Set Of Transactions Research Articles

Interest Rate Pass-Through and Consumption Response: The Deposit Channel

Abstract This study assesses a new mechanism, the deposit channel, in the transmission of interest rate shock to household consumption using an administrative panel data set of financial transactions for Turkey. Our empirical strategy exploits variation in consumers' adherence to the Islamic laws that forbid earning interest and employs a standard difference-in-difference design. Following an unanticipated announcement of interest rate hike, rate-sensitive consumers significantly reduce their overall spending, and the response persists throughout the post-announcement period. The response of debt payment, disparate exposure to inflation, exchange rate, and the demographic difference can hardly fully account for the documented consumption response heterogeneity.

Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets. As all non-empty subsets in frequent itemsets are still frequent itemsets, all frequent itemsets can be acquired only by finding all maximal frequent itemsets (MFIs), whose supersets are not frequent itemsets. In this study, an algorithm, named right-hand side expanding (RHSE), which can accurately find all MFIs, was proposed. First, an Expanding Operation was designed, which, starting from any given frequent itemset, could add items using certain rules and form some supersets of given frequent itemsets. In addition, these supersets were all MFIs. Next, this operator was used to add items by taking all frequent 1-itemsets as the starting point alternately, and all MFIs were found in the end. Due to the special design of the Expanding Operation, each MFI could be found. Moreover, the path found was unique, which avoided the algorithm redundancy in temporal and spatial complexity. This algorithm, which has a high operating rate, is applicable to the big data of high-dimensional mass transactions as it is capable of avoiding the computing redundancy and finding all MFIs. In the end, a detailed experimental report on 10 open standard transaction sets was given in this study, including the big data calculation results of million-class transactions.

DWORM: A Novel Algorithm To Maintain Large Itemsets in Deleted Items and/or Transactions Databases Without Re-Mining

Transactional databases can be updated by three cases; the addition of new transactions, the deletion of set of transactions, andor increasing or decreasing of the support of the itemsets. The update process affects the previously mined itemsets, some of the large items will be small and vice versa. Therefore the updated database should be re-mined to discover the changes in the hidden itemsets. There are many algorithms to avoid the re-mining process in the case of updating a database by addition and there is one algorithm in case of changing the value of the support. But there is no actual algorithm to avoid the re-mining in the case of deletion. This research presents a novel algorithm to manipulate this case. The proposed algorithm manipulates the three possibilities of the deletion that are deletion of one or more items from a transaction or set of transactions, deletion of a set of transactions, and deletion of items from transactions and deletion of set of transactions at the same time. The experimental result shows that the updating algorithm outperforms the re-mining process in considerable amount of execution time.

Data Mining Technology in Book Copyright Information Management Decision System

Today is an era of data “big bang”; Internet information technology is widely used in various fields of society. As an indispensable spiritual food in people’s daily life, books are increasing in number and scale. In order to better manage book information, people have introduced data mining technology. Based on this, this article takes the research and application of data mining technology in book copyright information management decision-making system as the theme, explores the role of data mining technology in book copyright information management, and aims to provide reference for our country’s book copyright information management and decision-making. This article first introduces the common algorithms of data mining technology and then elaborates on the advantages and effectiveness of the association rule method in data mining. Aiming at some defects of the original Apriori algorithm of the association rule method, an improved Apriori algorithm is proposed. After taking the library book information management system and database of a university in our province as the experimental research object, the performance gap between the two algorithms is compared through experiments, and it is concluded that when the number of transaction set item records is less than 1400, the Apriori algorithm performs better, and when the number of records in the transaction set is greater than 1400, the improved Apriori algorithm is obviously more advantageous. The research results show that the introduction and application of data mining technology make the information management of books more efficient and convenient, and it is more convenient for the management and decision-making of book copyright information.

