id: 06094995
dt: a
an: 06094995
au: Jiao, Min; Zhang, Yansong; Sun, Yan; Wang, Shan; Zhou, Xuan
ti: CDDTA-JOIN: one-pass OLAP algorithm for column-oriented databases.
so: Sheng, Quan Z. (ed.) et al., Web technologies and applications. 14th
    Asia-Pacific web conference, APWeb 2012, Kunming, China, April 11‒13,
    2012. Proceedings. Berlin: Springer (ISBN 978-3-642-29252-1/pbk).
    Lecture Notes in Computer Science 7235, 448-459 (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: OLAP; CDDTA-JOIN; predicate-vector; column-store
ci: 
li: doi:10.1007/978-3-642-29253-8_38
ab: Summary: Row-store commonly uses a volcano-style “once-a-tuple”
    pipeline processor for processing efficiency but looses the I/O
    efficiency when only a small part of columns are accessed in a wide
    table. The academic column-store usually uses “once-a-column” style
    processing for I/O and cache efficiency but it has to suffer multi-pass
    column scan for complex query. This paper focuses on how to achieve the
    maximal gains from storage models for both pipeline processing
    efficiency and column processing efficiency. Based on the
    “address-value” mapping for surrogate key in dimension table, we
    can map incremental primary keys as offset addresses, so the foreign
    keys in fact table can be utilized as native join index for dimensional
    tuples. We use predicate vector as bitmap vector filters for dimensions
    to enable star-join as pipeline operator and pre-generate hash
    aggregators for aggregat based on the column. Using these approaches,
    star-join and pre-grouping can be completed in one-pass scan on
    dimensional attributes in fact table, and the following aggregate
    column scanning responses for the sparse accessing aggregation. We can
    gain both I/O efficiency for vector processing and CPU efficiency for
    pipeline aggregating. We perform the experiments for both simulated
    algorithm based on the column and the commercial column-store database.
rv: