This book constitutes the refereed post-conference proceedings of the Second BenchCouncil International Federated Intelligent Computing and Block Chain Conferences, FICC 2020, held in Qingdao, China, in October/ November 2020.The 32 full papers and 6 short papers presented were carefully reviewed and selected from 103 submissions. The papers of this volume are organized in topical sections on AI and medical technology; AI and big data; AI and block chain; AI and education technology; and AI and financial technology.

This book comprehensively illustrates quality-ware scheduling in key-value stores. In addition, it provides scheduling strategies and a prototype framework of quality-aware scheduler as well as a demonstration of online applications. The book offers a rich blend of theory and practice which is suitable for students, researchers and practitioners interested in distributed systems, NoSQL key-value stores and scheduling.

Database research and development has been remarkably successful over the past three decades. Now the field is facing new challenges posted by the rapid advances of technology, especially the penetration of the Web and Internet into everyone's daily life. The economical and financial environment where database systems are used has been changing dramatically. In addition to being able to efficiently manage a large volume of operational data generated internally, the ability to manage data in cyberspace, extract relevant information, and discover knowledge to support decision making is critical to the success of any organization. In order to provide researchers and practitioners with a forum to share their experiences in tackling problems in managing and using data, information, and knowledge in the age of the Internet and Web, the First International Conference on Web-Age Information Management (WAIM 2000) was held in Shanghai, China, June 21-23. The inaugural conference in its series was well received. Researchers from 17 countries and regions, including Austria, Australia, Bahrain, Canada, China, France, Germany, Japan, Korea, Malaysia, The Netherlands, Poland, Singapore, Spain, Taiwan, UK, and USA submitted their recent work. Twenty-seven regular and 14 short papers contained in these proceedings were presented during the two-day conference. These papers cover a large spectrum of issues, from classical data management such as object-oriented modeling, spatial and temporal databases to recent hits like data mining, data warehousing, semi-structured data, and XML.

This illustrative compendium analyzes the load balancing problem in distributed stream processing systems and explores a set of high-performance real-time processing scheme based on key-based balancing strategy, join-matrix model and fault tolerance mechanisms.The volume succinctly provides the theoretical support for the proposed techniques. Through a rich set of experiments and comparisons with the other state-of-the-art techniques using both standard benchmarks and real data sets, the book comprehensively verifies the correctness and effectiveness of the proposed methods.This unique title is an excellent reference text for researchers in the fields of distributed stream processing, parallel system, cloud computing, etc.

As mobile devices continue becoming a larger part of our lives, the development of location acquisition technologies to track moving objects have focused the minds of researchers on issues ranging from longitude and latitude coordinates, speed, direction, and timestamping, as part of parameters needed to calculate the positional information and locations of objects, in terms of time and position in the form of trajectory streams. Recently, recent advances have facilitated various urban applications such as smart transportation and mobile delivery services.Unlike other books on spatial databases, mobile computing, data mining, or computing with spatial trajectories, this book is focused on smart transportation applications.This book is a good reference for advanced undergraduates, graduate students, researchers, and system developers working on transportation systems.

As mobile devices continue becoming a larger part of our lives, the development of location acquisition technologies to track moving objects have focused the minds of researchers on issues ranging from longitude and latitude coordinates, speed, direction, and timestamping, as part of parameters needed to calculate the positional information and locations of objects, in terms of time and position in the form of trajectory streams. Recently, recent advances have facilitated various urban applications such as smart transportation and mobile delivery services.Unlike other books on spatial databases, mobile computing, data mining, or computing with spatial trajectories, this book is focused on smart transportation applications.This book is a good reference for advanced undergraduates, graduate students, researchers, and system developers working on transportation systems.

Conceived as a cross between natural language processing methods and biological sequences in DNA, RNA and protein, biological language model is a new scientific research topic in bioinformatics that has been extensively studied by the authors. The basic theory and applications of this model are presented in this book to serve as an reference for graduate students and researchers.