Problems on Shortest k-Node Cycles and Paths

The paper is devoted to the construction of mathematical models for problems on the shortest cycles and paths, that pass through a given number of nodes of a directed graph. Such cycles and paths are called k-node, where 1<k <n, n is the number of nodes in the graph. Section 1 formulates two problems for finding the shortest k-node cycle – a mixed Boolean and linear programming problem and a discrete programming problem. Both problems include constraints from the classical assignment problem, describing a one-time entry into a node and a one-time exit from a node for those nodes through which the cycle passes. The cycle connectivity in the first problem is ensured by modeling the flow problem, and in the second problem, it is ensured by using the A. Tucker constraints for the travelling salesman problem. Section 2 establishes a connection between the formulations of both problems from Section 1 and the travelling salesman problem and investigates the efficiency of their solution using modern versions of gurobi and cplex programs and the AMPL modeling language. Section 3 contains the formulation of the shortest k-node path problem, which is represented by a mixed Boolean and linear programming problem. With its help, the optimal routes were found for visiting the wine-making points of the Malopolskie Wine Route in the direction Lviv-Wroclaw-Lviv (Section 4). Here a map for the 20 most visited wine-making points of the Malopolskie Wine Route and a table of the distances between them and the distances from them to Lviv and Wroclaw, calculated using the Google Maps web service, are presented. The developed mathematical models of the problems of finding the shortest k-node paths and cycles and the developed software in the AMPL modeling language can be used for the design and arrangement of technical objects, optimization of the transportation of products, analysis and forecasting of economic processes, determination of optimal routes when planning passenger and freight traffic, optimal organization of the process of managing a set of transactions and queries during their implementation in network databases and other classes of applied optimization problems. Keywords: digraph, shortest path, Boolean variable, linear programming, Hamiltonian cycle, Hamiltonian path, travelling salesman problem, AMPL, gurobi, cplex.

Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining

The solution space of a frequent itemset generally presents exponential explosive growth because of the high-dimensional attributes of big data. However, the premise of the big data association rule analysis is to mine the frequent itemset in high-dimensional transaction sets. Traditional and classical algorithms such as the Apriori and FP-Growth algorithms, as well as their derivative algorithms, are unacceptable in practical big data analysis in an explosive solution space because of their huge consumption of storage space and running time. A multi-objective optimization algorithm was proposed to mine the frequent itemset of high-dimensional data. First, all frequent 2-itemsets were generated by scanning transaction sets based on which new items were added in as the objects of population evolution. Algorithms aim to search for the maximal frequent itemset to gather more non-void subsets because non-void subsets of frequent itemsets are all properties of frequent itemsets. During the operation of algorithms, lethal gene fragments in individuals were recorded and eliminated so that individuals may resurge. Finally, the set of the Pareto optimal solution of the frequent itemset was gained. All non-void subsets of these solutions were frequent itemsets, and all supersets are non-frequent itemsets. Finally, the practicability and validity of the proposed algorithm in big data were proven by experiments.

Transport infrastructure, accessibility and residential property values: evidence from Hong Kong

PurposeHong Kong, like many other developed cities and countries, invests heavily in transport development. This study investigates whether the speculative benefits of future improvements in accessibility, brought about by impending transport development, will be capitalized into nearby residential property values even prior to the opening of the development.Design/methodology/approachDeviating from the standard hedonic price approach, the present study employed a fixed-effects model with a large data set of residential property transactions in the vicinity of three-stations situated along a newly proposed mass-transit-railway line in Hong Kong.FindingsThe results suggest that the values of residential properties close to stations do reflect the accessibility enhancements to be brought about by transport improvements even before the opening of the line. Results revealed a 6.5% of property value premium after the announcement of construction; and higher up to 6.7% after the operation of the line. This indicates that forthcoming new transport-infrastructure development produces changes in spatial price-gradients for neighbouring residential properties. Findings indicate that potential buyers/investors recognized the positive benefits of the planned transportation development, even before completion of the project, and are ready to pay a premium for those properties close to railway stations, representing clear evidence that residential property prices/values, near stations, reflect anticipated accessibility enhancements brought about by transport improvements.Originality/valueThis study, using a novel approach – a fixed-effects model to capture the speculative benefits of future improvements in transport infrastructure – provides a positive hypothesis that expected benefits of future improvements in accessibility are capitalized into property values.

