Demo Data from EDBT 2026 / reviewers in the wild / expert
Sherman Lau
dblp:08/517
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2
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
2 papers |
Information retrieval · 57% Indexing and storage engines · 43% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › indexing
index compression |
0.1 | 1 | 2009 | Efficient Index Compression in DB2 LUW · Proc. VLDB Endow. 2009 |
Indexing and storage engines
multidimensional clustering |
0.1 | 1 | 2007 | Efficient Bulk Deletes for Multi Dimensionally Clustered Tables in DB2 · VLDB 2007 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Efficient Index Compression in DB2 LUWabstractIn database systems, the cost of data storage and retrieval are important components of the total cost and response time of the system. A popular mechanism to reduce the storage footprint is by compressing the data residing in tables and indexes. Compressing indexes efficiently, while maintaining response time requirements, is known to be challenging. This is especially true when designing for a workload spectrum covering both data warehousing and transaction processing environments. DB2 Linux, UNIX, Windows (LUW) recently introduced index compression for use in both environments. This uses techniques that are able to compress index data efficiently while incurring virtually no performance penalty for query processing. On the contrary, for certain operations, the performance is actually better. In this paper, we detail the design of index compression in DB2 LUW and discuss the challenges that were encountered in meeting the design goals. We also demonstrate its effectiveness by showing performance results on typical customer scenarios. Bishwaranjan Bhattacharjee, Lipyeow Lim, Timothy Malkemus, George A. Mihaila, Kenneth A. Ross, Sherman Lau, Cathy McCarthur, Zoltan Toth, Reza Sherkat |
Proc. VLDB Endow. | 6 |
| 2007 | Efficient Bulk Deletes for Multi Dimensionally Clustered Tables in DB2
Bishwaranjan Bhattacharjee, Timothy Malkemus, Sherman Lau, Sean Mckeough, Jo-Anne Kirton, Robin Von Boeschoten, John Kennedy |
VLDB | 3 |