Demo Data from EDBT 2026 / reviewers in the wild / expert
Vinita Subramanian
dblp:185/4344
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Query processing and optimization · 50% Indexing and storage engines · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Query processing and optimization › query execution
in-memory query processing |
0.2 | 1 | 2016 | Accelerating Analytics with Dynamic In-Memory Expressions · Proc. VLDB Endow. 2016 |
Indexing and storage engines › column store
main-memory column store |
0.2 | 1 | 2016 | Accelerating Analytics with Dynamic In-Memory Expressions · Proc. VLDB Endow. 2016 |
Methods — techniques the papers use, named apart from their topics
in-memory storage indexes · 0.2SIMD vector processing · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Accelerating Analytics with Dynamic In-Memory ExpressionsabstractOracle Database In-Memory (DBIM) accelerates analytic workload performance by orders of magnitude through an in-memory columnar format utilizing techniques such as SIMD vector processing, in-memory storage indexes, and optimized predicate evaluation and aggregation. With Oracle Database 12.2, Database In-Memory is further enhanced to accelerate analytic processing through a novel lightweight mechanism known as Dynamic In-Memory Expressions (DIMEs). The DIME mechanism automatically detects frequently occurring expressions in a query workload, and then creates highly optimized, transactionally consistent, in-memory columnar representations of these expression results. At runtime, queries can directly access these DIMEs, thus avoiding costly expression evaluations. Furthermore, all the optimizations introduced in DBIM can apply directly to DIMEs. Since DIMEs are purely in-memory structures, no changes are required to the underlying tables. We show that DIMEs can reduce query elapsed times by several orders of magnitude without the need for costly pre-computed structures such as computed columns or materialized views or cubes. Aurosish Mishra, Shasank Chavan, Allison Holloway, Tirthankar Lahiri, Zhen Hua Liu, Sunil Chakkappen, Dennis Lui, Vinita Subramanian, Maria Colgan, Jesse Kamp, Niloy Mukherjee, Vineet Marwah |
Proc. VLDB Endow. | 8 |