Wyniki wyszukiwania - Biblioteka Nauki

1

Integrating simulation into data mining

100%

Bubeník P. , Horák F.

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2015

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tom Vol. 18, nr 1

8--12

EN

This article describes a way to integrate a simulation into a data mining technology, particularly with respect to CRISP-DM standard. Aim of this approach is to enable data mining in various cases, when available data do not meet all the requirements for data mining analysis. Solution is primarily tied to manufacturing companies environment, where there are many processes, that can be simulated, and thus the acquisition of sufficient volume of data for further analysis is possible.

PL

W artykule opisano sposób integracji oprogramowania symulacyjnego z technologią eksploracji danych, z szczególnym uwzględnieniem standardu CRISP-DM. Celem takiego podejścia jest pozyskanie danych w przypadkach, gdy dostępne dane nie spełniają wszystkich wymagań związanych z analizą w systemie eksploracji danych. Zaproponowane rozwiązanie jest przede wszystkim związane z praktyką produkcyjną, gdzie realizowanych jest wiele procesów, które można komputerowo zasymulować, a tym samym można pozyskać wystarczające ilości danych do dalszych analiz.

2

Data engineering in CRISP-DM process production data – case study

83%

Brzozowska J. , Pizoń J. , Baytikenova G. , Gola A. , Zakimova A. , Piotrowska K.

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tom Vol. 19, no 3

83--95

EN

The paper describes one of the methods of data acquisition in data mining models used to support decision-making. The study presents the possibilities of data collection using the phases of the CRISP-DM model for an organization and presents the possibility of adapting the model for analysis and management in the decision-making process. The first three phases of implementing the CRISP-DM model are described using data from an enterprise with small batch production as an example. The paper presents the CRISP-DM based model for data mining in the process of predicting assembly cycle time. The developed solution has been evaluated using real industrial data and will be a part of methodology that allows to estimate the assembly time of a finished product at the quotation stage, i.e., without the detailed technology of the product being known.

3

Machine learning-based business rule engine data transformation over high-speed networks

67%

Neelima K. , Vasundra S.

Computer Assisted Methods in Engineering and Science

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2023

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tom Vol. 30, no. 1

55--71

EN

Raw data processing is a key business operation. Business-specific rules determine howthe raw data should be transformed into business-required formats. When source datacontinuously changes its formats and has keying errors and invalid data, then the effectiveness of the data transformation is a big challenge. The conventional data extraction andtransformation technique produces a delay in handling such data because of continuousfluctuations in data formats and requires continuous development of a business rule engine.The best business rule engines require near real-time detection of business rule and datatransformation mechanisms utilizing machine learning classification models. Since data iscombined from numerous sources and older systems, it is challenging to categorize andcluster the data and apply suitable business rules to turn raw data into the business-required format. This paper proposes a methodology for designing ensemble machine learning techniques and approaches for classifying and segmenting registered numbersof registered title records to choose the most suitable business rule that can convert theregistered number into the format the business expects, allowing businesses to provide customers with the most recent data in less time. This study evaluates the suggested modelby gathering sample data and analyzing classification machine learning (ML) models todetermine the relevant business rule. Experimentation employed Python, R, SQL storedprocedures, Impala scripts, and Datameer tools.

4

Zastosowanie modelu Cross Industry Standard Process for Data Mining (CRISP-DM) w badaniach postaw i opinii pracowników

67%

Kulikowski K.

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2015

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tom z. 82

111--121

PL

Celem artykułu jest przedstawienie modelu Cross Industry Standard Process for Data Mining (CRISP-DM) jako kompleksowego modelu zbierania i analizy danych w badaniach postaw i opinii pracowników. Model CRISP-DM przez ustrukturyzowanie i porządkowanie procesu badania może usprawnić zarządzanie nim, a także umożliwić efektywniejsze odkrywanie wiedzy ze zgromadzonych danych.

EN

The aim of this study is to present a Cross Industry Standard Process for Data Mining (CRISP-DM), as a model of collecting and analyzing data from employees attitudes and opinions research. CRISP-DM model through structuring and organization of the research process can improve research management and enable more efficient knowledge discovery from collected data.

5

Eksploracja danych w kontekście procesu Knowledge Discovery In Databases (KDD) i metodologii Cross-Industry Standard Process For Data Mining (CRISP-DM)

59%

Mirończuk M. , Maciak T.

Metody Informatyki Stosowanej

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2009

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tom nr 2 (19)

65-79

EN

Article aims at introducing for the readers few problems connected with KDD process, Data Mining project modeling with the use of CRlSP-DM The systemized knowledge, aproaches to and generic terms was presented in the article. In the first part article describes approach to Data Exploration as one of the KDD cycle, which is specialized Knowledge Discovery process. Then article takes the subject of CRlSP-DM method. The context of method usage depending on scale and integration of project, which they concern - ivestigate of useing text mining in Inteligent Decission Support System (IDSS) develop by informatic faculty of Fire Service. At the end of the article the summary was made, which contains common features between the two looks on the exploration and extracting knowledge from data bases.