Abstract
Filtering is a popular approach to reduce information traffic in subscription-based systems, especially if most subscribers are located on mobile devices. The filters are placed between the subscribers and the subscription server. With increasing number of filters from subscribers, filtering may become a bottleneck and challenge the scalability of such systems. In this paper, we propose to use filter indexing to solve this problem. We propose and study four filter-indexing schemes: Ad-Hoc Indexing Scheme (AIS), Group Indexing Scheme (GIS), Group-Sort Indexing Scheme (GSIS) and B’ Tree Indexing Scheme (BTIS). We evaluate the performance of these four indexing schemes with respect to scalability and other factors. Among the proposed schemes, we find that GSIS is the most efficient indexing scheme for searching and BTIS has the best performance for updating and inserting filters.
Keywords
- Hash Function
- Leaf Node
- Internal Node
- Hash Table
- Large Subscription
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Xie, W., Navathe, S.B., Prasad, S.K. (2005). Filter Indexing: A Scalable Solution to Large Subscription Based Systems. In: Zhou, L., Ooi, B.C., Meng, X. (eds) Database Systems for Advanced Applications. DASFAA 2005. Lecture Notes in Computer Science, vol 3453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408079_27
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DOI: https://doi.org/10.1007/11408079_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25334-1
Online ISBN: 978-3-540-32005-0
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