Download:
by Donghui Zhang A, Dimitrios Gunopulos A, Vassilis J. Tsotras A, Bernhard Seeger B
http://www.ccs.neu.edu/home/donghui/publications/hta_journal.pdf
Add To MetaCart
Abstract:
Temporal and spatio-temporal aggregations are important but costly operations for applications that maintain time-evolving data (data warehouses, temporal databases, etc.). In this paper we examine the problem of computing such aggregates over data streams. The aggregates are maintained using multiple levels of temporal granularities: older data is aggregated using coarser granularities while more recent data is aggregated with finer detail. We present specialized indexing schemes for dynamically and progressively maintaining temporal and spatio-temporal aggregates. Moreover, these schemes can be parameterized. The levels of granularity as well as their corresponding index sizes (or validity lengths) can be dynamically adjusted. This provides a useful trade-off between aggregation detail and storage space. Analytical and experimental results show the efficiency of the proposed structures. We first address the temporal aggregation problem. A general framework of aggregating at multiple time granularities is then proposed. Finally we show how to utilize this framework to solve the range temporal and spatio-temporal aggregation problems.
Citations
|
1541
|
Computational Geometry: An Introduction
– Preparata, Shamos
- 1985
|
|
194
|
Geometric range searching and its relatives
– Agarwal, Erickson
- 1999
|
|
161
|
Mining high-speed data streams
– Domingos, Hulten
- 2000
|
|
132
|
An asymptotically optimal multiversion B-tree
– Becker, Gschwind, et al.
- 1996
|
|
90
|
Multidimensional divide-and-conquer
– Bentley
- 1980
|
|
56
|
Expiring Data in a Warehouse
– Garcia-Molina, Labio, et al.
- 1998
|
|
55
|
Incremental computation and maintenance of temporal aggregates
– Yang, Widom
- 2001
|
|
54
|
View maintenance issues for the chronicle data model
– Jagadish, Mumick, et al.
- 1995
|
|
52
|
Computing temporal aggregates
– Kline, Snodgrass
- 1995
|
|
50
|
Geometric range searching
– Matousek
- 1994
|
|
48
|
Progressive approximate aggregate queries with a multi-resolution tree structure
– Lazaridis, Mehrotra
- 2001
|
|
35
|
A Glossary of Time Granularity Concepts
– Bettini, Dyreson, et al.
- 1998
|
|
30
|
Efficient aggregation over objects with extent
– Zhang, Tsotras, et al.
- 2002
|
|
27
|
Parallel algorithms for computing temporal aggregates
– Gendrano, Huang, et al.
- 1999
|
|
25
|
Processing temporal aggregates in parallel
– Ye, Keane
- 1997
|
|
24
|
Implementing Historical Aggregates in TempIS
– Tuma
- 1992
|
|
22
|
Scalable algorithms for large temporal aggregation
– Moon, Lopez, et al.
- 2000
|
|
17
|
Symbolic representation of user-defined time granularities
– Bettini, Sibi
- 1999
|
|
7
|
Semantic compression of temporal data
– Bettini
- 2001
|
|
6
|
The K-D-B Tree
– Robinson
- 1981
|
|
1
|
insert 〈10, 3〉 : 1 (figure 18c), both the two records on the right should be split. To ensure minimum update cost, we require that an insertion causes at most one split in a page. As shown in the figure, only the record whose region contains the inserted
– To
|
