Transaction Database Research Articles

Compact Tree Structures for Mining High Utility Itemsets

High Utility Item set Mining (HUIM) from large transaction databases has garnered significant attention as it accounts for the revenue of the items purchased in a transaction. Existing tree-based HUIM algorithms discard unpromising items and require at most two database scans for their construction. Hence, whenever utility threshold is changed, the trees have to be reconstructed from scratch. In this regard, the current study proposes to not only incorporate all the items in the tree structure but compactly represent transaction information. The proposed trees namely- Utility Prime Tree (UPT), Prime Cantor Function Tree (PCFT), and String based Utility Prime Tree (SUPT) store transaction-level information in a node unlike item-based prefix trees. Experiments conducted on both real and synthetic datasets compare the execution time and memory of these tree structures with a proposed Utility Count Tree (UCT) and existing IHUP, UP-Growth trees. Due to transaction-level encoding, these structures consume significantly less memory when compared to the tree structures in the literature.

Mining Transactional Databases for Frequent and High-Utility Fuzzy Sequential Patterns With Time Intervals

The field of data mining is progressing rapidly presenting researchers with many opportunities for research. Sequential pattern mining is a popular and long established technique in data mining which extracts data in the form of sequential patterns satisfying a threshold value such as utility, support, profit or a combination of these. The application of fuzzy theory in sequential pattern mining has also been favored leading to more natural liinguistic representation. Researchers have proposed various hybrid algorithms with fuzzification of any one parameter such as time or quantity. This paper proposes a hybrid fuzzy algorithm for mining of frequent and high utility sequential patterns with fuzzification of both purchase time and purchase quantity parameters, thereby giving more useful fuzzy sequential patterns. Experimental results also prove that the proposed algorithm is better than the existing algorithms.

The Effects of Party Size and Dining Time on Restaurant Patrons’ Tipping Behavior: Results from a Large-Scale Transaction Database

The Effects of Party Size and Dining Time on Restaurant Patrons’ Tipping Behavior: Results from a Large-Scale Transaction Database

Copyright Transaction Mode and Copyright Protection Risk Analysis of Green Industry from the Perspective of Information Environment

At present, the requirements of the current environment and background require us to have higher requirements for the data mode of copyright transaction. According to the previous way to conduct copyright transactions, it will cause a lot of unnecessary trouble, and according to the establishment of the copyright transaction database, we analyse the data types and patterns of copyright transactions and related copyright transaction protection measures and risk prevention. According to the copyright transaction database, we adopt BS network model, CIF copyright transaction data set model, CI data set model, data interactive query technology, and block matching technology to deal with the copyright transaction data model and copyright transaction risk prevention under the background of Informa ionization as follows: (1) we use BS network model, CIF copyright transaction data set model, and CI data set model to analyse and detect the copyright transaction data before transaction, which better reflects the network model effect of self‐enhancing operation rate of copyright transaction data and effectively avoids overfitting problem. (2) By using interactive data query technology, the data presented in the preparation work before the transaction is more detailed, the samples are normalized, and the collected transaction data are tested for accuracy, which can eliminate potential dangers. (3) The method of similarity feature reconstruction of copyright transaction data set completes the extraction of association matching features of copyright transaction data system, which provides strong support for us to prevent risks and effectively match data in copyright transaction.

The Effects of Party Size and Dining Time on Restaurant Patrons’ Tipping Behavior: Results from a Large-Scale Transaction Database

The Effects of Party Size and Dining Time on Restaurant Patrons’ Tipping Behavior: Results from a Large-Scale Transaction Database

Modifying Transactional Databases to Hide Sensitive Association Rules

Although firms recognize the value in sharing data with supply chain partners, many remain reluctant to share for fear of sensitive information potentially making its way to competitors. Approaches that can help hide sensitive information could alleviate such concerns and increase the number of firms that are willing to share. Sensitive information in transactional databases often manifests itself in the form of association rules. The sensitive association rules can be concealed by altering transactions so that they remain hidden when the data are mined by the partner. The problem of hiding these rules in the data are computationally difficult (NP-hard), and extant approaches are all heuristic in nature. To our knowledge, this is the first paper that introduces the problem as a nonlinear integer formulation to hide the sensitive association rule while minimizing the alterations needed in the data set. We apply transformations that linearize the constraints and derive various results that help reduce the size of the problem to be solved. Our results show that although the nonlinear integer formulations are not practical, the linearizations and problem-reduction steps make a significant impact on solvability and solution time. This approach mitigates potential risks associated with sharing and should increase data sharing among supply chain partners.