City consumption profile: a city perspective on the spending behavior of citizens

In designing the city of the future, city managers and urban planners are driven by specific citizens’ behaviors. In fact, economic and financial behaviors, and specifically, which goods and services citizens purchase and how they allocate their spending, are playing a central role in planning targeted services. In this context, cashless payments provide an invaluable data source to identify such spending behaviors. In this work, we propose a methodology to extract the consumption behaviors of a large sample of customers through credit card transaction data. The main outcome of the methodology is a concise representation of the economic behavior of people residing in a city, the so-called city consumption profile. We inferred the city consumption profile from a network-based representation of the similarity among the customers in terms of purchase allocation; on top of which we applied a community detection algorithm to identify the representative consumption profiles. By applying the above methodology to a set of credit card transactions of an Italian financial group, we showed that cities, even geographically close, exhibit different profiles which makes them unique. Specifically, usage patterns focused on a single type of good/service—mono-categorical consumption profile—are the main factors leading to the differences in the city profiles. Our analysis also showed that there is a group of consumption profiles common to all cities, made up by purchases of primary goods/services, such as food or clothing. In general, the city consumption profile represents a tool for understanding the economic behaviors of the citizens and for comparing different cities. Moreover, city planners and managers may use it in the outline of city services tailored to the citizens’ needs.

IMPACT OF BLOCK CHAIN IN COMMUNICATION AND NETWORK

A block chain network is a technical infrastructure that provides ledger and smart contract (chain code) services to applications. Primarily, smart contracts are used to generate transactions which are subsequently distributed to every peer node in the network where they are immutably recorded on their copy of the ledger. The users of applications might be end users using client applications or block chain network administrators. First and foremost, block chain is a public electronic ledger built around a P2P system that can be openly shared among disparate users to create an unchangeable record of transactions, each time-stamped and linked to the previous one. Every time a set of transactions is added, that data becomes another block in the chain.

RFM-based repurchase behavior for customer classification and segmentation

Customer behavior modeling and classification are well-studied areas for applications in retail. Past studies implemented the purchase behavior modeling based on the physical behavior of a subject. In this research, we apply the recency, frequency, and monetary (RFM) model and data modeling techniques to detect behavior patterns for a customer. Each transaction attributed to a customer is part of one's behavior, and an instance of the feature vector, it is modeled on a set of transactions to constitute repurchase behavior. The proposed scheme is validated by simulating a publicly accessible real-world data set with a need-tailored multi-layer perceptron (MLP) and also support vector machine (SVM) and decision tree classification (DTC) methods. The experiments yield a high customer classification rate of more than 97% for the different numbers of the customers. Empirical analysis shows that eight transactions are sufficient to classify a customer with high accuracy.

Helix: A Fair Blockchain Consensus Protocol Resistant to Ordering Manipulation

We present Helix , a blockchain-based consensus protocol for fair ordering of transactions among nodes in a distributed network. Helix advances in rounds, in each an elected primary node proposes a potential block (a successive set of transactions). For being included in the blockchain, a block must pass validation by an elected committee of nodes. Nodes have two primary preferences. First, to be elected as committee members. Additionally, because each transaction is associated with one of the network nodes, nodes would like to prioritize their own transactions over those of others. Our definition of fairness incorporates three key elements. First, the process of electing nodes to committees is random and unpredictable. Second, a correlated sampling scheme is used to guarantee random selection and ordering of pending transactions in blocks. Third, transactions are encrypted to hide their associations with nodes and prevent censorship. Through the corresponding threshold decryption process we obtain an unpredictable and non-manipulable randomness beacon, which serves both the election process and the correlated sampling scheme. We define a quantitative measure of fairness in the protocol, prove theoretically that fairness manipulation in Helix is significantly limited, and present experiments evaluating fairness in practice.

Exploring Decomposition for Solving Pattern Mining Problems

This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strategy. The approximation-based strategy takes into account only the clusters, whereas the exact strategy takes into account both clusters and shared items between clusters. To boost the performance of the CBPM, a GPU-based implementation is investigated. To evaluate the CBPM framework, we perform extensive experiments on several pattern mining problems. The results from the experimental evaluation show that the CBPM provides a reduction in both the runtime and memory usage. Also, CBPM based on the approximate strategy provides good accuracy, demonstrating its effectiveness and feasibility. Our GPU implementation achieves significant speedup of up to 552× on a single GPU using big transaction databases.

