Quantitative Databases Research Articles

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.

An efficient strategy for mining high-efficiency itemsets in quantitative databases

The classic problems in itemset mining involve finding frequent itemsets and high-utility itemsets. However, frequent itemset mining has the disadvantage of not paying attention to the profit of products, while high-utility itemset mining does not address the issue of the cost price of the products. Therefore, neither can locate products with high-efficiency value on investment. To overcome these problems, the high-efficiency itemset mining (HEIM) problem was proposed. Despite its practicality, this issue has received little attention. The algorithms proposed to exploit high-efficiency itemsets (HEI) still use ineffective strategies on dense databases and unstrict upper bounds, requiring a lot of time and memory. To address the current issues with HEIM, the paper proposes tight upper bounds for the early pruning of candidates. Several techniques are also proposed, such as combining similar transactions and saving promising transaction locations, to reduce the cost of database scanning. Finally, the techniques are combined to propose a novel way to implement the MHEI (an efficient strategy for Mining High-Efficiency Itemsets in quantitative databases) to optimize the HEIM process. The experimental process also shows that the proposed algorithm has performance better than the state-of-the-art algorithm in HEIM.

Identifying potential drug targets for varicose veins through integration of GWAS and eQTL summary data.

Varicose veins (VV) are a common chronic venous disease that is influenced by multiple factors. It affects the quality of life of patients and imposes a huge economic burden on the healthcare system. This study aimed to use integrated analysis methods, including Mendelian randomization analysis, to identify potential pathogenic genes and drug targets for VV treatment. This study conducted Summary-data-based Mendelian Randomization (SMR) analysis and colocalization analysis on data collected from genome-wide association studies and cis-expression quantitative trait loci databases. Only genes with PP.H4 > 0.7 in colocalization were chosen from the significant SMR results. After the above analysis, we screened 12 genes and performed Mendelian Randomization (MR) analysis on them. After sensitivity analysis, we identified four genes with potential causal relationships with VV. Finally, we used transcriptome-wide association studies and The Drug-Gene Interaction Database data to identify and screen the remaining genes and identified four drug targets for the treatment of VV. We identified four genes significantly associated with VV, namely, KRTAP5-AS1 [Odds ratio (OR) = 1.08, 95% Confidence interval (CI): 1.05-1.11, p = 1.42e-10] and PLEKHA5 (OR = 1.13, 95% CI: 1.06-1.20, p = 6.90e-5), CBWD1 (OR = 1.05, 95% CI: 1.01-1.11, p = 1.42e-2) and CRIM1 (OR = 0.87, 95% CI: 0.81-0.95, p = 3.67e-3). Increased expression of three genes, namely, KRTAP5-AS1, PLEKHA5, and CBWD1, was associated with increased risk of the disease, and increased expression of CRIM1 was associated with decreased risk of the disease. These four genes could be targeted for VV therapy. We identified four potential causal proteins for varicose veins with MR. A comprehensive analysis indicated that KRTAP5-AS1, PLEKHA5, CBWD1, and CRIM1 might be potential drug targets for varicose veins.

Machine Learning Exploration of the Relationship Between Drugs and the Blood-Brain Barrier: Guiding Molecular Modification.

This study aimed to improve the efficiency of pharmacotherapy for CNS diseases by optimizing the ability of drug molecules to penetrate the Blood-Brain Barrier (BBB). We established qualitative and quantitative databases of the ADME properties of drugs and derived characteristic features of compounds with efficient BBB penetration. Using these insights, we developed four machine learning models to predict a drug's BBB permeability by assessing ADME properties and molecular topology. We then validated the models using the B3DB database. For acyclovir and ceftriaxone, we modified the Hydrogen Bond Donors and Acceptors, and evaluated the BBB permeability using the predictive model. The machine learning models performed well in predicting BBB permeability on both internal and external validation sets. Reducing the number of Hydrogen Bond Donors and Acceptors generally improves BBB permeability. Modification only enhanced BBB penetration in the case of acyclovir and not ceftriaxone. The machine learning models developed can accurately predict BBB permeability, and many drug molecules are likely to have increased BBB penetration if the number of Hydrogen Bond Donors and Acceptors are reduced. These findings suggest that molecular modifications can enhance the efficacy of CNS drugs and provide practical strategies for drug design and development. This is particularly relevant for improving drug penetration of the BBB.

