Keith Griffin

A Semantic Processing Framework for IoT-Enabled Communication Systems

Enterprise Collaboration Systems are designed in such a way to maximise the efficiency of communication and collaboration within the enterprise. With users becoming mobile, the Internet of Things can play a crucial role in this process, but is far from being seamlessly integrated in modern online communications. In this paper, we showcase the use of a solution that goes beyond today’s ad-hoc integration and processing of heterogeneous data sources for static and streaming data, providing more flexible and efficient processing techniques that can bridge the gap between IoT and online Enterprise Communication Systems. We document the technologies used for sensor deployment, sensor data acquisition based on the OpenIoT framework, and stream federation. Our main contributions are the following, i) we present a conceptual architecture of IoT-enabled Communication Systems, that builds upon existing frameworks for semantic data acquisition, and tools to enable continuous processing, discovery and federation of dynamic data sources based on Linked Data; ii) we present a semantic information model for representing and linking IoT data, social data and personal data by re-using and extending the existing standard semantic models; iii) we evaluate the performance of virtualisation of IoT sources based on OpenIoT in our testbed and show the impact of transmission, annotation and data storage, as well as initial results on scalability of RDF stream query processing in such a framework, providing guidelines and directions for optimisation.

Real-time data analytics and event detection for IoT-enabled communication systems

Abstract Enterprise Communication Systems are designed in such a way to maximise the efficiency of communication and collaboration within the enterprise. With users becoming mobile, the Internet of Things (IoT) can play a crucial role in this process, but is far from being seamlessly integrated into modern online communications. In this paper, we present a semantic infrastructure for gathering, integrating and reasoning upon heterogeneous, distributed and continuously changing data streams by means of semantic technologies and rule-based inference. Our solution exploits semantics to go beyond today’s ad-hoc integration and processing of heterogeneous data sources for static and streaming data. It provides flexible and efficient processing techniques that can transform low-level data into high-level abstractions and actionable knowledge, bridging the gap between IoT and online Enterprise Communication Systems. We document the technologies used for acquisition and semantic enrichment of sensor data, continuous semantic query processing for integration and filtering, as well as stream reasoning for decision support. Our main contributions are the following, (i) we define and deploy a semantic processing pipeline for IoT-enabled Communication Systems, which builds upon existing systems for semantic data acquisition, continuous query processing and stream reasoning, detailing the implementation of each component of our framework; (ii) we present a rich semantic information model for representing and linking IoT data, social data and personal data in the Enterprise Communication scenario, by reusing and extending existing standard semantic models; (iii) we define and develop an expressive stream reasoning component as part of our framework, based on continuous query processing and non-monotonic reasoning for semantic streams, (iv) we conduct experiments to comparatively evaluate the performance of our data acquisition and semantic annotation layer based on OpenIoT, and the performance of our expressive reasoning layer in the scenario of Enterprise Communication.

Evaluation of Asynchronous Event Mechanisms for Browser-based Real-time Communication Integration

Desktop based real-time communication applications are commonly used for presence based instant messaging and telephony applications. Such applications use installed desktop components to handle real-time asynchronous events on the client originating from the communication system. When the client is based in a web browser these installed components are not available however browser-based communication applications are required to handle the same type of asynchronous events. Moreover, browser-based clients which typically run over HTTP are challenged by HTTP itself which is designed to be a synchronous request-response protocol. We contend that a suitable mechanism can be found to deliver asynchronous real-time events to browser-based applications

Defining a call control interface for browser-based integrations using representational state transfer

While integrating telephony call control signaling into a desktop application is not a new concept, the emergence of web browser-based applications drives the need for feature parity between desktop and browser-based telephony call control applications. Desktop call control applications typically have the advantage of being able to use local protocol stacks or API libraries. A browser-based application does not have the same capabilities by default, especially when running on a constrained device. Traditional telephony call control APIs and architectures do not currently lend themselves for use with web browser-based applications without requiring some form of download which is not desirable. Is it possible to design a call control interface that can be consumed in browser-based applications natively without requiring additional downloads? Representational State Transfer (REST) is a resource oriented architectural style which, when used with HTTP as a uniform interface offers the possibility to use an existing defined set of interfaces to manipulate state transitions for resources. We propose that the REST architectural style can be used with an identified interface design methodology to define an interface for telephony call control capable of being consumed by browser-based applications. Furthermore by utilizing existing industry standards as an example, we suggest that existing standards can be extended to support browser-based integrations using REST.

Approaches for Real-Time Integration of Semantic Web Data in Distributed Enterprise Systems

The widespread use of social platforms in contemporary organizations leads to the generation of large amount of content shared through various social tools. While Semantic Web technologies provide data integration capabilities the two major remaining challenges include real-time integration of social data and limit the information overload experienced by the knowledge workers through content personalization. This paper focuses on the former and presents a comparison of current practices in real-time integration of RDF data and their performance. We give an overview of popular architectural approaches and existing RDF update formats used for modeling changes in RDF data.

Browser based Communications Integration using Representational State Transfer

Web browser based real time communications applications such as instant messaging, call control and presence aware applications differ from traditional desktop based communications applications. Browser based applications typically rely on Hypertext Transfer Protocol (HTTP) as an application level communications protocol. Traditional desktop applications have used a variety of client/server protocols and techniques. Browser based applications offer many advantages but also introduce many constraints. This paper investigates the possible use of Representational State Transfer (REST) based architectures for real time communications integration systems. We look at REST in the context of an enterprise unified communication system using telephony and presence as representative features of a unified communications system. We contend that a REST based architecture offers benefits for thin clients in a unified communication environment.

Towards Near Real-Time Social Recommendations for the Enterprise

The widespread use of social platforms in contemporary organizations leads to the generation of large amounts of content shared through various social tools. This information is distributed and often unstructured, making it difficult to fully exploit its value in an enterprise context. While Semantic Web technologies allow for publishing meaningful and structured data, major challenges include: (1) real-time integration of distributed social data, and (2) content personalization to identify relevant pieces of information and present them to users to limit the information overload. We propose to combine Semantic Web technologies with standardized transport protocols, such as XMPP, to provide an efficient and open source layer for aggregation of distributed social data in an enterprise. In addition, we propose a personalisation approach, which is able to provide filtered and personalised access on top of such distributed social data.

Real-Time Data Aggregation in Distributed Enterprise Social Platforms

The widespread use of social platforms in contemporary organizations leads to the generation of large amount of content shared through various social tools. This information is distributed and often unstructured, making it difficult to fully exploit its value in enterprise context. While Semantic Web technologies allow for publishing meaningful and structured data, major challenges include: (1) real-time integration of distributed social data, and (2) content personalization to identify relevant pieces of information and present them to users to limit the information overload. This research in progress paper draws from an enterprise use case and discussed practices in real-time integration of social data in distributed social platforms. We propose to combine Semantic Web technologies with standardized transport protocols to provide efficient and open source layer for aggregation of distributed social data in an enterprise. We also show how our component can facilitate development of personalised social platforms.