Готові списки джерел за темами / Transactional datasets

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Добірка наукової літератури з теми "Transactional datasets"

Автор: Grafiati

Опубліковано: 23 вересня 2022

Оновлено: 28 січня 2023

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Статті в журналах з теми "Transactional datasets"

AL Bouna, Bechara, Chris Clifton, and Qutaibah Malluhi. "Anonymizing transactional datasets." Journal of Computer Security 23, no. 1 (March 15, 2015): 89–106. http://dx.doi.org/10.3233/jcs-140517.

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Puri, Vartika, Parmeet Kaur, and Shelly Sachdeva. "ADT." International Journal of Information Security and Privacy 15, no. 3 (July 2021): 83–105. http://dx.doi.org/10.4018/ijisp.2021070106.

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Data anonymization is commonly utilized for the protection of an individual's identity when his personal or sensitive data is published. A well-known anonymization model to define the privacy of transactional data is the km-anonymity model. This model ensures that an adversary who knows up to m items of an individual cannot determine which record in the dataset corresponds to the individual with a probability greater than 1/k. However, the existing techniques generally rely on the presence of similarity between items in the dataset tuples to achieve km-anonymization and are not suitable when transactional data contains tuples without many common values. The authors refer to this type of transactional data as diverse transactional data and propose an algorithm, anonymization of diverse transactional data (ADT). ADT is based on slicing and generalization to achieve km-anonymity for diverse transactional data. ADT has been experimentally evaluated on two datasets, and it has been found that ADT yields higher privacy protection and causes a lower loss in data utility as compared to existing methods.

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Vu, Duc Thi, and Huy Duc Nguyen. "Mining High Utility Itemsets in Massive Transactional Datasets." Acta Cybernetica 20, no. 2 (2011): 331–46. http://dx.doi.org/10.14232/actacyb.20.2.2011.6.

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Liu, Xiangwen, Xia Feng, and Yuquan Zhu. "Transactional Data Anonymization for Privacy and Information Preservation via Disassociation and Local Suppression." Symmetry 14, no. 3 (February 25, 2022): 472. http://dx.doi.org/10.3390/sym14030472.

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Ubiquitous devices in IoT-based environments create a large amount of transactional data on daily personal behaviors. Releasing these data across various platforms and applications for data mining can create tremendous opportunities for knowledge-based decision making. However, solid guarantees on the risk of re-identification are required to make these data broadly available. Disassociation is a popular method for transactional data anonymization against re-identification attacks in privacy-preserving data publishing. The anonymization algorithm of disassociation is performed in parallel, suitable for the asymmetric paralleled data process in IoT where the nodes have limited computation power and storage space. However, the anonymization algorithm of disassociation is based on the global recoding mode to achieve transactional data km -anonymization, which leads to a loss of combinations of items in transactional datasets, thus decreasing the data quality of the published transactions. To address the issue, we propose a novel vertical partition strategy in this paper. By employing local suppression and global partition, we first eliminate the itemsets which violate km-anonymity to construct the first km-anonymous record chunk. Then, by the processes of itemset creating and reducing, we recombine the globally partitioned items from the first record chunk to construct remaining km-anonymous record chunks. The experiments illustrate that our scheme can retain more association between items in the dataset, which improves the utility of published data.

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Tang, Huijun, Le Wang, Yangguang Liu, and Jiangbo Qian. "Discovering Approximate and Significant High-Utility Patterns from Transactional Datasets." Journal of Mathematics 2022 (November 16, 2022): 1–17. http://dx.doi.org/10.1155/2022/6975130.

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Mining high-utility pattern (HUP) on transactional datasets has been widely discussed, and various algorithms have been introduced to settle this problem. However, the time-space efficiency of the algorithms is still limited, and the mining system cannot provide timely feedback on relevant information. In addition, when mining HUP from taxonomy transactional datasets, a large portion of the quantitative results are just accidental responses to the user-defined utility constraints, and they may have no statistical significance. To address these two problems, we propose two corresponding approaches named Sampling HUP-Miner and Significant HUP-Miner. Sampling HUP-Miner pursues a sample size of a transitional dataset based on a theoretical guarantee; the mining results based on such a sample size can be an effective approximation to the results on the whole datasets. Significant HUP-Miner proposes the concept of testable support, and significant HUPs could be drawn timely based on the constraint of testable support. Experiments show that the designed two algorithms can discover approximate and significant HUPs smoothly and perform well according to the runtime, pattern numbers, memory usage, and average utility.