THE INCREASE IN THE NUMBER OF DENGUE CASES IN BRAZIL AND THE CORRELATION WITH DEFORESTATION AND GLOBALIZATION

Brazil has faced a significant increase in the number of dengue cases in recent decades, which represents a challenge for public health authorities. Several factors may contribute to this increase, including deforestation and globalization, which have a direct impact on the proliferation of the Aedes aegypti mosquito, the dengue vector. The aims of this study are to investigate the relationship between increased deforestation and globalization and the increase in dengue cases in Brazil and to analyse the mechanisms by which deforestation and globalization can influence the spread of dengue. The methodology in question is a systematic review, using the narrative method, using quantitative and qualitative factors to reach the expected result. The DataSus/Tabnet, Terrabrasilis, PubMed, Scopus, Web of Science and Virtual Health Library databases were also used, using the health sciences descriptors “Dengue hospitalizations”, “Deforestation and Arboviruses”, “Deforestation and Dengue” and “Globalization and Dengue”, totalling 84 works and 8 works of literature were used in the formulation of this review and 2 references were formulated through the quantitative databases. Deforestation can increase the incidence of dengue, as it creates favorable environments for the reproduction of the transmitting mosquito, such as the accumulation of water in tires, cans and abandoned containers. In addition, deforestation can lead to the migration of populations from rural to urban areas, increasing exposure to endemic areas of the disease. Globalization can also contribute to the increase in dengue cases by facilitating the transport of infected people and the mosquito vector between different regions. Increased international trade and tourism can expand the spread of the dengue virus to new areas. The increase in the number of dengue cases in Brazil is related to deforestation and globalization, which create favourable conditions for the propagation of the mosquito vector and the spread of the dengue virus between different regions. Disease control measures must take these factors into account and include actions to monitor deforestation, vector control and public health education in order to reduce the incidence of dengue in the country.

T2FM: A novel hashtable based type-2 fuzzy frequent itemsets mining

Association rule mining (ARM) is an important research issue in the field of data mining that aims to find relations among different items in binary databases. The conventional ARM algorithms consider the frequency of the items in binary databases, which is not sufficient for real time applications. In this paper, a novel hash table based Type-2 fuzzy mining algorithm (T2FM) with an efficient pruning strategy is presented for discovering multiple fuzzy frequent itemsets from quantitative databases. The algorithm employs a hash table based structure for efficient storage and retrieval of item/itemset which reduces the search efficiency to O(1) or constant time. Previously, type-2 based Apriori and FP-growth based fuzzy frequent itemsets mining were proposed, which required large amounts of computation and a greater number of candidate generation and processing. Meanwhile, the proposed approach reduces a huge amount of computation by finding the common keys before the actual intersection operation takes place. An efficient pruning strategy is proposed to avoid unpromising candidates in order to speed up the computations. Several experiments are carried out to verify the efficiency of the approach in terms of runtime and memory for different minimum support threshold and the results show that the designed approach provides better performance compared to the state-of-the-art algorithms.

Integrated safety for sea and air transportation during the COVID-19 pandemic

Climate change and COVID-19 have disrupted food supply chains and exacerbated food security challenges (Rasul, 2021). The safety of sea and air transportation in food delivery is also a disruption that impacts logistics in the food value chain (Singh et al., 2021). Integration of sea and air transportation safety during the COVID-19 pandemic, with a focus on the impact of climate change on food supplies and the rapid spread of viruses through air travel, is essential to ensure that food distribution is not disrupted. The research investigates the response of countries and major international organizations such as the World Health Organization (WHO), the International Maritime Organization (IMO), and the International Civil Aviation Organization (ICAO). The methodology used in the study was a systematic literature review (SLR) by identifying 875 journals in the first stage. Then, through strict screening, 50 articles were selected and further analyzed. Covering continents, specific countries, and important ports, the research uses quantitative databases such as the Scopus journal index. The study results reveal the long-term impact of the COVID-19 pandemic on the aviation and maritime sectors and its influence on global passenger and goods transportation. SLR’s findings also bring together insights from diverse research on safety integration across different modes of transportation during the pandemic. Research provides recommendations — climate-related changes for safe transportation, applicable in normal and pandemic circumstances. In conclusion, this article comprehensively explains maintaining a safe and efficient transportation network amidst complex challenges.

