Practical selectivity estimation through adaptive sampling
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Data cube approximation and histograms via wavelets
Proceedings of the seventh international conference on Information and knowledge management
Locally adaptive dimensionality reduction for indexing large time series databases
ACM Transactions on Database Systems (TODS)
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Histogram-Based Approximation of Set-Valued Query-Answers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Approximate Query Processing Using Wavelets
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Approximate Answers to Aggregate Queries on a Data Cube
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Online Amnesic Approximation of Streaming Time Series
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Generalized Dimension-Reduction Framework for Recent-Biased Time Series Analysis
IEEE Transactions on Knowledge and Data Engineering
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The problem of statistics and aggregate maintenance over data streams has gained popularity in recent years, especially in telecommunication network monitoring, web-click streams, stock tickers and other time- variant data. The amount of data generated in such applications can become too large to store, or if stored too large to scan multiple times. So we consider queries over data streams that are biased towards the more recent values. Approximate query processing has emerged as a cost-effective approach for dealing with huge data volumes and stringent response time requirements of today's Decision Support systems. So we propose the use of wavelets as an effective tool for general purpose approximate query processing in modern, high dimensional applications. We develop a method called Adaptive framework for Recent-biased approximations, aiming at making traditional dimension reduction techniques actionable in Recent-biased time series analysis. In this framework time series data are first partitioned into segments and a dimension reduction technique is applied to each segment. Then more coefficients are kept for more recent data while fewer left for older data. This guarantees extremely fast response times since the query execution engine can do bulk of its processing over compact sets of wavelet coefficients essentially postponing the expansion into relational tuples until the end-result of the query. An extensive experimental study with synthetic as well as real life data sets establishes the effectiveness of our wavelet based approach compared to the traditional methods.