In-memory Distributed Computation
In-Memory Distributed Computation is a distributed data processing paradigm in which computational tasks are executed across a cluster of interconnected nodes that primarily store and manipulate data in main memory, thereby distinguishing itself from disk-based approaches through its emphasis on low-latency access and high-throughput processing of large-scale datasets.
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6.4K
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231
Authors
83
Institutions
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2017–2026
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University of California, Berkeley
Berkeley, United States
4 h-index
23 publications
35.4K citations
The Ohio State University
Columbus, United States
2 h-index
11 publications
567 citations

University of Manchester
Manchester, United Kingdom
2 h-index
9 publications
366 citations
IBM Research - Thomas J. Watson Research Center
Yorktown Heights, United States
2 h-index
13 publications
534 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
4 publications
692 citations
University of California San Diego
San Diego, United States
2 h-index
4 publications
69 citations
Intel (United States)
Santa Clara, United States
2 h-index
2 publications
249 citations
ETH Zurich
Zurich, Switzerland
2 h-index
3 publications
585 citations
1–8 of 83
PVLDB · 3 publications · 248 citations
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Conference · 1 publications · 224 citations
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1 publications · 14 citations
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