Retail Database Research Articles

Venous thromboembolism prophylaxis in shoulder surgery: a break-even cost-effectiveness study

Venous thromboembolism (VTE) is a serious complication following orthopedic shoulder surgery; however, research is limited involving the break-even cost-effectiveness of VTE prophylaxis. The purpose of this study was to determine if the cost of aspirin and enoxaparin would break even for VTE prevention in patients following shoulder surgery. A drug retail database was used to obtain the lowest price for a course of aspirin (81 mg) and enoxaparin (40 mg) to perform a break-even cost analysis. Our institutional purchasing records were then searched to estimate the cost of treating a symptomatic VTE. The TriNetX national database was then queried to establish a rate of VTE after shoulder surgery. A break-even cost analysis was performed by determining the absolute risk reduction. This value was used to calculate the number of patients who are treated to prevent a single VTE while breaking even on cost. Sensitivity analyses were performed for drugs that did not break-even at the database derived VTE rates. A full medication course of aspirin and enoxaparin were found to cost $1.18 and $125.37, respectively. The cost of treating a symptomatic VTE was determined to be $9,407.00. Data from the TriNetX database showed rates of symptomatic VTE following shoulder arthroplasty, hemiarthroplasty, and arthroscopic rotator cuff repair were 1.60%, 1.50%, and 0.68%, respectively. Aspirin broke even on cost for all procedures if the initial rate decreased by an ARR of 0.01% (NNT=7,972). Similarly, enoxaparin broke even for shoulder arthroplasty and hemiarthroplasty if the initial rate of VTE decreased by an ARR of 1.33% (NNT=75). Enoxaparin did not break-even at the initial VTE rate for arthroscopic rotator cuff repair, however sensitivity analysis found enoxaparin was if the cost could be obtained for $60.00 or less. Enoxaparin broke even if the cost of treating a symptomatic VTE was $20,000.00 or higher. The cost of a three-week course of twice daily aspirin or once daily enoxaparin breaks even for VTE prophylaxis following shoulder arthroplasty and hemiarthroplasty if they reduce the VTE rate by a calculated absolute risk reduction. Given the lower rate of VTE observed for patients undergoing arthroscopic rotator cuff repair, only the three-week course of aspirin broke even under these conditions. Once-daily enoxaparin did not break-even at current market rate. Further research is needed to help determine optimal VTE prophylaxis after shoulder surgery.

Recency, Frequency, Monetary Value, Clustering, and Internal and External Indices for Customer Segmentation from Retail Data

While there are several ways to identify customer behaviors, few extract this value from information already in a database, much less extract relevant characteristics. This paper presents the development of a prototype using the recency, frequency, and monetary attributes for customer segmentation of a retail database. For this purpose, the standard K-means, K-medoids, and MiniBatch K-means were evaluated. The standard K-means clustering algorithm was more appropriate for data clustering than other algorithms as it remained stable until solutions with six clusters. The evaluation of the clusters’ quality was obtained through the internal validation indexes Silhouette, Calinski Harabasz, and Davies Bouldin. When consensus was not obtained, three external validation indexes were applied: global stability, stability per cluster, and segment-level stability across solutions. Six customer segments were obtained, identified by their unique behavior: lost customers, disinterested customers, recent customers, less recent customers, loyal customers, and best customers. Their behavior was evidenced and analyzed, indicating trends and preferences. The proposed method combining recency, frequency, monetary value (RFM), K-means clustering, internal indices, and external indices achieved return rates of 17.50%, indicating acceptable selectivity of the customers.

