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.
['Gong Chen', 'Junghoo Cho', 'Mark Hansen']
On the brink: searching for drops in sensor data