Data Science and Knowledge Discovery

• Autore: Portela Filipe
• Pubblicazione: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
• Descrizione fisica: 1 online resource (254 p.)
• Soggetto topico: Computer science; Information technology industries
• Soggetto non controllato: activity recognition; adaptation process; ArcGIS; artificial intelligence; attribution; authorship; automation; big data; Big Data; box-counting framework; chatbots; classification; content base image retrieval; COVID-19; crisis reporting; customer relationship management (CRM); dashboard; data analysis; data analytics; data augmentation; data mining; data science; databases; decision systems; deep features; deep learning; digital humanities; digital infrastructures; distracted driving; driving behavior; driving operation area; e-commerce; economic determinants of open data; ESP32 microcontroller; feature extraction; forensic intelligence; fractal dimension; geoinformation technology; governance and social institutions; humanities; ICT; information systems; interdisciplinary research; internet of things; ioCOVID19; journalists; linked open data; LoRaWAN; machine learning; media analytics; media criticism; multimedia document retrieval; n/a; neural networks; news media; open government data; prediction by partial matching; public health; rough sets; rule based systems; SARS-CoV-2; script Python; semantic information retrieval; smart homes; social sciences; spatio-temporal; territorial road network; text mining; textbook research; The Things Network; Web Intelligence; WebGIS
• Persona (resp. second.): PortelaFilipe
• Sommario/riassunto: 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.
• Titolo autorizzato: Data Science and Knowledge Discovery
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910576878103321
• Lo trovi qui: Univ. Federico II