Analysis Of Process Data Research Articles

AUTO-CDD: automatic cleaning dirty data using machine learning techniques

Cleaning the dirty data has become very critical significance for many years, especially in medical sectors. This is the reason behind widening research in this sector. To initiate the research, a comparison between currently used functions of handling missing values and Auto-CDD is presented. The developed system will guarantee to overcome processing unwanted outcomes in data Analytical process; second, it will improve overall data processing. Our motivation is to create an intelligent tool that will automatically predict the missing data. Starting with feature selection using Random Forest Gini Index values. Then by using three Machine Learning Paradigm trained model was developed and evaluated by two datasets from UCI (i.e. Diabetics and Student Performance). Evaluated outcomes of accuracy proved Random Forest Classifier and Logistic Regression gives constant accuracy at around 90%. Finally, it concludes that this process will help to get clean data for further analytical process.

Accessing rural health services: Results from a qualitative narrative gerontological study.

ObjectiveExplore how older adults’ talk about accessing rural community health services.MethodsA qualitative narrative gerontological approach explored issues related to accessing health services in their community. Semi‐structured digitally recorded individual interviews were undertaken with 32 community‐dwelling older people aged between 75 and 93 years. A narrative data analytic process was undertaken. The COnsolidated criteria for REporting Qualitative research guidelines were followed to ensure rigour in this study.ResultsThree collective narratives resulted from the data analytic process: (a) “accessing local health services”; (b) “accessing specialist services”; and (c) “accessing emergency services.”ConclusionsNarrators identified a number of issues related to accessing rural health services. These included long waiting times, lack of continuity in care provision by doctors and difficulties accessing specialist and emergency services. Nurses were frequently cited as a reliable point of contact for these older people. Expansion of nursing roles would enhance the provision of rural health‐care services.

Multi-criteria Performance Evaluation Framework for Rehabilitation Service Operations

Rehabilitation or post-disaster relief is an important service operation performed by the government and non-government authorities after a calamity. Managing such services at a short notice requires a lot of back office preparations. The services offered during the rehabilitation process are usually carried out in different phases, and it often takes long time for the situation to return to normalcy. Evaluating performances of such services would not only ensure a proper planning but also provide better-organized efforts to mitigate the aftereffects of such instances occurring in future. In this work, we propose a generic framework for evaluating the performances of the services offered during the rehabilitation at various phases. This two-stage methodology uses multi-criteria decision-making tools such as data envelopment analysis and analytic hierarchy process. An application of Malmquist Productivity Index is also illustrated to track the improvements over a number of such occurrences. The approach provides an opportunity to identify the scope of improvements, if such situation/s repeat in future. To understand the scope of the work, a broad classification scheme (of various types of disasters) is proposed in this article. This scheme of categorization is based on dimensions of the frequency, the impact and the duration of occurrence. The proposed methodology is systematic and easy to implement, which makes it a favourable candidate for the acceptance by a wider group of practising managers.

Evaluation of government‐supported provincial innovation competitiveness—Evidence from China

AbstractGovernment support plays a crucial role in enhancing regional innovation capabilities and creating distinctive innovation clusters. Using the innovation competitiveness indicators of the 31 provinces in China, this paper examines the provincial government collaboration and innovation competitiveness values through Super‐SBM data envelopment analysis and analytic hierarchy process. Then, the particle swarm optimization (PSO) algorithm is employed to relate government collaboration to innovation competition and cooperation among provinces in hope of exploring the developmental trends of innovation cluster optimization. Research findings show that only 25.8% of the provincial government support proves effective for regional innovation, and the competitiveness values of the provinces are generally low and need to be improved. By incorporating the role of government support into the innovation competition and cooperation among provinces, there has been a tendency of innovation activities clustering in innovative regions and three major clusters have formed. The trend of clustering will continue to evolve outwards so that the overall innovation level of China would be enhanced.

Studying treatment-effect heterogeneity in precision medicine through induced subgroups

ABSTRACTPrecision medicine, in the sense of tailoring the choice of medical treatment to patients’ pretreatment characteristics, is nowadays gaining a lot of attention. Preferably, this tailoring should be realized in an evidence-based way, with key evidence in this regard pertaining to subgroups of patients that respond differentially to treatment (i.e., to subgroups involved in treatment–subgroup interactions). Often a-priori hypotheses on subgroups involved in treatment–subgroup interactions are lacking or are incomplete at best. Therefore, methods are needed that can induce such subgroups from empirical data on treatment effectiveness in a post hoc manner. Recently, quite a few such methods have been developed. So far, however, there is little empirical experience in their usage. This may be problematic for medical statisticians and statistically minded medical researchers, as many (nontrivial) choices have to be made during the data-analytic process. The main purpose of this paper is to discuss the major concepts and considerations when using these methods. This discussion will be based on a systematic, conceptual, and technical analysis of the type of research questions at play, and of the type of data that the methods can handle along with the available software, and a review of available empirical evidence. We will illustrate all this with the analysis of a dataset comparing several anti-depressant treatments.

