Uso de Data Mining e do ECR para incremento da competitividade de pequenas e médias empresas: um estudo multicaso sobre varejo e indústria alimentícia

O setor supermercadista sofreu grandes alterações nos últimos anos, principalmente com o avanço das tecnologias, a competição, a concentração e algumas insuficiências em seus processos. Estes e outros fatores favoreceram ao surgimento do movimento de ECR (Resposta de Consumidor Eficiente) que procura criar um relacionamento mais forte entre indústria e varejo através de novas visões para suas estratégias operacionais. A evolução das tecnologias de informação permitiram ao setor varejista gerar uma maior volume de dados a partir, principalmente, de seus check-outs. Entretanto, estes dados nem sempre são armazenados de forma correta ou utilizados de forma a se aproveitar a plenitude das informações neles contidas. O processo de transformar os dados em informação e conhecimento vem evoluindo constantemente. Uma das atuais metodologias de trabalhar dados é o Data Mining ou Mineração de Dados, que pode ser descrito como sendo uma variedade de ferramentas e estratégias que processam dados aumentando a utilidade destes em bancos de dados. Este trabalho analisa através de um estudo multicaso exploratório na região de Ribeirão Preto, no interior de São Paulo, a avaliação da capacidade do uso da tecnologia Data Mining para o fortalecimento do movimento ECR, principalmente em pequenos e médios varejistas e indústrias alimentícias, no sentido de oferecer a estes um diferencial de negociação para formação de alianças estratégias.

Diseño y desarrollo de un servicio big data en la nube para búsqueda, compartición de ficheros y data mining

En esta memoria se presenta el diseño y desarrollo de una aplicación en la nube destinada a la compartición de objetos y servicios. El desarrollo de esta aplicación surge dentro del proyecto de I+D+i, SITAC: Social Internet of Things – Apps by and for the Crowd ITEA 2 11020, que trata de crear una arquitectura integradora y un “ecosistema” que incluya plataformas, herramientas y metodologías para facilitar la conexión y cooperación de entidades de distinto tipo conectadas a la red bien sean sistemas, máquinas, dispositivos o personas con dispositivos móviles personales como tabletas o teléfonos móviles. El proyecto innovará mediante la utilización de un modelo inspirado en las redes sociales para facilitar y unificar las interacciones tanto entre personas como entre personas y dispositivos. En este contexto surge la necesidad de desarrollar una aplicación destinada a la compartición de recursos en la nube que pueden ser tanto lógicos como físicos, y que esté orientada al big data. Ésta será la aplicación presentada en este trabajo, el “Resource Sharing Center”, que ofrece un servicio web para el intercambio y compartición de contenido, y un motor de recomendaciones basado en las preferencias de los usuarios. Con este objetivo, se han usado tecnologías de despliegue en la nube, como Elastic Beanstalk (el PaaS de Amazon Web Services), S3 (el sistema de almacenamiento de Amazon Web Services), SimpleDB (base de datos NoSQL) y HTML5 con JavaScript y Twitter Bootstrap para el desarrollo del front-end, siendo Python y Node.js las tecnologías usadas en el back end, y habiendo contribuido a la mejora de herramientas de clustering sobre big data. Por último, y de cara a realizar el estudio sobre las pruebas de carga de la aplicación se ha usado la herramienta ApacheJMeter.

Towards a model-driven engineering approach of data mining

Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

Integrating the development of data mining and data warehouses via model-driven engineering

Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.

Open business intelligence: on the importance of data quality awareness in user-friendly data mining

Citizens demand more and more data for making decisions in their daily life. Therefore, mechanisms that allow citizens to understand and analyze linked open data (LOD) in a user-friendly manner are highly required. To this aim, the concept of Open Business Intelligence (OpenBI) is introduced in this position paper. OpenBI facilitates non-expert users to (i) analyze and visualize LOD, thus generating actionable information by means of reporting, OLAP analysis, dashboards or data mining; and to (ii) share the new acquired information as LOD to be reused by anyone. One of the most challenging issues of OpenBI is related to data mining, since non-experts (as citizens) need guidance during preprocessing and application of mining algorithms due to the complexity of the mining process and the low quality of the data sources. This is even worst when dealing with LOD, not only because of the different kind of links among data, but also because of its high dimensionality. As a consequence, in this position paper we advocate that data mining for OpenBI requires data quality-aware mechanisms for guiding non-expert users in obtaining and sharing the most reliable knowledge from the available LOD.

Towards a reverse engineering approach for guiding user in applying data mining

Comunicación presentada en las XVI Jornadas de Ingeniería del Software y Bases de Datos, JISBD 2011, A Coruña, 5-7 septiembre 2011.