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Puri, Vartika, Parmeet Kaur, and Shelly Sachdeva. "Effective Removal of Privacy Breaches in Disassociated Transactional Datasets." Arabian Journal for Science and Engineering 45, no. 4 (January 28, 2020): 3257–72. http://dx.doi.org/10.1007/s13369-020-04353-5.

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Al-Bana, Mohamed Reda, Marwa Salah Farhan, and Nermin Abdelhakim Othman. "An Efficient Spark-Based Hybrid Frequent Itemset Mining Algorithm for Big Data." Data 7, no. 1 (January 14, 2022): 11. http://dx.doi.org/10.3390/data7010011.

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Frequent itemset mining (FIM) is a common approach for discovering hidden frequent patterns from transactional databases used in prediction, association rules, classification, etc. Apriori is an FIM elementary algorithm with iterative nature used to find the frequent itemsets. Apriori is used to scan the dataset multiple times to generate big frequent itemsets with different cardinalities. Apriori performance descends when data gets bigger due to the multiple dataset scan to extract the frequent itemsets. Eclat is a scalable version of the Apriori algorithm that utilizes a vertical layout. The vertical layout has many advantages; it helps to solve the problem of multiple datasets scanning and has information that helps to find each itemset support. In a vertical layout, itemset support can be achieved by intersecting transaction ids (tidset/tids) and pruning irrelevant itemsets. However, when tids become too big for memory, it affects algorithms efficiency. In this paper, we introduce SHFIM (spark-based hybrid frequent itemset mining), which is a three-phase algorithm that utilizes both horizontal and vertical layout diffset instead of tidset to keep track of the differences between transaction ids rather than the intersections. Moreover, some improvements are developed to decrease the number of candidate itemsets. SHFIM is implemented and tested over the Spark framework, which utilizes the RDD (resilient distributed datasets) concept and in-memory processing that tackles MapReduce framework problem. We compared the SHFIM performance with Spark-based Eclat and dEclat algorithms for the four benchmark datasets. Experimental results proved that SHFIM outperforms Eclat and dEclat Spark-based algorithms in both dense and sparse datasets in terms of execution time.

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R., Sujatha, and Dr S. Ravichandran. "MAX-MiBit-An Algorithm To Discover Maximal Frequent Itemsets From Large Transactional Datasets." International Journal of Research in Advent Technology 7, no. 4 (April 10, 2019): 326–29. http://dx.doi.org/10.32622/ijrat.742019122.

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Yan, Hua, Keke Chen, Ling Liu, and Joonsoo Bae. "Determining the best K for clustering transactional datasets: A coverage density-based approach." Data & Knowledge Engineering 68, no. 1 (January 2009): 28–48. http://dx.doi.org/10.1016/j.datak.2008.08.005.

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Kahanda, Indika, and Jennifer Neville. "Using Transactional Information to Predict Link Strength in Online Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 3, no. 1 (March 19, 2009): 74–81. http://dx.doi.org/10.1609/icwsm.v3i1.13957.

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Many scientific fields analyzing and modeling social networks have focused on manually-collected datasets where the friendship links are sparse (due to the costs of collection) but relatively noise-free (i.e. they indicate strong relationships). In online social networks, where the notion of ``friendship'' is broader than what would generally be considered in sociological studies, the friendship links are denser but the links contain noisier information (i.e., some weaker relationships). However, the networks also contain additional transactional events among entities (e.g., communication, file transfers) that can be used to infer the true underlying social network. With this aim in mind, we develop a supervised learning approach to predict link strength from transactional information. We formulate this as a link prediction task and compare the utility of attribute-based, topological, and transactional features. We evaluate our approach on public data from the Purdue Facebook network and show that we can accurately predict strong relationships. Moreover, we show that transactional-network features are the most influential features for this task.

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Більше джерел

Дисертації з теми "Transactional datasets"

Sakaguchi, Hiroaki. "Psychology of financial decisions, using large transaction datasets." Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/97208/.

