Extraction of Classification Rules in Databases through Metaheuristic Procedures Based on GRASP

2014 ◽  
Vol 945-949 ◽  
pp. 3369-3375
Author(s):  
Genival Pavanelli ◽  
Maria Teresinha Arns Steiner ◽  
Anderson Roges Teixeira Góes ◽  
Alessandra Memari Pavanelli ◽  
Deise Maria Bertholdi Costa
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Knowledge Discovery In Databases ◽  
Personal Knowledge ◽  
Professional Experience ◽  
Search Procedure ◽  
Adaptive Search ◽  
Classification Rules ◽  
Constant Attention ◽  
Whole Process

The process of knowledge management in the several areas of society requires constant attention to the multiplicity of decisions to be made about the activities in organizations that constitute them. To make these decisions one should be cautious in relying only on personal knowledge acquired through professional experience, since the whole process based on this method would be slow, expensive and highly subjective. To assist in this management, it is necessary to use mathematical tools that fulfill the purpose of extracting knowledge from database. This article proposes the application of Greedy Randomized Adaptive Search Procedure (GRASP) as Data Mining (DM) tool within the process called Knowledge Discovery in Databases (KDD) for the task of extracting classification rules in databases.

scholarly journals Contributions of KDD to the Knowledge Management Process

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Hércules Antonio Do Prado ◽  
Paulo de Tarso Costa de Sousa ◽  
Eduardo Amadeu Moresi ◽  
Marcelo Ladeira
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Knowledge Creation ◽  
Federal District ◽  
Knowledge Discovery In Databases ◽  
Post Processing ◽  
Data Set ◽  
Processing Step ◽  
And Storage

Knowledge Discovery in Databases (KDD), as any organizational process, is carried out beneath a Knowledge Management (KM) model adopted (even informally) by a corporation. KDD is grossly described in three steps: pre-processing, data mining, and post-processing. The latter is mainly related to the task of transforming in knowledge the patterns issued in the data mining step. On the other hand, KM comprises the following phases, in which knowledge is the subject of the actions: identification of abilities, acquisition, selection and validation, organization and storage, sharing, application, and creation. Although there are many overlaps between KDD and KM, one of them is broadly recognized: the point in which knowledge arises. This paper concerns a study aimed at clarifying relations between the overlapping areas of KDD and knowledge creation, in KM. The work is conducted by means of a case study using the data from the Electoral Court of the Federal District (ECFD), Brazil. The study was developed over a 1.717.000-citizens data set from which data mining models were built by applying algorithms from Weka. It was observed that, although the importance of Information Technology is well recognized in the KM realm, the techniques of KDD deserve a special place in the knowledge creation phase of KM. Moreover, beyond the overlap of post- processing and knowledge creation, other steps of KDD can contribute significantly to KM. An example is the fact that one important decision taken from the ECFD board was taken on the basis of a knowledge acquired from the pre-processing step of KDD.

Designing an Automated Composter for Food Waste Management with the Implementation of Internet of Things

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
N. A. Fakharulrazi ◽  
◽  
F. Yakub ◽  
M. N. Baba ◽  
L. F. Zhao ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Food Waste ◽  
High Efficiency ◽  
Animal Feed ◽  
Modern Technology ◽  
Constant Attention ◽  
Whole Process ◽  
Handling Cost ◽  
Unpleasant Odor ◽  
Environmentally Friendly Method

