We study the approximate range searching for three variants of the clustering problem with a set P of n points in d-dimensional Euclidean space and axis-parallel rectangular range queries: the k-median, k-means, and k-center range-clustering query problems. We present data structures and query algorithms that compute (1 + ε)-approximations to the optimal clusterings of P ∩Q efficiently for a qu...

In this study Synthetic Aperture Radar (SAR) raw data compression techniques are introduced. Two encoding approaches are generally used. Direct encoding in spatial domain and transform encoding in transform domain. A well-known spatial domain compression technique is based on Block Adaptive Quantization (BAQ)[3,6]. BAQ encoding is implemented by dividing the SAR Raw data set into data blocks of...

Within the image reproduction pipeline, display devices limit the maximum dynamic range. The dynamic range of natural scenes can exceed three orders of magnitude. Many films and newer camera sensors can capture this range, and people can discriminate intensities spanning this range. Display devices, however, such as CRTs, LCDs, and print media, are limited to a dynamic range of roughly one to t...

In this paper, a new and novel data structure is proposed to dynamically insert and delete segments. Unlike the standard segment trees[3], the proposed data structure permits insertion of a segment with interval range beyond the interval range of the existing tree, which is the interval between minimum and maximum values of the end points of all the segments. Moreover, the number of nodes in th...

Analysis of range queries on spatial (multidimensional) data is both important and challenging. Most previous analysis attempts have made certain simplifying assumptions about the datasets and/or queries to keep the analysis tractable. As a result, they may not be universally applicable. This paper proposes a set of five analysis techniques to estimate the selectivity and number of index nodes ...

In this paper, we study the problem of supporting range sum queries on a compressed sequence of values. For a sequence of n k-bit integers, k ≤ O(log n), our data structures require asymptotically the same amount of storage as the compressed sequence if compressed using the Lempel-Ziv algorithm. The basic structure supports range sum queries in O(log n) time. With an increase by a constant fact...

Probabilistic range query is an important type of query in the area of uncertain data management. A probabilistic range query returns all the data objects within a specific range from the query object with a probability no less than a given threshold. In this paper, we assume that each uncertain object stored in the database is associated with a multi-dimensional Gaussian distribution, which de...

We develop external data structures for storing points in one or two dimensions, each moving along a linear trajectory, so that a range query at a given time tq can be answered eeciently. The novel feature of our data structures is that the number of I/Os required to answer a query depends not only on the size of the data set and on the number of points in the answer but also on the diierence b...

We extend contention adapting trees (CA trees), a family of concurrent data structures for ordered sets, to support linearizable range queries, range updates, and operations that atomically operate on multiple keys such as bulk insertions and deletions. CA trees differ from related concurrent data structures by adapting themselves according to the contention level and the access patterns to sca...

There is a growing interest in the analysis of data in warehouses. Data warehouses can be extremely large and typical queries frequently take too long to answer. Manageable and portable summaries return interactive response times in exploratory data analysis. Obtaining the best estimates for smaller response times and storage needs is the objective of simple data reduction techniques that usual...