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Behavioral studies show that people tend to use various decision heuristics which discard part of the available information, simplify the decision problem, and find a good enough answer. In addition, people’s decision and behavior may change because they learn from experience. This thesis investigates people’s heuristic decision making and learning from experience in two frequent real-world financial decision contexts—credit card repayments and stock trading. Chapter 1 reviews the literature about decision heuristics and nudges. Literature about learning from experience is also reviewed. Chapter 2 shows that automatic minimum credit card repayment as a default nudge has the adverse effect of reducing repayments by allowing card holders to neglect their monthly bill. Chapter 3 examines whether people learn from the negative feedback provided by credit card fees. We show that cardholders tend to adapt to late payment fees, which are typically due to forgetting a repayment, by setting up an automatic repayment. On the other hand, cash advance and over-limit fees are due to card holders’ liquidity needs rather than their mistakes, and thus, they do not learn from experiencing those fees. Our findings are contrast to those in a previous study in the US suggesting that people learn from all three types of fees. Chapter 4 shows evidence of people’s heuristic processing of numerical information in the context of credit card repayments. We find a strong tendency of card holders repaying at several prominent numbers. We also find people’s preference for round numbers. Conducting an online experiment, Chapter 5 confirms the anchoring effect of numerical information in a credit card bill, as in previous studies, and finds a false consensus bias where people who usually repay only the minimum greatly overestimate the commonness of minimum repayments among others. However, a social nudge phrase in a mock bill fails to correct the false belief, and thus, dose not reduce the likelihood of people repaying only the minimum. Chapter 6 presents a two-stage model of the choice of a stock to sell. Typically investors show a disposition effect, being more likely to sell a stock in gain than loss, other things equal. In our model, investors first decide whether to sell a stock in the domain of gains or losses, and only then, evaluate stocks within the chosen domain. As evidence for the model, our analysis shows that the likelihood of an individual stock being sold is inversely proportional to the number of stocks in the same domain in the portfolio but is not sensitive to the number of stocks in the other domain. Our findings indicate that existing estimation methods of the disposition effect result in substantial biases because those estimations assume that all stocks in a portfolio are simultaneously evaluated across domains of gains and losses. Chapter 7 summarizes the findings and implications of Chapters 2-6. Plans and suggestions for future research are also discussed.

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Alchicha, Élie. "Confidentialité Différentielle et Blowfish appliquées sur des bases de données graphiques, transactionnelles et images." Thesis, Pau, 2021. http://www.theses.fr/2021PAUU3067.

