ProPolyne: A Fast Wavelet-Based Algorithm for Progressive Evaluation of Polynomial Range-Sum Queries

Schmidt, Rolfe R.; Shahabi, Cyrus

doi:10.1007/3-540-45876-x_41

Cited by 18 publications

(33 citation statements)

References 16 publications

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“…These exact methods produce considerable savings, but are based on the assumption that some relation must be scanned in order to answer the batch of queries. This will not be the case when relations are stored using pre-aggregation techniques such as prefixsums [8], wavelets [14], or general Iterative Data Cubes [12].…”

Section: Related Workmentioning

confidence: 99%

“…Wavelets provide an update efficient storage format for which range-queries can be answered quickly. Furthermore, as pointed out in [14], query vectors for polynomial range-sums have excellent low I/O wavelet approximations, making progressive approximations fast and accurate. As an added benefit, these evaluation methods are complementary to powerful new wavelet-based approximate storage strategies [5].…”

Section: Contributionsmentioning

confidence: 99%

“…The functions χ R are very simple, and it has been shown that χ R has at most O(2 d log d N ) nonzero coefficients, and they can be computed quickly [13,14,17]. Thus Equation 1 can be used to evaluate Count queries in time O(2 d log d N ).…”

Section: Progressive Count Queriesmentioning

confidence: 99%

“…Extensive research has been done developing techniques for exact [2,3,8,12,14], approximate [4,6,10,15], and progressive [9,11,18] evaluation of single range-sum queries.…”

Section: Related Workmentioning

confidence: 99%

“…This technique is efficient as an exact method, shares I/O optimally, and uses data structures that can be updated quickly. It is based on ProPolyne [14], a technique for the progressive evaluation of single polynomial range-sum queries, and reuses much of its machinery. We begin by exhibiting the technique for range Count queries using Haar wavelets.…”

Section: Minimizing Ssementioning

confidence: 99%

See 4 more Smart Citations

How to evaluate multiple range-sum queries progressively

Schmidt

Shahabi

2002

Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems - PODS '02

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Decision support system users typically submit batches of range-sum queries simultaneously rather than issuing individual, unrelated queries. We propose a wavelet based technique that exploits I/O sharing across a query batch to evaluate the set of queries progressively and efficiently. The challenge is that now controlling the structure of errors across query results becomes more critical than minimizing error per individual query. Consequently, we define a class of structural error penalty functions and show how they are controlled by our technique. Experiments demonstrate that our technique is efficient as an exact algorithm, and the progressive estimates are accurate, even after less than one I/O per query.

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Section: Related Workmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Progressive Count Queriesmentioning

confidence: 99%

“…Extensive research has been done developing techniques for exact [2,3,8,12,14], approximate [4,6,10,15], and progressive [9,11,18] evaluation of single range-sum queries.…”

Section: Related Workmentioning

confidence: 99%

Section: Minimizing Ssementioning

confidence: 99%

See 3 more Smart Citations

How to evaluate multiple range-sum queries progressively

Schmidt

Shahabi

2002

Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems - PODS '02

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Very Large Databases

Garofalakis

Polyzotis

2009

Wiley Encyclopedia of Computer Science and Engineering

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Change Detection in Time Series Data Using Wavelet Footprints

Sharifzadeh

Azmoodeh

Shahabi

2005

Lecture Notes in Computer Science

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Abstract. In this paper, we propose a novel approach to address the problem of change detection in time series data. Our approach is based on wavelet footprints proposed originally by the signal processing community for signal compression. We, however, exploit the properties of footprints to capture discontinuities in a signal. We show that transforming data using footprints generates nonzero coefficients only at the change points. Exploiting this property, we propose a change detection query processing scheme which employs footprint-transformed data to identify change points, their amplitudes, and degrees of change efficiently and accurately. Our analytical and empirical results show that our approach outperforms the best known change detection approach in terms of both performance and accuracy. Furthermore, unlike the state of the art approaches, our query response time is independent of the number of change points and the user-defined change threshold.

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ProPolyne: A Fast Wavelet-Based Algorithm for Progressive Evaluation of Polynomial Range-Sum Queries

Cited by 18 publications

References 16 publications

How to evaluate multiple range-sum queries progressively

How to evaluate multiple range-sum queries progressively

Very Large Databases

Change Detection in Time Series Data Using Wavelet Footprints

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