Proceedings Of The International Conference On Data Engineering 2015 Daeng 2015

These proceedings gather outstanding research papers presented at the Second International Conference on Data Engineering 2015 (DaEng-2015) and offer a consolidated overview of the latest developments in databases, information retrieval, data mining and knowledge management. The conference brought together researchers and practitioners from academia and industry to address key challenges in these fields, discuss advanced data engineering concepts and form new collaborations. The topics covered include but are not limited to: • Data engineering • Big data • Data and knowledge visualization • Data management • Data mining and warehousing • Data privacy & security • Database theory • Heterogeneous databases • Knowledge discovery in databases • Mobile, grid and cloud computing • Knowledge management • Parallel and distributed data • Temporal data • Web data, services and information engineering • Decision support systems • E-Business engineering and management • E-commerce and e-learning • Geographical information systems • Information management • Information quality and strategy • Information retrieval, integration and visualization • Information security • Information systems and technologies

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 two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

This book encapsulates and occupies recent advances and state-of-the-art applications of nature-inspired computing (NIC) techniques in the field of bioinformatics and computational biology, which would aid medical sciences in various clinical applications. This edited volume covers fundamental applications, scope, and future perspectives of NIC techniques in bioinformatics including genomic profiling, gene expression data classification, DNA computation, systems and network biology, solving personalized therapy complications, antimicrobial resistance in bacterial pathogens, and computer-aided drug design, discovery, and therapeutics. It also covers the role of NIC techniques in various diseases and disorders, including cancer detection and diagnosis, breast cancer, lung disorder detection, disease biomarkers, and potential therapeutics identifications.

This book unfolds ways to transform data into innovative solutions perceived as new remarkable and meaningful value. It offers practical views of the concepts and techniques readers need to get the most out of their large-scale research and data mining projects. It strides them through the data-analytical thinking, circumvents the difficulty in deciphering complex data systems and obtaining commercialization value from the data. Also known as data-driven science, soft computing and data mining disciplines cover a broad spectrum, an interdisciplinary field of scientific methods and processes. The book, Recent Advances in Soft Computing and Data Mining, delivers sufficient knowledge to tackle a wide range of issues seen in complex systems. This is done by exploring a vast combination of practices and applications by incorporating these two domains. To thrive in these data-driven ecosystems, researchers, data analysts, and practitioners must choose the best design to approach the problem with the most efficient tools and techniques. To thrive in these data-driven ecosystems, researchers, data analysts, and practitioners must understand the design choice and options of these approaches, thus to better appreciate the concepts, tools, and techniques used.

This book features high-quality research papers presented at the 3rd International Conference on Computational Intelligence in Pattern Recognition (CIPR 2021), held at the Institute of Engineering and Management, Kolkata, West Bengal, India, on 24 – 25 April 2021. It includes practical development experiences in various areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.

The study of software engineering and its applications to system engineering is critical in computer science research. Modern research methodologies, as well as the use of machine and statistical learning in software engineering research, are covered in this book. This book contains the refereed proceedings of the Software Engineering Perspectives in Systems part of the 11th Computer Science On-line Conference 2022 (CSOC 2022), which was held in April 2022 online.