TY - GEN
T1 - Thematic event processing
AU - Hasan, Souleiman
AU - Curry, Edward
N1 - Publisher Copyright:
Copyright © 2014 ACM.
PY - 2014/12/8
Y1 - 2014/12/8
N2 - Event-based systems follow a decoupled mode of interaction between event producers and consumers in space, time, and synchronization to enable scalability within distributed systems. We recognize a fourth dimension of coupling due to the need for mutual agreements on terms that describe event types, attributes, and values. Semantic coupling is challenging in large-scale, open, and heterogeneous environments such as the Internet of Things (IoT). It requires event producers and consumers to agree on event semantics and can limit scalability due to the difficulties in establishing such agreements. In this paper we propose a new thematic event processing approach based on enhancing events and subscriptions with terms representing their themes to clarify their domains and meanings in addition to their pay- load. Experiments conducted using large heterogeneous sets of smart-city and energy management events suggest up to 85% of matching accuracy at a rate of 500 events/sec of throughput. This represents around 15% improvement in accuracy and 150% in throughput over non-thematic approaches. This suggests the viability of thematic event processing to scale to environments such as the IoT.
AB - Event-based systems follow a decoupled mode of interaction between event producers and consumers in space, time, and synchronization to enable scalability within distributed systems. We recognize a fourth dimension of coupling due to the need for mutual agreements on terms that describe event types, attributes, and values. Semantic coupling is challenging in large-scale, open, and heterogeneous environments such as the Internet of Things (IoT). It requires event producers and consumers to agree on event semantics and can limit scalability due to the difficulties in establishing such agreements. In this paper we propose a new thematic event processing approach based on enhancing events and subscriptions with terms representing their themes to clarify their domains and meanings in addition to their pay- load. Experiments conducted using large heterogeneous sets of smart-city and energy management events suggest up to 85% of matching accuracy at a rate of 500 events/sec of throughput. This represents around 15% improvement in accuracy and 150% in throughput over non-thematic approaches. This suggests the viability of thematic event processing to scale to environments such as the IoT.
KW - Approximate matching
KW - Distributional semantics
KW - Event processing
KW - Internet of things
KW - Semantic matching
KW - Theme tags
KW - Uncertainty
UR - https://www.scopus.com/pages/publications/84920418518
U2 - 10.1145/2663165.2663335
DO - 10.1145/2663165.2663335
M3 - Conference Publication
AN - SCOPUS:84920418518
T3 - Proceedings of the 15th International Middleware Conference, Middleware 2014
SP - 109
EP - 120
BT - Proceedings of the 15th International Middleware Conference, Middleware 2014
PB - Association for Computing Machinery
T2 - 15th International Middleware Conference, Middleware 2014
Y2 - 8 December 2014 through 12 December 2014
ER -
