Abstract: Disclosed is a variable-size sampling method under a data-streaming environment, including: calculating a maximum window size that satisfies a lower limitation of a predetermined uniformity confidence level at all times; inputting a data stream to be sampled; comparing a data stream length input until a current time point with the maximum window size; inspecting a sample size and a sampling fraction if the maximum window size is larger than the data stream length; performing sampling by generating a slot to increase the sample size if the current sample size is smaller than a predetermined percentage (P %) of the data stream; and directly performing sampling without generating a slot if the current sample size is equal to or larger than the predetermined percentage (P %) of the data stream. As a result, degradation of uniformity confidence during variable-size sampling under a real-time streaming environment can be prevented to improve sampling performance.

Abstract: Disclosed is a variable-size sampling method under a data-streaming environment, including: calculating a maximum window size that satisfies a lower limitation of a predetermined uniformity confidence level at all times; inputting a data stream to be sampled; comparing a data stream length input until a current time point with the maximum window size; inspecting a sample size and a sampling fraction if the maximum window size is larger than the data stream length; performing sampling by generating a slot to increase the sample size if the current sample size is smaller than a predetermined percentage (P %) of the data stream; and directly performing sampling without generating a slot if the current sample size is equal to or larger than the predetermined percentage (P %) of the data stream. As a result, degradation of uniformity confidence during variable-size sampling under a real-time streaming environment can be prevented to improve sampling performance.

Abstract: A subsequence matching method in time-series databases, reduces the number of points stored in the multidimensional index and can store individual points directly in the index by dividing the data sequence into disjoint windows using duality in constructing windows. The method reduces false alarms and improves performance by searching the index using the individual points that represent sliding windows of the query sequence and by comparing the points used in the query and the points stored in the index. Moreover, the method can create the index much faster than the previous method by reducing the number of calls to the feature extraction function that is a major part of CPU overhead in the index creation.