Probabilistic Customer Purchase Evolution Graph

Following the evolutionary theory, this paper defines the commodity consumption gene of the customer journey in the retail transaction, so as to discuss the distribution and evolution process of the consumer group. First of all, through relevant retail research, each transaction is defined as a data point in the SPC space (sales-product-customer). And the customer journey is a pivot transformation of transaction data points in the SPC space. Next, customer purchase products are defined as consumption genes in the customer journey, forming customer consumption species. Furthermore, evolutionary operations with probabilities between consumer species (genes) can analyze the evolution of each customer’s purchase consumption over time in the retail database. And an algorithm for the Customer Purchase Evolution Graph (CPEG) is proposed. To prove the practicability, an one-year nearly 300,000 actual transactions from over 27,000 consumers is used to establishes the CPEGs with evolutionary probabilities of the overall customers and the CPEG of the SVIP. The CPEG of all customers can be used to see the main consumption distribution, the main consumption starting point (first purchase) behavior and repurchase behavior of overall customers and new customers. The CPEG of SVIP customers can further reveal the main consumption genes of mature customers (species) and the evolution process in their consumption journey. These findings can be of specific help to the company’s commodity strategy and operational marketing.

Footwear segmentation and recommendation supported by deep learning: an exploratory proposal

The management of an online footwear retail store - also known as marketplace - usually involves activities that directly or indirectly end up to interface with customers, wherein communication efficiency and effectiveness is crucially relevant. Critical factors concerning entities developing remote business in these areas or similar include: (i) production of appealing catalogues and (ii) digital tools to shorten the distance between customers and marketplaces. The former requires using specific third parties - often technically complex - to arrange and prepare photographic entries acquired in studio-like environments. This can delay the diffusion of products supply that may result in financial losses. The latter prevents the retailer of reaching critical mass at a higher potential. Considering such issues, this paper proposes a couple of modules for footwear marketplaces, powered by deep learning: one to segment shoes as a fully automatic background removal tool for easing and quickening catalogue creation activities in a back-office perspective; and another to provide visual search services that allow a customer to submit photographs of footwear of interest to obtain recommendations of similar products directly retrieved from online retail databases, establishing another digital bridge with potential buyers. Preliminary implementation and pilot tests point out Mask-RCNN as a promising approach for shoes segmentation. The same applies to ResNet101 and Xception, but for shoes recommendation, based on multi-label classification.

Grouping Goods with the Association Rule Method Using A priori Algorithms in Modern Retail Stores

The proliferation of retail companies in Indonesia makes competition quite fierce in this business. The availability of detailed information on products purchased from each customer transaction in the database will become data garbage, which continues to grow every day. Company managers will certainly be interested in knowing whether certain groups of products are consistently purchased together. They can use this data for optimal grouping and placement of products, and managers can also use this information for cross-selling, for promotions, for catalog design and to identify customer segments based on purchasing patterns. In this study, data mining with Association rule techniques was used to explore patterns of attribute relationships and frequent itemsets in the Retail database. The a priori paradigm is used to find large itemsets in the determination of association rules. The integration of association rules with the a priori paradigm has managed to find a number of patterns of relationships between attributes in retail databases.

Venous Thromboembolism Chemoprophylaxis in Knee Arthroscopy: A Break-Even Analysis of Cost

Background: Symptomatic venous thromboembolism (VTE) is a serious and costly complication after knee arthroscopy. There continues to be debate regarding the use of VTE prophylaxis after knee arthroscopy, and minimal research has explored its cost-effectiveness. Hypothesis: Both aspirin and enoxaparin would be cost-effective in preventing symptomatic VTE. Study Design: Economic and decision analysis; Level of evidence, 3. Methods: The literature was searched and the TriNetX research database was queried to determine a range of initial rates of VTE. An open-access retail database was used to determine the mean retail price for aspirin (325 mg) and enoxaparin (30 mg and 40 mg). Our institutional records were used to determine the cost of treating VTE. A “break-even” analysis was then performed to determine the absolute risk reduction necessary to make these drugs cost-effective. This value was then used to calculate the number of patients who would need to be treated (NNT) to prevent a single VTE while still breaking even on cost. Results: The cost of treating VTE was $9407 (US Dollars). Aspirin (325 mg), enoxaparin (30 mg), and enoxaparin (40 mg) were found to cost $1.86, $188.72, and $99.99, respectively. The low, TriNetX, and high rates of VTE were 0.34%, 0.86%, and 10.9%, respectively. Aspirin was cost-effective at all 3 rates if the initial rate decreased by 0.02% (NNT = 5058). Both formulations of enoxaparin were cost-effective at the high initial rate if they decreased by 2.01% (NNT = 50) and 1.06% (NNT = 94), respectively. However, at the low and TriNetX rates, the 2 doses of enoxaparin were not cost-effective because their final break-even rate exceeded the initial VTE rate. Conclusion: Aspirin and, in some cases, enoxaparin are cost-effective treatments for VTE prophylaxis after knee arthroscopy.

