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Performance and resource optimization is an important
research problem in data intensive distributed comput-
ing. We present a new batched stream processing model
that captures query correlations to expose I/O and com-
putation redundancies for optimizations. The model is
inspired by our empirical study on a trace from a pro-
duction large-scale data processing cluster, which reveals
significant redundancies caused by strong temporal and
spatial correlations among queries.
We have developed Comet, a query processing
system that embraces the batched stream processing

Complex on-demand data retrieval and processing is a characteristic of several applications and com-
bines the notions of querying & search, information filtering & retrieval, data transformation & analysis,
and other data manipulations. Such rich tasks are typically represented by data processing graphs, hav-
ing arbitrary data operators as nodes and their producer-consumer interactions as edges. Optimizing
and executing such graphs on top of distributed architectures is critical for the success of the corre-