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.

The Creation of a Context to Knowledge Management and Innovation

2018 ◽  
pp. 383-396
Author(s):  
Rodrigo Dos Santos Costa
Keyword(s):  
Knowledge Management ◽  
Data Analysis ◽  
Tacit Knowledge ◽  
Knowledge Creation ◽  
Central Axis ◽  
The Internet ◽  
Information Economy ◽  
Public Data ◽  
And Storage ◽  
The Web

In spite of a contemporary discussion about the management of knowledge and the deep use of technologies focused on architecture, organization and knowledge detection based on organization inner data analysis, as well as public data available on the internet, it is necessary a critic look above the organization knowledge creation processes even as the load of tacit knowledge there is in an organization. It is observed that the evolution of technologies, such as mobile computing, the web, besides the architecture of the computers and their ability of handling and storage data, has brought to the information economy or the age of knowledge, diverting focus on people, the central axis of organizational knowledge, and their ability to reason, infer, make decisions, and above them all the processes of knowledge creation focused on the collaborative solution of problems and generation of innovation based on the socialization of knowledge.

Combining Data-Driven and User-Driven Evaluation Measures to Identify Interesting Rules

2009 ◽  
pp. 38-55 ◽  
Author(s):  
Solange Oliveira Rezende ◽  
Edson Augusto Melanda ◽  
Magaly Lika Fujimoto ◽  
Roberta Akemi Sinoara ◽  
Veronica Oliveira de Carvalho
Keyword(s):  
Data Mining ◽  
Data Driven ◽  
Objective Measures ◽  
Post Processing ◽  
Rule Mining ◽  
Evaluation Measures ◽  
Combining Data ◽  
Pros And Cons ◽  
The Subject

Association rule mining is a data mining task that is applied in several real problems. However, due to the huge number of association rules that can be generated, the knowledge post-processing phase becomes very complex and challenging. There are several evaluation measures that can be used in this phase to assist users in finding interesting rules. These measures, which can be divided into data-driven (or objective measures) and user-driven (or subjective measures), are first discussed and then analyzed for their pros and cons. A new methodology that combines them, aiming to use the advantages of each kind of measure and to make user’s participation easier, is presented. In this way, data-driven measures can be used to select some potentially interesting rules for the user’s evaluation. These rules and the knowledge obtained during the evaluation can be used to calculate user-driven measures, which are used to aid the user in identifying interesting rules. In order to identify interesting rules that use our methodology, an approach is described, as well as an exploratory environment and a case study to show that the proposed methodology is feasible. Interesting results were obtained. In the end of the chapter tendencies related to the subject are discussed.

A Hybrid Method for Prediction and Assessment Efficiency of Decision Making Units

2013 ◽  
Vol 5 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Iman Rahimi ◽  
Reza Behmanesh ◽  
Rosnah Mohd. Yusuff
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Rules ◽  
Large Data ◽  
Poultry Meat ◽  
Small Data ◽  
Data Set ◽  
Data Mining Techniques ◽  
Decision Making Units

The objective of this article is an evaluation and assessment efficiency of the poultry meat farm as a case study with the new method. As it is clear poultry farm industry is one of the most important sub- sectors in comparison to other ones. The purpose of this study is the prediction and assessment efficiency of poultry farms as decision making units (DMUs). Although, several methods have been proposed for solving this problem, the authors strongly need a methodology to discriminate performance powerfully. Their methodology is comprised of data envelopment analysis and some data mining techniques same as artificial neural network (ANN), decision tree (DT), and cluster analysis (CA). As a case study, data for the analysis were collected from 22 poultry companies in Iran. Moreover, due to a small data set and because of the fact that the authors must use large data set for applying data mining techniques, they employed k-fold cross validation method to validate the authors’ model. After assessing efficiency for each DMU and clustering them, followed by applied model and after presenting decision rules, results in precise and accurate optimizing technique.

Knowledge Management of Healthcare by Clinical-Pathways

2010 ◽  
Vol 09 (02) ◽  
pp. 119-125 ◽  
Author(s):  
Tomoyoshi Yamazaki ◽  
Katsuhiro Umemoto
Keyword(s):  
Knowledge Management ◽  
Theoretical Model ◽  
Case Studies ◽  
Knowledge Creation ◽  
Clinical Pathways ◽  
Medical Doctors ◽  
New Knowledge ◽  
Healthcare Process ◽  
Knowledge Intensive

Healthcare is a knowledge-intensive service provided by professionals, such as medical doctors, nurses, and pharmacists. Clinical-pathways are used by many healthcare organisations (HCOs) as a tool for performing the healthcare process, sharing and utilising knowledge from different professionals. In this paper, case studies were performed at two HCOs that use clinical-pathways actively in the healthcare process. Theoretical model construction, sharing, utilisation, and creation of the knowledge by different professionals, were tested by the case study of two HCOs which use clinical pathways actively. The theoretical model was a knowledge creation model which creates new knowledge continuously. In this theoretical model, clinical-pathways are suggested to be an effective tool for knowledge management in healthcare.

