6,847 research outputs found
An indexing method for answering queries on moving objects
We consider the problem of indexing a set of objects moving in d-dimensional spaces along linear trajectories. A simple external-memory indexing scheme is proposed to efficiently answer general range queries. The following are examples of the queries that can be answered by the proposed method: report all moving objects that will (i) pass between two given points within a specified time interval; (ii) become within a given distance from some or all of a given set of other moving objects. Our scheme is based on mapping the objects to a dual space, where queries about moving objects are transformed into polyhedral queries concerning their speeds and initial locations. We then present a simple method for answering such polyhedral queries, based on partitioning the space into disjoint regions and using a B+-tree to index the points in each region. By appropriately selecting the boundaries of each region, we guarantee an average search time that matches a known lower bound for the problem. Specifically, for a fixed d, if the coordinates of a given set of N points are statistically independent, the proposed technique answers polyhedral queries, on the average, in O((N/B)1-1/d ·(log B N)1/d +K/B) I/O\u27s using O(N/B) space, where B is the block size, and K is the number of reported points. Our approach is novel in that, while it provides a theoretical upper bound on the average query time, it avoids the use of complicated data structures, making it an effective candidate for practical applications. The proposed index is also dynamic in the sense that it allows object insertion and deletion in an amortized update cost of log B (N) I/O\u27s. Experimental results are presented to show the superiority of the proposed index over other methods based on R-trees. © 2005 Springer Science + Business Media, Inc
An indexing method for answering queries on moving objects
We consider the problem of indexing a set of objects moving in d-dimensional spaces along linear trajectories. A simple external-memory indexing scheme is proposed to efficiently answer general range queries. The following are examples of the queries that can be answered by the proposed method: report all moving objects that will (i) pass between two given points within a specified time interval; (ii) become within a given distance from some or all of a given set of other moving objects. Our scheme is based on mapping the objects to a dual space, where queries about moving objects are transformed into polyhedral queries concerning their speeds and initial locations. We then present a simple method for answering such polyhedral queries, based on partitioning the space into disjoint regions and using a B+-tree to index the points in each region. By appropriately selecting the boundaries of each region, we guarantee an average search time that matches a known lower bound for the problem. Specifically, for a fixed d, if the coordinates of a given set of N points are statistically independent, the proposed technique answers polyhedral queries, on the average, in O((N/B)1-1/d ·(log B N)1/d +K/B) I/O\u27s using O(N/B) space, where B is the block size, and K is the number of reported points. Our approach is novel in that, while it provides a theoretical upper bound on the average query time, it avoids the use of complicated data structures, making it an effective candidate for practical applications. The proposed index is also dynamic in the sense that it allows object insertion and deletion in an amortized update cost of log B (N) I/O\u27s. Experimental results are presented to show the superiority of the proposed index over other methods based on R-trees. © 2005 Springer Science + Business Media, Inc
An efficient indexing scheme for multi-dimensional moving objects
We consider the problem of indexing a set of objects moving in d-dimensional space along linear trajectories. A simple disk-based indexing scheme is proposed to efficiently answer queries of the form: report all objects that will pass between two given points within a specified time interval. Our scheme is based on mapping the objects to a dual space, where queries about moving objects translate into polyhedral queries concerning their speeds and initial locations. We then present a simple method for answering such polyhedral queries, based on partitioning the space into disjoint regions and using a B-tree to index the points in each region. By appropriately selecting the boundaries of each region, we can guarantee an average search time that almost matches a known lower bound for the problem. Specifically, for a fixed d, if the coordinates of a given set of N points are statistically independent, the proposed technique answers polyhedral queries, on the average, in O((N/B)1-1/d.(logB N)1/d + K/B) I/O\u27s using O(N/B) space, where B is the block size, and K is the number of reported points. Our approach is novel in that, while it provides a theoretical upper bound on the average query time, it avoids the use of complicated data structures, making it an effective candidate for practical applications. © Springer-Verlag Berlin Heidelberg 2003