A hybrid approach using rough set theory and hypergraph for feature selection on high-dimensional medical datasets

‘Curse of Dimensionality’—massive generation of high-dimensional medical datasets from various biomedical applications hardens the data analytic process for precise medical diagnosis. The design of an efficient feature selection technique for finding the optimal feature subset can be devised as a prominent solution to the above-said challenge. Further, it also improves the accuracy and minimizes the computational complexity of the learning model. The state-of-the-art feature selection techniques based on heuristic and statistical functions suffer from significant challenges in terms of classification accuracy, time complexity, etc. Hence, this paper presents Rough Set Theory and Hypergraph (RSHGT)-based feature selection technique to identify the optimal feature subset for accurate medical diagnosis. Experimental validations using six medical datasets from the Kent Ridge Biomedical dataset repository prove the efficiency of RSHGT in terms of reduct size, accuracy, precision, recall, and time complexity.

Knowledge based non-functional software distinguishing quality effort estimation using fuzzy use case point approach

Knowledge management along with data analytic process provides the collection of information which helps to examine both qualitative and quantitative software information. During the knowledge based data examination process, software effort estimation (SEE) is an organised approach to increase the efficiency of a software development process in various organisations. Software effort estimation based on use case point (UCP) methods provides only fixed estimation value which cannot deal with the uncertain and ambiguous conditions of the particular software. This work attempts to provide a fuzzy effort estimation procedure for use case model based on fuzzy inference rules. This paper also proposes a metric to calculate the enhanced UCPs with the fuzzy membership function for examining the software distinguishing quality. The proposed approach has been validated against the goal-driven UCP model, tree-boost model, Regression model and traditional use-case model. The obtained results show the better performance than those obtained by the existing methods.

Knowledge based non-functional software distinguishing quality effort estimation using fuzzy use case point approach

Knowledge management along with data analytic process provides the collection of information which helps to examine both qualitative and quantitative software information. During the knowledge based data examination process, software effort estimation (SEE) is an organised approach to increase the efficiency of a software development process in various organisations. Software effort estimation based on use case point (UCP) methods provides only fixed estimation value which cannot deal with the uncertain and ambiguous conditions of the particular software. This work attempts to provide a fuzzy effort estimation procedure for use case model based on fuzzy inference rules. This paper also proposes a metric to calculate the enhanced UCPs with the fuzzy membership function for examining the software distinguishing quality. The proposed approach has been validated against the goal-driven UCP model, tree-boost model, Regression model and traditional use-case model. The obtained results show the better performance than those obtained by the existing methods.

A Novel Neutrosophic Data Analytic Hierarchy Process for Multi-Criteria Decision Making Method: A Case Study in Kuala Lumpur Stock Exchange

This paper proposes a multi-criteria decision making method called the neutrosophic data analytic hierarchy process (NDAHP) for the single-valued neutrosophic set (SVNS). This method is an extension of the neutrosophic analytic hierarchy process (NAHP) designed to handle actual datasets which consist of crisp values. The proposed NDAHP method uses an objective weighting mechanism whereas all other existing versions of the AHP, fuzzy AHP, and other fuzzy based AHP method in literature such as the NAHP and picture fuzzy AHP uses a subjective weighting mechanism to arrive at the decision. This makes the proposed NDAHP method effective as the weightage of the criteria which forms the input of the evaluation matrix are determined in an objective manner using actual data collected for the problem, and hence will not change according to the opinions of different decision makers which are subjective. The proposed NDAHP method is applied to a multi-criteria decision making problem related to the ranking of the financial performance of five public listed petrochemical companies trading in the main board of the Kuala Lumpur Stock Exchange (KLSE). Actual dataset of 15 financial indices for the five petrochemical companies for 2017 obtained from Yahoo! Finance was used in this paper. Following this, a brief comparative study is conducted to evaluate the performance of our NDAHP algorithm against the results of other existing SVNS-based decision making methods in the literature. The results are compared against actual results obtained from KLSE. To further verify the rankings obtained through each method, the Spearman and Pearson ranking tests are carried out on each of the decision making methods that are studied. It is proved that NDAHP produces the most accurate results, and this was further verified from the results of the Spearman and Pearson ranking tests.