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 Knowledge Management Processes and Organisational Effectiveness in Nigerian Deposit Money Banks

2021 ◽  
Keyword(s):  
Knowledge Management ◽  
Knowledge Creation ◽  
Survey Design ◽  
Principal Component ◽  
Intangible Asset ◽  
Organisational Effectiveness ◽  
Knowledge Processes ◽  
Management Processes ◽  
And Storage ◽  
Knowledge Management Processes

The main objective of the study was to examine the effect of knowledge management processes on organisational effectiveness in Nigerian deposit money banks. Specifically, the study examines knowledge creation, acquisition, sharing and storage and organisational effectiveness in Nigerian deposit money banks. The data for the study was generated through a questionnaire which yielded 359 valid responses and was determined scientifically using Krejcie & Morgan (1970) from selected Nigerian deposit money banks with their operational head offices in Lagos-Nigeria as the population of the study. The study adopted survey design and the validity results of KMO, and Bartletts test indicate that variables are highly significant, and principal component analysis was suitable (.837). The test-retest result of reliability reported a reliability index of 0.872. Multiple regressions with the aid of SPSS version 21 were used to test the formulated hypotheses. Findings of the study show that knowledge creation (32.3%), acquisition (62.1%), sharing (42.4%), and storage (46.2%) have a positive significant effect on organisational effectiveness. The study recommends among others that management of Nigerian deposit money banks should aptly designed and encourage knowledge processes in their banks since it constitutes a valuable intangible asset for creating and sustaining exceptional competitive advantages in the Nigerian banking industry. Keywords: Deposit Money Banks, Organisational Effectiveness, Knowledge Management Processes

scholarly journals Quantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks

2021 ◽  
Vol 2042 (1) ◽  
pp. 012002
Author(s):  
Roberto Castello ◽  
Alina Walch ◽  
Raphaël Attias ◽  
Riccardo Cadei ◽  
Shasha Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
State Of The Art ◽  
Solar Panel ◽  
Post Processing ◽  
Processing Step ◽  
Recent Method

Abstract The integration of solar technology in the built environment is realized mainly through rooftop-installed panels. In this paper, we leverage state-of-the-art Machine Learning and computer vision techniques applied on overhead images to provide a geo-localization of the available rooftop surfaces for solar panel installation. We further exploit a 3D building database to associate them to the corresponding roof geometries by means of a geospatial post-processing approach. The stand-alone Convolutional Neural Network used to segment suitable rooftop areas reaches an intersection over union of 64% and an accuracy of 93%, while a post-processing step using building database improves the rejection of false positives. The model is applied to a case study area in the canton of Geneva and the results are compared with another recent method used in the literature to derive the realistic available area.

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA C4.5 TEHADAP PENGARUH PENJUALAN KOPI PADA PT. JPW INDONESIA

2020 ◽  
Vol 3 (1) ◽  
pp. 40-54
Author(s):  
Ikong Ifongki
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Rules ◽  
Large Data ◽  
Added Value ◽  
Data Set ◽  
Use Of Data ◽  
Decision Tree Classification ◽  
C4.5 Algorithm

Data mining is a series of processes to explore the added value of a data set in the form of knowledge that has not been known manually. The use of data mining techniques is expected to provide knowledge - knowledge that was previously hidden in the data warehouse, so that it becomes valuable information. C4.5 algorithm is a decision tree classification algorithm that is widely used because it has the main advantages of other algorithms. The advantages of the C4.5 algorithm can produce decision trees that are easily interpreted, have an acceptable level of accuracy, are efficient in handling discrete type attributes and can handle discrete and numeric type attributes. The output of the C4.5 algorithm is a decision tree like other classification techniques, a decision tree is a structure that can be used to divide a large data set into smaller sets of records by applying a series of decision rules, with each series of division members of the resulting set become similar to each other. In this case study what is discussed is the effect of coffee sales by processing 106 data from 1087 coffee sales data at PT. JPW Indonesia. Data samples taken will be calculated manually using Microsoft Excel and Rapidminer software. The results of the calculation of the C4.5 algorithm method show that the Quantity and Price attributes greatly affect coffee sales so that sales at PT. JPW Indonesia is still often unstable.

Knowledge Creation

2011 ◽  
pp. 527-538 ◽  
Author(s):  
Nilmini Wickramasinghe
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Organizational Performance ◽  
Knowledge Creation ◽  
Business Processes ◽  
Business Environment ◽  
Sustainable Competitive Advantage ◽  
Continuous Innovation ◽  
Knowledge Spiral ◽  
Business Challenges

Knowledge management (KM) is a newly emerging approach aimed at addressing today’s business challenges to increase efficiency and efficacy of core business processes, while simultaneously incorporating continuous innovation. The need for knowledge management is based on a paradigm shift in the business environment where knowledge is now considered to be central to organizational performance and integral to the attainment of a sustainable competitive advantage (Davenport & Grover, 2001; Drucker, 1993). Knowledge creation is not only a key first step in most knowledge management initiatives, but also has far reaching implications on consequent steps in the KM process, thus making knowledge creation an important focus area within knowledge management. Currently, different theories exist for explaining knowledge creation. These tend to approach the area of knowledge creation from either a people perspective—including Nonaka’s Knowledge Spiral, as well as Spender’s and Blackler’s respective frameworks—or from a technology perspective—namely, the KDD process and data mining.

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