Transaction processing is fundamental for many modern applications. These applications require the backend transaction processing engines to be available at all times as well as provide a massive horizontal scale for intensive transaction requests.Concurrency Control and Recovery features recent progress in research in online transaction processing. The book also showcases the authors' research on a highly scalable OLTP system. Its contents include the designs of an efficient multiple version storage engine, a scalable range optimistic concurrency control, high-performance Paxos-based log replication, global snapshot isolation, and fast follower recovery.This book is written for professionals, researchers, and graduate students specialising in database systems and its related fields.

This unique compendium provides a novel research on how time influences the conversions of advertising and product recommendation in E-commerce. It proposes time-aware conversion prediction models to solve the problem - what products should be recommended for a given period to maximize conversion? The volume also presents a series of researches on how to build data-driven attribution models to allocate the time-sensitive contribution of advertisements to the conversion. This must-have reference text will be invaluable for researchers, professionals, academics and graduate students keen in databases and artificial intelligence.

This book presents the recent achievements on the processing of representative user generated content (UGC) on E-commerce websites. This large size of UGC is valuable information for data mining to help customer/object profiling. It provides a comprehensive overview on the concept of customer credibility, object-oriented review summarization technology and content-based collaborative filtering algorithm. It covers a feedback mechanism which is designed to discover customer credibility, which is used to define the professional degree of review content; product-oriented review summarization for restaurants or trip arrangements, and introduced content-based collaborative filtering for product recommendation.

This book presents the recent achievements on the processing of representative user generated content (UGC) on E-commerce websites. This large size of UGC is valuable information for data mining to help customer/object profiling. It provides a comprehensive overview on the concept of customer credibility, object-oriented review summarization technology and content-based collaborative filtering algorithm. It covers a feedback mechanism which is designed to discover customer credibility, which is used to define the professional degree of review content; product-oriented review summarization for restaurants or trip arrangements, and introduced content-based collaborative filtering for product recommendation.

This book provides a comprehensive introduction on opinion analysis for online reviews. It offers the newest research on opinion mining, including theories, algorithms and datasets. A new feature presentation method is highlighted for sentiment classification. Then, a three-phase framework for sentiment classification is proposed, where a set of sentiment classifiers are selected automatically to make predictions. Such predictions are integrated via ensemble learning. Finally, to solve the problem of combination explosion encountered, a greedy algorithm is devised to select the base classifiers.

This book provides a comprehensive introduction on opinion analysis for online reviews. It offers the newest research on opinion mining, including theories, algorithms and datasets. A new feature presentation method is highlighted for sentiment classification. Then, a three-phase framework for sentiment classification is proposed, where a set of sentiment classifiers are selected automatically to make predictions. Such predictions are integrated via ensemble learning. Finally, to solve the problem of combination explosion encountered, a greedy algorithm is devised to select the base classifiers.

Data uncertainty widely exists in many applications, and an uncertain data stream is a series of uncertain tuples that arrive rapidly. However, traditional techniques for deterministic data streams cannot be applied to deal with data uncertainty directly due to the exponential growth of possible solution space.This book provides a comprehensive overview of the authors' work on querying and mining uncertain data streams. Its contents include some important discoveries dealing with typical topics such as top-k query, ER-Topk query, rarity estimation, set similarity, and clustering.Querying and Mining Uncertain Data Streams is written for professionals, researchers, and graduate students in data mining and its various related fields.

Data uncertainty widely exists in many applications, and an uncertain data stream is a series of uncertain tuples that arrive rapidly. However, traditional techniques for deterministic data streams cannot be applied to deal with data uncertainty directly due to the exponential growth of possible solution space.This book provides a comprehensive overview of the authors' work on querying and mining uncertain data streams. Its contents include some important discoveries dealing with typical topics such as top-k query, ER-Topk query, rarity estimation, set similarity, and clustering.Querying and Mining Uncertain Data Streams is written for professionals, researchers, and graduate students in data mining and its various related fields.