Interactive Event Driven Knowledge Discovery From Data Streams

With the proliferation of sensor data, a critical challenge is to interpret and extract knowledge from large-scale heterogeneous observational data. Most of the existing knowledge discovery frameworks relay on data mining techniques to extract interesting patterns. The problem of finding such patterns is NP-complete and the property of interestingness is not monotone since a pattern may be interesting, even if its sub-patterns are not. In this book a framework for interactive knowledge discovery from heterogeneous high-dimensional temporal data is presented. First, a high-level pattern formulation language is introduced. The language consists of an event model for fusing and abstracting data streams, a time model for representing temporal relations, and a set of expressive operators for formulating patterns. Based on these operators, a visual and interactive framework is designed that combines data-driven and hypothesis-driven analyses.

This book constitutes the refereed proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2006, held in Singapore in April 2006. The 67 revised full papers and 33 revised short papers presented together with 3 invited talks were carefully reviewed and selected from 501 submissions. The papers are organized in topical sections on Classification, Ensemble Learning, Clustering, Support Vector Machines, Text and Document Mining, Web Mining, Bio-Data Mining, and more.

Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents

The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022. The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.

This book constitutes the workshop proceedings of the 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, held in Gold Coast, QLD, Australia, in May 2018. The 23 full papers presented were carefully selected and reviewed from 44 submissions to the four following workshops: the 5th International Workshop on Big Data Management and Service, BDMS 2018; the Third International Workshop on Big Data Quality Management, BDQM 2018; the Second International Workshop on Graph Data Management and Analysis, GDMA 2018; and the 5th International Workshop on Semantic Computing and Personalization, SeCoP 2018.

Featuring papers from the Second International Conference on Computational Finance and its Applications, the text includes papers that encompass a wide range of topics such as risk management, derivatives pricing, credit risk, trading strategies, portfolio management and asset allocation, and market analysis.

Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.

This book constitutes the revised selected papers from the 5th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2015, held in Vienna, Austria in December 2015. The 8 papers presented in this volume were carefully reviewed and selected from 22 submissions. They cover theoretical issues related to process representation, discovery and analysis, or provide practical and operational experiences in process discovery and analysis. They focus mainly on the adoption of process mining algorithms in conjunction and coordination with other techniques and methodologies.