Alessandra Mileo

Support for context-aware monitoring in home healthcare

This paper tackles the problem of supporting independent living and well-being for people that live in their homes and have no critical chronic condition. The paper assumes the presence of a monitoring system equipped with a pervasive sensor network and a non-monotonic reasoning engine. The rich set of sensors that can be used for monitoring in home environments and their sheer number make it quite complex to provide a correct interpretation of collected data for a particular patient. For this reason, we introduce a logic-based context model for situation assessment combined with high level declarative feedback policy specification, and we use logic programming techniques to reason about different pieces of knowledge for prevention.

CityPulse: Large Scale Data Analytics Framework for Smart Cities

Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they have a profound impact on different domains, such as healthcare, environmental monitoring, urban systems, and control and management applications, among several other areas. Cities currently face an increasing demand for providing services that can have an impact on peoples everyday lives. The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams. To goal is to break away from silo applications and enable cross-domain data integration. The CityPulse framework integrates multimodal, mixed quality, uncertain and incomplete data to create reliable, dependable information and continuously adapts data processing techniques to meet the quality of information requirements from end users. Different than existing solutions that mainly offer unified views of the data, the CityPulse framework is also equipped with powerful data analytics modules that perform intelligent data aggregation, event detection, quality assessment, contextual filtering, and decision support. This paper presents the framework, describes its components, and demonstrates how they interact to support easy development of custom-made applications for citizens. The benefits and the effectiveness of the framework are demonstrated in a use-case scenario implementation presented in this paper.

CityBench: A Configurable Benchmark to Evaluate RSP Engines Using Smart City Datasets

With the growing popularity of Internet of Things (IoT) and IoT-enabled smart city applications, RDF stream processing (RSP) is gaining increasing attention in the Semantic Web community. As a result, several RSP engines have emerged, which are capable of processing semantically annotated data streams on the fly. Performance, correctness and technical soundness of few existing RSP engines have been evaluated in controlled settings using existing benchmarks like LSBench and SRBench. However, these benchmarks focus merely on features of the RSP query languages and engines, and do not consider dynamic application requirements and data-dependent properties such as changes in streaming rate during query execution or changes in application requirements over a period of time. This hinders wide adoption of RSP engines for real-time applications where data properties and application requirements play a key role and need to be characterised in their dynamic setting, such as in the smart city domain.

A Logical Approach to Home Healthcare with Intelligent Sensor-Network Support

This paper describes an intelligent home healthcare system characterized by a wireless sensor network (WSN) and a reasoning component. The aim of the system is to allow constant and unobtrusive monitoring of a patient in order to enhance autonomy and increase quality of life. Data collected by the sensor network are used to support a reasoning component, which is based on answer set programming (ASP), in performing three main reasoning tasks: (i) continuous contextualization of the physical, mental and social state of a patient, (ii) prediction of possibly risky situations and (iii) identification of plausible causes for the worsening of a patients health. Starting from different data sources (sensor data, test results, inference results) the reasoning component applies expressive logic rules aimed at correct interpretation of incomplete or inconsistent contextual information, and evaluates correlation rules expressed by clinicians. The expressive power of ASP allows efficient enough reasoning to support prevention, while declarativity simplifies rule-specification and allows automatic encoding of knowledge. Preliminary evaluations show that the combination of an ASP-based reasoning component and a WSN is a good solution for creating a home-based healthcare system.

StreamRule: a nonmonotonic stream reasoning system for the semantic web

Stream reasoning is an emerging research field focused on dynamic processing and continuous reasoning over huge volumes of streaming data. Finding the right trade-off between scalability and expressivity is a key challenge in this area. In this paper, we want to provide a baseline for exploring the applicability of complex reasoning to the Web of Data based on a solution that combines results and approaches from database research, stream processing, and nonmonotonic logic programming.

Using linked data to mine RDF from wikipedia's tables

The tables embedded in Wikipedia articles contain rich, semi-structured encyclopaedic content. However, the cumulative content of these tables cannot be queried against. We thus propose methods to recover the semantics of Wikipedia tables and, in particular, to extract facts from them in the form of RDF triples. Our core method uses an existing Linked Data knowledge-base to find pre-existing relations between entities in Wikipedia tables, suggesting the same relations as holding for other entities in analogous columns on different rows. We find that such an approach extracts RDF triples from Wikipedias tables at a raw precision of 40%. To improve the raw precision, we define a set of features for extracted triples that are tracked during the extraction phase. Using a manually labelled gold standard, we then test a variety of machine learning methods for classifying correct/incorrect triples. One such method extracts 7.9 million unique and novel RDF triples from over one million Wikipedia tables at an estimated precision of 81.5%.

QoS-Aware Complex Event Service Composition and Optimization Using Genetic Algorithms

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 about physical or social 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 compositions best suit users’ quality-of-service requirements. In this paper, we address this issue by first providing a quality-of-service aggregation schema for event service compositions and then developing a genetic algorithm to efficiently create optimal compositions.

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.

A Logic Programming approach for Access Control over RDF

The Resource Description Framework (RDF) is an interoperable data representation format suitable for interchange and integration of data, especially in Open Data contexts. However, RDF is also becoming increasingly attractive in scenarios involving sensitive data, where data protection is a major concern. At its core, RDF does not support any form of access control and current proposals for extending RDF with access control do not fit well with the RDF representation model. Considering an enterprise scenario, we present a modelling that caters for access control over the stored RDF data in an intuitive and transparent manner. For this paper we rely on Annotated RDF, which introduces concepts from Annotated Logic Programming into RDF. Based on this model of the access control annotation domain, we propose a mechanism to manage permissions via application-specific logic rules. Furthermore, we illustrate how our Annotated Query Language (AnQL) provides a secure way to query this access control annotated RDF data.

Approximate Continuous Query Answering over Streams and Dynamic Linked Data Sets

To perform complex tasks, RDF Stream Processing Web applications evaluate continuous queries over streams and quasi-static background data. While the former are pushed in the application, the latter are continuously retrieved from the sources. As soon as the background data increase the volume and become distributed over the Web, the cost to retrieve them increases and applications become unresponsive. In this paper, we address the problem of optimizing the evaluation of these queries by leveraging local views on background data. Local views enhance performance, but require maintenance processes, because changes in the background data sources are not automatically reflected in the application. We propose a two-step query-driven maintenance process to maintain the local view: it exploits information from the query e.g.,i?źthe sliding window definition and the current window content to maintain the local view based on user-defined Quality of Service constraints. Experimental evaluation show the effectiveness of the approach.