Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Supporting fast search in time series for movement patterns in multiple scales
Proceedings of the seventh international conference on Information and knowledge management
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Approximate Queries and Representations for Large Data Sequences
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Supporting Content-Based Searches on Time Series via Approximation
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic query tools for time series data sets: timebox widgets for interactive exploration
Information Visualization
Online event-driven subsequence matching over financial data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A decade of progress in indexing and mining large time series databases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Skyline Index for Time Series Data
IEEE Transactions on Knowledge and Data Engineering
Change detection in time series data using wavelet footprints
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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Sensor networks have been widely used to collect data about the environment. When analyzing data from these systems, people tend to ask exploratory questions---they want to find subsets of data, namely signal, reflecting some characteristics of the environment. In this paper, we study the problem of searching for drops in sensor data. Specifically, the search is to find periods in history when a certain amount of drop over a threshold occurs in data within a time span. We propose a framework, SegDiff, for extracting features, compressing them, and transforming the search into standard database queries. Approximate results are returned from the framework with the guarantee that no true events are missed and false positives are within a user specified tolerance. The framework efficiently utilizes space and provides fast response to users' search. Experimental results with real world data demonstrate the efficiency of our framework with respect to feature size and search time.