Transaction Database Research Articles

Comparative analysis of the Express.js and ElysiaJS frameworks in the context of web application development

JavaScript-based programming frameworks allow more and more web applications to be built, aiming to become the fastest platform for a particular application. The aim of this paper is, among other things, to conduct study to obtain performance results of two server-based programming libraries in the application of an online transaction database structure. The database structure, the TPC-C benchmark queries and the Bombardier HTTP(S) query measurement tool were used to perform the tests. The research thesis "The Express.js framework has better performance than ElysiaJS in terms of TPS measurement tests in TCP-C structure" was set. The results obtained did confirm the thesis.

Method for Mining High-utility Patterns in Transaction Stream Data based on Linked List Structure

Mining valuable patterns in data streams presentsa significant challenge in the field of data mining. Thistask is crucial as it allows for the identification of highlyprofitable item sets within transaction databases. However, asnew transactions are continually added, new valuable patternsemerge, thus changing the usefulness of previously analyzeddata. It is essential to promptly update information regardingthese changes to enable effective business decision-making.Consequently, existing mining methods applied to transactionflow datasets require considerable time to identify newpatterns and update information related to new transactions.This article focuses on the research and proposal of a newtransaction stream data mining method called High-UtilityStream Linked-List Mining. The method utilizes a linkedlist structure known as the High-Utility Stream Linked List(HUSLL) to store information about patterns in the database.Mining and updating transaction information are directlyperformed on the HUSLL structure. Experimental resultsdemonstrate that this novel mining method exhibits moreefficient execution times compared to previous solutions.

Mining actionable repetitive positive and negative sequential patterns

Repetitive positive and negative sequential patterns (PNSPs) recognize the repetitive characteristics of positive (occurring) and negative (nonoccurring) sequential patterns, thereby providing more comprehensive information than traditional PNSPs. However, existing repetitive PNSP mining methods produce numerous conflict patterns that do not benefit decision-making. To address this issue, we propose an actionable repetitive PNSP mining method, namely ARPNSP, for transaction databases. First, we propose the definition of negative occurrence under the self-adaptive gap and nonoverlapping conditions, which makes it possible to identify whether a pattern is actionable via correlation analysis. Second, we propose an offset sequence definition by adding a dummy character at the head of the sequences, which determines the population of repetitive PNSP. Finally, we utilize the bitmap structure to represent databases and occurrences of patterns, which avoids rescanning data sequences to calculate support. To the best of our knowledge, this study is the first attempt at actionable repetitive PNSP mining. Extensive experiments on real-world datasets show that ARPNSP can efficiently discover more actionable PNSPs with high correlation than the considered methods.

The Application of Big Data in the Management of Ideological and Political Education in the Development of Education Network

Based on a brief analysis of the current situation of university education management and research on intelligent algorithms, this article constructs a university education management system based on big data. For the clustering and prediction modules in higher education management, corresponding algorithms are used for optimization design. A fuzzy clustering algorithm based on entropy weight is proposed to address the shortcomings of the C-means clustering algorithm. This algorithm adds weighting coefficients on the basis of improvement and continuously updates the clustering centers. The prediction module uses Apriori algorithm to map and compress the target transaction database, reduces the number of candidate item sets through pruning process, and designs simulation experiments to measure the performance of the algorithm. The simulation results show that the clustering results of this algorithm are closer to the actual clustering situation, with shorter running time and better algorithm performance.

PALF: Replicated Write-Ahead Logging for Distributed Databases

Distributed databases have been widely researched and developed in recent years due to their scalability, availability, and consistency guarantees. The write-ahead logging (WAL) system is one of the most vital components in a database. It is still a non-trivial problem to design a replicated logging system as the foundation of a distributed database with the power of ACID transactions. This paper proposes PALF, a Paxos-backed Append-only Log File System, to address these challenges. The basic idea behind PALF is to co-design the logging system with the entire database for supporting database-specific functions and to abstract the functions as PALF primitives to power other distributed systems. Many database functions, including transaction processing, database restore, and physical standby databases, have been built based on PALF primitives. Evaluation shows that PALF greatly outperforms well-known implementations of consensus protocols and is fully competent for distributed database workloads. PALF has been deployed as a component of the OceanBase 4.0 database and has been made open-source along with it.

