Abstract

The suitability of

Abstract. Efficient subsumption checking, deciding whether a subscription or publication is covered by a set of previously defined subscriptions, is of paramount importance for publish/subscribe systems. It provides the core system functionality-matching of publications to subscriber needs expressed as subscriptions-and additionally, reduces the overall system load and generated traffic since the covered subscriptions are not propagated in distributed environments. As the subsumption problem was shown previously to be co-NP complete and existing solutions typically apply pairwise comparisons to detect the subsumption relationship, we propose a 'Monte Carlo type' probabilistic algorithm for the general subsumption problem. It determines whether a publication/subscription is covered by a disjunction of subscriptions in O(k m d), where k is the number of subscriptions, m is the number of distinct attributes in subscriptions, and d is the number of tests performed to answer a subsumption question. The probability of error is problem-specific and typically very small, and sets an upper bound on d. Our experimental results show significant gains in term of subscription set reduction which has favorable impact on the overall system performance as it reduces the total computational costs and networking traffic. Furthermore, the expected theoretical bounds underestimate algorithm performance because it performs much better in practice due to introduced optimizations, and is adequate for fast forwarding of subscriptions in case of high subscription rate.

The paper proposes a novel approach to mobility in publish/subscribe (pub/sub)

Peer-to-peer networks have been identified as promising architectural concept for developing search scenarios across digital library collections. Digital libraries typically offer sophisticated search over their local content, however, search methods involving a network of such stand-alone components are currently quite limited. We present an architecture for highly-efficient search over digital library collections based on structured P2P networks. As the standard single-term indexing strategy faces significant scalability limitations in distributed environments, we propose a novel indexing strategy-key-based indexing. The keys are term sets that appear in a restricted number of collection documents. Thus, they are discriminative with respect to the global document collection, and ensure scalable search costs. Moreover, key-based indexing computes posting list joins during indexing time, which significantly improves query performance. As search efficient solutions usually imply costly indexing procedures, we present experimental results that show acceptable indexing costs while the retrieval performance is comparable to the standard centralized solutions with TF-IDF ranking.