Instant Loading for Main Memory Databases
Summary: Instant Loading delivers wire-speed CSV bulk loading for main-memory DBs by optimizing load phases on multi-core CPUs. A single-node data-staging model with instantaneous load-work-unload cycles across archives enables rapid in-memory loading and fast subsequent queries. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tobias Muehlbauer
- 2. Wolf Roediger
- 3. Robert Seilbeck
- 4. Angelika Reiser
- 5. Alfons Kemper
- 6. Thomas Neumann
Incoming Citations (Sorted by Pagerank)
Showing 20 of 20 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,504 | Supporting Scalable Analytics with Latency Constraints | 2015 | VLDB | 4.3341665e-05 |
| 3,548 | Adaptive Query Processing on RAW Data | 2014 | VLDB | 6.9859242e-05 |
| 52 | Database Architecture Optimized for the new Bottleneck: Memory Access | 1999 | VLDB | 0.00066474881 |
| 3,075 | Instant Recovery for Main-Memory Databases | 2015 | CIDR | 7.6108216e-05 |
| 1,343 | NoDB: Efficient Query Execution on Raw Data Files | 2012 | SIGMOD | 0.00012482538 |
| 2,367 | Here are my Data Files. Here are my Queries. Where are my Results? | 2011 | CIDR | 8.9511058e-05 |
| 2,973 | Parallel In-Situ Data Processing with Speculative Loading | 2014 | SIGMOD | 7.7902322e-05 |
| 2,964 | In-Memory Performance for Big Data | 2015 | VLDB | 7.80643e-05 |
| 6,666 | Mainlining Databases: Supporting Fast Transactional Workloads on Universal Columnar Data File Formats | 2021 | VLDB | 4.9691571e-05 |
| 9,918 | Shared Load(ing): Efficient Bulk Loading into Optimized Storage | 2020 | CIDR | 4.2561557e-05 |