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Kan Lv

dblp:274/6334 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 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
Indexing and storage engines · 67% Information retrieval · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Indexing and storage engines
bitmap index
0.412020
APPLE: a new compression scheme for bitmap indexes: poster abstract · SenSys 2020
Indexing and storage engines › bitmap index
compressed bitmap index
0.412020
APPLE: a new compression scheme for bitmap indexes: poster abstract · SenSys 2020
Information retrieval › indexing
index compression
0.412020
APPLE: a new compression scheme for bitmap indexes: poster abstract · SenSys 2020

Methods — techniques the papers use, named apart from their topics

run-length encoding · 0.4packed position lists · 0.4
YearPublicationVenuePosition
2020 APPLE: a new compression scheme for bitmap indexes: poster abstract
abstract
Compressed bitmap indexes are increasingly used in databases and search engines. By exploiting bit-level parallelism and bitwise operations, e.g. AND/OR operations, they can significantly accelerate the development of many areas. The Word Aligned Hybrid (WAH) bitmap compression scheme using run-length encoding (RLE), is commonly recognized as the most efficient scheme in terms of CPU-performance. This paper presents a new form of compressed bitmap indexes named Adaptive Partitioned Position List Encoding (APPLE), which uses packed position lists for compression. For experiments, we compare it with Huffman encoding, and two enhanced variants of WAH : Concise and COMPAX. Our empirical results show this scheme achieves significant improvement.
Ge Ma, Guowei Zhu, Kan Lv, Qiyang Huang, Weixi Gu
SenSys4