Decision Making Methods for Evaluation of Efficiency of General Insurance Companies in Malaysia: A Comparative Study

This paper proposes an integration of two neutrosophic based multi-criteria decision making methods, namely the neutrosophic data analytical hierarchy process (NDAHP) and the Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) with maximizing deviation method, both based on the single-valued neutrosophic set (SVNS) to evaluate the efficiency of general insurance companies in Malaysia. The level of efficiency of insurance companies is a subjective and vague matter, as the efficiency can be further branched into operational efficiency, investment efficiency, underwriting efficiency, and risk management efficiency. Hence relying on entirely objective decision making methods based on crisp data might not address the problem effectively, and therefore fuzzy based decision making methods are highly appropriate to be used in this situation. Our proposed decision making algorithm uses an integrated weighting mechanism that takes into consideration both the objective and subjective weights of the data attributes. The objective weighting mechanism handles the actual datasets that were used which consists of crisp values, whereas the subjective weighing mechanism handles the opinions of the experts in the general insurance industry who were surveyed in this study. This makes the proposed method a more holistic approach to evaluate the efficiency of general insurance companies in Malaysia as previous researches in this area are generally based on the actual datasets without consideration of the opinions and evaluations of the industry experts, or vice-versa. The proposed decision making algorithm is applied on actual datasets of management expenses, net commission, net earned premium and the net investment income for 19 selected general insurance companies in Malaysia over a two-year period from 2016 to 2017. The results obtained are then discussed and the possible reasons for the results are analyzed. A comprehensive comparative study of the results obtained via our proposed method and two other commonly used methods are then presented, analyzed and discussed.

An integrated AHP-DEA methodology for evaluation and ranking of production methods in industrial environments

Decision making about the superior method to produce products is essential to growth and dynamism of productive organisations. In this regard, utilising a model to evaluate the comprehensive performance of different manufacturing methods is significant. In addition, defining and finding the suitable indexes of decision making is required to the proper implementation of performance evaluation system. Thus, in this study, it is attempted to select the best production method and to supply material using data envelopment analysis (DEA) technique and analytic hierarchy process (AHP). In this regard, the performance of ball valve production in Petro Nab Sepahan Manufacturing Company is assessed and ranked using these two methods by considering dimensions such as quality, cost and function as well as acquired measures.

“Men don’t cry”: An Interpretative Phenomenological Analysis of Black South African Men’s Experience of Divorce

The decision to divorce marks a turning point for every individual involved. It can be viewed as more than just a legal process. From a psychological perspective, it does not matter who initiated the divorce, since it always comes with emotional ramifications for all those involved. Statistically, there is a high rate of divorce in South Africa and there have been significant shifts in trends over time. While black South African men’s experience of divorce has been relatively neglected in the research on divorce, it is important for understanding contemporary social arrangements and processes, and, in particular, for broadening the understanding of black South African men’s lives. How black South African men describe their experience and respond to marital dissolution may point to their positions in the gender-structured community as well as illuminate how they interpret the nature of social practice, marriage, divorce and their position in society. The aim of the research reported on in this paper was to explore black South African men’s experience of divorce. The theoretical framework underpinning this qualitative study was broadly that of Symbolic Interactionism, with Interpretative Phenomenological Analysis (IPA) employed as both the research design and data analytic theory and process. The eight participants were volunteers who were recruited purposively. In keeping with IPA guidelines, data-collection proceeded by means of biographical questionnaires and semi-structured interviews. The emerging themes were grouped into three superordinate themes, namely, perceptions of divorce, social support, and experiencing of pain. Each superordinate theme had corresponding subordinate themes and experiential claims. Weed’s (2008) recommendations for the interpretative synthesis of interview data were applied.

