Spatial crowdsourcing with mobile agents in vehicular networks

In the last years, the automotive industry has shown interest in the addition of computing and communication devices to cars, thanks to technological advances in these fields, in order to meet the increasing demand of “connected” applications and services. Although vehicular ad hoc networks (VANETs) have not been fully developed yet, they could be used in a near future as a means to provide a number of interesting applications and services that need the exchange of data among vehicles and other data sources. In this paper, we propose a spatial crowdsourcing schema for the opportunistic collection of information within an interest area in a city or region (e.g., measures about the environment, such as the concentration of certain gases in the atmosphere, or information such as the availability of parking spaces in an area), using vehicular ad hoc communications. We present a method that exploits mobile agent technology to accomplish the distributed collection and querying of data among vehicles in such a scenario. Our proposal is supported by an extensive set of realistic simulations that prove the feasibility of the approach

Real-time Selection of Video Streams for Live TV Broadcasting Based on Query-by-Example Using a 3D Model

The emergence of low-cost cameras with nearly professional features in the consumer market represents a new important source of video information. For example, using an increasing number of these cameras in live TV broadcastings enables obtaining varied contents without affecting the production costs. However, searching for interesting shots (e.g., a certain view of a specific car in a race) among many video sources in real-time can be difficult for a Technical Director (TD). So, TDs require a mechanism to easily and precisely represent the kind of shot they want to obtain abstracting them from the need to be aware of all the views provided by the cameras. In this paper we present our proposal to help a TD to visually define, using an interface for the definition of 3D scenes, an interesting sample view of one or more objects in the scenario. We recreate the views of the cameras in a 3D engine and apply 3D geometric computations on their virtual view, instead of analyzing the real images they provide, to enable an efficient and precise real-time selection. Specifically, our system computes a similarity measure to rank the candidate cameras. Moreover, we present a prototype of the system and an experimental evaluation that shows the interest of our proposal

Special Issue on Smart Data and Semantics in a Sensor World

Introduction Since its first inception in 2001, the application of the Semantic Web [1, 2] has carried out an extensive use of ontologies [3–5], reasoning, and semantics in diverse fields, such as Information Integration, Software Engineering, Bioinformatics, eGovernment, eHealth, and social networks. This widespread use of ontologies has led to an incredible advance in the development of techniques to manipulate, share, reuse, and integrate information across heterogeneous data sources. In recent years, the growth of the IoT (Internet of Things) required to face the challenges of “Big Data” [6–10]. The cost of sensors is decreasing, while their use is expanding. Moreover, the use of multiple personal smart devices is an emerging trend and all of them can embed sensors to monitor the surrounding environment. Therefore, the number of available sensors is exploding. On the one hand, the flows of sensor data are massive and continuous, and the data could be obtained in real time or with a delay of just a few seconds. Then, the volume of sensor data is increasing continuously every day. On the other hand, the variety of data being generated is also increasing, due to plenty of different devices and different measures to record. There are many kinds of structured and unstructured sensor data in diverse formats. Moreover, data veracity, which is the degree of accuracy or truthfulness of a data set, is an important aspect to consider. In the context of sensor data, it represents the trustworthiness of the data source and the processing of data. The need for more accurate and reliable data was always declared, but often overlooked for the sake of larger and cheaper..

Location-aware recommendation systems: Where we are and where we recommend to go

Recommendation systems have been successfully used to provide items of interest to the users (e.g., movies, music, books, news, images). However, traditional recommenda- tion systems do not take into account the location as a relevant factor when providing suggestions. On the other hand, nowadays, there exist an increasing amount of geo- referenced data and users are usually interested only in nearby items (e.g., restaurants, museums, cinemas). Hence, the emergence of location-aware recommendation systems have acquired a great attention by the research community in the last decade. In this paper, we provide a survey of location-aware rec- ommendation systems in mobile computing scenarios. Firstly, we describe briefly the fundamentals of recommendation sys- tems. Then, we introduce some of the most relevant existing approaches for location-aware recommendation. Moreover, we present the main applications of this type of systems in several recommendation scenarios, such as music, news, restaurants, etc. Finally, we discuss new avenues and open issues in the area