Scalable and Efficient Approach for High Temporal Fuzzy Utility Pattern Mining.

Fuzzy utility (FU) pattern mining with an advantage in human reasoning has become one of the interesting topics in studies of knowledge discovery. The discovered information in FU pattern mining from real-life quantitative databases with item profits is suitable for interpreting data from a human perspective because it is not expressed using numerical values but linguistic terms which consist of natural languages. State-of-the-art approaches in this literature provide extended results by considering temporal factors, such as seasons, which can be influential in real-life situations. However, they still suffer from scalability issues because they are based on level-wise approaches which generate a number of candidates. In this article, we propose a scalable and efficient approach with a novel data structure for mining high temporal FU patterns without generating candidates. Efficient pruning techniques and algorithms are presented to improve the performance of the proposed approach. Performance experiments on both real and synthetic datasets show that the suggested algorithm has better performance than the state-of-the-art algorithms in terms of runtime, memory usage, and scalability.

Global Street Experiment: A Geospatial Database of Pandemic-induced Street Transitions

Street experiment is a tactical urbanism practice that involves implementing temporary changes in street use through regulatory or physical interventions, aimed at people-centric street transition. During the Covid-19 pandemic, cities worldwide implemented street experiments to accommodate the need for socially-distanced physical and commercial activities. However, we know little about the locations and urban environments of these pandemic-induced street experiments on a global scale. This knowledge gap hinders us from understanding where these experiments took place, the conditions of the neighbourhoods involved, and the factors contributing to their longevity beyond the pandemic. We thus developed a geospatial database to document the pandemic-induced street experiments (PISE Database), enabling quantitative analysis of these interventions. We mapped the locations and calculated the neighbourhood environment attributes of 539 street experiments in 333 cities. Our contributions are twofold. Firstly, we enhanced the comparability of built environment indicators between cities, thereby advancing the construction of global geospatial datasets. Specifically, we established a standardised template encompassing unified neighbourhood-level built environment indicators and methodologies, for which we devised relative values to facilitate comparisons between different cities and defined study areas using the 15-minute walking city and Urban Centre concepts. Secondly, we conducted primary analyses based on spatial and temporal visualisations of the street experiment locations and durations, shedding light on locational patterns and development trajectories during times of crisis. This global, quantitative approach complements the growing body of local and often qualitative studies. Our work improves existing global quantitative databases and provides a robust foundation for future research on tactical urbanism.

A new model for cost estimation construction project using Hybrid importance regression ensemble method

Cost estimating entails gathering and evaluating historical data, as well as using quantitative models, methodologies, tools, and databases to forecast the cost of a program in the future. At the early stages of the building design process, the cost is considered one of the most important elements in making decisions. During the design phase of a project, cost estimating is quite essential. To compelet a construction project successfully, it is critical to design a usable model and method for cost estimation in construction projects. For the above reason, this study has developed a hybrid method to conduct an accurate cost estimation in construction projects in Iraq. This study also conducted a rigorous survey to find five main influential factors with thirty-six sub-factors in the Iraqi market. It was evaluated through previous studies, questionnaires, and surveys of twenty projects to build a matrix factors database for construction projects. This work gathered the construction cost factors from relevant research and expert views. In the second step, the researcher ranked the factors within the Importance Regression Ensemble Method then the K-Nearest Neighbor Method was applied to specify the effect of the near-effective factors on the cost. The outcome of this study will be helpful to construction professionals in estimating a construction project effectively.