Mining Approximate Frequent Itemsets Using Pattern Growth Approach

Approximate frequent itemsets (AFI) mining from noisy databases are computationally more expensive than traditional frequent itemset mining. This is because the AFI mining algorithms generate large number of candidate itemsets. This article proposes an algorithm to mine AFIs using pattern growth approach. The major contribution of the proposed approach is it mines core patterns and examines approximate conditions of candidate AFIs directly with single phase and two full scans of database. Related algorithms apply Apriori-based candidate generation and test approach and require multiple phases to obtain complete AFIs. First phase generates core patterns, and second phase examines approximate conditions of core patterns. Specifically, the article proposes novel techniques that how to map transactions on approximate FP-tree, and how to mine AFIs from the conditional patterns of approximate FP-tree. The approximate FP-tree maps transactions on shared branches when the transactions share a similar set of items. This reduces the size of databases and helps to efficiently compute the approximate conditions of candidate itemsets. We compare the performance of our algorithm with the state of the art AFI mining algorithms on benchmark databases. The experiments are analyzed by comparing the processing time of algorithms and scalability of algorithms on varying database size and transaction length. The results show pattern growth approach mines AFIs in less processing time than related Apriori-based algorithms.

Efficient mining of cross-level high-utility itemsets in taxonomy quantitative databases

In contrast to frequent itemset mining (FIM) algorithms that focus on identifying itemsets with high occurrence frequency, high-utility itemset mining algorithms can reveal the most profitable sets of items in transaction databases. Several algorithms were proposed to perform the task efficiently. Nevertheless, most of them ignore the item categorizations. This useful information is provided in many real-world transaction databases. Previous works, such as CLH-Miner and ML-HUI Miner were proposed to solve this limitation to discover cross-level and multi-level HUIs. However, the CLH-Miner has a long runtime and high memory usage. To address these drawbacks, this study extends tight upper bounds to propose effective pruning strategies. A novel algorithm named FEACP (Fast and Efficient Algorithm for Cross-level high-utility Pattern mining) is introduced, which adopts the proposed strategies to efficiently identify cross-level HUIs in taxonomy-based databases. It can be seen from a thorough performance evaluation that FEACP can identify useful itemsets of different abstraction levels in transaction databases with high efficiency, that is up to 8 times faster than the state-of-the-art algorithm on the tested sparse databases and up to 177 times on the tested dense databases. FEACP reduces memory usage by up to half over the CLH-Miner algorithm.

Analysis of Top-K Utility Pattern Mining Framework

A new algorithm of utility-list structure has been proposed in this paper which based on the framework of top-k utility mining analysed. There are many techniques which extracts top keyword within search space relates with various algorithms. Like, High Utility Pattern Mining, which is detection of high utility itemsets in a transactional database; provides the fundamental task in database and data mining community, such as quantity, cost, weight and profits concern, to extract remarkable knowledge and efficient database patterns. Different from the support based mining models; the utility oriented mining framework integrates the utility theory to provide more informative and useful patterns. These could not be directly performed on the utility mining techniques with the help of proposed method in this paper would find a top-k search space itemsets. This paper is focused on analysis of many identical high top-k utility patterns mining algorithm, such as mining for Top-K High Utility Itemsets, Solutions to Utility Big Data Analysis, Negative Sequential Patterns Mining, Efficient High Pattern Mining with Tighter Upper Bounds and Tighter Upper Bound including Average-Utility for Mining High Average-Utility Patterns. However there are some issues that need to resolve. These are discussed in this paper and efficient proposed the analysis of the various utility

Valuation of University-Originated Technologies: A Predictive Analytics Approach

Experts have difficulty assessing the economic value of university-originated technologies due to the high level of uncertainty associated with the commercialization of early stage and basic technologies. This article proposes a random forest approach to the valuation of university-originated technologies that integrates monetary value and patent value models for technology valuation. First, a technological characteristics-value matrix was constructed after defining a total of 23 indicators from the U.S. Patent and Trademark and Scopus databases and extracting the value of university-originated technologies from technology transaction databases. Second, a random forest model, an ensemble machine learning model based on a multitude of decision trees, was employed to assess the economic value of university-originated technologies. Finally, the performance of our approach was assessed using quantitative metrics. A case study of the technologies registered in the Office of Technology Licensing of Stanford University confirms, with statistically significant outcomes, that our method is valuable as a complementary tool for the valuation of university-originated technologies.

