This book presents concepts and techniques for describing and analyzing large-scale time-series data streams, which, for example, is critical for complex real-world data in telecommunications, bioinformatics, and finance databases. The work aims at efficient discovery in time series, rather than at prediction, and presents rapid-discovery techniques for finding portions of time series with many events (i.e., gamma-ray scatterings) and finding closely related time series (i.e., highly correlated price histories or musical melodies). Database and online Web Services researchers and professionals will appreciate the book's algorithmic contributions, as well as its practical aspects and many case studies.  Graduate students studying databases or interested in massive time-series data will find the book an essential resource.