Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation

Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations.Comment: Software at https://github.com/lemire/OLAPTagClou

Principal component and multiple correspondence analysis for handling mixed variables in the smoothed location model

The issue of classifying objects into groups when the measured variables are mixtures of continuous and binary variables has attracted the attention of statisticians. Among the discriminant methods in classification, Smoothed Location Model (SLM) is used to handle data that contains both continuous and binary variables simultaneously. However, this model is infeasible if the data is having a large number of binary variables. The presence of huge binary variables will create numerous multinomial cells that will later cause the occurrence of large number of empty cells. Past studies have shown that the occurrence of many empty cells affected the performance of the constructed smoothed location model. In order to overcome the problem of many empty cells due to large number of measured variables (mainly binary), this study proposes four new SLMs by combining the existing SLM with Principal Component Analysis (PCA) and four types of Multiple Correspondence Analysis (MCA). PCA is used to handle large continuous variables whereas MCA is used to deal with huge binary variables. The performance of the four proposed models, SLM+PCA+Indicator MCA, SLM+PCA+Burt MCA, SLM+PCA+Joint Correspondence Analysis (JCA), and SLM+PCA+Adjusted MCA are compared based on the misclassification rate. Results of a simulation study show that SLM+PCA+JCA model performs the best in all tested conditions since it successfully extracted the smallest amount of binary components and executed with the shortest computational time. Investigations on a real data set of full breast cancer also showed that this model produces the lowest misclassification rate. The next lowest misclassification rate is obtained by SLM+PCA+Adjusted MCA followed by SLM+PCA+Burt MCA and SLM+PCA+Indicator MCA models. Although SLM+PCA+Indicator MCA model gives the poorest performance but it is still better than a few existing classification methods. Overall, the developed smoothed location models can be considered as alternative methods for classification tasks in handling large number of mixed variables, mainly the binary

Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation

Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations

Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation

Increasingly, business projects are ephemeral. New Business Intelligence tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds are a popular community-driven visualization technique. Hence, we investigate tag-cloud views with support for OLAP operations such as roll-ups, slices, dices, clustering, and drill-downs. As a case study, we implemented an application where users can upload data and immediately navigate through its ad hoc dimensions. To support social networking, views can be easily shared and embedded in other Web sites. Algorithmically, our tag-cloud views are approximate range top-k queries over spontaneous data cubes. We present experimental evidence that iceberg cuboids provide adequate online approximations. We benchmark several browser-oblivious tag-cloud layout optimizations

NationalConference on Construction, sustainable Infrastructure and Valorization of waste-2023

Gandhi Institute of Excellent Technocrats, Ghangapatna, BBSR is organizing a conference, “National Conference on Construction, sustainable Infrastructure and Valorization of waste-2023” on 6th& 7thOctober 2023, at GIET, Ghangapatna, BBSR. The conference provides a platform for deliberations on developing solutions that mitigate the impact of infrastructure on ecology and environment. Research and case studies on challenges, underlying opportunities and innovative ideas for the development of sustainable infrastructure will be presented and discussed.https://www.interscience.in/conf_proc_volumes/1089/thumbnail.jp

OLEMAR: An Online Environment for Mining Association Rules in Multidimensional Data

Data warehouses and OLAP (online analytical processing) provide tools to explore and navigate through data cubes in order to extract interesting information under different perspectives and levels of granularity. Nevertheless, OLAP techniques do not allow the identification of relationships, groupings, or exceptions that could hold in a data cube. To that end, we propose to enrich OLAP techniques with data mining facilities to benefit from the capabilities they offer. In this chapter, we propose an online environment for mining association rules in data cubes. Our environment called OLEMAR (online environment for mining association rules), is designed to extract associations from multidimensional data. It allows the extraction of inter-dimensional association rules from data cubes according to a sum-based aggregate measure, a more general indicator than aggregate values provided by the traditional COUNT measure. In our approach, OLAP users are able to drive a mining process guided by a meta-rule, which meets their analysis objectives. In addition, the environment is based on a formalization, which exploits aggregate measures to revisit the definition of the support and the confidence of discovered rules. This formalization also helps evaluate the interestingness of association rules according to two additional quality measures: lift and loevinger. Furthermore, in order to focus on the discovered associations and validate them, we provide a visual representation based on the graphic semiology principles. Such a representation consists in a graphic encoding of frequent patterns and association rules in the same multidimensional space as the one associated with the mined data cube. We have developed our approach as a component in a general online analysis platform called Miningcubes according to an Apriori-like algorithm, which helps extract inter-dimensional association rules directly from materialized multidimensional structures of data. In order to illustrate the effectiveness and the efficiency of our proposal, we analyze a real-life case study about breast cancer data and conduct performance experimentation of the mining process