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Les données numériques jouent un rôle crucial dans notre vie quotidienne en communiquant, en enregistrant des informations, en exprimant nos pensées et nos opinions et en capturant nos moments précieux sous forme d'images et de vidéos numériques. Les données numériques présentent d'énormes avantages dans tous les aspects de la vie moderne, mais constituent également une menace pour notre vie privée. Dans cette thèse, nous considérons trois types de données numériques en ligne générées par les utilisateurs des médias sociaux et les clients du commerce électronique : les graphiques, les transactions et les images. Les graphiques sont des enregistrements des interactions entre les utilisateurs qui aident les entreprises à comprendre qui sont les utilisateurs influents dans leur environnement. Les photos postées sur les réseaux sociaux sont une source importante de données qui nécessitent des efforts d'extraction. Les ensembles de données transactionnelles représentent les opérations qui ont eu lieu sur les services de commerce électronique.Nous nous appuyons sur une technique de préservation de la vie privée appelée Differential Privacy (DP) et sa généralisation Blowfish Privacy (BP) pour proposer plusieurs solutions permettant aux propriétaires de données de bénéficier de leurs ensembles de données sans risque de violation de la vie privée pouvant entraîner des problèmes juridiques. Ces techniques sont basées sur l'idée de récupérer l'existence ou la non-existence de tout élément dans l'ensemble de données (tuple, ligne, bord, nœud, image, vecteur, ...) en ajoutant respectivement un petit bruit sur la sortie pour fournir un bon équilibre entre intimité et utilité.Dans le premier cas d'utilisation, nous nous concentrons sur les graphes en proposant trois mécanismes différents pour protéger les données personnelles des utilisateurs avant d'analyser les jeux de données. Pour le premier mécanisme, nous présentons un scénario pour protéger les connexions entre les utilisateurs avec une nouvelle approche où les utilisateurs ont des privilèges différents : les utilisateurs VIP ont besoin d'un niveau de confidentialité plus élevé que les utilisateurs standard. Le scénario du deuxième mécanisme est centré sur la protection d'un groupe de personnes (sous-graphes) au lieu de nœuds ou d'arêtes dans un type de graphes plus avancé appelé graphes dynamiques où les nœuds et les arêtes peuvent changer à chaque intervalle de temps. Dans le troisième scénario, nous continuons à nous concentrer sur les graphiques dynamiques, mais cette fois, les adversaires sont plus agressifs que les deux derniers scénarios car ils plantent de faux comptes dans les graphiques dynamiques pour se connecter à des utilisateurs honnêtes et essayer de révéler leurs nœuds représentatifs dans le graphique.Dans le deuxième cas d'utilisation, nous contribuons dans le domaine des données transactionnelles en présentant un mécanisme existant appelé Safe Grouping. Il repose sur le regroupement des tuples de manière à masquer les corrélations entre eux que l'adversaire pourrait utiliser pour violer la vie privée des utilisateurs. D'un autre côté, ces corrélations sont importantes pour les propriétaires de données dans l'analyse des données pour comprendre qui pourrait être intéressé par des produits, biens ou services similaires. Pour cette raison, nous proposons un nouveau mécanisme qui expose ces corrélations dans de tels ensembles de données, et nous prouvons que le niveau de confidentialité est similaire au niveau fourni par Safe Grouping.Le troisième cas d'usage concerne les images postées par les utilisateurs sur les réseaux sociaux. Nous proposons un mécanisme de préservation de la confidentialité qui permet aux propriétaires des données de classer les éléments des photos sans révéler d'informations sensibles. Nous présentons un scénario d'extraction des sentiments sur les visages en interdisant aux adversaires de reconnaître l'identité des personnes
Digital data is playing crucial role in our daily life in communicating, saving information, expressing our thoughts and opinions and capturing our precious moments as digital pictures and videos. Digital data has enormous benefits in all the aspects of modern life but forms also a threat to our privacy. In this thesis, we consider three types of online digital data generated by users of social media and e-commerce customers: graphs, transactional, and images. The graphs are records of the interactions between users that help the companies understand who are the influential users in their surroundings. The photos posted on social networks are an important source of data that need efforts to extract. The transactional datasets represent the operations that occurred on e-commerce services.We rely on a privacy-preserving technique called Differential Privacy (DP) and its generalization Blowfish Privacy (BP) to propose several solutions for the data owners to benefit from their datasets without the risk of privacy breach that could lead to legal issues. These techniques are based on the idea of recovering the existence or non-existence of any element in the dataset (tuple, row, edge, node, image, vector, ...) by adding respectively small noise on the output to provide a good balance between privacy and utility.In the first use case, we focus on the graphs by proposing three different mechanisms to protect the users' personal data before analyzing the datasets. For the first mechanism, we present a scenario to protect the connections between users (the edges in the graph) with a new approach where the users have different privileges: the VIP users need a higher level of privacy than standard users. The scenario for the second mechanism is centered on protecting a group of people (subgraphs) instead of nodes or edges in a more advanced type of graphs called dynamic graphs where the nodes and the edges might change in each time interval. In the third scenario, we keep focusing on dynamic graphs, but this time the adversaries are more aggressive than the past two scenarios as they are planting fake accounts in the dynamic graphs to connect to honest users and try to reveal their representative nodes in the graph. In the second use case, we contribute in the domain of transactional data by presenting an existed mechanism called Safe Grouping. It relies on grouping the tuples in such a way that hides the correlations between them that the adversary could use to breach the privacy of the users. On the other side, these correlations are important for the data owners in analyzing the data to understand who might be interested in similar products, goods or services. For this reason, we propose a new mechanism that exposes these correlations in such datasets, and we prove that the level of privacy is similar to the level provided by Safe Grouping.The third use-case concerns the images posted by users on social networks. We propose a privacy-preserving mechanism that allows the data owners to classify the elements in the photos without revealing sensitive information. We present a scenario of extracting the sentiments on the faces with forbidding the adversaries from recognizing the identity of the persons. For each use-case, we present the results of the experiments that prove that our algorithms can provide a good balance between privacy and utility and that they outperform existing solutions at least in one of these two concepts

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Kim, GwanSeon. "UTILIZING LARGE SCALE DATASETS TO EVALUATE ASPECTS OF A SUSTAINABLE BIOECONOMY." UKnowledge, 2019. https://uknowledge.uky.edu/agecon_etds/78.

