Actionable insights through association mining of exchange rates: a case study

Association mining is the methodology within data mining that researches associations among the elements of a given set, based on how they appear together in multiple subsets of that set. Extensive literature exists on the development of efficient algorithms for association mining computations, and the fundamental motivation for this literature is that association mining reveals actionable insights and enables better policies. This motivation is proven valid for domains such as retailing, healthcare and software engineering, where elements of the analyzed set are physical or virtual items that appear in transactions. However, the literature does not prove this motivation for databases where items are “derived items”, rather than actual items. This study investigates the association patterns in changes of exchange rates of US Dollar, Euro and Gold in the Turkish economy, by representing the percentage changes as “derived items” that appear in “derived market baskets”, the day on which the observations are made. The study is one of the few in literature that applies such a mapping and applies association mining in exchange rate analysis, and the first one that considers the Turkish case. Actionable insights, along with their policy implications, demonstrate the usability of the developed analysis approach

Data Mining in Electronic Commerce

Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transaction costs and better customer relations. Success depends on skilled information technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Inteligência competitiva e tecnologia da informação: um estudo exploratório

A pesquisa analisou como as empresas têm usado os conceitos de Inteligência Competitiva, como esta prática se beneficia através de aplicações de Tecnologia da Informação e como as áreas de Tecnologia da Informação e Inteligência Competitiva se alinham com a estratégia de negócios da empresa. Criou-se um quadro teórico para aplicação de uma pesquisa qualitativa em quatro empresas que utilizam processos de inteligência, cujos resultados foram coletados através de entrevistas subdivididas sob os temas de Gestão da Informação, Inteligência Competitiva e Tecnologia da informação. Os resultados comprovam a alta demanda de informações pelas empresas para monitoramento do ambiente de negócios e concorrentes, mas poucas formalizam setores de Gestão da informação e Gestão do conhecimento. Os setores internos de Tecnologia da Informação que demonstraram maior alinhamento com a estratégia de negócios da empresa foram aqueles de empresas cujos produtos e serviços se baseiam no desenvolvimento de novas tecnologias, nos outros casos, a participação do setor de Tecnologia da Informação se concentrou em oferecer suporte técnico às aplicações diversas. As conclusões principais apontam que o uso de Inteligência Competitiva traz benefícios efetivos no suporte de tomada de decisão, mas a utilização de seus conceitos e aplicações ainda é pouco clara nas empresas. A Gestão do Conhecimento organizacional, embora seja um elemento importante para o sucesso na utilização de informações internas para inovação e resolução de problemas, no entanto, ainda é pouco explorado pelas empresas analisadas. Conclui-se também que a maior integração dos setores de Tecnologia da Informação com a formulação de estratégias de negócio em empresas que não desenvolvam tecnologias pode conferir maior eficiência no planejamento e utilização de um sistema de Inteligência Competitiv

Lessons Learned from the ECML/PKDD Discovery Challenge on the Atherosclerosis Risk Factors Data

It becomes a good habit to organize a data mining cup, a competition or a challenge at machine learning or data mining conferences. The main idea of the Discovery Challenge organized at the European Conferences on Principles and Practice of Knowledge Discovery in Databases since 1999 was to encourage a collaborative research effort rather than a competition between data miners. Different data sets have been used for the Discovery Challenge workshops during the seven years. The paper summarizes our experience gained when organizing and evaluating the Discovery Challenge on the atherosclerosis risk factor data

DRIP - Data Rich, Information Poor: A Concise Synopsis of Data Mining

As production of data is exponentially growing with a drastically lower cost, the importance of data mining required to extract and discover valuable information is becoming more paramount. To be functional in any business or industry, data must be capable of supporting sound decision-making and plausible prediction. The purpose of this paper is concisely but broadly to provide a synopsis of the technology and theory of data mining, providing an enhanced comprehension of the methods by which massive data can be transferred into meaningful information

Sequential Event Prediction with Association Rules

We consider a supervised learning problem in which data are revealed sequentially and the goal is to determine what will next be revealed. In the context of this problem, algorithms based on association rules have a distinct advantage over classical statistical and machine learning methods; however, there has not previously been a theoretical foundation established for using association rules in supervised learning. We present two simple algorithms that incorporate association rules, and provide generalization guarantees on these algorithms based on algorithmic stability analysis from statistical learning theory. We include a discussion of the strict minimum support threshold often used in association rule mining, and introduce an "adjusted confidence" measure that provides a weaker minimum support condition that has advantages over the strict minimum support. The paper brings together ideas from statistical learning theory, association rule mining and Bayesian analysis