Exploiting Data Skew for Improved Query Performance

Analytic queries enable sophisticated large-scale data analysis within many commercial, scientific and medical domains today. Data skew is a ubiquitous feature of these real-world domains. In a retail database, some products are typically much more popular than others. In a text database, word frequencies follow a Zipf distribution with a small number of very common words, and a long tail of infrequent words. In a geographic database, some regions have much higher populations (and therefore data measurements) than others. Current systems do not make the most of caches for exploiting skew. In particular, a whole cache line may remain cache resident even though only a small part of the cache line corresponds to a popular data item. In this article, we propose a novel index structure for repositioning data items to concentrate popular items into the same cache lines. The net result is better spatial locality, and better utilization of limited cache resources. We develop a theoretical model for analyzing the cache utilization, and implement database operators that are efficient in the presence of skew. Our experimental evaluation on real and synthetic data shows that exploiting skew can significantly improve in-memory query performance. In some cases, our techniques can speed up queries by over an order of magnitude.

Association between school-based tobacco retailer exposures and young adolescent cigarette, cigar and e-cigarette use

BackgroundAssociations between retail tobacco availability and tobacco use have been mixed. This study examined associations between school-based retail environment exposures and current use of cigarettes, cigar products and e-cigarettes among...

Distributed elephant herding optimization for grid-based privacy association rule mining

PurposeAssociation rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.Design/methodology/approachThe primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.FindingsThe experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.Originality/valueData mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

MINING OF HIGH-UTILITY ITEMSETS WITH NEGATIVE UTILITY

The goal of the high-utility itemset mining task is to discover combinations of items that yield high profits from transactional databases. HUIM is a useful tool for retail stores to analyze customer behaviors. However, in the real world, items are found with both positive and negative utility values. To address this issue, we propose an algorithm named Modified Efficient High‐utility Itemsets mining with Negative utility (MEHIN) to find all HUIs with negative utility. This algorithm is an improved version of the EHIN algorithm. MEHIN utilizes 2 new upper bounds for pruning, named revised subtree and revised local utility. To reduce dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. An array‐based utility‐counting technique is also utilized to calculate upper‐bound efficiently. The MEHIN employs a novel structure called P-set to reduce the number of transaction scans and to speed up the mining process. Experimental results show that the proposed algorithms considerably outperform the state-of-the-art HUI-mining algorithms on negative utility in retail databases in terms of runtime.

Tracy–Singh Product and Genetic Whale Optimization Algorithm for Retrievable Data Perturbation for Privacy Preserved Data Publishing in Cloud Computing

Abstract This paper proposes a retrievable data perturbation model for overcoming the challenges in cloud computing. Initially, genetic whale optimization algorithm (genetic WOA) is developed by integrating genetic algorithm (GA) and WOA for generating the optimized secret key. Then, the input data and the optimized secret key are given to the Tracy–Singh product-based model for transforming the original database into perturbed database. Finally, the perturbed database can be retrieved by the client, if and only if the client knows the secret key. The performance of the proposed model is analyzed using three databases, namely, chess, T10I4D100K and retail databases from the FIMI data set based on the performance metrics, privacy and utility. Also, the proposed model is compared with the existing methods, such as Retrievable General Additive Data Perturbation, GA and WOA, for the key values 128 and 256. For the key value 128, the proposed model has the better privacy and utility of 0.18 and 0.83 while using the chess database. For the key value 256, the proposed model has the better privacy and utility of 0.18 and 0.85, using retail database. From the analysis, it can be shown that the proposed model has better privacy and utility values than the existing models.