Cross-Entropy Assisted Optimization Technique for High Utility Itemset Mining from the Transactional Database

Cross-Entropy Assisted Optimization Technique for High Utility Itemset Mining from the Transactional Database

Association Between Cost-Sharing and Buprenorphine Prescription Abandonment.

Prior studies suggest cost-sharing decreases buprenorphine dispensing. However, these studies used databases that only report prescriptions filled by patients, not those that were "abandoned." Consequently, the studies could not calculate the probability of buprenorphine prescription abandonment or evaluate whether cost-sharing is associated with abandonment. To evaluate the association between cost-sharing and buprenorphine prescription abandonment. Cross-sectional analysis of the IQVIA Formulary Impact Analyzer, a pharmacy transaction database representing 63% of U.S. retail pharmacies. The database includes transaction records ("claims") for prescriptions even if they are not filled. Buprenorphine claims in 2022 among commercially insured and Medicare patients. We evaluated the association between cost-sharing per 30-day supply and abandonment using logistic regression, controlling for patient characteristics, product type, and buprenorphine use in the prior 180days. We assessed for effect modification by prior buprenorphine use. Analyses included 2,346,994 and 1,242,596 buprenorphine prescription claims for commercially insured and Medicare patients, respectively. Among these claims, mean (SD) cost-sharing per 30-day supply was $28.1 (46.4) and $8.4 (20.2), and 1.5% and 1.2% were abandoned. Each $10 increase in cost-sharing per 30-day supply was associated with a 0.09 (95% CI: 0.09, 0.10) and 0.09 (95% CI: 0.08, 0.10) percentage-point increase in abandonment among commercially insured and Medicare patients. Among commercially insured and Medicare patients without prior buprenorphine use, respectively, a $10 increase in cost-sharing per 30-day supply was associated with a 0.12 (95% CI: 0.11, 0.14) and 0.13 (95% CI: 0.07, 0.18) percentage-point higher increase in the probability of abandonment compared with patients with > 90days of prior buprenorphine use. Among commercially insured and Medicare patients, buprenorphine prescription abandonment is rare and only minimally associated with cost-sharing. Findings suggest elimination of buprenorphine cost-sharing should only be one component of a larger, multi-faceted campaign to increase buprenorphine dispensing.

Association Between Cost Sharing and Naloxone Prescription Dispensing

Increasing access to naloxone (an opioid antagonist that can reverse overdose) could slow the US opioid epidemic. Prior studies suggest cost sharing may be a barrier to dispensing of naloxone prescriptions, but these studies were limited by their cross-sectional designs and use of databases that do not capture prescriptions that are not filled (abandoned). To evaluate the association between cost sharing and naloxone prescription abandonment (nondispensing of naloxone prescriptions). This cross-sectional, regression discontinuity analysis exploited the fact that deductibles typically reset at the beginning of the year in commercial and Medicare plans. The included data were derived from the 2020-2021 IQVIA Formulary Impact Analyzer (a pharmacy transactions database that represents 63% of prescriptions at US pharmacies). The analysis included claims for naloxone nasal spray among commercially insured patients and Medicare patients that occurred during the 60 days before January 1, 2021, through 59 days after January 1, 2021. Cost sharing, which is defined as the amount patients would have to pay to fill prescriptions. Local linear regression models were used to assess for abrupt changes in cost sharing and the probability of prescription abandonment on January 1, 2021. To estimate the association between cost sharing and prescription abandonment, a fuzzy regression discontinuity analysis was conducted. These analyses included naloxone claims for 71 306 commercially insured patients and 101 706 Medicare patients (40 019 [56.1%] and 61 410 [60.4%], respectively, were female). The commercially insured patients and Medicare patients accounted for 73 311 and 106 076 naloxone claims, respectively. On January 1, 2021, the mean cost sharing per claim increased by $15.0 (95% CI, $13.8-$16.2) for commercially insured patients and increased by $12.3 (95% CI, $10.9-$13.6) for Medicare patients and the probability of abandonment increased by 4.7 (95% CI, 3.2-6.2) percentage points and 2.8 (95% CI, 1.6-4.1) percentage points, respectively. The results from the fuzzy regression discontinuity analysis suggest a decision by commercial and Medicare plans to increase naloxone cost sharing by $10 would be associated with percentage-point increases of 3.1 (95% CI, 2.2-4.1) and 2.3 (95% CI, 1.4-3.2), respectively, in the probability of abandonment. The elimination of cost sharing might be associated with increased naloxone dispensing to commercially insured and Medicare patients.

