QOBST: An Adaptable Matching Model for Content-Based Pub/Sub Systems

The matching efficiency of content-based publish and subscribe model(pub/sub model) can be more optimized since users on Internet have different needs for data and popular data is always changing. In this paper, a brand new matching model - Quasi-Optimal Binary Search Tree (QOBST) which is adaptive was proposed. Based on the idea of optimal binary search tree and avoiding the complex construction process of optimal binary search tree, QOBST is designed as an approximate optimal solution with time complexity of O(log2N). The subscription division of QOBST can completely avoid the secondary matching. Its index structure is changed with the changes of subscription and inputting data, making it easier to match the data with high frequency. Experimental results show that QOBST can be applied to content-based pub/sub models, especially distributed content-based pub/sub models.

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