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Incremental computation for pattern detection queries is challenging.
iiHadoop performs incremental computation on the level of key-value pairs as presented in i2MapReduce [16].
Nearly all the previous methods that are capable of predicting the trajectory of drag anchors need a numerical incremental computation.
Better still, (mathsf {GRAPE}) is based on a principled approach by combining partial evaluation and incremental computation and can be modeled as fixpoint computation.
iiHadoop overcomes the limitations of existing systems and efficiently implements incremental computation using key-value pair level approach presented in i2MapReduce [16].
While forwarding the data along the linear paths (in red in Figure 3), the node partially accumulates them and therefore cooperates to incremental computation of the Radon-like CS measurements.
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In the incremental processing domain, many systems have been developed to support efficient incremental computations for one-step applications.
Some of these systems support only one-step incremental computations while many important algorithms need extensive iterative computation.
Additionally, systems such as Naiad[50] combine DSPS with batch processing techniques, allowing complex incremental computations on streaming data.
As seen in the previous section, iiHadoop 1.2.1 reduces the run time of iterative and incremental computations by approximately 23% compared to i2MapReduce.
Other systems extended the MapReduce framework but they only support one-step incremental computations (e.g., Incoop [13] and IncMR [14]).
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