This report introduces a data structure called T-Cube designed to dramatically improve response time to ad-hoc time series queries against large datasets. We have tested T-Cube on both synthetic and real world data (emergency room patient visits, pharmacy sales) containing millions of records. The results indicate that T-Cube responds to complex queries 1,000 times faster when compared to the state-of-the-art commercial time series extraction tools. This speedup has two main benefits: (1) It enables massive scale statistical mining of large collections of time series data, and (2) It allows its users to perform many complex ad-hoc queries without inconvenient delays. These benefits have been already found useful in applications related to practice of monitoring safety of food and agriculture, in detection of emerging patterns of failures in maintenance and supply management systems, as well as in the original application domain: bio-surveillance.

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