Abstract. Front-end design of mobile applications is a complex and multidisciplinary task, where many perspectives intersect and the user experience must be perfectly tailored to the application objectives. However, development of mobile user interactions is still largely a manual task, which yields to high risks of errors, inconsistencies and inefficiencies. In this paper we propose a model-driven approach to mobile application development based on the IFML standard. We propose an extension of the Interaction Flow Modeling Language tailored to mobile applications and we describe our implementation experience that comprises the development of automatic code generators for cross-platform mobile applications based on HTML5, CSS and JavaScript optimized for the Apache Cordova framework. We show the approach at work on a popular mobile application, we report on the application of the approach on an industrial application development project and we provide a productivity comparison with traditional approaches.

Abstract. Processing data streams is increasingly gaining momentum, given the need to process these flows of information in real-time and at Web scale. In this context, RDF Stream Processing (RSP) and Stream Reasoning (SR) have emerged as solutions to combine semantic technologies with stream and event processing techniques. Research in these areas has proposed an ecosystem of solutions to query, reason and perform real-time processing over heterogeneous and distributed data streams on the Web. However, so far one basic building block has been missing: a mechanism to disseminate and exchange RDF streams on the Web. In this work we close this gap, proposing TripleWave, a reusable and generic tool that enables the publication of RDF streams on the Web. The features of TripleWave were selected based on requirements of real use-cases, and support a diverse set of scenarios, independent of any specific RSP implementation. TripleWave can be fed with existing Web streams (e.g. Twitter and Wikipedia streams) or time-annotated RDF datasets (e.g. the Linked Sensor Data dataset). It can be invoked through both pull-and push-based mechanisms, thus enabling RSP engines to automatically register and receive data from TripleWave.

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In many crowd-based applications, the interaction with performers is decomposed in several tasks that, collectively, produce the desired results. Tasks interactions give rise to arbitrarily complex workflows. In this paper we propose methods and tools for designing crowd-based workflows as interacting tasks. We describe the modelling concepts that are useful in such framework, including typical workflow patterns, whose function is to decompose a cognitively complex task into simple interacting tasks so that the complex task is co-operatively solved. We then discuss how workflows and patterns are managed by CrowdSearcher, a system for designing, deploying and monitoring applications on top of crowd-based systems, including social networks and crowdsourcing platforms. Tasks performed by humans consist of simple operations which apply to homogeneous objects; the complexity of aggregating and interpreting task results is embodied within the framework. We show our approach at work on a validation scenario and we report quantitative findings, which highlight the effect of workflow design on the final result

Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage "better" driving behaviour through immediate feedback while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style. In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour. This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance policies and car rentals. Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined groundtruth on drivers classification. The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.