Frequency-based load shedding over a data stream of tuples

Usually the data generation rate of a data stream is unpredictable, and some data elements of the data stream cannot be processed in real time if the generation rate exceeds the capacity of a data stream processing algorithm. In order to overcome this situation gracefully, a load shedding technique is recommended. This paper proposes a frequency-based load shedding technique over a data stream of tuples. In many data stream processing applications, such as mining frequent patterns, data elements having high frequency can be considered more significant than others having low frequency. Based on this observation, in the proposed technique, only frequent elements of a data stream are processed in real time while the others are trimmed. The decision to shed a load from the data stream or not is controlled automatically by the data generation rate of a data stream. Consequently, an unnecessary load shedding operation is not allowed in the proposed technique.