Digital signatures over HMAC entangled chains

In this work we present a new digital signature method based on hash chains and the hash message authentication code (HMAC). Unlike Blockchain technology, our approach establishes that the database of transactions is distributed among users, so each one maintains a personal record called Singlechain. The Lizama HMAC protocol defines a double authentication mechanism that verifies the origin and destination of the signature. Our analysis shows that the system generates a block size of around 6 kilobytes to perform double authentication using SHA-256, making it a promising alternative for digital signature using public key infrastructure. Since the method is based on the mathematical properties of hash functions and hash chains, the security of the method can be properly evaluated and thereby achieve a robust method of digital signatures in the quantum era.

IoTranx: Transactions for Safer Smart Spaces

Smart spaces such as smart homes deliver digital services to optimize space use and enhance user experience. They are composed of an Internet of Things (IoT), people, and physical content. They differ from traditional computer systems in that their cyber-physical nature ties intimately with the users and the built environment. The impact of ill-programmed applications in such spaces goes beyond loss of data or a computer crash, risking potentially physical harm to the space and its users. Ensuring smart space safety is therefore critically important to successfully deliver intimate and convenient services surrounding our daily lives. By modeling smart space as a highly dynamic database, we present IoT Transactions, an analogy to database transactions, as an abstraction for programming and executing the services as the handling of the devices in smart space. Unlike traditional database management systems that take a “clear room approach,” smart spaces take a “dirty room approach” where imperfection and unattainability of full control and guarantees are the new normal. We identify Atomicity, Isolation, Integrity and Durability (AI 2 D) as the set of properties necessary to define the safe runtime behavior for IoT transactions for maintaining “permissible device settings” of execution and to avoid or detect and resolve “impermissible settings.” Furthermore, we introduce a lock protocol, utilizing variations of lock concepts, that enforces AI 2 D safety properties during transaction processing. We show a brief proof of the protocol correctness and a detailed analytical model to evaluate its performance.

LCM from FCA point of view: A CbO-style algorithm with speed-up features

LCM is an algorithm for enumeration of frequent closed itemsets in transaction databases. It is well known that when we ignore the required frequency, the closed itemsets are exactly intents of formal concepts in Formal Concept Analysis (FCA). We describe LCM in terms of FCA and show that LCM is basically the Close-by-One algorithm with multiple speed-up features for processing sparse data. We analyze the speed-up features and compare them with those of similar FCA algorithms, like FCbO and algorithms from the In-Close family.

An efficient HUIBA Algorithm for Mining High Utility Itemset

Utility Mining is to spot the itemsets with highest utilities, by considering profit, quantity, cost or other user preferences. Mining High Utility itemsets from a transaction database is to seek out itemsets that have utility above a user-specified threshold. Bio inspired algorithm is extremely efficient for mining High Utility Itemset(HUI), but it will not find all HUI in the database and the quality is poor within the number of discovered HUI. A replacement framework using BA algorithm is proposed to rectify this issue. The proposed algorithm is more efficient in terms of quality and convergence speed when put next to other algorithms.

Retracted: High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation

AbstractMining high utility itemsets (HUI) is a current thrust field in data mining that has received numerous methodologies for addressing it effectively. The difficulty with HUI is to locate a number of items that have a high degree of utility in comparison to other different sets in a transaction database. Traditional accurate HUIM algorithms usually have to solve the exponential problem of big search spaces when the size or number of different items in the database is quite vast. Evolutionary computation (EC)‐based algorithms have been offered as an alternate and successful technique to solving HUIM issues since they may generate a collection of approximately optimum solutions in a short amount of time. Many genetic algorithm (GA)‐based approaches have been developed in recent years to efficiently mine HUI from transaction databases. The selection technique, crossover probability, mutation probability, and finishing criteria of the genetic algorithm have an greater impact on generating a reasonably decent solution and the processing time. Particularly crossover is a convergence operation which is intended to pull the population toward a local minimum/maximum. During HUIM using GA both low and high crossover rate will have the problem of decreasing the quality of intermediate itemset and take longer time to converge to some optima and vice versa. This problem can be solved by adjusting the crossover rate adaptively depending on environmental inputs. The proposed approach describe a hybrid system that employs a reinforcement learning (RL) agent to adaptively calibrate the crossover operation to increase the performance of a genetic algorithm. To estimate state‐action utility values, the RL agent employs Q‐learning, which it then employs to execute high‐level adaptive control over the crossover operation in the genetic algorithm. To evaluate the performance of the proposed methodology, extensive experiments were conducted on a four benchmark datasets and compared with three state‐of‐art EC approaches HUPEUMU‐GRAM, Bio‐HUIF‐GA, HUIM‐BPSO, and one exact approach HUP‐Miner . The result analysis witnessed that proposed approach outperforms EC approaches in terms of execution time, discovered HUIs and convergence.

