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Fast Approximate Aggregation with Error Guarantee Using Encoded Bit-Slice Indexing

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Kakeru Ito
Ryogo Maeda
Qiong Chang
Jun Miyazaki
Published in
Information Integration and Web Intelligence
Publisher
Springer Nature Switzerland

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Abstract

This chapter explores the challenges of performing aggregation operations on large-scale multidimensional data and introduces two innovative methods, PBE+DAQ and PBE+DAQ/WN, to address these challenges. The text delves into the limitations of traditional approximate-aggregation methods, such as sampling and histogram-based techniques, and presents a detailed overview of deterministic approximate querying (DAQ) using bit-slice indexing. The proposed methods combine patch-based encoding (PBE) with DAQ to reduce the number of bit-slices to be read, thereby improving the throughput of aggregation operations. The chapter also discusses the extension of PBE+DAQ, known as PBE+DAQ/WN, which is designed to handle skew data more efficiently. Experimental results demonstrate the effectiveness of these methods, showing significant improvements in computation time and data reading efficiency compared to traditional DAQ. The conclusion highlights the potential of these methods to enhance the performance of approximate-aggregation operations in various applications.
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previous chapter Peak Pattern Based Similarity Search for High-Dimensional Spectral Data
next chapter Retrieving More Concrete Product Reviews by Query Rewriting with Retrieved Review Concretization
Title
Fast Approximate Aggregation with Error Guarantee Using Encoded Bit-Slice Indexing
Authors
Kakeru Ito
Ryogo Maeda
Qiong Chang
Jun Miyazaki
Copyright Year
2026
Book
Information Integration and Web Intelligence

Print ISBN: 978-3-032-11975-9

Electronic ISBN: 978-3-032-11976-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-11976-6_12
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