Sustainable evaluation and selection of potential third-party logistics (3PL) providers

PurposeThe purpose of this paper is to efficiently assist the decision makers in evaluating and selecting the most appropriate third-party logistics (3PL) provider from environmental sustainability perspective using a two-phase model based on data envelopment analysis (DEA) and analytic network process (ANP).Design/methodology/approachThe study uses an integrated approach of DEA and ANP as an evaluation and selection methodology to select an efficient and requisite 3PL. The integrated model is a sound technique for 3PL evaluation and selection. DEA effectively screens the maximally efficient 3PLs, whereas ANP easily performs the cumbersome process of weighting diverse criteria and ranks various alternatives according to their performance on the basis of these criteria. This study lists the prominent evaluation and selection criteria taking into consideration the environmental sustainability. Finally, a real-life industrial application is presented to demonstrate the proposed method.FindingsThe current research work is devoted to the emerging research topic of environmental sustainability in logistics industry and supply chain management. The results of study indicate that eco-efficient transportation and 3PLs lead better transportation planning, better inventory and warehouse management, lower inventory cost and sustainable supply chain operations. Also, the applied approach enables the decision makers to better understand the complex relationships of the relevant criteria in the decision-making process.Research limitations/implicationsThis study does not investigate the relationships between environmental performances and differentiation advantage from the perspective of 3PLs. Also, sensitivity analysis is not performed.Practical implicationsThe developed integrated model enables decision analysts to better understand the complete evaluation process of 3PL evaluation and selection considering environmental sustainability perspective. Furthermore, the developed approach provides a more flexible, simple, accurate, effective, and systematic decision support tool for 3PL evaluation and selection.Originality/valueThe current study is first of its kind to use the integrated approach in evaluation and selection of 3PLs from environmental sustainability perspective considering the inter-dependencies among the evaluation and selection criteria.

Evaluation and selection of third-party logistics providers using an integrated multi-criteria decision making approach

Today with business globalisation, the use of third-party logistics service providers (3PLSPs) is becoming prevalent. With the worldwide increasing use of 3PL services, the need for carefully evaluating and selecting the best 3PL is becoming much more critical. Supply chain managers always face the dilemma of selecting the right 3PL. The current study presents an efficient data envelopment analysis (DEA) and analytic hierarchy process (AHP)-based decision framework for 3PL evaluation and selection for an outsourcing arrangement. The analysis results demonstrate the effectiveness of the integrated DEA-AHP model as a simple and flexible decision analysis tool for 3PL evaluation and selection. Further, the application of decision framework has been demonstrated with a real case situation. Finally, the study discusses the implications of the results in a real-life context and concludes with the managerial insights identifying a few inherent limitations of the approach utilised.

A Holistic View of Data Warehousing in Education

Data warehousing (DW) is a widespread and essential practice in business organizations that support the data analytic and decision-making process. Despite the importance of DW in complex organizations, the adoption of a data warehouse (DWH) in education is apparently lower compared with other industries. To clarify this situation, this paper presents a systematic mapping that includes the study of empirical research papers from 2008 to 2018 on the topic of DW in education. For this paper, we applied a qualitative and quantitative approach based on a four-stage research method with the objective to have a holistic view of DWHs in education. After filtering and applying the proposed method, 34 relevant papers were identified and studied in detail. The study revealed interesting facts; for example, Kimball’s approach is the most applied methodology for DWH design in education. In addition, a mapping between this comprehensive collection of research papers covering educational DW and six dimensions of analysis (schema proposal, analysis of the user requirements, analysis of the business requirements, effectiveness, implementation, and data analysis) was performed. From this analysis, we discovered that the star schema is the most implemented approach. The purpose of the mapping was to explore and identify the priority areas of research and the research gaps within the academic community. These gaps are a source of opportunities to start new lines of research.

Evaluation and selection of third-party logistics providers using an integrated multi-criteria decision making approach

Today with business globalisation, the use of third-party logistics service providers (3PLSPs) is becoming prevalent. With the worldwide increasing use of 3PL services, the need for carefully evaluating and selecting the best 3PL is becoming much more critical. Supply chain managers always face the dilemma of selecting the right 3PL. The current study presents an efficient data envelopment analysis (DEA) and analytic hierarchy process (AHP)-based decision framework for 3PL evaluation and selection for an outsourcing arrangement. The analysis results demonstrate the effectiveness of the integrated DEA-AHP model as a simple and flexible decision analysis tool for 3PL evaluation and selection. Further, the application of decision framework has been demonstrated with a real case situation. Finally, the study discusses the implications of the results in a real-life context and concludes with the managerial insights identifying a few inherent limitations of the approach utilised.