A single‐phase algorithm for mining high utility itemsets using compressed tree structures

Mining high utility itemsets (HUIs) from transaction databases considers such factors as the unit profit and quantity of purchased items. Two-phase tree-based algorithms transform a database into compressed tree structures and generate candidate patterns through a recursive pattern-growth procedure. This procedure requires a lot of memory and time to construct conditional pattern trees. To address this issue, this study employs two compressed tree structures, namely, Utility Count Tree and String Utility Tree, to enumerate valid patterns and thus promote fast utility computation. Furthermore, the study presents an algorithm called single-phase utility computation (SPUC) that leverages these two tree structures to mine HUIs in a single phase by incorporating novel pruning strategies. Experiments conducted on both real and synthetic datasets demonstrate the superior performance of SPUC compared with IHUP, UP-Growth, and UP-Growth+ algorithms.

On-shelf utility mining from transaction database

As an important technique for dealing with transaction database in the field of data mining, utility-driven mining can be used to discover useful patterns (i.e., itemsets, sequences) which have a high utility. However, it has a bias towards the item/object combinations which have more exhibition period since they have more opportunity to generate a high utility. To address this, the on-shelf time period of items need to be considered, thus on-shelf utility mining (OSUM) can be applied in the application which is more closer to the actual situation. Currently several models have been proposed to deal with the OSUM problem, but they still suffer from the requirement that it needs to maintain a massive candidates in memory and to scan database many times. In this paper, we propose two effective one-phase algorithms named OSUMI (On-Shelf Utility Mining from transactIon database) and OSUMI+ (the improve version of OSUMI). Both OSUMI and OSUMI+ search all itemsets as a set-enumeration tree and discover the on-shelf itemsets with high utility in a more practical way. More precisely, in order to avoid the problems of high memory consumption, two algorithms apply some properties of the concept of on-shelf utility. Besides, two upper-bounds named subtree utility and local utility are applied to early filter out unpromising patterns and then prune the search space. Finally, an extensive experimental study on several real on-shelf datasets shows that our proposed algorithms can be significantly faster than the state-of-the-art algorithm.

Pembangunan Dashboard Inventory Pada Bisnis Ritel

In this study, it will be explained how to create a data mart in a retail business to answer the KPI (Key Performance Indicator) inventory of goods which will be visualized in the form of a dashboard to make it easier for company executives to see the condition of inventory in stores. In making a data mart, it will be explained how the ETL (Extraction, Transformation and Loading) process from a transactional database from 9 stores becomes a data mart. The data mart in this study was built using a star schema and produced 4 dimension tables and 1 fact table. Star schema is used to simplify the query process when you want to display data on the dashboard. After the dashboard has successfully visualized the data, then testing is carried out on users using the UEQ (User Experience Questionnaire) method to find out how the user experience when using the dashboard. From this study, it was found that the dashboard was able to visualize the total inventory of goods, consigned goods inventory, non-consigned trunk inventory in monthly, semester, quarterly, and annual periods. And excellent results on 6 scales of User Experience Questionnaire (UEQ).

Implementation Of Data Mining On Glasses Sales Using The Apriori Algorithm

The Optik Nasional is one of the most important optik in Tasikmalaya. Many of his everyday sales transactions, even longer sales, will increase as a result of his activities through the process of purchasing and selling glasses with diverse brands. If left to its own devices, sales transaction data devolves into useless junk. With the help of technological advancements, the ability to collect and process data is improving. Data mining is the process of extracting information and expertise from large amounts of data. Transaction data may be used and analysed to generate helpful information for improved sales and promotional goods, in addition to serving as an archive for the firm. The author utilises the Apriori algorithm to obtain information on the most popular and in-demand goods in a database transaction at a national optical firm, so that the results may be utilised to improve the growth of eyeglass sales and marketing.

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.