A dataflow platform for applications based on Linked Data

Modern software applications increasingly benefit from accessing the multifarious and heterogeneous Web of Data, thanks to the use of web APIs and Linked Data principles. In previous work, the authors proposed a platform to develop applications consuming Linked Data in a declarative and modular way. This paper describes in detail the functional language the platform gives access to, which is based on SPARQL (the standard query language for Linked Data) and on the dataflow paradigm. The language features interactive and meta-programming capabilities so that complex modules/applications can be developed. By adopting a declarative style, it favours the development of modules that can be reused in various specific execution context

Semantic web technologies for video surveillance metadata

Video surveillance systems are growing in size and complexity. Such systems typically consist of integrated modules of different vendors to cope with the increasing demands on network and storage capacity, intelligent video analytics, picture quality, and enhanced visual interfaces. Within a surveillance system, relevant information (like technical details on the video sequences, or analysis results of the monitored environment) is described using metadata standards. However, different modules typically use different standards, resulting in metadata interoperability problems. In this paper, we introduce the application of Semantic Web Technologies to overcome such problems. We present a semantic, layered metadata model and integrate it within a video surveillance system. Besides dealing with the metadata interoperability problem, the advantages of using Semantic Web Technologies and the inherent rule support are shown. A practical use case scenario is presented to illustrate the benefits of our novel approach

Update of time-invalid information in Knowledge Bases through Mobile Agents

In this paper, we investigate the use of a mobile, autonomous agent to update knowledge bases containing statements that lose validity with time. This constitutes a key issue in terms of knowledge acquisition and representation, because dynamic data need to be constantly re-evaluated to allow reasoning. We focus on the way to represent the time- validity of statements in a knowledge base, and on the use of a mobile agent to update time-invalid statements while planning for “information freshness” as the main objective. We propose to use Semantic Web standards, namely the RDF model and the SPARQL query language, to represent time-validity of information and decide how long this will be considered valid. Using such a representation, a plan is created for the agent to update the knowledge, focusing mostly on guaranteeing the time-validity of the information collected. To show the feasibility of our approach and discuss its limitations, we test its implementation on scenarios in the working environment of our research lab, where an autonomous robot is used to sense temperature, humidity, wifi signal and number of people on demand, updating the knowledge base with time- valid information

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Mixing the reactive with the personal: Opportunities for end-user programming in personal information management

The transition of personal information management (PIM) tools off the desktop to the Web presents an opportunity to augment these tools with capabilities provided by the wealth of real-time information readily available. In this chapter, we describe a personal information assistance engine that lets end-users delegate to it various simple context- and activity-reactive tasks and reminders. Our system, Atomate, treats RSS/ATOM feeds from social networking and life-tracking sites as sensor streams, integrating information from such feeds into a simple unified RDF world model representing people, places and things and their time-varying states and activities. Combined with other information sources on the web, including the user's online calendar, web-based e-mail client, news feeds and messaging services, Atomate can be made to automatically carry out a variety of simple tasks for the user, ranging from context-aware filtering and messaging, to sharing and social coordination actions. Atomate's open architecture and world model easily accommodate new information sources and actions via the addition of feeds and web services. To make routine use of the system easy for non-programmers, Atomate provides a constrained-input natural language interface (CNLI) for behavior specification, and a direct-manipulation interface for inspecting and updating its world model

Demonstration of a stream reasoning platform on low-end devices to enable personalized real-time cycling feedback

During amateur cycling training, analyzing sensor data in real-time would allow riders to receive immediate feedback on how they are performing, and adapt their training accordingly. In this paper, a solution with Semantic Web technologies is presented that gives such real-time personalized feedback, by integrating the data streams with domain knowledge, rider profiles {\&} other context data. This solution consists of a stream reasoning engine running on a low-end Raspberry Pi device, and a tablet app showing feedback based on the continuous query results. To demonstrate this in a static environment, a virtual training app is presented, allowing a user to simulate an amateur cycling training