Data Science and Knowledge Discovery

• Other Authors: Portela, Filipe (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Information technology industries; Computer science; 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
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.