IATSJ: Identification of anomalies in time series data using similarity join processing

There is continues capture of large streaming data vital for application such as intensive health care system, Sensor networks, Object tracking etc.,. Data reduction of these huge data stream is carried out by similarity join processing which tracks the abnormal contents in real time data. The identification of anomalies such as abnormalities in Electro Cardio Gram (ECG) of an heart patient, predicting future casualties in weather monitor monitoring system, and providing heuristics in object tracking has to be effectively carried out. To achieve this we propose Identification of Anomalies in Time Series Data using Similarity Join Processing (IATSJ) to identify the anomalies by using Alternate Multilevel Segment Mean (AMSM) technique which reduces the data dimension and applying similarity join processing on these reduced data using sliding window concept. Experimental results show that, the time and space efficiency of our approach in anomaly detection from the given time series is better than the existing methods.