Top-k/w publish/subscribe: finding k most relevant publications in sliding time window w

Existing content-based publish/subscribe systems are designed assuming that all matching publications are equally relevant to a subscription. As we cannot know in advance the distribution of publication content, the following two unwanted situations are highly possible: a subscriber either receives too many or only few publications. In this paper we present a new publish/subscribe model which is based on the sliding window computation model. Our model assumes that publications have different relevance to a subscription. In the model, a subscriber receives k most relevant publications published within a time window w, where k and w are parameters defined per each subscription. As a consequence, the arrival rate of incoming relevant publications per subscription is predefined, and does not depend on the publication rate. Since all relevant objects (i.e. publications in our case) cannot be kept in main memory, existing solutions immediately discard less relevant objects, and store only a small representative set for subsequent delivery. In this paper we develop a probabilistic criterion to decide upon the arrival of a new object whether it may become the top-k object at some future point in time and should thus be stored in a special publications queue. We show that by accepting typically very small probability of error, the queue length is reasonably small and does not significantly depend on publication rate. Furthermore, we experimentally evaluate our approach to demonstrate its applicability in practice

Fuzzy Logic-Based Event Notification in Sparse MANETs

In the Ad-Hoc InfoWare project, we develop a delay tolerant event notification service for sparse Mobile Ad-Hoc Networks for emergency and rescue operations. In most event notification solutions, subscriptions are formed with crisp values or crisp value ranges. Filtering mechanisms do not take into account more expressive subscriptions in terms of approximate predicates and complex aggregating relations among them. However, in emergency scenarios subscribers ’ interests often have gradual nature and subjective measure. Therefore, we design an intelligent event notification system allowing uncertainties to be modeled and complex matching semantics to be processed by fuzzy reasoning. Requiring more computational efforts, fuzzy logic introduces performance penalties in the whole network. We have developed a new subscription data structure and filtering algorithms, and evaluated and optimized it for runtime and space efficiency