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Data Science and Knowledge Discovery

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Bibliographic Details
Title: Data Science and Knowledge Discovery
Contributors: Portela, Filipe
Publisher Information: MDPI - Multidisciplinary Digital Publishing Institute
Publication Year: 2022
Collection: Directory of Open Access Books (DOAB)
Subject Terms: 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
Description: 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.
Document Type: other/unknown material
File Description: application/octet-stream
Language: English
Relation: ONIX_20220621_9783036543161_99; https://mdpi.com/books/pdfview/book/5505
Availability: https://directory.doabooks.org/handle/20.500.12854/84521
https://hdl.handle.net/20.500.12854/84521
https://mdpi.com/books/pdfview/book/5505
Rights: open access
Accession Number: edsbas.60CE8A6F
Database: BASE
Description
Abstract: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.