MLC-miner: Efficiently discovering multi-level closed high utility patterns from quantitative hierarchical transaction databases

High utility pattern mining (HUPM) extends frequent pattern mining (FPM) by including item significance and quantity, which determine their utility, in transaction databases. A pattern is a high utility if its utility satisfies the minimum threshold. Several efficient approaches have been developed to address this task. However, more extensive databases combined with lower thresholds can lead to an explosion in the number of discovered patterns. Use the closed version of the pattern to decrease patterns while keeping information. To improve real-world applicability, HUPM research now considers item connections. Categorizing items creates an abstraction model that can help decision-makers. This new mining task is generalized high utility pattern mining (GHUPM). However, GHUPM significantly increases the complexity of the mining task, resulting in longer mining times, higher memory usage, and a more significant number of discovered patterns. To date, none of the proposed approaches in GHUPM have utilized the closed representation to shrink the size of the result set. This study introduces MLC-Miner, a multi-level closed high utility pattern extraction technique, to fill this research gap. MLC-Miner combines several efficient methods to enhance mining performance and reduce memory footprints. Experimental results show that MLC-Miner can handle up to 5 million transactions efficiently.

Streamlined and Efficient Management of Non-Relational Cloud Database Transactions

Streamlined and Efficient Management of Non-Relational Cloud Database Transactions

A Novel Approach to Enhance the Efficiency of Apriori Algorithm

Data mining is the process of obtaining valuable or significant information from a large-scale database. One significantarea of research in the field of data mining is association rules mining. Apriori algorithm is one of the classical algorithmsin the association rule mining field. This research analyses the basic ideas and shortcomings of the Apriori algorithm andcompares several different styles of its major improvement strategies. Then it suggests an improved version of the Apriorialgorithm that utilizes a dataset summarization method, an optimized database mapping technique, an intersection operation,and a joined optimization strategy. These enhancements aim to address the low performance and efficiency by reducing thegeneration of candidate itemsets and minimizing the execution time. This addresses the issues of generating numerous candidate itemsets and repeatedly scanning the transaction database. After implementing the optimized algorithm, to verify its effectiveness, it has been applied to a groceries dataset, which is for market basket analysis. The improved Apriori algorithm demonstrated significant enhancements over the original algorithm in terms of reduced candidate itemsets and running time, leading to improved algorithm efficiency.

The analysis of association rules: Latent class analysis

AbstractAssociation rules are used to extract information from transactional databases with a collection of items also called “tokens” or “words.” The aim of association rule analysis is to indicate what and how items go with what items in a set of transactions called “documents.” This approach is used in the analysis of text records, of blogs in social media and of shopping baskets. We present here an approach to analyze documents using latent class analysis (LCA) clustering of document term matrices. A document term matrix (DTM) consists of rows referring to documents and columns corresponding to items. In binary weights, “1” indicates the presence of a term in a document and “0” otherwise. The clustering of similar documents provides stratified data sets used to enhance the interpretability of measures of interest such as lift, odds ratios and relative linkage disequilibrium. The article demonstrates the approach with two case studies. A first example consists of comments recorded in a survey aimed at pet owners. A second, much larger example, is based on online reviews to crocs sandals. Association rules describe combinations of terms in the pet survey and crocs reviews. In Section 3, we compute, for these case studies, association rule measures of interest defined in Section 2. We first introduce the case studies to motivate the methods proposed here. In Section 4, we provide a new approach with an enhanced interpretations of measures such as lift by comparing them across clusters derived from an LCA of the DTM. A key result is the application of clustered data in analyzing observational data. This enhances generalizability and interpretability of findings from text analytics. The article concludes with a discussion in Section 5.

