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220822 ||| eng |
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|a 9783036543161
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|a 9783036543154
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|a books978-3-0365-4315-4
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|a Portela, Filipe
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| 245 |
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|a Data Science and Knowledge Discovery
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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| 300 |
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|a 1 electronic resource (254 p.)
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|a machine learning
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|a data analytics
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|a spatio-temporal
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|a driving behavior
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|a data mining
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|a multimedia document retrieval
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|a ArcGIS
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|a journalists
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|a rule based systems
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|a WebGIS
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|a LoRaWAN
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|a media criticism
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|a data science
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|a authorship
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|a n/a
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|a information systems
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|a data analysis
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|a data augmentation
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|a digital humanities
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|a customer relationship management (CRM)
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|a textbook research
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|a big data
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|a deep learning
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|a humanities
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|a dashboard
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|a decision systems
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|a neural networks
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|a deep features
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|a semantic information retrieval
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|a distracted driving
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|a governance and social institutions
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|a interdisciplinary research
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|a automation
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|a media analytics
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|a Web Intelligence
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|a news media
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|a box-counting framework
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|a The Things Network
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|a fractal dimension
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|a feature extraction
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|a text mining
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|a SARS-CoV-2
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|a COVID-19
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|a ESP32 microcontroller
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|a Information technology industries / bicssc
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|a script Python
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|a geoinformation technology
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|a databases
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|a public health
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|a Computer science / bicssc
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|a chatbots
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|a forensic intelligence
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|a Big Data
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|a crisis reporting
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|a attribution
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|a internet of things
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|a social sciences
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|a linked open data
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|a territorial road network
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|a content base image retrieval
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|a artificial intelligence
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|a classification
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|a economic determinants of open data
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|a smart homes
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|a e-commerce
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|a rough sets
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|a open government data
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|a prediction by partial matching
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|a ioCOVID19
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|a activity recognition
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|a digital infrastructures
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|a adaptation process
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|a driving operation area
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|a ICT
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|a Portela, Filipe
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
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|a 10.3390/books978-3-0365-4315-4
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| 856 |
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|u https://directory.doabooks.org/handle/20.500.12854/84521
|z DOAB: description of the publication
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|u https://www.mdpi.com/books/pdfview/book/5505
|7 0
|x Verlag
|3 Volltext
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|a 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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