diversity of algorithms. However, more complex structural patterns are often hidden behind sequences. This article begins with the introduction of a model for the representation of sequential patterns—Sequential Patterns Graph—which motivates the search for new structural relation patterns. An integrative framework for the discovery of these patterns–Postsequential Patterns Mining–is then described which underpins the postprocessing of sequential patterns. A corresponding data-mining method based on sequential patterns postprocessing is proposed and shown to be effective in the search for concurrent patterns. From experiments conducted on three component algorithms, it is demonstrated that sequential patterns-based concurrent patterns mining provides an efficient method for structural knowledge discovery.

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Xu, Xun. "Integration Based on STEP Standards." In Integrating Advanced Computer-Aided Design, Manufacturing, and Numerical Control. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-714-0.ch011.

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The integration model (Model B) as discussed in the previous chapter makes use of exchangeable neutral data formats such as IGES (1980). Neutral data formats provide a middle tier to connect CAD and CAM systems. Thus, Model B can create a collaborative manufacturing environment and make the design data exchange possible for large projects at the international level. Yet, some problems still remain. IGES was designed to exchange geometrical information only, so additional design or manufacturing information (such as feature information) within a proprietary model is ignored. During data exchange, some information may become astray during data transfer; geometry stitching or model repair is often needed. Plus, IGES is not an international standard. As previously discussed, there are also problems common to both Models A and B (Figure 10.1). Different data formats (e.g. IGES and ISO 6983-1, 1982) are used in the designto- manufacturing chain. Data loss occurs in the transaction from design to manufacturing because only low-level, step-by-step sequential machining commands are passed onto the CNC controllers, leaving the complete product model behind. Of particular significance has been the endeavour made by the International Organization for Standardization to introduce the STEP Standard (i.e. ISO 10303-1 [1994]). Major aerospace and automotive companies have proven the value of STEP through production implementations resulting in savings of US $150 million per year (Gallaher, O’Connor & Phelps, 2002, PDES, Inc. 2006). Moreover, STEP has recently been extended to cater to manufacturing data modelling and execution with an aim to fill the information gap between CAD/CAPP/CAM and CNC. The standard is informally known as STEP-compliant Numerical Control, or otherwise STEP-NC for short. It was given an ISO name of “ISO 14649: Data model for Computerized Numerical Controllers (ISO 14649-1, 2003)”, which defines the STEP-NC Application Reference Model. With STEP being extended to model manufacturing information, a new paradigm of integrated CAD/CAPP/CAM/CNC is emerging. This is illustrated in Figure 11.1. The key to this paradigm is that no data conversion is required and the data throughout the design and manufacturing chain are preserved. This chapter focuses on the use of STEP standards to support data exchange between CAD systems as well as facilitate data flow between CAD, CAPP, CAM, and CNC systems. Also discussed are the specific integration issues between CAD and CAPP, CAPP and CAM, and CAM and CNC using STEP standards. STEP-NC data model is a relatively new member in the STEP family, but it completes the entire suite of STEP standards from design to NC machining. Both Physical File Implementation Method (ISO 10303-21, 1994) and XML Implementation Method (ISO/TS 10303-18, 2004) are presented as the two popular ways of implementing STEP and STEP-NC.

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Ruta, Dymitr, Christoph Adl, and Detlef Nauck. "New Churn Prediction Strategies in the Telecom Industry." In Intelligent Data Analysis. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-982-3.ch013.

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In the telecom industry, high installation and marketing costs make it six to 10 times more expensive to acquire a new customer than it is to retain an existing one. Prediction and prevention of customer churn is therefore a key priority for industrial research. While all the motives of customer decision to churn are highly uncertain there is a lot of related temporal data generated as a result of customer interaction with the service provider. The major problem with this data is its time discontinuity resulting from the transactional character of events they describe. Moreover, such irregular temporal data sequences are typically a chaotic mixture of different data types, which further hinders its exploitation for any predictive task. Existing churn prediction methods like decision trees typically classify customers into churners and non-churners based on the static data collected in a snapshot of time while completely ignoring the timing of churn and hence the circumstances of this event. In this work, we propose new churn prediction strategies that are suitable for application at different levels of the information content available in customers’ data. Gradually enriching the data information content from the prior churn rate and lifetime expectancy then typical static events data up to decay-weighted data sequences, we propose a set of new churn prediction tools based on: customer lifetime modelling, hidden markov model (HMM) of customer events, and the most powerful k nearest sequence (kNS) algorithm that deliver robust churn predictions at different levels of data availability. Focussing further on kNS we demonstrate how the sequential techprocessing of appropriately pre-processed data streams lead to better performance of customer churn prediction. Given histories of other customers and the current customer data, the presented kNS uses an original combination of sequential nearest neighbour algorithm and original sequence aggregation technique to predict the whole remaining customer data sequence path up to the churn event. On the course of experimental trials, it is demonstrated that the new kNS model better exploits time-ordered customer data sequences and surpasses existing churn prediction methods in terms of performance and capabilities offered.