Teaching and learning professionalism in medical education

Eeuwenlang hebben studenten Geneeskunde hun professionele normen en waarden ontwikkeld in een meester-gezel relatie met hun klinische docenten. Deze informele manier van leren lijkt niet voldoende meer om studenten voor te bereiden op werken als professional binnen de hedendaagse, complexe beroepspraktijk. Mede ingegeven door alarmerende berichten over dokters in de media, heerst het idee dat medische professionaliteit aan het afkalven is. Vandaar de roep om studenten expliciet te onderwijzen in professionaliteit. Professionaliteit is echter een complex te definiëren begrip. Dit maakt de vraag hoe professionaliteit nu het beste te leren en te onderwijzen is, lastig. In dit proefschrift wordt een overzicht gegeven van een geïntegreerde onderwijslijn professionele ontwikkeling. De rationale achter deze lijn is professionaliteit te beschouwen als een reflectieve, tweede orde competentie. Intervisiebijeenkomsten in kleine groepen – het ‘cement’ van deze lijn - waarin studenten gestructureerd zelfgekozen klinische ervaringen bespreken, lijken geschikt voor het ontwikkelen van deze reflectieve professionaliteit. Als onderdeel van een portfolio schrijven studenten reflectieve essays. Feedback hierop door de docent kan het beste geformuleerd worden als vraag, positief van toon zijn en zich richten op het verdiepen van het reflectieve niveau van de student. Docenten zijn belangrijke rolmodellen voor coassistenten, maar soms vertonen ze onprofessioneel gedrag in de docent-student relatie. Waar grenzen liggen tussen wat nog wel kan of juist niet, blijkt sterk individueel bepaald. Deze grote variatie in percepties maakt het moeilijk om eenduidige gedragscodes af te spreken. Aangeraden wordt studenten en docenten bewust te maken dat eigen grenzen wellicht heel anders zijn dan die van anderen.For centuries, medical students have developed their professional values and beliefs while participating in a traditional apprenticeship relationship with their clinical teachers. This implicit approach it is no longer felt to be sufficient to prepare students for professional practice. Prompted by alarming headlines in the media, the general public have come to realize that medical professionalism is under threat. Therefore, there is a widely acknowledged call for professionalism to be trained explicitly. However, the concept of professionalism is complex to define. This makes the question of how professionalism should be learned and taught a difficult one. In this thesis an overview is presented of a professional development course for clerks. The rationale underpinning the course is that professionalism is a reflective, second-order competence. Small group sessions in which students learn to reflect on self-selected topics provide a suitable training context in which professionalism can be developed. As part of their portfolio assignments, students have to write reflective essays. Our findings suggests that written feedback on students’ reflective essays should be formulated as questions, be positive in tone and tailored to the individual student’s reflective level in order to stimulate students to reflect at a slightly higher level. Teachers are important role models for young trainee doctors. Unfortunately, sometimes they show unprofessional behaviour. Perceptions of what is or is not appropriate behaviour differ largely. Applying a fixed code of conduct is there for troublesome. We recommend making students and teachers aware that other people’s boundaries might not be the same as their own

Perceptions of Health Professionals in Ghana on the Role of the Community in Chronic Disease Management

The prevalence of chronic disease has been on the rise in various developing countries. This study is an exploratory investigation of the perceptions of health professionals in Kumasi, Ghana on the role of the community in chronic disease management. Using the Chronic Care Model as a framework, the objective of the study was to examine the extent of community-healthcare system in chronic disease management and to explore the barriers to and facilitators of community involvement in chronic disease management. By exploring the availability of community resources that positively contribute to the management of these patients, the study fills an existing gap in the literature related to community involvement in chronic disease management. The study uses a mixed methods approach, utilizing both qualitative and quantitative data. Data were obtained from interviews with 50 healthcare professionals and a quantitative survey of 109 healthcare professionals. Research findings revealed that the family unit is the main form of support for patients with chronic diseases. Findings additionally revealed a lack of community support, resources, and community partnerships in the efforts towards effective health management for patients with chronic diseases in Ghana