Uday Kiran Rage – författare

This volume constitutes the papers of several workshops which were held in conjunction with the 27th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held as virtual event in April 2022.The 30 revised full papers presented in this book were carefully reviewed and selected from 65 submissions. DASFAA 2022 presents the following five workshops:·         First  workshop on Pattern mining and Machine learning in Big complex Databases (PMBD 2021)·         6th International Workshop on Graph Data Management and Analysis (GDMA 2022)·         First International Workshop on Blockchain Technologies (IWBT2022)·         8th International Workshop on Big Data Management and Service (BDMS 2022)·         First workshop on Managing Air Quality Through Data Science·         7th International Workshop on Big Data Quality Management (BDQM 2022).

This volume constitutes the papers of several workshops which were held in conjunction with the 27th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held as virtual event in April 2022.

The 30 revised full papers presented in this book were carefully reviewed and selected from 65 submissions. 

DASFAA 2022 presents the following five workshops:

·         First  workshop on Pattern mining and Machine learning in Big complex Databases (PMBD 2021)

·         6th International Workshop on Graph Data Management and Analysis (GDMA 2022)

·         First International Workshop on Blockchain Technologies (IWBT2022)

·         8th International Workshop on Big Data Management and Service (BDMS 2022)

·         First workshop on Managing Air Quality Through Data Science

·         7th International Workshop on Big Data Quality Management (BDQM 2022).

This book introduces pattern mining by presenting various pattern mining techniques and giving hands-on experience with each technique. Pattern mining is a popular data mining technique with many real-world applications, and involves discovering all user interest-based patterns that may exist in a database. Several models and numerous algorithms were described in the literature to find these patterns in binary databases, quantitative databases, uncertain databases, and streams. Since the lack of a Python toolkit containing these algorithms has limited the wide adaptability of pattern-mining techniques, the author developed Pattern Mining (PAMI) Python library, which currently contains 80+ algorithms to discover useful patterns in transactional databases, temporal databases, quantitative databases, and graphs.The book consists of three main parts:· Introduction: The first chapter introduces big data, types of learning techniques, and the importance of pattern mining. The second chapter introduces the PAMI library, its organizational structure, installation, and usage.· Pattern mining algorithms and examples: The following chapters present the state-of-the-art techniques for discovering user interest-based patterns in (1) transactional databases, (2) temporal databases, (3) quantitative databases, (4) uncertain databases, (5) sequential databases, and (6) graphs.· Applications: The book concludes with several applications, where the predicted knowledge using TensorFlow and PyTorch was transformed into a database to discover future trends or patterns.

This book introduces pattern mining by presenting various pattern mining techniques and giving hands-on experience with each technique. Pattern mining is a popular data mining technique with many real-world applications, and involves discovering all user interest-based patterns that may exist in a database. Several models and numerous algorithms were described in the literature to find these patterns in binary databases, quantitative databases, uncertain databases, and streams. Since the lack of a Python toolkit containing these algorithms has limited the wide adaptability of pattern-mining techniques, the author developed Pattern Mining (PAMI) Python library, which currently contains 80+ algorithms to discover useful patterns in transactional databases, temporal databases, quantitative databases, and graphs.The book consists of three main parts:· Introduction: The first chapter introduces big data, types of learning techniques, and the importance of pattern mining. The second chapter introduces the PAMI library, its organizational structure, installation, and usage.· Pattern mining algorithms and examples: The following chapters present the state-of-the-art techniques for discovering user interest-based patterns in (1) transactional databases, (2) temporal databases, (3) quantitative databases, (4) uncertain databases, (5) sequential databases, and (6) graphs.· Applications: The book concludes with several applications, where the predicted knowledge using TensorFlow and PyTorch was transformed into a database to discover future trends or patterns.