You’re dead to me, but should you be? Using a retail database to classify American malls by occupancy and sales and an extended analysis of dead and dying malls

ABSTRACTThe study uses a shopping center database to classify malls by occupancy and sales. A factor analysis is conducted and reduces the large number of variables to a smaller number of mall constructs. The constructs are used to predict mall group membership by occupancy and sales. The results indicate that population and income are important variables towards mall occupancy and sales. An analysis of dead malls follows in order to produce more novel information for the growing interest in dead and dying malls. It is found through a second discriminant analysis that the prototypical characteristics of dead malls include; smaller in size, less population and lower income market areas. There were varying effects of renovation dates, competition and market age. Overall, the study benefited from examining dead malls more closely and provides more groundwork for future dead mall studies.

Discovery of High Utility Patterns from Retail Database with adding constraints on LBHUP algorithm

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Brain storm-based Whale Optimization Algorithm for privacy-protected data publishing in cloud computing

Cloud computing serves as a major boost for the digital era since it handles data from a large number of users simultaneously. Besides the several useful characteristics, providing security to the data stored in the cloud platform is a major challenge for the service providers. Privacy preservation schemes introduced in the literature trying to enhance the privacy and utility of the data structures by modifying the database with the secret key. In this paper, an optimization scheme, Brain Storm based Whale Optimization Algorithm (BS-WOA), is introduced for identifying the secret key. The database from the data owner is modified with the optimal secret key for constructing the retrievable perturbation data for preserving the privacy and utility. The proposed BS-WOA is designed through the hybridization of Brain Storm Optimization and Whale Optimization Algorithm. Simulation of the proposed technique with the BS-WOA is done in the three standard databases, such as chess T10I4D100 K, and the retail databases. When evaluated for the key size of 256, the proposed BS-WOA achieved privacy value of 0.186 and utility value of 0.8777 for the chess database, and thus, has improved performance.

Effect of Store Brand Introduction on Channel Price Leadership: An Empirical Investigation

In this study, we conduct an empirical investigation of the impact of store brand introductions on the price leadership relations in a distribution channel between a retailer and national brand manufacturers. We analyze a multi-product category retail database from a major grocery chain, which captures both a period before and a period after the introduction of a store brand in each product category. By applying the time series approach to this data set, we show that store brand introductions frequently lead to price leadership changes, generally in a more favorable direction for the retailer than for the national brand manufacturer, evidenced by either the decay of the manufacturers’ price leadership or the rise of the retailer’s price leadership. However, such a change is not universal but tends to be concentrated among a certain quality tier of national brands, which is not always the low-tier, but sometimes the top-tier despite the low-price low-quality position of the store brand. The patterns detected in the data suggest that these changes are likely to reflect the retailer’s strategic effort to reshape the price leadership environment in a product category aided by the enhanced bargaining power and managerial sophistication that accompanied the store brand introductions.

Growth factors of store brands in different store formats in Italy

Since the beginning of the consumer crisis, which in Italy can be traced back to the second half of 2008, there has been a significant development of store brands? market share which has reached close to 25% in some leader groups (for example Coop, Conad, Esselunga). The acceleration of the rate of penetration of store brands differs by category and store format. The main reason for this result is the different consumer purchase behavior. In this framework, this work aims at analyzing the elements that have determined the store brands success in the main store format (hypermarket, supermarket, convenience store), through analysis of single retail mix levers management. The factors that have mostly influenced the growth in store brands, in each store format, are investigated throughout a complete informative retail database. The aim of this study is to verify whether the management of the individual retail mix levers produces the same results in different store formats. Additional enhancement to the understanding of store brands management and further support to modern distribution management policies are also provided. A relevant finding is that the performance of store formats depends on the use of specific retail mix levers.

