The pervasive and increasing deployment of smart meters allows collecting a huge amount of fine-grained energy data in different urban scenarios. The analysis of such data is challenging and opening ...up a variety of interesting and new research issues across energy and computer science research areas. The key role of computer scientists is providing energy researchers and practitioners with cutting-edge and scalable analytics engines to effectively support their daily research activities, hence fostering and leveraging data-driven approaches. This paper presents SPEC, a scalable and distributed engine to predict building-specific power consumption. SPEC addresses the full analytic stack and exploits a data stream approach over sliding time windows to train a prediction model tailored to each building. The model allows us to predict the upcoming power consumption at a time instant in the near future. SPEC integrates different machine learning approaches, specifically ridge regression, artificial neural networks, and random forest regression, to predict fine-grained values of power consumption, and a classification model, the random forest classifier, to forecast a coarse consumption level. SPEC exploits state-of-the-art distributed computing frameworks to address the big data challenges in harvesting energy data: the current implementation runs on Apache Spark, the most widespread high-performance data-processing platform, and can natively scale to huge datasets. As a case study, SPEC has been tested on real data of an heating distribution network and power consumption data collected in a major Italian city. Experimental results demonstrate the effectiveness of SPEC to forecast both fine-grained values and coarse levels of power consumption of buildings.
In recent years there has been a growing interest in the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces ...of information suitable to help visitors discover and follow the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present Museum Assistant (MusA), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these applications.
The overwhelming increase of parcel transports has prompted the need for effective and scalable intelligent logistics systems. In parallel, with the advent of Industry 4.0, a tight integration of ...Internet of Things technologies and Big Data analytics solution has become necessary to effectively manage industrial processes and to early predict product faults or service disruptions. In the context of good transports, the development of smart monitoring tools is particularly useful for couriers to ensure effective and efficient parcel deliveries. However, the existing predictive maintenance frameworks are not tailored to parcel delivery services. We present REDTag Service, an integrated framework to track and monitor the shipped packages. It relies on a network of IoT-enabled devices, called REDTags, allowing courier employees to easily collect the status of the package at each delivery step. The framework provides back-end functionalities for smart data transmission, management, storage, and analytics. A machine-learning process is included to promptly analyze the features describing event-related data to predict potential breaks of the goods in the packages. The framework provides also a dynamic view on the integrated data tailored to the different stakeholders, as well as on the prediction outcomes, enabling immediate feedback and model improvements. We analyze a real-world dataset including event-related data about parcel transports. To validate the hypothesis that the acquired data contains information relevant to predict the package status (i.e., broken or safe), we empirically analyze the performance of different, scalable classifiers. The experimental results confirm, in good approximation, the predictive power of the models extracted from the event-related features. To the best of the authors' knowledge, this work is the first attempt to address predictive maintenance in smart good transport logistics to predict package breaks from real-world data.
Experiencing Indoor Navigation on Mobile Devices Barberis, Claudia; Andrea, Bottino; Giovanni, Malnati ...
IT professional,
2014-Jan.-Feb., 2014-1-00, 20140101, Letnik:
16, Številka:
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Indoor GPS navigation is unreliable owing to poor communication with satellites and a lack of detailed maps. The authors discuss technologies that offer accurate indoor location and orientation data, ...presenting their system for indoor navigation, which runs on a smartphone. The Web extra at http://youtu.be/cfOSNjyD15A illustrates the prototypal system. Another Web extra, http://youtu.be/6DtHkihkiO4, provides a more detailed introduction about the topics and system discussed.
In many contexts where data is streamed on a large scale, such as video surveillance systems, there is a dual requirement: secure data storage and continuous access to audio and video content by ...third parties, such as human operators or specific business logic, even while the media files are still being collected. However, using transactions to ensure data persistence often limits system throughput and latency. This paper presents a solution that enables both high ingestion rates with transactional data persistence and near real-time, low-latency access to the stream during collection. This immediate access enables the prompt application of specialized data engineering algorithms during data acquisition. The proposed solution is particularly suitable for binary data sources such as audio and video recordings in surveillance systems, and it can be extended to various big data scenarios via well-defined general interfaces. The scalability of the approach is based on the microservice architecture. Preliminary results obtained with Apache Kafka and MongoDB replica sets show that the proposed solution provides up to 3 times higher throughput and 2.2 times lower latency compared to standard multi-document transactions.
