AbstractGiven a set of n objects, each characterized by d attributes specified at m fixed time instances, we are interested in the problem of designing space efficient indexing structures such that a class of temporal range search queries can be handled efficiently. When m=1, our problem reduces to the d-dimensional orthogonal search problem. We establish efficient data structures to handle several classes of the general problem. Our results include a linear size data structure that enables a query time of O(lognlogm+f) for one-sided queries when d=1, where f is the number of objects satisfying the query. A similar result is shown for counting queries. We also show that the most general problem can be solved with a polylogarithmic query tim...
We present a succinct representation of a set of n points on an n×n grid using bits to support ortho...
Temporal information plays a crucial role in many database applications, however support for queries...
We consider the problem of querying large scale multidimensional time series data to discover events...
Given a set of $n$ objects, each characterized by $d$ attributes specified at $m$ fixed time instan...
AbstractGiven a set of n objects, each characterized by d attributes specified at m fixed time insta...
AbstractIn this paper we describe space-efficient data structures for the two-dimensional range sear...
Abstract — We consider the problem of querying large scale multidimensional time series data to disc...
In this paper we present new data structures for two extensively studied variants of the orthogonal ...
Orthogonal range searches arise in many areas of application, most often, in database queries. Many ...
AbstractWe present the first adaptive data structure for two-dimensional orthogonal range search. Ou...
We consider the problem of dynamically indexing temporal observations about a collection of obje...
Abstract. We revisit the range minimum query problem and present a new O(n)-space data structure tha...
We revisit the orthogonal range searching problem and the exact l_infinity nearest neighbor searchin...
Given the lower bound of\Omega\Gamma n (d\Gamma1)=d ) for range query time complexity on n d-dime...
AbstractLet P be a set of n points that lie on an n×n grid. The well-known orthogonal range reportin...
We present a succinct representation of a set of n points on an n×n grid using bits to support ortho...
Temporal information plays a crucial role in many database applications, however support for queries...
We consider the problem of querying large scale multidimensional time series data to discover events...
Given a set of $n$ objects, each characterized by $d$ attributes specified at $m$ fixed time instan...
AbstractGiven a set of n objects, each characterized by d attributes specified at m fixed time insta...
AbstractIn this paper we describe space-efficient data structures for the two-dimensional range sear...
Abstract — We consider the problem of querying large scale multidimensional time series data to disc...
In this paper we present new data structures for two extensively studied variants of the orthogonal ...
Orthogonal range searches arise in many areas of application, most often, in database queries. Many ...
AbstractWe present the first adaptive data structure for two-dimensional orthogonal range search. Ou...
We consider the problem of dynamically indexing temporal observations about a collection of obje...
Abstract. We revisit the range minimum query problem and present a new O(n)-space data structure tha...
We revisit the orthogonal range searching problem and the exact l_infinity nearest neighbor searchin...
Given the lower bound of\Omega\Gamma n (d\Gamma1)=d ) for range query time complexity on n d-dime...
AbstractLet P be a set of n points that lie on an n×n grid. The well-known orthogonal range reportin...
We present a succinct representation of a set of n points on an n×n grid using bits to support ortho...
Temporal information plays a crucial role in many database applications, however support for queries...
We consider the problem of querying large scale multidimensional time series data to discover events...