Efficient ticket routing by resolution sequence mining

IT problem management calls for quick identification of resolvers to reported problems. The efficiency of this process highly depends on ticket routing—transferring problem ticket among various expert groups in search of the right resolver to the ticket. To achieve efficient ticket routing, wise decision needs to be made at each step of ticket transfer to determine which expert group is likely to be, or to lead to the resolver. In this paper, we address the possibility of improving ticket routing efficiency by mining ticket resolution sequences alone, without accessing ticket content. To demonstrate this possibility, a Markov model is developed to statistically capture the right decisions that have been made toward problem resolution, where the order of the Markov model is carefully chosen according to the conditional entropy obtained from ticket data. We also design a search algorithm, called Variable-order Multiple active State search (VMS), that generates ticket transfer recommendations based on our model. The proposed framework is evaluated on a large set of realworld problem tickets. The results demonstrate that VMS significantly improves human decisions: Problem resolvers can often be identified with fewer ticket transfers

Evidence-based discovery

Both data-driven and human-centric methods have been used to better understand the scientific process. We describe a new framework called evidence-based discovery, to reconcile the gulf between the data-driven and human-centered approaches. Our goal is to provide a vision statement for how these (and other) approaches can be unified in order to better understand the complex-decision making that occurs when creating new knowledge. Despite the inevitable challenges, the combination of data and human-centric methods are required to understand, characterize, and ultimately accelerate science.ye

An automated technique for identifying associations between medications, laboratory results and problems

AbstractBackgroundThe patient problem list is an important component of clinical medicine. The problem list enables decision support and quality measurement, and evidence suggests that patients with accurate and complete problem lists may have better outcomes. However, the problem list is often incomplete.ObjectiveTo determine whether association rule mining, a data mining technique, has utility for identifying associations between medications, laboratory results and problems. Such associations may be useful for identifying probable gaps in the problem list.DesignAssociation rule mining was performed on structured electronic health record data for a sample of 100,000 patients receiving care at the Brigham and Women’s Hospital, Boston, MA. The dataset included 272,749 coded problems, 442,658 medications and 11,801,068 laboratory results.MeasurementsCandidate medication-problem and laboratory-problem associations were generated using support, confidence, chi square, interest, and conviction statistics. High-scoring candidate pairs were compared to a gold standard: the Lexi-Comp drug reference database for medications and Mosby’s Diagnostic and Laboratory Test Reference for laboratory results.ResultsWe were able to successfully identify a large number of clinically accurate associations. A high proportion of high-scoring associations were adjudged clinically accurate when evaluated against the gold standard (89.2% for medications with the best-performing statistic, chi square, and 55.6% for laboratory results using interest).ConclusionAssociation rule mining appears to be a useful tool for identifying clinically accurate associations between medications, laboratory results and problems and has several important advantages over alternative knowledge-based approaches

Measuring the impact of learning at the workplace on organisational performance

Purpose: The purpose of this article is to explore the importance of workplace learning in the context of performance measurement on an organisational level. It shows how workplace learning analytics can be grounded on professional identity transformation theory and integrated into performance measurement approaches to understand its organisation-wide impact. Design/methodology/approach: In a conceptual approach, a framework to measure the organisation-wide impact of workplace learning interventions has been developed. As a basis for the description of the framework, related research on relevant concepts in the field of performance measurement approaches, workplace learning, professional identity transformation, workplace and social learning analytics are discussed. A case study in a European Public Employment Service is presented. The framework is validated by qualitative evaluation data from three case studies. Finally, theoretical as well as practical implications are discussed. Findings: Professional identity transformation theory provides a suitable theoretical framework to gain new insights into various dimensions of workplace learning. Workplace learning analytics can reasonably be combined with classical performance management approaches to demonstrate its organisation-wide impact. A holistic and streamlined framework is perceived as beneficial by practitioners from several European Public Employment Services. Research limitations/implications: Empirical data originates from three case studies in the non-profit sector only. The presented framework needs to be further evaluated in different organisations and settings. Practical implications: The presented framework enables non-profit organisations to integrate workplace learning analytics in their organisation-wide performance measurement, which raises awareness for the importance of social learning at the workplace. Originality/value: The paper enriches the scarce research base about workplace learning analytics and its potential links to organisation-wide performance measurement approaches. In contrast to most previous literature, a thorough conceptualisation of workplace learning as a process of professional identity transformation is used