FAST '04 Abstract
Pp. 73-86 of the Proceedings
Diamond: A Storage Architecture for Early Discard in Interactive Search
Larry Huston, Intel Research Pittsburgh; Rahul Sukthankar, Carnegie Mellon
University; Rajiv Wickremesinghe, Intel Research Pittsburgh; M. Satyanarayanan and Gregory R. Ganger, Carnegie Mellon University; Erik Riedel, Seagate Research; Anastassia Ailamaki, Carnegie Mellon University
This paper explores the concept of early discard for
interactive search of unindexed data. Processing data inside
storage devices using downloaded searchlet code enables
Diamond to perform efficient, application-specific filtering of
large data collections. Early discard helps users who are looking
for ``needles in a haystack'' by eliminating the bulk of the
irrelevant items as early as possible. A searchlet consists of a
set of application-generated filters that Diamond uses to determine
whether an object may be of interest to the user. The system
optimizes the evaluation order of the filters based on run-time
measurements of each filter's selectivity and computational cost.
Diamond can also dynamically partition computation between the
storage devices and the host computer to adjust for changes in
hardware and network conditions. Performance numbers show that
Diamond dynamically adapts to a query and to run-time system state.
An informal user study of an image retrieval application supports
our belief that early discard significantly improves the quality
of interactive searches.
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