Towards a Scalable Dynamic Spatial Database System
With the rise of GPS-enabled smartphones and other similar mobile devices,
massive amounts of location data are available. However, no scalable solutions
for soft real-time spatial queries on large sets of moving objects have yet
emerged. In this paper we explore and measure the limits of actual algorithms
and implementations regarding different application scenarios. And finally we
propose a novel distributed architecture to solve the scalability issues.Comment: (2012
Efficient Spatial Keyword Search in Trajectory Databases
An increasing amount of trajectory data is being annotated with text
descriptions to better capture the semantics associated with locations. The
fusion of spatial locations and text descriptions in trajectories engenders a
new type of top- queries that take into account both aspects. Each
trajectory in consideration consists of a sequence of geo-spatial locations
associated with text descriptions. Given a user location and a
keyword set , a top- query returns trajectories whose text
descriptions cover the keywords and that have the shortest match
distance. To the best of our knowledge, previous research on querying
trajectory databases has focused on trajectory data without any text
description, and no existing work has studied such kind of top- queries on
trajectories. This paper proposes one novel method for efficiently computing
top- trajectories. The method is developed based on a new hybrid index,
cell-keyword conscious B-tree, denoted by \cellbtree, which enables us to
exploit both text relevance and location proximity to facilitate efficient and
effective query processing. The results of our extensive empirical studies with
an implementation of the proposed algorithms on BerkeleyDB demonstrate that our
proposed methods are capable of achieving excellent performance and good
scalability.Comment: 12 page
Main Memory Adaptive Indexing for Multi-core Systems
Adaptive indexing is a concept that considers index creation in databases as
a by-product of query processing; as opposed to traditional full index creation
where the indexing effort is performed up front before answering any queries.
Adaptive indexing has received a considerable amount of attention, and several
algorithms have been proposed over the past few years; including a recent
experimental study comparing a large number of existing methods. Until now,
however, most adaptive indexing algorithms have been designed single-threaded,
yet with multi-core systems already well established, the idea of designing
parallel algorithms for adaptive indexing is very natural. In this regard only
one parallel algorithm for adaptive indexing has recently appeared in the
literature: The parallel version of standard cracking. In this paper we
describe three alternative parallel algorithms for adaptive indexing, including
a second variant of a parallel standard cracking algorithm. Additionally, we
describe a hybrid parallel sorting algorithm, and a NUMA-aware method based on
sorting. We then thoroughly compare all these algorithms experimentally; along
a variant of a recently published parallel version of radix sort. Parallel
sorting algorithms serve as a realistic baseline for multi-threaded adaptive
indexing techniques. In total we experimentally compare seven parallel
algorithms. Additionally, we extensively profile all considered algorithms. The
initial set of experiments considered in this paper indicates that our parallel
algorithms significantly improve over previously known ones. Our results
suggest that, although adaptive indexing algorithms are a good design choice in
single-threaded environments, the rules change considerably in the parallel
case. That is, in future highly-parallel environments, sorting algorithms could
be serious alternatives to adaptive indexing.Comment: 26 pages, 7 figure
Dynamic Data Structures for Document Collections and Graphs
In the dynamic indexing problem, we must maintain a changing collection of
text documents so that we can efficiently support insertions, deletions, and
pattern matching queries. We are especially interested in developing efficient
data structures that store and query the documents in compressed form. All
previous compressed solutions to this problem rely on answering rank and select
queries on a dynamic sequence of symbols. Because of the lower bound in
[Fredman and Saks, 1989], answering rank queries presents a bottleneck in
compressed dynamic indexing. In this paper we show how this lower bound can be
circumvented using our new framework. We demonstrate that the gap between
static and dynamic variants of the indexing problem can be almost closed. Our
method is based on a novel framework for adding dynamism to static compressed
data structures. Our framework also applies more generally to dynamizing other
problems. We show, for example, how our framework can be applied to develop
compressed representations of dynamic graphs and binary relations
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