Chinese families' knowledge, attitudes, and practices regarding seizure management for children with epilepsy: a mixed-methods study.

This study aimed to examine Chinese families' knowledge, attitudes, and practices regarding the management of acute seizures (AS) that occur outside the hospital in children with epilepsy (CWE) and factors that influence AS. A mixed-methods sequential explanatory study was conducted, which was integrated at the design and methods levels. In phase 1, a questionnaire was developed for this study, and a family functioning assessment was administered from Nov 2021 to Apr 2022. Multivariate logistic regression was used to analyze the knowledge, attitudes, and practices (KAP) and factors that influence AS. In phase 2, family caregivers (FCGs) were recruited from Jul to Aug 2022 to participate in a qualitative exploration, using semi-structured interviews and a combination of inductive and deductive methods. The setting was five children's specialty hospitals in different regions of China. The participants were FCGs of CWE. A total of 645 participants were included in the quantitative phase, and 15 FCGs (eight parents, five grandparents, and two others) were recruited for the qualitative phase. The FCGs' average total KAP score for AS management was 66.23 ± 15.12, with 45.42% of FCGs having a low level. Univariate and multivariate regression analyses showed that demographic factors, disease characteristics, and family function significantly predicted family management of AS. The three most salient themes and eight sub-themes from phase 2 were explored. The quantitative and qualitative databases were analyzed separately and combined through integration, and a conceptual model was constructed based on the individual and family self-management theory (IFSMT); the model consisted of context, knowledge, self-regulation, and promotion factors. Chinese families have a positive attitude toward the management of out-of-hospital AS in CWE, but lack practice and related knowledge. AS management for CWE families was associated with the demographic characteristics of FCGs, epilepsy, and family characteristics. The research findings expand the existing application requirements of an Acute Seizure Action Plan and patient safety. Our results also indicate a pressing need for localized development of AS emergency medicine in family medicine, the establishment of auxiliary information systems, the utilization of caregivers' positive psychological resources, and improvements in family function for intergenerational care.

Mining frequent weighted utility patterns with dynamic weighted items from quantitative databases

Mining frequent weighted utility patterns with dynamic weighted items from quantitative databases

Mining Significant Utility Discriminative Patterns in Quantitative Databases

Drawing a discriminative pattern in quantitative datasets is often represented to return a high utility pattern (HUP). The traditional methods output patterns with a utility above a pre-given threshold. Nevertheless, the current user-centered algorithm requires outputting the results in a timely manner to strengthen the interaction between the mining system and users. Pattern sampling can return results with a probability guarantee in a short time, and it could be a candidate technology to mine such discriminative patterns. In this paper, a novel approach named HUPSampler is proposed to sample one potential HUP, which is extracted with probability significance according to its utility in the database. HUPSampler introduces an interval constraint on the length of HUP and randomly extracts an integer k according to the utility proportion firstly; then, the HUPs could be obtained efficiently from a random tree by using a pattern growth way, and finally, it returns a HUP of length k randomly. The experimental study shows that HUPSampler is efficient in regard to memory usage, runtime, and utility distribution. In addition, case studies show that HUPSampler can be significantly used in analyzing the COVID-19 epidemic by identifying critical locations.

PARALLEL FUZZY FREQUENT ITEMSET MINING USING CELLULAR AUTOMATA

Finding frequent fuzzy itemsets in operational quantitative databases is a significant challenge for fuzzy association rule mining in the context of data mining. If frequent fuzzy itemsets are detected, the decision-making process and formulating strategies in businesses will be made more precise. Because the characteristic of these data models is a large number of transactions and unlimited and high-speed productions. This leads to limitations in calculating the support for itemsets containing fuzzy attributes. As a result, mining using parallel processing techniques has emerged as a potential solution to the issue of slow availability. This study presents a reinforced technique for mining frequent fuzzy sets based on cellular learning automata (CLA). The results demonstrate that frequent set mining can be accomplished with less running time when the proposed method is compared to iMFFP and NPSFF methods.

