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Detecting Tree Distributed Predicates

Authors: Min Shen; Ajay D. Kshemkalyani; Ashfaq A. Khokhar;

Detecting Tree Distributed Predicates

Abstract

In a large-scale locality-driven network, knowing the state of a local area is sometimes necessary due to either interactions being local and driven by neighborhood proximity or the users being interested in the state of a certain region. We propose locality-aware predicates that aim at detecting a predicate within a specified area. We model the area of interest as the set of processes that are within distance $k$ from the initiator process. By associating the predicate with a tree topology, we force the set of processes satisfying the predicate to form a tree with height no more than $k$. This enables the detection of the predicate within the area of interest. We also formalize several classes of locality-aware predicates, which deal with strong stable and stable predicates for both conjunctive and relational types. The algorithms to detect each class are also proposed. These algorithms associate a tree topology constraint with the predicate to be detected. Since a locality-aware predicate detects predicates only within the specified area, the complexities of the corresponding algorithms are thus scale-free. These properties make locality-aware predicate a natural fit for detecting distributed properties in systems such as modular robotics and wireless sensor networks.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
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