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Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows...

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Bibliographic Details
Main Author: Portela, Filipe
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
Machine Learning
Data Analytics
Spatio-temporal
Driving Behavior
Data Mining
Multimedia Document Retrieval
Arcgis
Journalists
Rule Based Systems
Webgis
Lorawan
Media Criticism
Data Science
Authorship
N/a
Information Systems
Data Analysis
Data Augmentation
Digital Humanities
Customer Relationship Management (crm)
Textbook Research
Big Data
Deep Learning
Humanities
Dashboard
Decision Systems
Neural Networks
Deep Features
Semantic Information Retrieval
Distracted Driving
Governance And Social Institutions
Interdisciplinary Research
Automation
Media Analytics
Web Intelligence
News Media
Box-counting Framework
The Things Network
Fractal Dimension
Feature Extraction
Text Mining
Sars-cov-2
Covid-19
Esp32 Microcontroller
Information Technology Industries / Bicssc
Script Python
Geoinformation Technology
Databases
Public Health
Computer Science / Bicssc
Chatbots
Forensic Intelligence
Crisis Reporting
Attribution
Internet Of Things
Social Sciences
Linked Open Data
Territorial Road Network
Content Base Image Retrieval
Artificial Intelligence
Classification
Economic Determinants Of Open Data
Smart Homes
E-commerce
Rough Sets
Open Government Data
Prediction By Partial Matching
Iocovid19
Activity Recognition
Digital Infrastructures
Adaptation Process
Driving Operation Area
Ict
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary:Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (254 p.)
ISBN:9783036543161
9783036543154
books978-3-0365-4315-4

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