Monitoring Agriculture Policy at the WTO: Insights from a New Quantitative Database

ABSTRACT In this paper, we build a quantitative database of the questions raised in the World Trade Organization (WTO) Committee on Agriculture (CoA) matching questions with the corresponding value of trade. Using this newly built database, we are able to show that (i) members’ concerns about trade policy measures applied by other members cover on average 16% of global trade in agriculture; (ii) the activity of the committee increases in the aftermath of a new agreement, during a food crisis and has shifted over time from questions about market access (MA) policies to questions on domestic support, thus pointing at the need to further cooperation on some of these issues; (iii) 20% of the questions relate to trade policy measures that have not been notified at the WTO, thus representing a significant source of information for researchers to fill the gaps in notifications; and (iv) there is a strong correlation between members participation in the CoA and their share of trade in agriculture, yet questions on seeds, oils and animal fats, cereals, and beverages are well above what one would expect on the basis of the trade importance of these sectors. These findings illustrate the importance of building quantitative databases from the information collected in WTO committees to analyse trends in trade policy concerns by members and factors affecting these trends and to assess the impact of discussions in committees on trade.

Fuzzy-driven periodic frequent pattern mining

Frequent pattern mining (FM) has a wide range of applications in the real world. But FM sometimes discovers many uninteresting patterns at the same time. Constraint-based FM, especially periodic constraint FM, becomes of interest, and it reduces redundant patterns. Most of the state-of-the-art algorithms for mining periodic frequent patterns (PFPs) are designed to find PFPs only in binary temporal databases. However, vast information in real situations is more suitable to be modelized as quantitative databases, in which most existing methods are inapplicable. Patterns obtained by those methods may change due to a tiny perturbation in the database, which is not expected in many situations. In this paper, we propose an algorithm (FP2M) to mine fuzzy-driven periodic frequent patterns and an algorithm (SFP2M) to find stable fuzzy-driven periodic frequent patterns in quantitative temporal databases. Fuzzy sets are used in our algorithms to deal with quantitative data since they provide a relaxed interval segmentation and are easy to implement. Novel pruning strategies and a new structure called Estimated Period Co-occurrence Structure (EPCS) are designed to speed up the mining process and improve efficiency. An improved method (SFP2M) is also presented by using the lability measurement to find stable patterns. Experimental evaluations on both real and synthetic datasets show good performance of the designed algorithms.

Evoked and transmitted culture models: Using bayesian methods to infer the evolution of cultural traits in history.

A central question in behavioral and social sciences is understanding to what extent cultural traits are inherited from previous generations, transmitted from adjacent populations or produced in response to changes in socioeconomic and ecological conditions. As quantitative diachronic databases recording the evolution of cultural artifacts over many generations are becoming more common, there is a need for appropriate data-driven methods to approach this question. Here we present a new Bayesian method to infer the dynamics of cultural traits in a diachronic dataset. Our method called Evoked-Transmitted Cultural model (ETC) relies on fitting a latent-state model where a cultural trait is a latent variable which guides the production of the cultural artifacts observed in the database. The dynamics of this cultural trait may depend on the value of the cultural traits present in previous generations and in adjacent populations (transmitted culture) and/or on ecological factors (evoked culture). We show how ETC models can be fitted to quantitative diachronic or synchronic datasets, using the Expectation-Maximization algorithm, enabling estimating the relative contribution of vertical transmission, horizontal transmission and evoked component in shaping cultural traits. The method also allows to reconstruct the dynamics of cultural traits in different regions. We tested the performance of the method on synthetic data for two variants of the method (for binary or continuous traits). We found that both variants allow reliable estimates of parameters guiding cultural evolution, and that they outperform purely phylogenetic tools that ignore horizontal transmission and ecological factors. Overall, our method opens new possibilities to reconstruct how culture is shaped from quantitative data, with possible application in cultural history, cultural anthropology, archaeology, historical linguistics and behavioral ecology.

