Semantic Event Model and Its Implication on Situation Detection

Events are at the core of reactive applications, which have become popular in many domains. Contemporary modeling tools lack the capability express the event semantics and relationships to other entities. This research is aimed at providing the system designer a tool to define and describe events and their relationships to other events, object and tasks. It follows the semantic data modeling approach, and applies it to events, by using the classification, aggregation, generalization and association abstractions in the event world. The model employs conditional generalizations that are specific to the event domain, and determine conditions in which an event that is classified to lower level class, is considered as a member of a higher-level event class, for the sake of reaction to the event. The paper describes the event model, its knowledge representation scheme and its properties, and demonstrates these properties through a comprehensive example

Semantic Event Model and its Implication on Situation Detection

Abstract -Events are at the core of reactive applications, which have become popular in many domains. Contemporary modeling tools lack the capability express the event semantics and relationships to other entities. This research is aimed at providing the system designer a tool to define and describe events and their relationships to other events, object and tasks. It follows the semantic data modeling approach, and applies it to events, by using the classification, aggregation, generalization and association abstractions in the event world. The model employs conditional generalizations that are specific to the event domain, and determine conditions in which an event that is classified to lower level class, is considered as a member of a higher-level event class, for the sake of reaction to the event. The paper describes the event model, its knowledge representation scheme and its properties, and demonstrates these properties through a comprehensive example

Events, Context, and Situations and Reactive Mobile Service Delivery

Mobile Users (MUs) require flexible, reactive service delivery due to their regularly changing location and activities and the lack of a wired Internet connection. A mobile service delivery system should be able to detect relevant events that occur such as change of location, availability of new last-minute specials, sales opportunities and safety issues and then reactively take action in response to these events. This paper describes a framework for delivering such a system. Issues addressed include MU and service states and events, context, situations and situation-action rules, and syntactically and semantically compatible XML schemas for their specification. A framework is proposed that is based on distributed, co-operating software agents and mobile data technologies

Efficient and Effective Event Pattern Management

The goal of this thesis is to reduce the barriers stopping more enterprises from accessing CEP technology by providing additional support in managing relevant business situations. Therefore we outline the role of event pattern management and present a methodology, methods and tools aiming at an efficient and effective event pattern management. We provide a meta model for event patterns, an event pattern life cycle methodology, methods for guidance, refinement and evolution

Web-oriented Event Processing

How can the Web be made situation-aware? Event processing is a suitable technology for gaining the necessary real-time results. The Web, however, has many users and many application domains. Thus, we developed multi-schema friendly data models allowing the re-use and mix from diverse users and application domains. Furthermore, our methods describe protocols to exchange events on the Web, algorithms to execute the language and to calculate access rights

Ontology-based context-aware model for event processing in an IoT environment

The Internet of Things (IoT) is more and more becoming one of the fundamental sources of data. The observations produced by these sources are made accessible with heterogeneous vocabularies, models and data formats. The heterogeneity factor in such an enormous environment complicates the task of sharing and reusing this data in a more intelligent way (other than the purposes it was initially set up for). In this research, we investigate these challenges, considering how we can transform raw sensor data into a more meaningful information. This raw data will be modelled using ontology-based information that is accessible through continuous queries for sensor streaming data.Interoperability among heterogeneous entities is an important issue in an IoT environment. Semantic modelling is a key element to support interoperability. Most of the current ontologies for IoT mainly focus on resources and services information. This research builds upon the current state-of-the-art ontologies to provide contextual information and facilitate sensor data querying. In this research, we present an Ontology to represent an IoT environment, with emphasis on temporal and geospatial context enrichment. Furthermore, the Ontology is used alongside a proposed syntax based on Description Logic to build an Event Processing Model. The aim of this model is to interconnect ontology-based reasoning with event processing. This model enables to perform event processing over high-level ontological concepts.The Ontology was developed using the NeOn methodology, which emphasises on the reuse and modularisation. The Competency Questions techniques was used to develop the requirements of this Ontology. This was later evaluated by domain experts in software engineering and cloud computing. The ontology was evaluated based on its completeness, conciseness, consistency and expandability, over 70% of the domain experts agreed on the core modules, concepts and relationships within the ontology. The resulted Ontology provides a core IoT ontology that could be used for further development within a specific IoT domain. IIThe proposed Ontology-Based Context-Aware model for Event-Processing in an IoT environment “OCEM-IoT”, implements all the time operators used in complex event processing engines. Throughput and latency were used as performance comparison metrics for the syntax evaluation; the results obtained show an improved performance over existing event processing languages

Event Processing and Stream Reasoning with ETALIS

This thesis presents the ETALIS Language for Events (ELE), a declarative rule-based language for Event Processing (EP) and Stream Reasoning (SR). ELE features a well-defined semantics, and provides strong event processing and reasoning capabilities. In this work we present ELE and show how its EP and SR capabilities have the potential to provide powerful real time intelligence. We provide a prototype implementation of the language, and present evaluation results for a few implemented scenarios