Preface

Open data is freely usable, reusable, or redistributable by anybody, provided there are safeguards in place that protect the data's integrity and transparency. Analysis and processing of public open data (POD) repositories in order to obtain relevant information from query log data.

This book describes how retrieved data can improve different learning qualities of digital networking, particularly performance and reliability. The book also describes developing artificial intelligence (AI) and machine learning or related models, knowledge acquisition problems, and feature assessment by incorporating data sources (blogs, search query logs, document collection) as well as interactive data (images, videos, and their explanations, multi-channel handling data).

The search query log created by manual intervention with the POD repository is a good source of knowledge. The data in the search query log is generated from users who interact with online communities. However, there is an understanding of the concept with economic models in specific sectors, for example, the telecom sector, where prices are appropriately designed and implemented. There is a significant gap between recently evolved extraction methodologies of POD repositories and their applicability across numerous organizational processes.

This book is useful for undergraduates, scientists, and professionals working in open data. It includes five chapters.