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This dissertation combines large scale datasets to evaluate crop prediction, land values, and consumption of a crop being considered to advance a sustainable bioeconomy. In chapter 2, we propose a novel application of the multinomial logit (MNL) model to estimate the conditional transition probabilities of crop choice for the state of Kentucky. Utilizing the recovered transition probabilities the forecast distributions of total acreages for alfalfa, corn, soybeans, tobacco, and wheat produced in the state from 2010 to 2015 can be recovered. The Cropland Data Layer is merged with the Common Land Unit dataset to allow for the identification of crop choice at the field level. Our findings show there are higher probabilities of planting soybeans or wheat after corn relative to corn after corn, tobacco, or alfalfa. In addition, the transition probability of the crop rotation demonstrates that corn will be planted after soybean, and vice versa and that alfalfa has a lower probability of being rotated with other crops from year to year. These findings are expected with traditional crop rotation in the U.S., and a characteristic of a perennial crop, especially for alfalfa. Finally, forecasting results indicate that there are significantly wider distributions in corn and soybean, whereas there is a little variation in the tobacco, wheat and alfalfa acres in the simulation. In chapter 3, we identify critical consumer-demographic characteristics that are associated with the consumption of products containing hemp and investigate their effect on total expenditure in the U.S. To estimate the likelihood of market participation and consumption level, the Heckman selection model, is employed using the maximum likelihood estimation procedure utilizing Nielsen consumer panel data from 2008 to 2015. Results indicate marketing strategies targeting consumers with higher education and income levels can attract new customers and increase sales from current consumers for this burgeoning market. Head-of-household age in different regions shows mixed effects on decisions to purchase hemp products and consumption levels. Findings will provide a basic understanding of a consumer profile and overall hemp market that has had double-digit growth over the last six years. As the industry continues to move forward, policymakers are going to need a deeper understanding of the factors driving the industry if they are going to create regulations that support the development of the industry. In chapter 4, we investigate the factors that affect agricultural land values by proposing a new rich dataset, Zillow Transaction and Assessment Data (ZTRAX) provided by Zillow from 2009 to 2014. we also examine whether National Commodity Crop Productivity Index (NCCPI) could be a good indicator of land values or not by comparing two different regression models between county-level cash rent and parcel-level NCCPI. Finally, this study incorporates flexible functional forms of the parcel size to test the parcel size and land values relations. Findings show that factors influencing agricultural land values in states with heterogeneous agricultural lands such as Kentucky are not different from other states with relatively homogeneous agricultural lands. This study also provides suggestive evidence that there is a non-linear relationship between parcel size and land values. Furthermore, we find that a disaggregated NCCPI at parcel-level could be considered an acceptable indicator to estimate agricultural values compared to an aggregated cash rent at county-level.

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Частини книг з теми "Transactional datasets"

Raj, Shashi, and Dharavath Ramesh. "Applying Partition Method to Adopt Spark-Based Eclat Algorithm for Large Transactional Datasets." In Algorithms for Intelligent Systems, 131–44. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3951-8_11.

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Taraba, Peter. "Clustering for Binary Featured Datasets." In Transactions on Engineering Technologies, 127–42. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2191-7_10.

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Al Bouna, Bechara, Chris Clifton, and Qutaibah Malluhi. "Using Safety Constraint for Transactional Dataset Anonymization." In Lecture Notes in Computer Science, 164–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39256-6_11.

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Cabiddu, Daniela, and Marco Attene. "Processing Large Geometric Datasets in Distributed Environments." In Transactions on Computational Science XXIX, 97–120. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54563-8_6.

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Memon, Neelam, and Jianhua Shao. "MR-RBAT: Anonymizing Large Transaction Datasets Using MapReduce." In Data and Applications Security and Privacy XXIX, 3–18. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20810-7_1.