Context:Kotlin is a new programming language representing an alternative to Java; they both target the same JVM and can safely coexist in the same application. Kotlin is advertised as capable to ...solve several known limitations of Java. Recent surveys show that Kotlin achieved a relevant diffusion among Java developers. Goal:We planned to empirically assess a few typical promises of Kotlin w.r.t. known Java’s limitations, in terms of development effectiveness, maintainability, and ease of development. Method:Our experiment involved 27 teams of 4 people each that completed a set of maintenance tasks (both defect correction and feature addition) on Android apps written in either Java or Kotlin. In addition to the number of fixed defects, effort, and code size, we collected, though a questionnaire, the participants’ perceptions about the avoidance of known pitfalls. Results:We did not observe any significant difference in terms of maintainability between the two languages.We found a significant difference regarding the amount of code written, which constitutes evidence of better conciseness of Kotlin. Concerning ease of development, the frequency of NullPointerExceptions reported by the subjects was significantly lower when developing in Kotlin. On the other hand, no significant difference was found in the occurrence of other common Java pitfalls. Finally, the IDE support was deemed better for Java than Kotlin. Conclusions:Some of the promises of Kotlin to be a ”better Java” have been confirmed by our empirical assessment. Evidence suggests that the effort in transitioning to Kotlin can provide some advantages to Java developers, especially regarding code conciseness.Our results may serve as the basis for further investigations on the properties of the language.
The use of theory-based knowledge in machine learning models has a major impact on many engineering and physics problems. The growth of deep learning algorithms is closely related to an increasing ...demand for data that is not acceptable or available in many use cases. In this context, the incorporation of physical knowledge or a priori constraints has proven beneficial in many tasks. On the other hand, this collection of approaches is context-specific, and it is difficult to generalize them to new problems. In this paper, we experimentally compare some of the most commonly used theory-injection strategies to perform a systematic analysis of their advantages. Selected state-of-the-art algorithms were reproduced for different use cases to evaluate their effectiveness with smaller training data and to discuss how the underlined strategies can fit into new application contexts.
Digital technologies allow teachers and students to experience new pedagogical approaches leveraging on interactivity and collaboration. Among the available techniques, digital storytelling (DST) has ...been usually regarded as an activity that can both enrich the teaching practices and foster students' active behaviour. This paper aims at analysing to what extent a DST platform proposing the collaborative writing of a fictional story and leveraging on an active learning technique such as role-play can affect a variety of dimensions, namely students' performance, commitment, creativity and social skills. Combining the analysis of the data-log automatically collected by the system with the evaluations given by teachers before and after the activity, this article shows that the use of a DST tool in the teaching practice can have positive effects on students, including the ones that usually manifest moderate scores for the previously mentioned dimensions. Additionally, semi-structured interviews and direct observations provide an insight on how the benefits stemming from the use of a DST tool in the classroom can especially lie on the collaborative process that is activated.
The availability of infotainment contents to be accessed while driving constitutes an added value to both drivers and passengers, improving their travel experience. These contents can be already ...on-board, diffused through broadcast networks (e.g., FM radio) or delivered on demand by servers as data streams. Vehicular ad-hoc networks (VANET) allow envisaging a new way to access contents based on epidemic data dissemination, tearing down the costs for both the user and the content publisher, and increasing system scalability. In this paper, we present the design and the evaluation of a collaborative system for content diffusion and retrieval among traveling vehicles. This system relies on multicast epidemic dissemination of messages and exploits vehicles mobility and their local storage capabilities. In order to assess its performance, several simulations have been carried out, using real-world maps and realistic mobility models, and varying intrinsic system parameters. Experimental results suggest that the system is feasible, produces reliable results, almost independent of the mobility scenario, and can be used as a peer-to-peer download accelerator for improving client-server access.
The availability of multimedia contents to be played back while driving constitutes an added value to both drivers and passengers, improving the travel experience. These contents can be already ...on-board, diffused through broadcast networks or delivered on demand by servers as data streams. Vehicular ad-hoc networks allow envisaging a new way to access multimedia contents based on epidemic data dissemination, tearing down the costs for both the user and the content publisher, and increasing system scalability. In this paper, we propose VET, a collaborative system for content diffusion and retrieval among traveling vehicles that relies on multicast epidemic dissemination of messages and exploits vehicles mobility and their local storage capabilities.
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