# Top- k queries on temporal data

Top- k queries on temporal data The database community has devoted extensive amount of efforts to indexing and querying temporal data in the past decades. However, insufficient amount of attention has been paid to temporal ranking queries. More precisely, given any time instance t , the query asks for the top- k objects at time t with respect to some score attribute. Some generic indexing structures based on R-trees do support ranking queries on temporal data, but as they are not tailored for such queries, the performance is far from satisfactory. We present the Seb -tree, a simple indexing scheme that supports temporal ranking queries much more efficiently. The Seb -tree answers a top- k query for any time instance t in the optimal number of I/Os in expectation, namely, $${O\left({\rm log}_B\,\frac{N}{B}+\frac{k}{B}\right)}$$ I/Os, where N is the size of the data set and B is the disk block size. The index has near-linear size (for constant and reasonable k max values, where k max is the maximum value for the possible values of the query parameter k ), can be constructed in near-linear time, and also supports insertions and deletions without affecting its query performance guarantee. Most of all, the Seb -tree is especially appealing in practice due to its simplicity as it uses the B-tree as the only building block. Extensive experiments on a number of large data sets, show that the Seb -tree is more than an order of magnitude faster than the R-tree based indexes for temporal ranking queries. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

# Top- k queries on temporal data

, Volume 19 (5) – Oct 1, 2010
19 pages

/lp/springer_journal/top-k-queries-on-temporal-data-4Os2uRQgjT
Publisher
Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-010-0186-6
Publisher site
See Article on Publisher Site

### Abstract

The database community has devoted extensive amount of efforts to indexing and querying temporal data in the past decades. However, insufficient amount of attention has been paid to temporal ranking queries. More precisely, given any time instance t , the query asks for the top- k objects at time t with respect to some score attribute. Some generic indexing structures based on R-trees do support ranking queries on temporal data, but as they are not tailored for such queries, the performance is far from satisfactory. We present the Seb -tree, a simple indexing scheme that supports temporal ranking queries much more efficiently. The Seb -tree answers a top- k query for any time instance t in the optimal number of I/Os in expectation, namely, $${O\left({\rm log}_B\,\frac{N}{B}+\frac{k}{B}\right)}$$ I/Os, where N is the size of the data set and B is the disk block size. The index has near-linear size (for constant and reasonable k max values, where k max is the maximum value for the possible values of the query parameter k ), can be constructed in near-linear time, and also supports insertions and deletions without affecting its query performance guarantee. Most of all, the Seb -tree is especially appealing in practice due to its simplicity as it uses the B-tree as the only building block. Extensive experiments on a number of large data sets, show that the Seb -tree is more than an order of magnitude faster than the R-tree based indexes for temporal ranking queries.

### Journal

The VLDB JournalSpringer Journals

Published: Oct 1, 2010

## You’re reading a free preview. Subscribe to read the entire article.

### DeepDyve is your personal research library

It’s your single place to instantly
that matters to you.

over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month ### Explore the DeepDyve Library ### Search Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly ### Organize Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place. ### Access Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals. ### Your journals are on DeepDyve Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more. All the latest content is available, no embargo periods. DeepDyve ### Freelancer DeepDyve ### Pro Price FREE$49/month
\$360/year

Save searches from
PubMed

Create lists to

Export lists, citations