260 citations · 266 across the 3 of their papers we have counts for
6 papers
Approximate Queries and Representations for Large Data Sequences
Hagit Shatkay, Stanley B. Zdonik
Many new database application domains such as experimental sciences and medicine are characterized by large sequences as their main form of data. Using approximate representation c…
Precision and Recall for Time Series
Nesime Tatbul, Tae Jun Lee, Stan Zdonik +2
Classical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time. Yet, many real-world anomalies are range-base…
Precision and Recall for Range-Based Anomaly Detection
Tae Jun Lee, Justin Gottschlich, Nesime Tatbul +2
Classical anomaly detection is principally concerned with point-based anomalies, anomalies that occur at a single data point. In this paper, we present a new mathematical model to…
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection
Tae Jun Lee, Justin Gottschlich, Nesime Tatbul +2
This short paper describes our ongoing research on Greenhouse - a zero-positive machine learning system for time-series anomaly detection.
S-Store: Streaming Meets Transaction Processing
John Meehan, Nesime Tatbul, Stan Zdonik +10
Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two mo…
The Lowell Database Research Self Assessment
Serge Abiteboul, Rakesh Agrawal, Phil Bernstein +26
A group of senior database researchers gathers every few years to assess the state of database research and to point out problem areas that deserve additional focus. This report su…