Remapping California's wildland urban interface: A property-level time-space framework, 2000–2020

Wildland fire poses a significant risk to property and people, and these risks tend to be highest in places where flammable, wildland vegetation abuts urban development. Traditionally, efforts to map these areas of intersection, known as the Wildland Urban Interface (WUI), have relied on satellite-derived land cover and census data. While these types of data are valuable for broadly delineating WUI areas, they are constrained in their ability to specifically identify at-risk residential properties and their characteristics over time. This article addresses these challenges through a multi-time-step, property-level framework that enhances current approaches based on moving window mapping, the Time Step Moving Window (TSMW) framework. We implement this framework on approximately 10 million geo-located property records from Zillow's Transaction and Assessment Database for California. Using this framework, we generate estimates of California's WUI from 2000 to 2020 and find that our time stepped building scale maps have allowed us to classify residential homes at-risk to burns with greater accuracy compared to time stepped Census polygon based maps. Based on these maps, we further estimate that approximately $1.34 Trillion, or 40%, of California's improved residential property value is situated within the WUI. Our approach provides a high-precision strategy for delineating at-risk residences and calculating localized financial costs associated with future hazard events.

Evidence of increasing wildfire damage with decreasing property price in Southern California fires.

Across the Western United States, human development into the wildland urban interface (WUI) is contributing to increasing wildfire damage. Given that natural disasters often cause greater harm within socio-economically vulnerable groups, research is needed to explore the potential for disproportionate impacts associated with wildfire. Using Zillow Transaction and Assessment Database (ZTRAX), hereafter "Zillow", real estate data, we explored whether lower-priced structures were more likely to be damaged during the most destructive, recent wildfires in Southern California. Within fire perimeters occurring from 2000-2019, we matched property price data to burned and unburned structures. To be included in the final dataset, fire perimeters had to surround at least 25 burned and 25 unburned structures and have been sold at most seven years before the fire; five fires fit these criteria. We found evidence to support our hypothesis that lower-priced properties were more likely to be damaged, however, the likelihood of damage and the influence of property value significantly varied across individual fire perimeters. When considering fires individually, properties within two 2003 fires-the Cedar and Grand Prix-Old Fires-had statistically significantly decreasing burn damage with increasing property value. Occurring in 2007 and later, the other three fires (Witch-Poomacha, Thomas, and Woolsey) showed no significant relationship between price and damage. Consistent with other studies, topographic position, slope, elevation, and vegetation were also significantly associated with the likelihood of a structure being damaged during the wildfire. Driving time to the nearest fire station and previously identified fire hazard were also significant. Our results suggest that further studies on the extent and reason for disproportionate impacts of wildfire are needed. In the meantime, decision makers should consider allocating wildfire risk mitigation resources-such as fire-fighting and wildfire structural preparedness resources-to more socioeconomically vulnerable neighborhoods.

Deciphering Patterns in a small scale Case Analysis Study of the Apriori Algorithm in Market Basket Analysis using machine learning tools

Analysis of Transaction Database Using Apriori Algorithm. This study examines the use of the Apriori algorithm for analysing transaction databases. The Apriori algorithm is a fundamental technique in data mining that allows for the efficient discovery of frequent patterns and association rules in large datasets. The Apriori algorithm employs a two-step approach. Initially, it identifies frequent items in the database based on a user-defined minimum support threshold. Subsequently, it generates association rules that describe relationships between these frequent items based on metrics such as confidence and lift. This paper provides an in-depth explanation of the Apriori algorithm, emphasizing its strengths and limitations. Additionally, it presents various applications of the Apriori algorithm in real-world scenarios, such as shopping cart analysis, cross-selling and upselling, and customer segmentation. The significance of this study lies in its comprehensive analysis of the Apriori algorithm and its practical relevance in diverse data mining tasks. It serves as a valuable resource for researchers, practitioners, and anyone seeking to understand the fundamentals and applications of association rule mining. Keywords: Datamining, OLTP, Apriori algorithm, Item sets and Market Basket Analysis

Access Control System Using AI and Blockchain

The demands of a hyperconnected society that demand increased security, transparency, and user autonomy cause traditional access control to crumble. This abstract investigates how combining blockchain technology with artificial intelligence could revolutionize access control systems. By removing single points of failure and increasing accountability, blockchain's distributed ledger technology (DLT) creates irreversible trust via a shared, tamper-proof database of rights and transactions. By automating policies, smart contracts give people command over their digital assets. AI adds intelligence and flexibility. Real-time machine learning systems detect anomalies, dynamically assess behavior, and modify policy. Access requests are filtered by AI-driven risk assessment, and sensitive resources are protected by improved identity verification. The combination of AI's dynamic powers and blockchain's unchangeable base opens the door to a future where safe, user-focused access is commonplace.

