Welcome to the InfoSci Platform
Reference Hub1
Temporal Join with Hilbert Curve Mapping and Adaptive Buffer Management

Temporal Join with Hilbert Curve Mapping and Adaptive Buffer Management

Jaime Raigoza, Junping Sun
Copyright: © 2014 |Volume: 2 |Issue: 2 |Pages: 19
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781466656604|DOI: 10.4018/ijsi.2014040101
Cite Article Cite Article

MLA

Raigoza, Jaime, and Junping Sun. "Temporal Join with Hilbert Curve Mapping and Adaptive Buffer Management." IJSI vol.2, no.2 2014: pp.1-19. http://doi.org/10.4018/ijsi.2014040101

APA

Raigoza, J. & Sun, J. (2014). Temporal Join with Hilbert Curve Mapping and Adaptive Buffer Management. International Journal of Software Innovation (IJSI), 2(2), 1-19. http://doi.org/10.4018/ijsi.2014040101

Chicago

Raigoza, Jaime, and Junping Sun. "Temporal Join with Hilbert Curve Mapping and Adaptive Buffer Management," International Journal of Software Innovation (IJSI) 2, no.2: 1-19. http://doi.org/10.4018/ijsi.2014040101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Management of data with a time dimension increases the overhead of storage and query processing in large database applications especially with the join operation, which is a commonly used and expensive relational operator. The temporal join evaluation can be time consuming because temporal data are intrinsically multi-dimensional. Also, due to a limited buffer size, the long-lived data can be frequently swapped-in and swapped-out between disk and main memory thus resulting in a low cache hit ratio. The proposed index-based Hilbert-Temporal Join (Hilbert-TJ) join algorithm maps temporal data into Hilbert curve space that is inherently clustered, thus allowing for fast retrieval and storage. This paper also proposes the Adaptive Replacement Cache-Temporal Data (ARC-TD) buffer replacement policy which favors the cache retention of data pages in proportion to the average life span of the tuples in the buffer. By giving preference to tuples having long life spans, a higher cache hit ratio can be achieved. The caching priority is also balanced between recently and frequently accessed data. The comparison study consists of different join algorithms and buffer replacement policies. Additionally, the Hilbert-TJ algorithm offers support to both valid-time and transaction-time data.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.