543 research outputs found

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    Data mining with the SAP NetWeaver BI accelerator

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    The new SAP NetWeaver Business Intelligence accelerator is an engine that supports online analytical processing. It performs aggregation in memory and in query runtime over large volumes of structured data. This paper first briefly describes the accelerator and its main architectural features, and cites test results that indicate its power. Then it describes in detail how the accelerator may be used for data mining. The accelerator can perform data mining in the same large repositories of data and using the same compact index structures that it uses for analytical processing. A first such implementation of data mining is described and the results of a performance evaluation are presented. Association rule mining in a distributed architecture was implemented with a variant of the BUC iceberg cubing algorithm. Test results suggest that useful online mining should be possible with wait times of less than 60 seconds on business data that has not been preprocessed

    An Improved Apriori Algorithm for Association Rule Mining in Employability Analysis

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    The wide application of emerging advanced technologies causes significant changes in the development trend of the employment market. The lack of flexible and easy-to-implement analysis methods challenges general maritime education practitioners to understand the developing trends. This research proposed the improved Apriori algorithm to explore employment preference by identifying the association rule of the employability indicators and the employment status. The candidate generation methods are optimised based on the questionnaire design to generate fewer candidates. The minimum support value is automatically generated to reduce the reliance on analysis expertise and improve accuracy. To validate the algorithm, a questionnaire for the maritime graduate is used to collect employment data to test the efficiency and capability of the improved algorithm. The computation time for different data set sizes shows that the improvement could improve the algorithm\u27s effectiveness. The algorithm also successfully identifies significant employment preference that certain employment types emphasise specific employability skills, such as responsibility and core professional skills. The results suggest that the improved A algorithm could reduce the computing burden and identify the employment preference from questionnaire data. This research provides easy-to-use and flexible analysis tools, which could reduce the computing expertise required for education practitioners

    A genetic algorithm coupled with tree-based pruning for mining closed association rules

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    Due to the voluminous amount of itemsets that are generated, the association rules extracted from these itemsets contain redundancy, and designing an effective approach to address this issue is of paramount importance. Although multiple algorithms were proposed in recent years for mining closed association rules most of them underperform in terms of run time or memory. Another issue that remains challenging is the nature of the dataset. While some of the existing algorithms perform well on dense datasets others perform well on sparse datasets. This paper aims to handle these drawbacks by using a genetic algorithm for mining closed association rules. Recent studies have shown that genetic algorithms perform better than conventional algorithms due to their bitwise operations of crossover and mutation. Bitwise operations are predominantly faster than conventional approaches and bits consume lesser memory thereby improving the overall performance of the algorithm. To address the redundancy in the mined association rules a tree-based pruning algorithm has been designed here. This works on the principle of minimal antecedent and maximal consequent. Experiments have shown that the proposed approach works well on both dense and sparse datasets while surpassing existing techniques with regard to run time and memory

    Machine Learning-Driven Decision Making based on Financial Time Series

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    A data-driven approach to support the automation of thermostats in residential buildings

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    Programmable thermostats represent a significant advancement in home automation technology, offering the potential for maintaining comfort and energy efficiency. However, the frequent overriding of default schedules indicates the necessity of flexibility to accommodate the dynamic occupant behavior and requirements. This thesis delves into this challenge, leveraging data-driven insights to understand thermostat override behaviors and hence develop supportive automation strategies that minimize human interaction. The introductory focus of this research lies in examining how individual comfort preferences, outdoor conditions, and daily schedules influence thermostat override behaviors. The data set for this exploration comprises thermostat and occupancy data from two residential buildings in Quebec, Canada, equipped with ecobee smart thermostats from the heating and cooling seasons of 2017 to 2019. The research subsequently explores the frequency of override behaviors across different Heating, ventilation, and air conditioning (HVAC) modes, schedules, temperatures, and years. A key novelty of this research lies in its extensive exploration of occupancy, temperature, and setpoint trends over specific periods, facilitating the identification of patterns in thermostat override cycles and daily adjustments. Machine learning algorithms, such as decision trees and random forests, are employed to ascertain the importance of various features influencing thermostat override behaviors. Association rule mining techniques then reveal the relationship between variables, suggesting adaptive automation strategies based on temperature, occupancy, time, and outdoor conditions. After conducting a comparative data analysis for two households, we identified significant shifts in occupant behavior and temperature preferences. From these insights, we have derived four various automation strategies: temperature-based, occupancy-based, outdoor temperature-based, and time-of-day and weekday-based. These strategies exemplify the adaptability in occupant behaviors. Recognizing the factors that influence thermostat overrides makes it possible to equip smart thermostats with more intuitive automation strategies. These strategies can proactively adjust settings in line with user behavior and prevailing outdoor conditions, enhancing comfort and energy efficiency. To further fine-tune and widen the applicability of these strategies, it would be beneficial to conduct additional research with more extensive and diverse datasets

    Research on Network Data Algorithm Based on Association Rules

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    The network data algorithm on account of association can effectively describe the development process of historical data and predict the development trend of data. Draw support from the corresponding data algorithm to ameliorate the mining efficiency and execution efficiency of association, more users pay more attention to the rules, so it has important research and utilization value. On account of this, this paper first analyses the concept and mining process of data association, then studies the mining algorithm of data association, and finally gives the structure and utilization effect of cyber data algorithm on account of association

    Scalable visualization of event sequences

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    Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing

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    With the onset of the Information Era and the rapid growth of information technology, ample space for processing and extracting data has opened up. However, privacy concerns may stifle expansion throughout this area. The challenge of reliable mining techniques when transactions disperse across sources is addressed in this study. This work looks at the prospect of creating a new set of three algorithms that can obtain maximum privacy, data utility, and time savings while doing so. This paper proposes a unique double encryption and Transaction Splitter approach to alter the database to optimize the data utility and confidentiality tradeoff in the preparation phase. This paper presents a customized apriori approach for the mining process, which does not examine the entire database to estimate the support for each attribute. Existing distributed data solutions have a high encryption complexity and an insufficient specification of many participants' properties. Proposed solutions provide increased privacy protection against a variety of attack models. Furthermore, in terms of communication cycles and processing complexity, it is much simpler and quicker. Proposed work tests on top of a realworld transaction database demonstrate that the aim of the proposed method is realistic

    A survey on privacy in human mobility

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    In the last years we have witnessed a pervasive use of location-aware technologies such as vehicular GPS-enabled devices, RFID based tools, mobile phones, etc which generate collection and storing of a large amount of human mobility data. The powerful of this data has been recognized by both the scientific community and the industrial worlds. Human mobility data can be used for different scopes such as urban traffic management, urban planning, urban pollution estimation, etc. Unfortunately, data describing human mobility is sensitive, because people's whereabouts may allow re-identification of individuals in a de-identified database and the access to the places visited by indi-viduals may enable the inference of sensitive information such as religious belief, sexual preferences, health conditions, and so on. The literature reports many approaches aimed at overcoming privacy issues in mobility data, thus in this survey we discuss the advancements on privacy-preserving mo-bility data publishing. We first describe the adversarial attack and privacy models typically taken into consideration for mobility data, then we present frameworks for the privacy risk assessment and finally, we discuss three main categories of privacy-preserving strategies: methods based on anonymization of mobility data, methods based on the differential privacy models and methods which protect privacy by exploiting generative models for synthetic trajectory generation
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