Research on the Application of Improved Apriori Algorithm in Sport-Adaptive Online Teaching System Under Big Data Environment

Each student in an adaptive education system has significant differences in knowledge background, ability level and cognitive style. Therefore, to build an adaptive teaching system, it is necessary to establish an operable, reasonable and individualized student model by clarifying students’ abilities and differences. The improved Apriori algorithm under big data is the most classic association rule algorithm, which is generated by a set of candidates, and it uses the iterative method of hierarchical search to traverse a set of frequency items in the transaction database. After finding the set of frequency items, select the association according to the trust rules. This paper studies how to apply the improved Apriori algorithm to an adaptive online education system in a big data environment. An evolutionary algebra is taken with mean fit of 80, population size of 20, mean fit of 0.28, population size of 60, mean fit of 0.26, population size of 80, mean fit of 0.25, mean population size of 0.25. The population size is 200, and the average fitting is 0.24. The larger the error, the smaller the error between each indicator of the test paper and the corresponding value specified by the user. The improved Apriori algorithm in the big data environment has designed five themes of rule mining, which are mainly used for class management: class linkage, class category linkage, student basic information linkage, lecture and basic information linkage, and lecture mode linkage. They play the role of teaching assistants with a specific role.

The Research on the Improvement of FP-growth Algorithm

This paper focuses on the issue of low efficiency in the FP-growth algorithm for frequent pattern mining and proposes an improved algorithm, ICFM-growth. Experimental results demonstrate that the improved algorithm outperforms the FP-growth algorithm in terms of both runtime and space utilization. By studying the classical frequent pattern algorithms Apriori and FP-growth, the latter is an improved algo- rithm based on Apriori to address the problems of generating a large number of candidate itemsets and consuming significant memory space. While FP-growth exhibits superior mining efficiency compared to Apriori, it faces challenges when dealing with large and long transactional databases due to the construction of numerous FP-trees, which increases computational tasks and prolongs runtime, leading to lagging mining efficiency. To address this issue, the improved algorithm ICFM-growth is proposed. It constructs a co-occurrence frequency matrix to perform preliminary screening on the transaction set, focusing on high-frequency and co-occurring item pairs, thereby reducing unnecessary computa- tions. In the initial stage, as important item pairs have already been filtered, the algorithm can directly operate on these item pairs and items, rather than the entire dataset, thereby reducing the search space and computational complexity. Additionally, the structure of the FP-tree ena- blesthe algorithm to store and process data more efficiently, avoiding frequent scanning of the entire database, as seen in traditional Apriori algorithms. Finally, through simulation experiments on publicly available datasets such as Movie Data and House Data, validated using cross- validation, the ICFM-growth algorithm proves to be significantly superior to FP-growth in terms of time and space efficiency. It demonstrates faster runtime, lower memory consumption, and superior mining efficiency compared to FP-growth.

How export performance is mediated by innovation, owner characteristics, and location

We investigate how innovation affects rural nonfarm exports, and thus the U.S. trade deficit. Previous European studies indicate a positive link between R&D expenditures, patented innovation, and exports, but no comparable U.S. firm-level research exists. Using data from the Longitudinal Firm Trade Transactions Database and Annual Business Survey, we examine the relationship between innovation and exports for the United States. Employing a two-stage selection model to address endogeneity concerns, our findings suggest a significant connection between innovation and export. The study contributes to understanding the pivotal role of rural nonfarm exports and highlights policy implications for both trade and rural innovation.

NOC-NOC: Towards Performance-optimal Distributed Transactions

Substantial research efforts have been devoted to studying the performance optimality problem for distributed database transactions. However, they focus just on optimizing transactional reads, and thus overlook crucial factors, such as the efficiency of writes, which also impact the overall system performance. Motivated by a recent study on Twitter's workloads showing the prominence of write-heavy workloads in practice, we make a substantial step towards performance-optimal distributed transactions by also aiming to optimize writes, a fundamentally new dimension to this problem. We propose a new design objective and establish impossibility results with respect to the achievable isolation levels. Guided by these results, we present two new transaction algorithms with different isolation guarantees that fulfill this design objective. Our evaluation demonstrates that these algorithms outperform the state of the art.