All for One and One for All: Dynamic Injection of Situations in a Generic Context-Aware Application

In a new smart-world, users are getting accustomed to fast-responding applications that make their everyday tasks and daily life easier. In order to meet their expectancies, mobile applications are shifting towards a new era of context awareness. Nonetheless, it seems that context-aware applications are still struggling to provide the user with a real situation understanding. They only consider non-evolving limited scenarios and react to them using only generic services. To address these concerns, we have developed the Long Life Application, a dynamic context-aware situation-based distributed mobile application dedicated to assist end-users in their everyday needs. This application considers the requirements of the users and provides them with the appropriate services according to their current context. In this paper we focus in the way that the user''s context is considered by the application. We propose a hybrid approach that combines both high-level context (top-down approach), by injecting user-related context, and a low-level context (bottom-up approach), by inferring it from sensor data. Peer-review under responsibility of the Conference Program Chairs

Towards Trajectory-Based Recommendations in Museums: Evaluation of Strategies Using Mixed Synthetic and Real Data

Recommendation systems, which suggest items that are of potential interest to the user (e.g., regarding which books to read, which movies to watch, etc.) have grown in popularity due to the ever-increasing amount of data available, that can lead to significant user''s overload. In particular, in recent years, extensive research has focused on the so-called Context-Aware Recommender Systems (CARS), which exploit context data to offer more relevant recommendations. In this paper, we study this problem with a use case scenario: recommending items to observe in a museum. We propose a trajectory-based and user-based collaborative filtering approach, that considers context data such as the location of the user and his/her trajectory to offer personalized recommendations. Besides, we exploit DataGenCARS, a dataset synthetic generator designed to construct datasets for the evaluation of context-aware recommendation systems, to build a mixed scenario based on both real and synthetic data. The experimental results show the advantages of the proposed approach and the usefulness of DataGenCARS for practical evaluation with a real use-case scenario. Peer-review under responsibility of the Conference Program Chairs

NEREA: Named entity recognition and disambiguation exploiting local document repositories

In this work, we describe the design, development, and deployment of NEREA (Named Entity Recognizer for spEcific Areas), an automatic Named Entity Recognizer and Disambiguation system, developed in collaboration with professional documentalists. The aim of NEREA is to keep accurate and current information about the entities mentioned in a local repository, and then support building appropriate infoboxes, setting out the main data of these entities. It achieves a high performance thanks to the use of classification resources belonging to the local database. With this aim, the system performs tasks of named entity recognition and disambiguation by using three types of knowledge bases: local classification resources, global databases like DBpedia, and its own catalog created by NEREA. The proposed method has been validated with two different datasets and its operation has been tested in English and Spanish. The working methodology is being applied in a real environment of a media with promising results

Semantic traffic sensor data: The TRAFAIR experience

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city''s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective

On transformation of query scheduling strategies in distributed and heterogeneous database systems

This work considers a problem of optimal query processing in heterogeneous and distributed database systems. A global query sub- mitted at a local site is decomposed into a number of queries processed at the remote sites. The partial results returned by the queries are in- tegrated at a local site. The paper addresses a problem of an optimal scheduling of queries that minimizes time spend on data integration of the partial results into the final answer. A global data model defined in this work provides a unified view of the heterogeneous data structures located at the remote sites and a system of operations is defined to ex- press the complex data integration procedures. This work shows that the transformations of an entirely simultaneous query processing strate- gies into a hybrid (simultaneous/sequential) strategy may in some cases lead to significantly faster data integration. We show how to detect such cases, what conditions must be satisfied to transform the schedules, and how to transform the schedules into the more efficient ones