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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 usin...

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Guardat en:
Format: Online
Idioma:anglès
Publicat: MDPI - Multidisciplinary Digital Publishing Institute 2022
Matèries:
crisis reporting
chatbots
journalists
news media
COVID-19
textbook research
digital humanities
digital infrastructures
data analysis
content base image retrieval
semantic information retrieval
deep features
multimedia document retrieval
data science
open government data
governance and social institutions
economic determinants of open data
geoinformation technology
fractal dimension
territorial road network
box-counting framework
script Python
ArcGIS
internet of things
LoRaWAN
ICT
The Things Network
ESP32 microcontroller
decision systems
rule based systems
databases
rough sets
prediction by partial matching
spatio-temporal
activity recognition
smart homes
artificial intelligence
automation
e-commerce
machine learning
big data
customer relationship management (CRM)
distracted driving
driving behavior
driving operation area
data augmentation
feature extraction
authorship
text mining
attribution
neural networks
deep learning
forensic intelligence
dashboard
WebGIS
data analytics
SARS-CoV-2
Big Data
Web Intelligence
media analytics
social sciences
humanities
linked open data
adaptation process
interdisciplinary research
media criticism
classification
information systems
public health
data mining
ioCOVID19
n/a
thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries
thema EDItEUR::U Computing and Information Technology::UY Computer science
Accés en línia:ONIX_20220621_9783036543161_99
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Sumari: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.

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