A Taxonomy and Representation of Sources of Uncertainty in Active Systems

Proceedings of the 11th ACM International Conference on Distributed and Event-Based Systems

et al. 2017

Event processing systems involve the processing of high volume and variety data which has inherent uncertainties like incomplete event streams, imprecise event recognition etc. With the emergence of crowdsourcing platforms, the performance of event processing systems can be enhanced by including 'human-in-theloop' to leverage their cognitive ability. The resulting crowdsourced event processing can cater to the problem of event uncertainty and veracity by using humans to verify the results.This paper introduces the first hybrid crowd-enabled event processing engine. The paper proposes a list of five event crowd operators that are domain and language independent and can be used by any event processing framework. These operators encapsulate the complexities to deal with crowd workers and allow developers to define an event-crowd hybrid workflow. The operators are: Annotate, Rank, Verify, Rate, and Match. The paper presents a proof of concept of event crowd operators, schedulers, poolers, aggregators in an event processing system. The paper demonstrates the implementation of these operators and simulates the system with various performance metrics. The experimental evaluation shows that throughput of the system was 7.86 events per second with average latency of 7.16 seconds for 100 crowd workers. Finally, the paper concludes with avenues for future research in crowd-enabled event processing.

Section: Related Workmentioning

confidence: 99%

The Event Crowd

Yadav

Hassan

Proceedings of the 11th ACM International Conference on Distributed and Event-Based Systems

et al. 2017

“…A taxonomy for uncertainty in event processing proposed in Wasserkrug et al [2006] suggests that uncertainty can be classified in two orthogonal dimensions: element uncertainty and origin uncertainty. Element uncertainty includes uncertainties about event occurrence and event attributes.…”

Section: Uncertainty In Event Processingmentioning

confidence: 99%

Approximate Semantic Matching of Events for the Internet of Things

ACM Trans. Internet Technol.

Curry

2014

Event processing follows a decoupled model of interaction in space, time, and synchronization. However, another dimension of semantic coupling also exists and poses a challenge to the scalability of event processing systems in highly semantically heterogeneous and dynamic environments such as the Internet of Things (IoT). Current state-of-the-art approaches of content-based and concept-based event systems require a significant agreement between event producers and consumers on event schema or an external conceptual model of event semantics. Thus, they do not address the semantic coupling issue. This article proposes an approach where participants only agree on a distributional statistical model of semantics represented in a corpus of text to derive semantic similarity and relatedness. It also proposes an approximate model for relaxing the semantic coupling dimension via an approximation-enabled rule language and an approximate event matcher. The model is formalized as an ensemble of semantic and top-k matchers along with a probability model for uncertainty management. The model has been empirically validated on large sets of events and subscriptions synthesized from real-world smart city and energy management systems. Experiments show that the proposed model achieves more than 95% F 1 Score of effectiveness and thousands of events/sec of throughput for medium degrees of approximation while not requiring users to have complete prior knowledge of event semantics. In semantically loosely-coupled environments, one approximate subscription can compensate for hundreds of exact subscriptions to cover all possibilities in environments which require complete prior knowledge of event semantics. Results indicate that approximate semantic event processing could play a promising role in the IoT middleware layer.

“…A taxonomy for uncertain event processing has been proposed in [25]. It suggests two dimensions for uncertainty: element and origin uncertainty.…”

Section: Uncertainty In Event Processingmentioning

confidence: 99%

Thematic event processing

Proceedings of the 15th International Middleware Conference on - Middleware '14

Curry

2014

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 payload. 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.