Abstract P083: Modest Association Between the Number of Fast Food Restaurants in a Neighborhood with Cardiovascular Disease Incidence

Background: In recent years, there has been an increasing interest in the association between neighbourhood environments and cardiovascular disease (CVD) outcomes. Applying advanced linkage techniques to existing databases enables new and more in-depth research in this field. Aim: To investigate the association between fast food restaurant (FFR) density and incident CVD. Methods: Through linkage with the Dutch Population Register, Hospital Discharge Register and Cause of Death Register we obtained a cohort of 35+ years adults who at 01-01-2009 had lived at the same address for 15 years and were not previously admitted for CVD during this period. We followed these persons for one year for incident CVD. Trough linkage with the Locatus database, a nationwide retail database, we obtained information about the number of FFR’s within 500m, 1000m and 3000m buffers from the exact address. Logistic regression analyses was used to investigate the association between FFR density and CVD incidence. We adjusted for age, gender, marital status, country of origin, area-level SES , comorbidity, and degree of urbanisation. Results: In total 3,146,469 adults (46% male), aged 59.2 ± 12 were included. After one year 72,811 (2.3%) persons suffered from an incident CVD. The association with the number of FFR and incident CVD in both 500m, 1000m and 3000m buffers were shown in table 1. Conclusion: There is a modest association between FFR and incident CVD. The association was stronger when FFR’s are within 500m from a person’s home address and when there is a high number of FFR’s. Table 1: The association (OR (95% confidence interval)) with the number of FFR and incident CVD in both 500m, 1000m and 3000m buffers. Adjusted for age, gender, marital status, country of origin, area-level SES , comorbidity, and degree of urbanisation.

PMH49 - The Economic Consequences of the use of Antidepressants in Belgium

Antidepressants use is increasing worldwide but is particularly high in Belgium compared to other EU countries whereas suicide-rates in Belgium are among the highest. This could reflect misuse of antidepressants resulting in an important economic burden. The former is part of a separate abstract. The aim here was to evaluate potential savings that could be realized in case prescriptions behavior in Belgium would be similar to neighbor countries. Current spending levels for antidepressants and mood stabilizer prescriptions were determined in Belgium, France and the Netherlands. Volume and value sales were determined per country (average number of pills sold/capita) based on IMS Health Belgian National Retail Database available to IMS Health in both countries via the IMS MIDAS platform (2013 data). The potential country-level prescription cost in Belgium was estimated by multiplying average number of pills/person in France and the Netherlands, with the cost/pill in Belgium and extrapolating the result to the total population in Belgium, assuming that prescribing levels in Belgium were equal to those in France and the Netherlands and using Belgian prices. In Belgium the number of pills per capita is respectively 45% and 48% higher compared to France and the Netherlands (34.17 vs. 23.62 vs. 23.15 pills/person). With a 30% smaller population than the Netherlands, spending in Belgium is 4 times higher. In France, having a 6 times larger population compared to Belgium, spending is only 3 times higher. The estimated savings for the Belgian payer applying the usage (pill/person) of respectively France and the Netherlands and current cost/pill in Belgium were €67 and €70 Mn, meaning around 30% of current spending in Belgium. By comparing Belgium’s prescription patterns to France and the Netherlands, we estimated important potential savings due to antidepressant mis-use/over-use that could be re-invested in for example mental health prevention.

Qualitative and quantitative combinations of crisp and rough clustering schemes using dominance relations

Due to their unsupervised learning nature, analyzing the semantics of clustering schemes can be difficult. Qualitative information such as preference relations may be useful in semantic analysis of clustering process. This paper describes a framework based on preference or dominance relations that helps us qualitatively analyze a clustering scheme. This qualitative interpretation is shown to be useful for combining clustering schemes that are based on different criteria. The qualitative combination can be used to analyze its quantitative counterpart and can also be used instead of the quantitative combination. The paper further extends the framework to accommodate rough set based clustering. The usefulness of the approach is illustrated using a synthetic retail database.

Closed constrained gradient mining in retail databases

Incorporating constraints into frequent itemset mining not only improves data mining efficiency, but also leads to concise and meaningful results. In this paper, a framework for closed constrained gradient itemset mining in retail databases is proposed by introducing the concept of gradient constraint into closed itemset mining. A tailored version of CLOSET+, LCLOSET, is first briefly introduced, which is designed for efficient closed itemset mining from sparse databases. Then, a newly proposed weaker but antimonotone measure, top-X average measure, is proposed and can be adopted to prune search space effectively. Experiments show that a combination of LCLOSET and the top-X average pruning provides an efficient approach to mining frequent closed gradient itemsets