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Yoshimura, Yuji, Alexander Amini, Stanislav Sobolevsky, Josep Blat, and Carlo Ratti. "Analysis of Customers’ Spatial Distribution Through Transaction Datasets." In Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVII, 177–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-53416-8_11.

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Patra, Bidyut Kr, and Sukumar Nandi. "Tolerance Rough Set Theory Based Data Summarization for Clustering Large Datasets." In Transactions on Rough Sets XIV, 139–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21563-6_8.

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Phan, Trong Nhan, Josef Küng, and Tran Khanh Dang. "An Adaptive Similarity Search in Massive Datasets." In Transactions on Large-Scale Data- and Knowledge-Centered Systems XXIII, 45–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49175-1_3.

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Poulis, Giorgos, Aris Gkoulalas-Divanis, Grigorios Loukides, Spiros Skiadopoulos, and Christos Tryfonopoulos. "SECRETA: A Tool for Anonymizing Relational, Transaction and RT-Datasets." In Medical Data Privacy Handbook, 83–109. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23633-9_5.

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Al-E’mari, Salam, Mohammed Anbar, Yousef Sanjalawe, and Selvakumar Manickam. "A Labeled Transactions-Based Dataset on the Ethereum Network." In Communications in Computer and Information Science, 61–79. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6835-4_5.

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Тези доповідей конференцій з теми "Transactional datasets"

Babu, M. Vinaya, and M. Sreedevi. "A Comprehensive Study on Enhanced Clustering Technique of Association Rules over Transactional Datasets." In 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2021. http://dx.doi.org/10.1109/i-smac52330.2021.9640681.

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Zheng, Wenbo, Lan Yan, Chao Gou, and Fei-Yue Wang. "Federated Meta-Learning for Fraudulent Credit Card Detection." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/642.

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Credit card transaction fraud costs billions of dollars to card issuers every year. Besides, the credit card transaction dataset is very skewed, there are much fewer samples of frauds than legitimate transactions. Due to the data security and privacy, different banks are usually not allowed to share their transaction datasets. These problems make traditional model difficult to learn the patterns of frauds and also difficult to detect them. In this paper, we introduce a novel framework termed as federated meta-learning for fraud detection. Different from the traditional technologies trained with data centralized in the cloud, our model enables banks to learn fraud detection model with the training data distributed on their own local database. A shared whole model is constructed by aggregating locallycomputed updates of fraud detection model. Banks can collectively reap the benefits of shared model without sharing the dataset and protect the sensitive information of cardholders. To achieve the good performance of classification, we further formulate an improved triplet-like metric learning, and design a novel meta-learning-based classifier, which allows joint comparison with K negative samples in each mini-batch. Experimental results demonstrate that the proposed approach achieves significantly higher performance compared with the other state-of-the-art approaches.

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Cardoso, Fabian Corrêa, Juan Malska, Paulo Ramiro, Giancarlo Lucca, Eduardo N. Borges, Viviane de Mattos, and Rafael Berri. "BovDB: A data set of stock quotes for Machine Learning on all companies from B3 between 1995 and 2020." In Dataset Showcase Workshop. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/dsw.2021.17411.

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Stock markets are responsible for the movement of huge amounts of financial resources around the world. This market generates a high volume of transaction data, which after being analyzed are very useful for many applications. In this paper we present BovDB, a data set that was built considering as source the Brazilian Stock Exchange (B3) with information related to the years between 1995 and 2020. We have approached the events’ impact on the stocks by applying a cumulative factor to correct prices. The results were compared with public data from InfoMoney and BR Investing, showing that our methods are valid and in accordance with the market standards. BovDB data set can be used as a benchmark for different applications and is publicly available for any researcher on GitHub.

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WANG, Cheng. "The Behavioral Sign of Account Theft: Realizing Online Payment Fraud Alert." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/636.

