This book provides an introduction to the field of periodic pattern mining, reviews state-of-the-art techniques, discusses recent advances, and reviews open-source software. Periodic pattern mining is a popular and emerging research area in the field of data mining. It involves discovering all regularly occurring patterns in temporal databases. One of the major applications of periodic pattern mining is the analysis of customer transaction databases to discover sets of items that have been regularly purchased by customers. Discovering such patterns has several implications for understanding the behavior of customers. Since the first work on periodic pattern mining, numerous studies have been published and great advances have been made in this field. The book consists of three main parts: introduction, algorithms, and applications. The first chapter is an introduction to pattern mining and periodic pattern mining. The concepts of periodicity, periodic support, search space exploration techniques, and pruning strategies are discussed. The main types of algorithms are also presented such as periodic-frequent pattern growth, partial periodic pattern-growth, and periodic high-utility itemset mining algorithm. Challenges and research opportunities are reviewed. The chapters that follow present state-of-the-art techniques for discovering periodic patterns in (1) transactional databases, (2) temporal databases, (3) quantitative temporal databases, and (4) big data. Then, the theory on concise representations of periodic patterns is presented, as well as hiding sensitive information using privacy-preserving data mining techniques. The book concludes with several applications of periodic pattern mining, including applications in air pollution data analytics, accident data analytics, and traffic congestion analytics.

This book constitutes the refereed proceedings of the 6th International Conference on Big Data analytics, BDA 2018, held in Warangal, India, in December 2018. The 29 papers presented in this volume were carefully reviewed and selected from 93 submissions. The papers are organized in topical sections named: big data analytics: vision and perspectives; financial data analytics and data streams; web and social media data; big data systems and frameworks; predictive analytics in healthcare and agricultural domains; and machine learning and pattern mining.

This book provides an introduction to the field of periodic pattern mining, reviews state-of-the-art techniques, discusses recent advances, and reviews open-source software. Periodic pattern mining is a popular and emerging research area in the field of data mining. It involves discovering all regularly occurring patterns in temporal databases. One of the major applications of periodic pattern mining is the analysis of customer transaction databases to discover sets of items that have been regularly purchased by customers. Discovering such patterns has several implications for understanding the behavior of customers. Since the first work on periodic pattern mining, numerous studies have been published and great advances have been made in this field. The book consists of three main parts: introduction, algorithms, and applications. The first chapter is an introduction to pattern mining and periodic pattern mining. The concepts of periodicity, periodic support, search space exploration techniques, and pruning strategies are discussed. The main types of algorithms are also presented such as periodic-frequent pattern growth, partial periodic pattern-growth, and periodic high-utility itemset mining algorithm. Challenges and research opportunities are reviewed. The chapters that follow present state-of-the-art techniques for discovering periodic patterns in (1) transactional databases, (2) temporal databases, (3) quantitative temporal databases, and (4) big data. Then, the theory on concise representations of periodic patterns is presented, as well as hiding sensitive information using privacy-preserving data mining techniques. The book concludes with several applications of periodic pattern mining, including applications in air pollution data analytics, accident data analytics, and traffic congestion analytics.

The three-volume set LNCS 13245, 13246 and 13247 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held online, in April 2021. The total of 72 full papers, along with 76 short papers, are presented in this three-volume set was carefully reviewed and selected from 543 submissions. Additionally, 13 industrial papers, 9 demo papers and 2 PhD consortium papers are included. The conference was planned to take place in Hyderabad, India, but it was held virtually due to the COVID-19 pandemic.

The three-volume set LNCS 13245, 13246 and 13247 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held online, in April 2021. The total of 72 full papers, along with 76 short papers, are presented in this three-volume set was carefully reviewed and selected from 543 submissions. Additionally, 13 industrial papers, 9 demo papers and 2 PhD consortium papers are included. The conference was planned to take place in Hyderabad, India, but it was held virtually due to the COVID-19 pandemic.

The three-volume set LNCS 13245, 13246 and 13247 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held online, in April 2021. The total of 72 full papers, along with 76 short papers, are presented in this three-volume set was carefully reviewed and selected from 543 submissions. Additionally, 13 industrial papers, 9 demo papers and 2 PhD consortium papers are included. The conference was planned to take place in Hyderabad, India, but it was held virtually due to the COVID-19 pandemic.

This book constitutes the proceedings of the 8th International Conference on Big Data Analytics, BDA 2020, which took place during December 15-18, 2020, in Sonepat, India. The 11 full and 3 short papers included in this volume were carefully reviewed and selected from 48 submissions; the book also contains 4 invited and 3 tutorial papers. The contributions were organized in topical sections named as follows: data science systems; data science architectures; big data analytics in healthcare; information interchange of Web data resources; and business analytics.

This volume constitutes refereed proceedings of the Third International Conference on Smart Applications and Data Analysis, SADASC 2020, held in Marrakesh, Morocco. Due to the COVID-19 pandemic the conference has been postponed to June 2020. The 24 full papers and 3 short papers presented were thoroughly reviewed and selected from 44 submissions. The papers are organized according to the following topics: ontologies and meta modeling; cyber physical systems and block-chains; recommender systems; machine learning based applications; combinatorial optimization; simulations and deep learning.

Part of the 'Occasional Papers' series of CSSSC, this essay is a brief, and sharply posed, exchange between Dipesh Chakrabarty and Ranajit Das Gupta on working class consciousness in Bengal. it posits that this consciousness is not a mechanical outcome of the capitalist mode of production, it is not a thing but a process; that even failure must be taken on board in order to flesh out that process; that not only was the working class present (and therefore conscious) of its own making, but drew from rich pre-capitalist cultural traditions of dissent, rebellion and republicanism. The essay asks pertinent questions about the morality of labour, history of peasant revolts, capitalist intervention, religious discrimination among labourers etc.