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Communications of the ACM
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Communications of the ACM
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
A case for intelligent disks (IDISKs)
ACM SIGMOD Record
Active disks: programming model, algorithms and evaluation
Proceedings of the eighth international conference on Architectural support for programming languages and operating systems
Active Storage for Large-Scale Data Mining and Multimedia
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Tradeoffs Between Quality of Results and Resource Consumption in a Recognition System
ASAP '02 Proceedings of the IEEE International Conference on Application-Specific Systems, Architectures, and Processors
Active disks: remote execution for network-attached storage
Active disks: remote execution for network-attached storage
Biosequence Similarity Search on the Mercury System
Journal of VLSI Signal Processing Systems
Visions for application development on hybrid computing systems
Parallel Computing
Mercury BLASTP: Accelerating Protein Sequence Alignment
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Acceleration of ungapped extension in Mercury BLAST
Microprocessors & Microsystems
Auto-pipe and the X language: a pipeline design tool and description language
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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In many data mining applications, the size of the database is not only extremely large, it is also growing rapidly. Even for relatively simple searches, the time required to move the data off magnetic media, cross the system bus into main memory, copy into processor cache, and then execute code to perform a search is prohibitive. We are building a system in which a significant portion of the data mining task (i.e., the portion that examines the bulk of the raw data) is implemented in fast hardware, close to the magnetic media on which it is stored. Furthermore, this hardware can be replicated allowing mining tasks to be performed in parallel, thus providing further speedup for the overall mining application. In this paper, we describe a general framework under which this can be accomplished and provide initial performance results for a set of applications.