Decision Making and Decision Styles of Project Managers: A Preliminary Exploration Using Data Mining Techniques

2015 ◽  
pp. 278-297 ◽  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Project Managers ◽  
Data Mining Techniques ◽  
Decision Styles ◽  
Using Data

Using Data Mining Techniques to Discover Patterns in an Airline's Flight Hours Assignments

2021 ◽  
pp. 107-126
Author(s):  
Francisco Javier Villar Martín ◽  
Jose Luis Castillo Sequera ◽  
Miguel Angel Navarro Huerga
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information System ◽  
Predictive Modeling ◽  
Data Model ◽  
Free Software ◽  
Data Mining Techniques ◽  
Visualization Tools ◽  
Using Data

The quality of a company's information system is essential and also its physical data model. In this article, the authors apply data mining techniques in order to generate knowledge from the information system's data model, and also to discover and understand hidden patterns within data that regulate the planning of flight hours of pilots and copilots in an airline. With this objective, they use Weka free software which offers a set of algorithms and visualization tools geared to data analysis and predictive modeling of information systems. Firstly, they apply clustering to study the information system and analyze data model; secondly, they apply association rules to discover connection patterns in data; and finally, they generate a decision tree to classify and extract more specific patterns. The authors suggest conclusions according these information system's data to improve future decision making in an airline's flight hours assignments.

Sistema bioinformático ganadero para la toma de decisiones en procesos de transferencia de embriones [Cattle’s bioinformatics system for decision making in embryo transfer processes]

2014 ◽  
Author(s):  
Nestor Jaime Castaño Pérez ◽  
Félix Antonio Céspedes Giraldo ◽  
Octavio Isaza Londoño ◽  
Jhon Fredy Betancur Pérez
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Embryo Transfer ◽  
Sql Server ◽  
Process Data ◽  
Data Mining Techniques ◽  
Analysis And Design ◽  
Biological Variables ◽  
Toma De Decisiones ◽  
Using Data

Resumen La transferencia de embriones al útero de hembras receptoras bovinas, ha sido demostrada como una práctica que aumenta la incidencia de gestaciones. El estudio tuvo como objetivo el desarrollo de un sistema informático que permite la captura, análisis y gestión de la información de los procesos en la transferencia de embriones para la toma de decisiones. Se recolectaron, monitorearon y analizaron los datos del proyecto de ‘súper-ovulación y transferencia de embriones en bovinos’, en relación a todas las variables biológicas antes, durante y después de un tratamiento, en hembras donantes y receptoras. Se digitalizaron datos físicos y, en paralelo, se realizó un análisis y diseño para la construcción de un sistema informático para la captura datos en tiempo real; utilizando las tecnologías de programación web en móviles; HTML5, Jquery Mobile, en servidor, Microsoft .NET, de sincronización con sistemas heterogéneos, AJAX, y de almacenamiento en base datos local en móviles, SQLite y en servidor Microsoft SQL Server. Se utilizaron técnicas de minería de datos con la herramienta MATLAB® 7.10.0.499 (R2010a), y así se obtuvo un modelo matemático predictivo y fiable en la toma de decisiones con respecto a la obtención de mayores índices de preñez en bovinos. Palabras clave: Minería de datos, transferencia de embriones bovinos, biotecnología bovina, arquitectura de software distribuida en dispositivos móviles.   Abstract Embryo transfer to receptor female cattle has been proved as a practice that raises the incidence of gestations. This study has the objective of developing an informatics system that allows for the capture, analysis and management of information related to the process of embryo transfer that facilitates decision making in this specific process. Data related to the ‘Super-ovulation and embryo transfer in cattle’ was collected, monitored and analyzed, considering all biological variables ex-ante and ex-post a treatment in female donors and receptors. Physical data was digitized and in parallel a process of analysis and design was developed in order to build an information system that allows for real time data collection; using web programming technologies in mobile, HTML5, jQuery Mobile, server, Microsoft. NET, synchronization with heterogeneous systems, AJAX, and storage in mobile local database, SQLite and Microsoft SQL Server. Stored data was analyzed using data mining techniques with MATLAB® 7.10.0.499 (R2010a). By using data mining techniques on biological variables, it was possible to obtain a reliable predictive mathematical model in decision making related to the embryo transfer process that allows to raise the pregnancy success rate in cattle. Keywords: Data mining, cattle embryo transfer, cattle biotechnology, distributed software architecture for mobile devices.

scholarly journals Heart Diseases Forecast using Data Mining Techniques and Tools

2019 ◽  
Vol 9 (2) ◽  
pp. 2726-2729
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Heart Diseases ◽  
Complex Data ◽  
Huge Amount ◽  
Data Mining Techniques ◽  
Statistical Knowledge ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Using Data

Data Mining have always been a field and combination of both computer science and statistical knowledge. From the beginning it is used to ascertain designs, patterns and arrangements which are formed in the information pool. The motive of the data mining development is to produce useful information from the pool of raw data and convert it into useful information which can be used for future arrangements. The tools which are used in data mining are helpful in predicting the future trends and predictions across the market, which also help in decision making and building the knowledge to make decisions. The “Healthcare Industry” is generally information rich. It has been collecting data to improve the continuing problems and help to identify the solutions for that problems. Data mining techniques can be used to predict heart conditions from the voluminous and complex data which are kept by the hospitals for decision making which are difficult to analyze by outmoded methods. Unfortunately, outmoded methods are less accurate in discovering hidden information from effective decision making. Data mining helps in altering the huge amount of data into knowledge driven which takes, as compared to others, less time and effort for the prediction and with greater accuracy. Our effort is to apply different data mining techniques that are used to solve the problem of biased forecasts and decision making and help in calculating the results with more accuracy.

scholarly journals Application and Analysis of Retail Inventory Using Data Mining Techniques

2020 ◽  
pp. 27-33
Author(s):  
MD Imtiaz Uddin Adnan ◽  
Redoyan Raz ◽  
Tanvir Ahmed ◽  
A. H. M Saiful Islam
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Inventory Management ◽  
21St Century ◽  
Research Paper ◽  
Retail Sales ◽  
Data Mining Techniques ◽  
Using Data ◽  
Almost All ◽  
Attention To Detail

Data mining is one of the most essential tools for gathering information from different datasets in almost all recent industries. In this 21st-century, data mining gained attention because of its significance in decision making, and it has become a key component in various industries such as retail. Inventory management requires pre-planned goals and attention to detail, and prioritizing items that require less attention can be a waste of time and resources. Learning indications about customers’ shopping patterns by showing associations among various provides significant value in managing retail inventory. In the present research paper, popular data mining techniques have been applied and analyzed for multi-item inventory management in retail sales stores to show how data mining techniques can optimize and organize the retail inventory.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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