Abstract Methods for Approximate Query Processing (AQP) are essential for dealing with
massive data. They are often the only means of providing interactive response times when …

Given a time series data stream, the generation of error-bounded Piecewise Linear
Representation (error-bounded PLR) is to construct a number of consecutive line segments …

Event sequences capture system and user activity over time. Prior research on sequence
mining has mostly focused on discovering local patterns appearing in a sequence. While …

In this paper we investigate algorithms and lower bounds for summarization problems over a
single pass data stream. In particular we focus on histogram construction and K-center …

Recently, much study has been directed toward summarizing event data, in the hope that the
summary will lead us to a better understanding of the system that generates the events …

Discrete random variables are essential ingredients in various artificial intelligence
problems. These include the estimation of the probability of missing the deadline in a series …

Approximating points by piecewise linear functions is an intensively researched topic in
computational geometry. In this paper, we study, based on the uniform error metric, an array …

Histograms are a common technique for density estimation and they have been widely used
as a tool in exploratory data analysis. Learning histograms from static and stationary data is …

Hierarchical synopsis structures offer a viable alternative in terms of efficiency and flexibility
in relation to traditional summarization techniques such as histograms. Previous research on …

Constructing Haar wavelet synopses with guaranteed maximum error on data
approximations has many real world applications. In this paper, we take a novel approach …