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SharkDB: An In-Memory Storage System for Massive Trajectory Data

Summary: SharkDB partitions trajectories into time-aligned frames stored as columnar in-memory blocks, enabling frame-level compression and cache-friendly processing. This frame-based in-memory column store enables parallel analytics on massive trajectories and outperforms disk/tuple-based designs for variable-length data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4983
Venue
SIGMOD
Year
2015
Pagerank
5.9786915e-05
Overall Rank
4,713 | 67.22%
DOI
10.1145/2723372.2735368

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
1,776 Distributed Trajectory Similarity Search 2017 VLDB 0.00010593716
2,192 DITA: Distributed In-Memory Trajectory Analytics 2018 SIGMOD 9.3185895e-05
6,974 ROLL: Fast In-Memory Generation of Gigantic Scale-free Networks 2016 SIGMOD 4.8780906e-05
11,655 Top-k Queries over Digital Traces 2019 SIGMOD 4.1945683e-05
11,686 The Maximum Trajectory Coverage Query in Spatial Databases 2019 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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