Composting food waste is a delicate procedure that requires specific infrastructure and machinery that can gradually transform the wastes to nutrient-rich manure. Nevertheless, it also desires a constant attention by experts to achieve a quality outcome. Therefore, automatic composting machinery is a promising new idea as modern technology is taking over the world with it high efficiency. The objective of this paper is to build a fully automated composting machine that can help to reduce food waste using a more efficient and environmentally friendly method. This machine has its special features of heating, cooling and grinding which is simple and easy to use for every consumer at just one touch of a button. In addition, it uses a special filter to eliminate unpleasant odor to ensure consumer’s space of mind. The composting process uses node microcontroller (MCU) to run its operation and Internet of Things (IoT) with a developed mobile application to measure the amount of food waste, current process and its moisture content before turning the waste into high nutrient flakes at around 10% of its original volume. It will also notify the consumer when the whole process is done and the final product is ready to use. The produced flakes are good for nurturing soils, use as fertilizer, and renewable source of energy or animal feed. The benefit is to help reduce handling cost of waste at landfill. Excessive logistical energy is required to send food waste to landfill if conventional equipment is applied. This product has a high potential to penetrate the end users who usually cooks at home and also the industrial food manufacturers whether from medium to large which produces a lot of raw waste. Essentially, this machine allows food waste, through implementation of IoT to be converted to usable fertilizer.

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

Semantic Desktop for the End-UserSemantic Desktop für Anwender

i-com ◽  
2009 ◽  
Vol 8 (3) ◽  
pp. 25-32 ◽  
Author(s):  
Gunnar Aastrand Grimnes ◽  
Benjamin Adrian ◽  
Sven Schwarz ◽  
Heiko Maus ◽  
Kinga Schumacher ◽  
...  
Keyword(s):  
Knowledge Management ◽  
Knowledge Work ◽  
Personal Knowledge ◽  
Term Study ◽  
The Core ◽  
Personal Knowledge Management ◽  
Long Term Study ◽  
Core Components

AbstractThis article describes the Semantic Desktop. We give insights into the core services that aim to improve personal knowledge management on the desktop. We describe these core components of our Semantic Desktop system and give evaluation results. Results of a long-term study reveal effects of using the Semantic Desktop on personal knowledge work.

scholarly journals Proposta de metodologia para a criação de etiqueta de classificação – estudo de caso: desempenho escolar

2016 ◽  
Vol 23 (1) ◽  
pp. 177-191
Author(s):  
Anderson Roges Teixeira Góes ◽  
Maria Teresinha Arns Steiner
Keyword(s):  
Data Mining ◽  
Support Vector Machines ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Support Vector ◽  
Vector Machines

Resumo A qualidade na educação tem sido objeto de muita discussão, seja nas escolas e entre seus gestores, seja na mídia ou na literatura. No entanto, uma análise mais profunda na literatura parece não indicar técnicas que explorem bancos de dados com a finalidade de obter classificações para o desempenho escolar, nem tampouco há um consenso sobre o que seja “qualidade educacional”. Diante deste contexto, neste artigo, é proposta uma metodologia que se enquadra no processo KDD (Knowledge Discovery in Databases, ou seja, Descoberta de Conhecimento em Bases de Dados) para a classificação do desempenho de instituições de ensino, de forma comparativa, com base nas notas obtidas na Prova Brasil, um dos itens integrantes do Índice de Desenvolvimento da Educação Básica (IDEB) no Brasil. Para ilustrar a metodologia, esta foi aplicada às escolas públicas municipais de Araucária, PR, região metropolitana de Curitiba, PR, num total de 17, que, por ocasião da pesquisa, ofertavam Ensino Fundamental, considerando as notas obtidas pela totalidade dos alunos dos anos iniciais (1º. ao 5º. ano do ensino fundamental) e dos anos finais (6º. ao 9º. ano do ensino fundamental). Na etapa de Data Mining, principal etapa do processo KDD, foram utilizadas três técnicas de forma comparativa para o Reconhecimento de Padrões: Redes Neurais Artificiais; Support Vector Machines; e Algoritmos Genéticos. Essas técnicas apresentaram resultados satisfatórios na classificação das escolas, representados por meio de uma “Etiqueta de Classificação do Desempenho”. Por meio desta etiqueta, os gestores educacionais poderão ter melhor base para definir as medidas a serem adotadas junto a cada escola, podendo definir mais claramente as metas a serem cumpridas.

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