SPICE Frame Work: A Tool for Analysis of Large Processual Data

The paradigm wars have led to dominance of instrumentality over purpose in the application of research methods resulting in journals showing distinct but hidden preferences for methodology driven articles. The bigger impact has been very poor theory development. Theories are crucially dependent upon establishment of causal linkages between factors identified in a phenomenon. It requires qualitative case based methodology to generate it and once established quantitative methods allow extension and validation of the theory. The very process of theory generation has been hampered by the debate on issues of validity and replication. What limits in numbers of cases must be sourced to successfully establish causal phenomenon is still a subject of debate. When multiple cases are used as part of case research strategy, issues of presentation of findings and critically ability to use strategies in theorirising from volume of process data come to the fore. Missing is a framework which would enable use of multiple case based designs regularly in research on management and allow use of generic heuristics to analyze process data. This paper generates such a generic framework. “SPICE” – an acronym standing for five generic dimensions-enables processual analysis of data from multiple case research designs. It generates patterns from the analysis resulting in establishing of causal mechanisms leading to generation of hypotheses and their verification. This framework will enable greater use of multiple case based research designs and thus better ways to generate theories.

Hierarchy grey relational analysis using DEA and AHP

Purpose This paper aims to apply an integrated data envelopment analysis (DEA) and analytic hierarchy process (AHP) approach to a multi-hierarchy grey relational analysis (GRA) model. Consistent with the most real-life applications, the authors focus on a two-level hierarchy in which the attributes of similar characteristics can be grouped into categories. Nevertheless, the proposed approach can be easily extended to a three-level hierarchy in which attributes might also belong to different sub-categories and further be linked to categories. Design/methodology/approach The procedure of incorporating the DEA and AHP methods in a two-level GRA may be broken down into a series of steps. The first three steps are under the heading of attributes and the latter three steps are under the heading of categories as follows: computing the grey relational coefficients of attributes for each alternative using the basic GRA model which further provides the required (output) data for an additive DEA model; computing the priority weights of attributes and categories using the AHP method which provides a priori information on the adjustments of attributes and categories in additive DEA models; computing the grey relational grades of attributes in each category for alternatives using an additive DEA model; converting the grey relational grades of attributes to the grey relational coefficients of categories; computing the grey relational grades of categories for alternatives using an additive DEA model; computing the dissimilarity grades of categories for the tied alternatives using an additive DEA exclusion model. Findings The proposed approach provides a more reasonable and encompassing measure of performance in a hierarchy GRA, based on which the overall ranking position of alternatives is obtained. A case study of a wastewater treatment technology selection verifies the effectiveness of this approach. Originality/value This research is a step forward to overcome the current shortcomings in a hierarchy GRA by extracting the benefits from both the objective and subjective weighting methods.

Data Analytic Process of a Nationwide Population-Based Study on Obesity Using the National Health Information Database Presented by the National Health Insurance Service 2006-2015.

BackgroundIn Korea, the prevalence of obesity has steadily increased, and the socioeconomic burden of obesity has increased along with it. In 2015, the National Health Insurance Service (NHIS) signed a memorandum of understanding with the Korean Society for the Study of Obesity (KSSO), providing limited open access to its databases so that the status of obesity and obesity management could be investigated.MethodsUsing NHIS databases, we analyzed nationwide population-based studies for obesity using the definition of obesity (body mass index ≥25 kg/m2) in subjects over the age of 20. Age and sex standardization were used for all data.ResultsThe KSSO released the ‘Obesity Fact Sheet 2016’ using the 2006–2015 NHIS Health Checkup database. The prevalence of obesity steadily increased from 28.7% in 2006 to 32.4% in 2015, and the prevalence of abdominal obesity also steadily increased from 18.4% in 2009 to 20.8% in 2015. The prevalence of class II obesity steadily increased from 2006 to 2015, such that the total prevalence was 4.8% in 2015 (5.6% in men and 4.0% in women). The highest prevalence of obesity was found in Jeju Island, while the lowest prevalence was found in Daegu City. The highest prevalence of abdominal obesity was also found in Jeju Island, while the lowest prevalence was found in Gwangju City.ConclusionBased on the Obesity Fact Sheet 2016, a strategy for reducing the prevalence of obesity is needed, especially in Korean men.

Integrated optimisation technique based on computer-aided capacity and safety evaluation for managing downstream lane-drop merging area of signalised junctions

This study provides an integrated strategy, encompassing microscopic simulation, safety assessment, and multi-attribute decision-making, to optimize traffic performance at downstream merging area of signalized intersections. A Fuzzy Cellular Automata (FCA) model is developed to replicate microscopic movement and merging behavior. Based on simulation experiment, the proposed FCA approach is able to provide capacity and safety evaluation of different traffic scenarios. The results are then evaluated through data envelopment analysis (DEA) and analytic hierarchy process (AHP). Optimized geometric layout and control strategies are then suggested for various traffic conditions. An optimal lane-drop distance that is dependent on traffic volume and speed limit can thus be established at the downstream merging area.