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Larisa Han

dblp:43/10826 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2012
—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 · 61% Database theory · 39%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Query processing and optimization
aggregate query processing
0.112012
Aggregation in Probabilistic Databases via Knowledge Compilation · Proc. VLDB Endow. 2012
Database theory
probabilistic databases
0.112012
Aggregation in Probabilistic Databases via Knowledge Compilation · Proc. VLDB Endow. 2012
Query processing and optimization
probabilistic query processing
0.112012
Aggregation in Probabilistic Databases via Knowledge Compilation · Proc. VLDB Endow. 2012
Database theory › query complexity
query tractability
0.012012
Aggregation in Probabilistic Databases via Knowledge Compilation · Proc. VLDB Endow. 2012

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

semiring · 0.1semimodule · 0.1knowledge compilation · 0.1decomposition tree · 0.1
YearPublicationVenuePosition
2012 Aggregation in Probabilistic Databases via Knowledge Compilation
abstract
This paper presents a query evaluation technique for positive relational algebra queries with aggregates on a representation system for probabilistic data based on the algebraic structures of semiring and semimodule. The core of our evaluation technique is a procedure that compiles semimodule and semiring expressions into so-called decomposition trees, for which the computation of the probability distribution can be done in time linear in the product of the sizes of the probability distributions represented by its nodes. We give syntactic characterisations of tractable queries with aggregates by exploiting the connection between query tractability and polynomial-time decomposition trees. A prototype of the technique is incorporated in the probabilistic database engine SPROUT. We report on performance experiments with custom datasets and TPC-H data.
Robert Fink, Larisa Han, Dan Olteanu
Proc. VLDB Endow.2