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

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Exploring conflicts in rule-based Sensor Networks

This paper addresses rule conflicts within wireless sensor networks. The work is situatedwithin psychiatric ambulatory assessment settings where patients are monitored in andaround their homes. Detecting behaviours within these settings favours sensor networks,while scalability and resource concerns favour processing data on smart nodes incorporatingrule engines. Such monitoring involves personalisation, thereby becoming important toprogram node rules on the fly. Since rules may originate from distinct sources and changeover time, methods are required to maintain rule consistency. Drawing on lessons fromFeature Interaction, the paper contributes novel approaches for detecting and resolving rule-conflict across sensor networks

Dealing with Inconsistency and Incompleteness in Data Integration

Marco Schaerf, Giuseppe Di Battista, Domenico SaccÃ

Overcoming database heterogeneity to facilitate social networks: the Colombian displaced population as a case study

In this paper we describe a two-step approach for the publication of data about displaced people in Colombia, whose lack of homogeneity represents a major barrier for the application of adequate policies. This data is available in heterogeneous data sources, mainly relational, and is not connected to social networking sites. Our approach consists in a first step where ontologies are automatically derived from existing relational databases, exploiting the semantics underlying the SQL-DDL schema description, and a second step where these ontologies are aligned with existing ontologies (FOAF in our example), facilitating a better integration of data coming from multiple sources