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As a matter of fact, it is usually taken for granted that the occurrence of unauthorized behaviors is necessary for the fraud detection in online payment services. However, we seek to break this stereotype in this work. We strive to design an ex-ante anti-fraud method that can work before unauthorized behaviors occur. The feasibility of our solution is supported by the cooperation of a characteristic and a finding in online payment fraud scenarios: The well-recognized characteristic is that online payment frauds are mostly caused by account compromise. Our finding is that account theft is indeed predictable based on users' high-risk behaviors, without relying on the behaviors of thieves. Accordingly, we propose an account risk prediction scheme to realize the ex-ante fraud detection. It takes in an account's historical transaction sequence, and outputs its risk score. The risk score is then used as an early evidence of whether a new transaction is fraudulent or not, before the occurrence of the new transaction. We examine our method on a real-world B2C transaction dataset from a commercial bank. Experimental results show that the ex-ante detection method can prevent more than 80\% of the fraudulent transactions before they actually occur. When the proposed method is combined with an interim detection to form a real-time anti-fraud system, it can detect more than 94\% of fraudulent transactions while maintaining a very low false alarm rate (less than 0.1\%).

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Huy, Thach Nguyen, Sombut Foitong, Sornchai Udomthanapong, Ouen Pinngern, Sio-Iong Ao, Alan Hoi-Shou Chan, Hideki Katagiri, Osca Castillo, and Li Xu. "Effects of Distance between Classes and Training Dataset Size on Imbalance Datasets." In IAENG TRANSACTIONS ON ENGINEERING TECHNOLOGIES VOLUME I: Special Edition of the International MultiConference of Engineers and Computer Scientists 2008. AIP, 2009. http://dx.doi.org/10.1063/1.3078140.

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Carswell, James D., Keith Gardiner, and Marco Neumann. "Wireless spatio-semantic transactions on multimedia datasets." In the 2004 ACM symposium. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/967900.968143.

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Alweshah, Mohammed, Wael Ahmad AlZoubi, and Abdulsalam Alarabeyyat. "Cluster based data reduction method for transaction datasets." In 2015 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2015. http://dx.doi.org/10.1109/iscaie.2015.7298332.

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Lin, Wangli, Li Sun, Qiwei Zhong, Can Liu, Jinghua Feng, Xiang Ao, and Hao Yang. "Online Credit Payment Fraud Detection via Structure-Aware Hierarchical Recurrent Neural Network." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/505.

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Online credit payment fraud detection plays a critical role in financial institutions due to the growing volume of fraudulent transactions. Recently, researchers have shown an increased interest in capturing users’ dynamic and evolving fraudulent tendencies from their behavior sequences. However, most existing methodologies for sequential modeling overlook the intrinsic structure information of web pages. In this paper, we adopt multi-scale behavior sequence generated from different granularities of web page structures and propose a model named SAH-RNN to consume the multi-scale behavior sequence for online payment fraud detection. The SAH-RNN has stacked RNN layers in which upper layers modeling for compendious behaviors are updated less frequently and receive the summarized representations from lower layers. A dual attention is devised to capture the impacts on both sequential information within the same sequence and structural information among different granularity of web pages. Experimental results on a large-scale real-world transaction dataset from Alibaba show that our proposed model outperforms state-of-the-art models. The code is available at https://github.com/WangliLin/SAH-RNN.

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Doan, Thang, Neil Veira, and Brian Keng. "Generating Realistic Sequences of Customer-Level Transactions for Retail Datasets." In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. http://dx.doi.org/10.1109/icdmw.2018.00122.

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Morency, Catherine, Martin Trepanier, Bruno Agard, Basile Martin, and Joel Quashie. "Car sharing system: what transaction datasets reveal on users' behaviors." In 2007 IEEE Intelligent Transportation Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/itsc.2007.4357656.

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Alviarez, Vanessa, Michele Fioretti, Ken Kikkawa, and Monica Morlacco. Two-Sided Market Power in Firm-to-Firm Trade. Inter-American Development Bank, August 2021. http://dx.doi.org/10.18235/0003493.

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Firms in global value chains (GVCs) are granular and exert bargaining power over the terms of trade. We show that these features are crucial to understanding the well-established variation in prices and pass-through across importers and exporters. We develop a novel theory of prices in GVCs, which tractably nests a wide range of bilateral concentration and bargaining power configurations. We test and evaluate the models predictions using a novel dataset merging transaction-level U.S. import data with balance sheet data for both U.S. importers and foreign exporters. Our pricing framework enhances traditional frameworks in the literature in accurately predicting price changes following a tariff shock. The results shed light on the role of firms in determining the tariff pass-through onto import prices.

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