Tao Li

Event mining encompasses techniques for automatically and efficiently extracting valuable knowledge from historical event/log data. The field, therefore, plays an important role in data-driven system management. Event Mining: Algorithms and Applications presents state-of-the-art event mining approaches and applications with a focus on computing system management. The book first explains how to transform log data in disparate formats and contents into a canonical form as well as how to optimize system monitoring. It then shows how to extract useful knowledge from data. It describes intelligent and efficient methods and algorithms to perform data-driven pattern discovery and problem determination for managing complex systems. The book also discusses data-driven approaches for the detailed diagnosis of a system issue and addresses the application of event summarization in Twitter messages (tweets). Understanding the interdisciplinary field of event mining can be challenging as it requires familiarity with several research areas and the relevant literature is scattered in diverse publications. This book makes it easier to explore the field by providing both a good starting point for readers not familiar with the topics and a comprehensive reference for those already working in this area.

Algorithms and Applications

Event Mining

Wojciech Cellary

Hua Wang

Yanchun Zhang

Jianyong Wang

Shu-Ching Chen

Dingding Wang

This two volume set LNCS 9418 and LNCS 9419 constitutes the proceedings of the 16th International Conference on Web Information Systems Engineering, WISE 2015, held in Miami, FL, USA, in November 2015. The 53 full papers, 17 short and 14 special sessions and invited papers, presented in these proceedings were carefully reviewed and selected from 189 submissions. The papers cover the areas of big data techniques and applications, deep/hidden Web, integration of Web and internet, linked open data, semantic Web, social network computing, social Web and applications, social Web models, analysis and mining, Web-based applications, Web-based business processes and Web services, Web data integration and mashups, Web data models, Web information retrieval, Web privacy and security, Web-based recommendations, and Web search.

16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part I

Web Information Systems Engineering – WISE 2015

This two volume set LNCS 9418 and LNCS 9419 constitutes the proceedings of the 16th International Conference on Web Information Systems Engineering, WISE 2015, held in Miami, FL, USA, in November 2015. The 53 full papers, 17 short and 14 special sessions and invited papers, presented in these proceedings were carefully reviewed and selected from 189 submissions. The papers cover the areas of big data techniques and applications, deep/hidden web, integration of web and internet, linked open data, semantic web, social network computing, social web and applications, social web models, analysis and mining, web-based applications, web-based business processes and web services, web data integration and mashups, web data models, web information retrieval, web privacy and security, web-based recommendations, and web search.

16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part II

Mitsunori Ogihara

George Tzanetakis

The research area of music information retrieval has gradually evolved to address the challenges of effectively accessing and interacting large collections of music and associated data, such as styles, artists, lyrics, and reviews. Bringing together an interdisciplinary array of top researchers, Music Data Mining presents a variety of approaches to successfully employ data mining techniques for the purpose of music processing. The book first covers music data mining tasks and algorithms and audio feature extraction, providing a framework for subsequent chapters. With a focus on data classification, it then describes a computational approach inspired by human auditory perception and examines instrument recognition, the effects of music on moods and emotions, and the connections between power laws and music aesthetics. Given the importance of social aspects in understanding music, the text addresses the use of the Web and peer-to-peer networks for both music data mining and evaluating music mining tasks and algorithms. It also discusses indexing with tags and explains how data can be collected using online human computation games. The final chapters offer a balanced exploration of hit song science as well as a look at symbolic musicology and data mining. The multifaceted nature of music information often requires algorithms and systems using sophisticated signal processing and machine learning techniques to better extract useful information. An excellent introduction to the field, this volume presents state-of-the-art techniques in music data mining and information retrieval to create novel ways of interacting with large music collections.

Tao Li

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