QoS-Aware Stream Federation and Optimization Based on Service Composition

Abstract

The proliferation of sensor devices and services along with the advances in event processing brings many new opportunities as well as challenges. It is now possible to provide, analyze and react upon real-time, complex events in urban environments. When existing event services do not provide such complex events directly, an event service composition maybe required. However, it is difficult to determine which event service candidates (or service compositions) best suit users' and applications' quality-of-service requirements. A sub-optimal service composition may lead to inaccurate event detection, lack of system robustness etc. In this paper, the authors address these issues by first providing a quality-of-service aggregation schema for complex event service compositions and then developing a genetic algorithm to efficiently create near-optimal event service compositions. The authors evaluate their approach with both real sensor data collected via Internet-of-Things services as well as synthesised datasets.

Automated discovery and integration of semantic urban data streams: The ACEIS middleware

“…The event service composer creates service composition plans to detect the complex events specified by event requests based on event patterns. We refer readers to [11,12] for further details of the composition algorithm used by the event service composer.…”

“…As shown in Section 1, Event service modelling and composition are steps taken before implementation and execution, and have been discussed thoroughly in our previous work. We refer interested readers to [11,12] for a detailed description on these topics. In this section, for the sake of completeness, we describe the key features of the ontology used for describing event services and event requests as well as the discovery and integration mechanism for the sensor data streams.…”

“…A Complex Event Service (CES) ontology 12 had been developed to describe event services and requests. The CES ontology is an extension of OWL-S, which is an ontology to describe, discover and compose semantic web services.…”

“…Listing 2 shows a snippet of a sensor service request matched by the traffic sensor service. When the discovery component finds all service candidates suitable for the request, a Simple-Additive-Weighting algorithm [12] is used to rank the service candidates based on q d , q r and pref. This matching and ranking process can be accelerated by using a SPARQL query as a filter.…”

“…Figure 1 illustrates the life cycle of event services (by the analogy to Web Service life cycle). Our previous work have discussed the modelling [11], planning [12] and adaptation [13] aspects. In this paper, we present the big picture of ACEIS to show how different parts come together, with a focus on how the implementation is carried out for event services, and how the execution can be optimised.…”