Publishing LO(D)D: Linked Open (Dynamic) Data for Smart Sensing and Measuring Environments

The paper proposes a distributed framework that provides a systematic way to publish environment data which is being updated continuously; such updates might be issued at specific time intervals or bound to some environment- specific event. The framework targets smart environments having networks of devices and sensors which are interacting with each other and with their respective environments to gather and generate data and willing to publish this data. This paper addresses the issues of supporting the data publishers to maintain up-to-date and machine understandable representations, separation of views (static or dynamic data) and delivering up-to-date information to data consumers in real time, helping data consumers to keep track of changes triggered from diverse environments and keeping track of evolution of the smart environment. The paper also describes a prototype implementation of the proposed architecture. A preliminary use case implementation over a real energy metering infrastructure is also provided in the paper to prove the feasibility of the architectur

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

A component-based middleware framework for configurable and reconfigurable Grid computing

Significant progress has been made in the design and development of Grid middleware which, in its present form, is founded on Web services technologies. However, we argue that present-day Grid middleware is severely limited in supporting projected next-generation applications which will involve pervasive and heterogeneous networked infrastructures, and advanced services such as collaborative distributed visualization. In this paper we discuss a new Grid middleware framework that features (i) support for advanced network services based on the novel concept of pluggable overlay networks, (ii) an architectural framework for constructing bespoke Grid middleware platforms in terms of 'middleware domains' such as extensible interaction types and resource discovery. We believe that such features will become increasingly essential with the emergence of next-generation e-Science applications. Copyright (c) 2005 John Wiley & Sons, Ltd

Efficient State Update Exchange in a CPS Environment for Linked Data-based Digital Twins

International audienceThis paper addresses the problem of reducing the number of messages needed to exchange state updates between the Cyber-Physical System (CPS) components that integrate with the rest of the CPS through Digital Twins in order to maintain uniform communication interface and carry out their tasks correctly and safely. The main contribution is a proposed architecture and the discussion of its suitability to support correct execution of complex tasks across the CPS. A new State Event Filtering component is presented to provide event-based communication among Digital Twins that are based on the Linked Data principles while keeping the fan-out limited to ensure the scalability of the architecture

Methods and Tools for Management of Distributed Event Processing Applications

Die Erfassung und Verarbeitung von Ereignissen aus cyber-physischen Systemen bietet Anwendern die Möglichkeit, kontinuierlich über Leistungsdaten und aufkommende Probleme unterrichtet zu werden (Situational Awareness) oder Wartungsprozesse zustandsabhängig zu optimieren (Condition-based Maintenance). Derartige Szenarien verlangen aufgrund der Vielzahl und Frequenz der Daten sowie der Anforderung einer echtzeitnahen Auswertung den Einsatz geeigneter Technologien. Unter dem Namen Event Processing haben sich dabei Technologien etabliert, die in der Lage sind, Datenströme in Echtzeit zu verarbeiten und komplexe Ereignismuster auf Basis räumlicher, zeitlicher oder kausaler Zusammenhänge zu erkennen. Gleichzeitig sind heute in diesem Bereich verfügbare Systeme jedoch noch durch eine hohe technische Komplexität der zugrunde liegenden deklarativen Sprachen gekennzeichnet, die bei der Entwicklung echtzeitfähiger Anwendungen zu langsamen Entwicklungszyklen aufgrund notwendiger technischer Expertise führt. Gerade diese Anwendungen weisen allerdings häufig eine hohe Dynamik in Bezug auf Veränderungen von Anforderungen der zu erkennenden Situationen, aber auch der zugrunde liegenden Sensordaten hinsichtlich ihrer Syntax und Semantik auf. Der primäre Beitrag dieser Arbeit ermöglicht Fachanwendern durch die Abstraktion von technischen Details, selbständig verteilte echtzeitfähige Anwendungen in Form von sogenannten Echtzeit-Verarbeitungspipelines zu erstellen, zu bearbeiten und auszuführen. Die Beiträge der Arbeit lassen sich wie folgt zusammenfassen: 1. Eine Methodik zur Entwicklung echtzeitfähiger Anwendungen unter Berücksichtigung von Erweiterbarkeit sowie der Zugänglichkeit für Fachanwender. 2. Modelle zur semantischen Beschreibung der Charakteristika von Ereignisproduzenten, Ereignisverarbeitungseinheiten und Ereigniskonsumenten. 3. Ein System zur Ausführung von Verarbeitungspipelines bestehend aus geographisch verteilten Ereignisverarbeitungseinheiten. 4. Ein Software-Artefakt zur graphischen Modellierung von Verarbeitungspipelines sowie deren automatisierter Ausführung. Die Beiträge werden in verschiedenen Szenarien aus den Bereichen Produktion und Logistik vorgestellt, angewendet und evaluiert

Streaming MASSIF : cascading reasoning for efficient processing of iot data streams

In the Internet of Things (IoT), multiple sensors and devices are generating heterogeneous streams of data. To perform meaningful analysis over multiple of these streams, stream processing needs to support expressive reasoning capabilities to infer implicit facts and temporal reasoning to capture temporal dependencies. However, current approaches cannot perform the required reasoning expressivity while detecting time dependencies over high frequency data streams. There is still a mismatch between the complexity of processing and the rate data is produced in volatile domains. Therefore, we introduce Streaming MASSIF, a Cascading Reasoning approach performing expressive reasoning and complex event processing over high velocity streams. Cascading Reasoning is a vision that solves the problem of expressive reasoning over high frequency streams by introducing a hierarchical approach consisting of multiple layers. Each layer minimizes the processed data and increases the complexity of the data processing. Cascading Reasoning is a vision that has not been fully realized. Streaming MASSIF is a layered approach allowing IoT service to subscribe to high-level and temporal dependent concepts in volatile data streams. We show that Streaming MASSIF is able to handle high velocity streams up to hundreds of events per second, in combination with expressive reasoning and complex event processing. Streaming MASSIF realizes the Cascading Reasoning vision and is able to combine high expressive reasoning with high throughput of processing. Furthermore, we formalize semantically how the different layers in our Cascading Reasoning Approach collaborate

Mapping the NGSI-LD Context Model on Top of a SPARQL Event Processing Architecture: Implementation Guidelines

NGSI-LD is an open specification released by ETSI which proposes an information model and an API for an easy to use and standard management of context information. The NGSI-LD information model is framed within an ontology and adopts JSON-LD as serialization format for context information. This paper presents an approach to the implementation of the NGSI-LD specification over a SPARQL Event Processing Architecture. This work is being developed within the European-Brasilian H2020 SWAMP project focused on implementing an Internet of Things platform providing services for smart water management in agriculture