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This book presents an overview of techniques for discovering high-utility patterns (patterns with a high importance) in data. It introduces the main types of high-utility patterns, as well as the theory and core algorithms for high-utility pattern mining, and describes recent advances, applications, open-source software, and research opportunities. It also discusses several types of discrete data, including customer transaction data and sequential data. The book consists of twelve chapters, seven of which are surveys presenting the main subfields of high-utility pattern mining, including itemset mining, sequential pattern mining, big data pattern mining, metaheuristic-based approaches, privacy-preserving pattern mining, and pattern visualization. The remaining five chapters describe key techniques and applications, such as discovering concise representations and regular patterns.
This two volume set of LNCS 11029 and LNCS 11030 constitutes the refereed proceedings of the 29th International Conference on Database and Expert Systems Applications, DEXA 2018, held in Regensburg, Germany, in September 2018. The 35 revised full papers presented together with 40 short papers were carefully reviewed and selected from 160 submissions. The papers of the first volume discuss a range of topics including: Big data analytics; data integrity and privacy; decision support systems; data semantics; cloud data processing; time series data; social networks; temporal and spatial databases; and graph data and road networks. The papers of the second volume discuss a range of the following topics: Information retrieval; uncertain information; data warehouses and recommender systems; data streams; information networks and algorithms; database system architecture and performance; novel database solutions; graph querying and databases; learning; emerging applications; data mining; privacy; and text processing.
Although the field of intelligent systems has grown rapidly in recent years, there has been a need for a book that supplies a timely and accessible understanding of this important technology. Filling this need, Case Studies in Intelligent Computing: Achievements and Trends provides an up-to-date introduction to intelligent systems.This edited book
This book constitutes the thoroughly refereed short papers, workshops and doctoral consortium papers of the 21th European Conference on Advances in Databases and Information Systems, ADBIS 2017, held in Nicosia, Cyprus, in September 2017. The 25 full and 4 short workshop papers and the 12 short papers of the main conference were carefully reviewed and selected from 160 submissions. The papers from the following workshops have been included in the proceedings: the first workshop on Data-Driven Approaches for Analyzing and Managing Scholarly Data, AMSD 2017; the first workshop on Novel Techniques for Integrating Big Data, BigNovelTI 2017; the first international workshop on Data Science: Methodologies and Use-Cases, DaS 2017; the second international workshop on Semantic Web for Cultural Heritage, SW4CH 2017.
This book presents new communication and networking technologies, an area that has gained significant research attention from both academia and industry in recent years. It also discusses the development of more intelligent and efficient communication technologies, which are an essential part of current day-to-day life, and reports on recent innovations in technologies, architectures, and standards relating to these technologies. The book includes research that spans a wide range of communication and networking technologies, including wireless sensor networks, big data, Internet of Things, optical and telecommunication networks, artificial intelligence, cryptography, next-generation networks, cloud computing, and natural language processing. Moreover, it focuses on novel solutions in the context of communication and networking challenges, such as optimization algorithms, network interoperability, scalable network clustering, multicasting and fault-tolerant techniques, network authentication mechanisms, and predictive analytics.
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 14th International Conference on Advanced Data Mining and Applications, ADMA 2018, held in Nanjing, China in November 2018. The 23 full and 22 short papers presented in this volume were carefully reviewed and selected from 104 submissions. The papers were organized in topical sections named: Data Mining Foundations; Big Data; Text and Multimedia Mining; Miscellaneous Topics.
This book constitutes the thoroughly refereed short papers, workshops and doctoral consortium papers of the 22th European Conference on Advances in Databases and Information Systems, ADBIS 2018, held in Budapest, Hungary, in September 2018. The 20 full and the 4 short workshop papers as well as the 3 doctoral consortium papers were carefully reviewed and selected from 54 submissions to the workshops and 6 submissions to the doctoral consortium. Furthermore, there are 10 short papers included, which were accepted for the main conference. The papers are organized according to the 6 workshops and the doctoral consortium: ADBIS 2018 short papers; First Workshop on Advances on Big Data Management, Analytics, Data Privacy and Security, BigDataMAPS 2018; First International Workshop on New Frontiers on Meta-data Management and Usage, M2U 2018; First Citizen Science Applications and Citizen Databases Workshop, CSADB 2018; First International Workshop on Articial Intelligence for Question Answering, AI*QA 2018; First International Workshop on BIG Data Storage, Processing and Mining for Personalized MEDicine, BIGPMED 2018; First Workshop on Current Trends in Contemporary Information Systems and Their Architectures, ISTREND 2018; Doctoral Consortium.