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Savla, Sagar, and Sharma Chakravarthy. "A Single Pass Algorithm for Discovering Significant Intervals in Time-Series Data." In Data Warehousing and Mining. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-951-9.ch207.

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Sensor-based applications, such as smart homes, require prediction of event occurrences for automating the environment using time-series data collected over a period of time. In these applications, it is important to predict events in tight and accurate intervals to effectively automate the application. This article deals with the discovery of significant intervals from time-series data. Although there is a considerable body of work on sequential mining of transactional data, most of them deal with time-point data and make several passes over the entire data set in order to discover frequently occurring patterns/events. We propose an approach in which significant intervals representing intrinsic nature of data are discovered in a single pass. In our approach, time-series data is folded over a periodicity (day, week, etc.) in which the intervals are formed. Significant intervals are discovered from this interval data that satisfy the criteria of minimum confidence and maximum interval length specified by the user. Both compression and working with intervals contribute towards improving the efficiency of the algorithm. In this article, we present a new single-pass algorithm for detecting significant intervals; discuss its characteristics, advantages, and disadvantages; and analyze it. Finally, we compare the performance of our algorithm with previously developed level-wise and SQL-based algorithms for significant interval discovery (SID).

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Ivask, Mari, Eve Aruvee, and Kristjan Piirimäe. "Database of Environmental Decision Support Tools." In Transactional Environmental Support System Design. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2824-3.ch005.

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This chapter investigates modeling and data sources for environmental decision support for local authorities and private managers. A metadatabase of 198 computerized environmental decision tools was collected using a questionnaire with two sequential web-pages of 4+16 questions (http://tess.ttu.ee/). Half of the software applications are freely downloadable and well described online. The database contains 42 references reported as forestry tools, 50 as agriculture or apiculture tools, and 15 targeting to either amenity areas or tourism and access-based recreation. The most domain-general toolbox, InVEST, integrates models which support management of natural capital, including pollination of crops and production of timber, but is limited in types of natural capital and management situations. Agricultural toolboxes DSSAT and MicroLEIS cover soil quality issues well but ignore management of agricultural landscapes where natural and semi-natural habitats provide wild crop pollination, biocontrol of pests, and other services. The most prominent sustainable forestry toolboxes are UBC-FM and NED-2. The latter contains simulation models and qualitative knowledge bases, but complex forest management conditions require more flexible metamodeling approaches. For the management of recreational sites, no integrated toolkit exists. As most of the existing environmental tools support option assessment, future development should address also earlier decision-making steps.

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Conference papers on the topic "Sequential transaction data"

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Sitanggang, Imas Sukaesih, Nor Azura Husin, Anita Agustina, and Naghmeh Mahmoodian. "Sequential pattern mining on library transaction data." In 2010 International Symposium on Information Technology (ITSim 2010). IEEE, 2010. http://dx.doi.org/10.1109/itsim.2010.5561316.

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Izza, Yacine, Said Jabbour, Badran Raddaoui, and Abdelahmid Boudane. "On the Enumeration of Association Rules: A Decomposition-based Approach." 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.2020/176.

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While traditional data mining techniques have been used extensively for finding patterns in databases, they are not always suitable for incorporating user-specified constraints. To overcome this issue, CP and SAT based frameworks for modeling and solving pattern mining tasks have gained a considerable audience in recent years. However, a bottleneck for all these CP and SAT-based approaches is the encoding size which makes these algorithms inefficient for large databases. This paper introduces a practical SAT-based approach to discover efficiently (minimal non-redundant) association rules. Firs

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Liu, Jundi, Steven Hwang, Walter Yund, Linda Ng Boyle, and Ashis G. Banerjee. "Predicting Purchase Orders Delivery Times Using Regression Models With Dimension Reduction." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85710.

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In current supply chain operations, the transactions among suppliers and original equipment manufacturers (OEMs) are sometimes inefficient and unreliable due to limited information exchange and lack of knowledge about the supplier capabilities. For the OEMs, majority of downstream operations are sequential, requiring the availabilities of all the parts on time to ensure successful executions of production schedules. Therefore, accurate prediction of the delivery times of purchase orders (POs) is critical to satisfying these requirements. However, such prediction is challenging due to the suppl

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Banerjee, Ashis Gopal, Walter Yund, Dan Yang, Peter Koudal, John Carbone, and Joseph Salvo. "A Hybrid Statistical Method for Accurate Prediction of Supplier Delivery Times of Aircraft Engine Parts." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47605.

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Aircraft engine assembly operations require thousands of parts provided by several geographically distributed suppliers. A majority of the operation steps are sequential, necessitating the availability of all the parts at appropriate times for these steps to be completed successfully. Thus, being able to accurately predict the availabilities of parts based on supplier deliveries is critical to minimizing the delays in meeting the customer demands. However, such accurate prediction is challenging due to the large lead times of these parts, limited knowledge of supplier capacities and capabiliti

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