Memory Pool Publisher Algorithm for Preventing Malicious Fork in the Bitcoin Environment

Abstract— Blockchain technology is used by most Bitcoin systems to store all historical transaction information. Blockchain is a chain of blocks similar to the linked list structure and can be changed to a fork structure, in which there are two types of forks: useful fork or an intentional fork structure. A useful fork may appear when the rules of the Bitcoin system are updated. On the other hand, the intentional fork may appear when a miner has supercomputer properties, generates a set of blocks as a private branch, and does not publish this branch to the blockchain until its length exceeds the length of the main branch. A set of blockchain transactions will be rollbacked when the intentional fork occurs in the Bitcoin system, user waiting times will increase, and miner rewards will illegally increase. A Memory pool publisher algorithm is suggested in this paper to avoid the fork issues in the Bitcoin system, for instance: intentional fork, rollback problem, users waiting time. The proposed algorithm is to make the system a single publisher and divide the block's construction into two phases. A miner constructs a block and sends it to the memory pool as the first phase. The memory pool will send the construction block to the blockchain as the second phase. The findings indicate that the proposed algorithm has a strong potential to avoid the blockchain's intentional fork problem and thus minimize user waiting times for the rollback problem.

Measuring Customer Lifetime Value in a Semi-Contractual Transaction Setting with Artificially Truncated Duration

The measurement of customer lifetime value (CLV) has long been of primary interest to marketing research. To date, almost all CLV modeling studies have been conducted in either the contractual or the non-contractual transaction setting. The semi-contractual transaction setting, however, has been increasingly observed lately but rarely studied. In this article, we deal with the CLV measurement in one type of semi-contractual transaction setting, in which firms can unilaterally terminate their transaction relationship with customers under certain conditions, resulting customers’ survival duration being artificially truncated. We propose an innovative model to capture CLV in the setting, which consists of two sub-models. The first sub-model is a novel customer base analysis (CBA) model, BG-t/BB. It adds an “artificially truncated” feature to the classic BG/BB model. This BG-t/BB model is then augmented by a monetary value (MV) sub-model, Gamma-Gamma, so that we can capture each customer’s overall monetary value. We also develop and report a systematic plan for data collection and model testing, and then discuss how our model can be used to support managers to determine the optimal customer termination timing.

К вопросу об основных и вспомогательных сделках в жилищном праве

The article examines the classification of unilateral transactions in civil law into main and auxiliary ones. The author gives the examples of transactions that can be attributed to each of these groups, and critically evaluates the position on the characterization of the main transactions as a set of auxiliary transactions that are interconnected with each other and are made sequentially. The article analyzes the definitions of the concept «auxiliary contract» available in the legal literature, as well as the types of such transactions in civil law. The study of the classification issue allowed to carry out its application to unilateral transactions. Special attention is paid to the problem of the ambiguous position of regulatory transactions in the analyzed classification, which leads to a confusion of concepts in the doctrine and in judicial practice. As a result of the consideration of the transactions classification into mandatory and regulatory transactions, the author justifies the position that regulatory transactions cannot be unambiguously attributed to the basic or auxiliary transactions, gives the examples of unilateral transactions in housing law, determines their place in the analyzed classification, and concludes on the significance of the considered classification for housing law and civil science in general.

Die Grenzen der Therapie verhandeln

To a large extent, the history of psychiatric therapy is the history of incomplete cures, defeated hopes, and ever enduring chronicity. Yet while the historiography of psychiatry has paid much attention to the therapeutic successes claimed by psychiatrists, it has had only little to say about its failures. This article offers both a framework for analyzing therapeutic failure in the aftermath of the mid-twentieth century psychiatric revolution and an exploration of the ways in which it has been problematized in three corpuses of publications from families of people with mental illness, psychiatric survivors, and nurses, respectively. I suggest that failure should be understood as a situated concept that emerged, historically, from the complex set of transactions among psychiatrists, their clients, and the wider society. Specifically, I highlight three levels of analysis when addressing therapeutic failure. The first relates to the broader system of expectations surrounding medical practices. At the second level, failure emerges in the course of the administration of a given treatment from the observation that a cure or at least some relief has not been obtained for a given patient. At the third and last level, failure refers to the inability of a clinician to perform his prescribed role. The changing dynamics of treatment administration in the wake of the neuroleptic revolution, the evolving dangers of therapy, and the transformations in the relationship between therapists and patients is then highlighted to offer a broad understanding of failure as an experience and a concept for the various protagonists of the psychiatric drama.