Ferramentas open source de Data Mining

Em época de crise financeira, as ferramentas open source de data mining representam uma nova tendência na investigação, educação e nas aplicações industriais, especialmente para as pequenas e médias empresas. Com o software open source, estas podem facilmente iniciar um projeto de data mining usando as tecnologias mais recentes, sem se preocuparem com os custos de aquisição das mesmas, podendo apostar na aprendizagem dos seus colaboradores. Os sistemas open source proporcionam o acesso ao código, facilitando aos colaboradores a compreensão dos sistemas e algoritmos e permitindo que estes o adaptem às necessidades dos seus projetos. No entanto, existem algumas questões inerentes ao uso deste tipo de ferramenta. Uma das mais importantes é a diversidade, e descobrir, tardiamente, que a ferramenta escolhida é inapropriada para os objetivos do nosso negócio pode ser um problema grave. Como o número de ferramentas de data mining continua a crescer, a escolha sobre aquela que é realmente mais apropriada ao nosso negócio torna-se cada vez mais difícil. O presente estudo aborda um conjunto de ferramentas de data mining, de acordo com as suas características e funcionalidades. As ferramentas abordadas provém da listagem do KDnuggets referente a Software Suites de Data Mining. Posteriormente, são identificadas as que reúnem melhores condições de trabalho, que por sua vez são as mais populares nas comunidades, e é feito um teste prático com datasets reais. Os testes pretendem identificar como reagem as ferramentas a cenários diferentes do tipo: performance no processamento de grandes volumes de dados; precisão de resultados; etc. Nos tempos que correm, as ferramentas de data mining open source representam uma oportunidade para os seus utilizadores, principalmente para as pequenas e médias empresas, deste modo, os resultados deste estudo pretendem ajudar no processo de tomada de decisão relativamente às mesmas.

Fuzzy logic in web data mining for website assessment

This paper proposes a novel application of fuzzy logic to web data mining for two basic problems of a website: popularity and satisfaction. Popularity means that people will visit the website while satisfaction refers to the usefulness of the site. We will illustrate that the popularity of a website is a fuzzy logic problem. It is an important characteristic of a website in order to survive in Internet commerce. The satisfaction of a website is also a fuzzy logic problem that represents the degree of success in the application of information technology to the business. We propose a framework of fuzzy logic for the representation of these two problems based on web data mining techniques to fuzzify the attributes of a website.

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes. which may be (ens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far. most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be, predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply-demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour. electricity), demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. Crown Copyright (C) 2004 Published by Elsevier B.V. All rights reserved.

Data mining and simulation: a grey relationship demonstration

Fuzzy data has grown to be an important factor in data mining. Whenever uncertainty exists, simulation can be used as a model. Simulation is very flexible, although it can involve significant levels of computation. This article discusses fuzzy decision-making using the grey related analysis method. Fuzzy models are expected to better reflect decision-making uncertainty, at some cost in accuracy relative to crisp models. Monte Carlo simulation is used to incorporate experimental levels of uncertainty into the data and to measure the impact of fuzzy decision tree models using categorical data. Results are compared with decision tree models based on crisp continuous data.

IJDWM Special Issue: Advances in Data Mining Applications

This special issue is a collection of the selected papers published on the proceedings of the First International Conference on Advanced Data Mining and Applications (ADMA) held in Wuhan, China in 2005. The articles focus on the innovative applications of data mining approaches to the problems that involve large data sets, incomplete and noise data, or demand optimal solutions.

Diagnosis of diabetes mellitus: case of different cutoff values: A data mining approach

Objective: An estimation of cut-off points for the diagnosis of diabetes mellitus (DM) based on individual risk factors. Methods: A subset of the 1991 Oman National Diabetes Survey is used, including all patients with a 2h post glucose load >= 200 mg/dl (278 subjects) and a control group of 286 subjects. All subjects previously diagnosed as diabetic and all subjects with missing data values were excluded. The data set was analyzed by use of the SPSS Clementine data mining system. Decision Tree Learners (C5 and CART) and a method for mining association rules (the GRI algorithm) are used. The fasting plasma glucose (FPG), age, sex, family history of diabetes and body mass index (BMI) are input risk factors (independent variables), while diabetes onset (the 2h post glucose load >= 200 mg/dl) is the output (dependent variable). All three techniques used were tested by use of crossvalidation (89.8%). Results: Rules produced for diabetes diagnosis are: A- GRI algorithm (1) FPG>=108.9 mg/dl, (2) FPG>=107.1 and age>39.5 years. B- CART decision trees: FPG >=110.7 mg/dl. C- The C5 decision tree learner: (1) FPG>=95.5 and 54, (2) FPG>=106 and 25.2 kg/m2. (3) FPG>=106 and =133 mg/dl. The three techniques produced rules which cover a significant number of cases (82%), with confidence between 74 and 100%. Conclusion: Our approach supports the suggestion that the present cut-off value of fasting plasma glucose (126 mg/dl) for the diagnosis of diabetes mellitus needs revision, and the individual risk factors such as age and BMI should be considered in defining the new cut-off value.

Load profiling and data mining techniques in electricity deregulated market

This paper presents load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, a data mining technique, to determine the load profiles for different types of customers. In this paper, the current load profiling methods are compared using data mining techniques, by analysing and evaluating these classification techniques. The objective of this study is to determine the best load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the deregulated market. This paper focuses mainly on the comparative analysis of the classification techniques selected; a forthcoming paper will focus on the detection and prediction methods.