NDLI: Semi-Streamed Index Join for near-real time execution of ETL transformations

Content Provider	IEEE Xplore Digital Library
Author	Bornea, M.A. Deligiannakis, A. Kotidis, Y. Vassalos, V.
Copyright Year	2011
Description	Author affiliation: Athens U. of Econ and Business, Greece (Bornea, M.A.; Kotidis, Y.; Vassalos, V.) \|\| Technical University of Crete, Greece (Deligiannakis, A.)
Abstract	Active data warehouses have emerged as a new business intelligence paradigm where data in the integrated repository is refreshed in near real-time. This shift of practices achieves higher consistency between the stored information and the latest updates, which in turn influences crucially the output of decision making processes. In this paper we focus on the changes required in the implementation of Extract Transform Load (ETL) operations which now need to be executed in an online fashion. In particular, the ETL transformations frequently include the join between an incoming stream of updates and a disk-resident table of historical data or metadata. In this context we propose a novel Semi-Streaming Index Join (SSIJ) algorithm that maximizes the throughput of the join by buffering stream tuples and then judiciously selecting how to best amortize expensive disk seeks for blocks of the stored relation among a large number of stream tuples. The relation blocks required for joining with the stream are loaded from disk based on an optimal plan. In order to maximize the utilization of the available memory space for performing the join, our technique incorporates a simple but effective cache replacement policy for managing the retrieved blocks of the relation. Moreover, SSIJ is able to adapt to changing characteristics of the stream (i.e. arrival rate, data distribution) by dynamically adjusting the allocated memory between the cached relation blocks and the stream. Our experiments with a variety of synthetic and real data sets demonstrate that SSIJ consistently outperforms the state-of-the-art algorithm in terms of the maximum sustainable throughput of the join while being also able to accommodate deadlines on stream tuple processing.
Starting Page	159
Ending Page	170
File Size	382057
Page Count	12
File Format	PDF
ISBN	9781424489596
ISSN	10636382
e-ISBN	9781424489602
e-ISBN	9781424489589
DOI	10.1109/ICDE.2011.5767906
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-11
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Indexes Radiation detectors Heuristic algorithms Data warehouses Buffer storage Throughput Memory management
Content Type	Text
Resource Type	Article

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