Collaborative Recommendation based on Implication Field

Recently, recommender systems has grown rapidly in both quantity and quality and has attracted many studies aimed at improving their quality. Especially, collaborative fil-tering techniques based on rule mining model combined with statistical implication analysis (SIA) technique also achieved some interesting results. This has shown the potential of SIA to improve the performance of recommender systems. However, it is still not rich and there are several problems to be solved for better results such as the problem of non-binary data processing, dealing with bottleneck case of data partitioning method according to the number of transactions on the very sparse transaction sets during training and testing the model, and not paying attention to exploiting the trend of variation of statistical implication. In order to contribute to solving these problems, the paper focuses on proposing a new data partitioning method, and developing the recommendation model based on equipotential planes mining generated by variation of implication intensity or implication index in the implication field on both binary and non-binary data to improve the recommendations further. Experimental results have shown the success of this new approach through its quality comparison with collaborative filtering recommendation models as well as existing SIA-based ones.

Mining method of massive data of mobile library under information asymmetry facing large-scare database

The current method generates a large number of candidate sets when mining data in mobile libraries under asymmetric information, and the mining time and efficiency are poor. To this end, a new method for mobile library massive data mining based on improved Apriori algorithm is proposed to collect, clean and reduce massive data. Calculate the reader's interest distance by analyzing the borrowed historical data, and use the Apriori algorithm to find the association rules in the frequent itemsets of the data. In order to make up for the shortcomings of the current method, while filtering out infrequent candidate sets, the corresponding transaction set is also collaboratively filtered, which can reduce the amount of calculation and time consumption. Experimental results show that the proposed method can mine more valuable rules. The improved execution time is only 10 s, the CPU utilization exceeds 90%, and the acceleration ratio exceeds 1.81 s, which can better meet the needs of decision makers.

Trustzone-based secure lightweight wallet for hyperledger fabric

With the development of blockchain-based digital currencies, the security of digital wallets becomes more and more important. As far as we know, there is no safe lightweight wallet in hyperledger fabric. To solve the problem, we proposed a Trustzone-based Secure Lightweight Wallet for Hyperledger Fabric (hereafter referred to as TSLWHF). Firstly, we designed an Unspent Transaction Output (UTXO) set of transactions under blockchain and a signature verification mechanism for transactions, which made it possible to implement the lightweight wallet in hyperledger fabric. Then, we implemented a reliable protection mechanism for private keys and wallet’s address, which solved the problem that users’ information might be stolen or replaced. Meanwhile, the transaction verification results are guaranteed not to be tampered by hackers through verifying transactions in Trusted Execution Environment (TEE) and encrypting local block headers. Finally, to demonstrate utility, we deployed the system in hyperledger fabric and trustzone. Experiments showed that the wallet reduces the size of locally stored data while protecting the security of user’s assets. The time spent on TSLWHF to execute a transaction is 0.589s, which improves transaction’s performance compared to Bitcoin wallet.

Privacy-Preserving Multiobjective Sanitization Model in 6G IoT Environments

The next revolution of the smart industry relies on the emergence of the Industrial Internet of Things (IoT) and 5G/6G technology. The properties of such sophisticated communication technologies will change our perspective of information and communication by enabling seamless connectivity and bring closer entities, data, and “things.” Terahertz-based 6G networks promise the best speed and reliability, but they will face new man-in-the-middle attacks. In such critical and high-sensitive environments, the security of data and privacy of information still a big challenge. Without privacy-preserving considerations, the configuration state may be attacked or modified, thus causing security problems and damage to data. In this article, motivated by the need to secure 6G IoT networks, an ant colony optimization (ACO) approach is presented by adopting multiple objectives as well as using transaction deletion to secure confidential and sensitive information. Each ant in the population is represented as a set of possible deletion transactions for hiding sensitive information. We utilize the use of a prelarge concept to assist in the reduction of multiple database scans in the evaluation progress. We then also adopt external solutions to maintain discovered Pareto solutions, thus improving effectiveness to find optimized solutions. Experiments are conducted comparing our methodology to state-of-the-art bioinspired particle swarm optimization (PSO) as well as genetic algorithm (GA). Our strong results clearly show that the designed approach achieves fewer side effects while maintaining low computational cost overall (Chen et al., 2020).