Fuzzy Association Rules Mining based on Type-2 Fuzzy Sets over Data Stream

Fuzzy set theory aims to solve the problem of mining more valuable human reasoning knowledge from quantitative databases, and to narrow the gap between computer and human understanding. Fuzzy Frequent Itemset (FFI) mining, which is very important in fuzzy association rule mining, converts quantitative values into language, and finds fuzzy frequency itemset according to the defined membership function. In the traditional fuzzy frequent pattern mining method, only the language item with the largest scalar base is considered, and the uncertainty of the membership function is not considered. At the same time, it is mainly considered for the fixed data set. This paper proposes a Fuzzy Frequent Pattern mining algorithm based on the Type-2 Fuzzy Set (T2FS) theory of the data stream. Under the T2FS theory, the data stream is dynamically divided based on the sliding window method, and the ambiguity is quickly found from the numerical data stream. In order to reduce the search space and improve over-mining, this paper designs a dynamic compressed lists structure and trees method. Under different minimum support degrees and sliding window sizes, many experiments have been carried out, and the experiments have verified the efficiency and effectiveness of the designed method in terms of running time and memory usage. The results can also contribute to exchange the information and preferences among the people who cooperate in decision-making.

Quantitative risk assessment and safety databases in Romanian coal mining: preliminary systematic approach

Risk management is becoming increasingly more complex. Risk assessment, approached quantitatively, requires a factual database to define the likelihood of adverse health effects of workplace-related injuries and exposures, and it attempts to balance scientific knowledge with concerns of staff, investigators and administration. Practical guidance should be provided for Romanian coal mining companies to make progress in risk assessment process. Guidance is given on how to effectively introduce quantitative risk assessment in mining industry, the main goal being to highlight that the most valuable resource remains experience gained by effectively performing the process. Analyzing how various parameters are described/used, the paper aims to establish the place and role of quantitative risk analysis mining. Possibilities of developing safety/reliability database in coal mining are investigated. The block diagram describing the conceptual structure of a database on failures, safety of equipment and workers in the mining industry was developed. Because mining relies heavily on complex technologies - permanent mining facilities and large mobile equipment and support services - often located in isolated and hostile environments, the implementation of quantitative risk analysis and the development of a realistic database could be considered as a resilience business strategy and conversion of available knowledge into management actions.

Smoking-Interaction Loci Affect Obesity Traits: A Gene-Smoking Stratified Meta-Analysis of 545,131 Europeans

Introduction: Although many studies have investigated the association between smoking and obesity, very few have analyzed how obesity traits are affected by interactions between genetic factors and smoking. Here, we aimed to identify the loci that affect obesity traits via smoking status-related interactions in European samples. Methods: We performed stratified analysis based on the smoking status using both the UK Biobank (UKB) data (N = 334,808) and the Genetic Investigation of ANthropometric Traits (GIANT) data (N = 210,323) to identify gene-smoking interaction for obesity traits. We divided the UKB subjects into two groups, current smokers and nonsmokers, based on the smoking status, and performed genome-wide association study (GWAS) for body mass index (BMI), waist circumference adjusted for BMI (WCadjBMI), and waist-hip ratio adjusted for BMI (WHRadjBMI) in each group. And then we carried out the meta-analysis using both GWAS summary statistics of UKB and GIANT for BMI, WCadjBMI, and WHRadjBMI and computed the stratified p values (p_stratified) based on the differences between meta-analyzed estimated beta coefficients with standard errors in each group. Results: We identified four genome-wide significant loci in interactions with the smoking status (p_stratified < 5 × 10⁻⁸): rs336396 (INPP4B) and rs12899135 (near CHRNB4) for BMI, and rs998584 (near VEGFA) and rs6916318 (near RSPO3) for WHRadjBMI. Moreover, we annotated the biological functions of the SNPs using expression quantitative trait loci (eQTL) and GWAS databases, along with publications, which revealed possible mechanisms underlying the association between the smoking status-related genetic variants and obesity. Conclusions: Our findings suggest that obesity traits can be modified by the smoking status via interactions with genetic variants through various biological pathways.