Distributed online data analytics has attracted significant research interest in recent years with the advent of Fog and Cloud computing. The popularity of novel distributed applications such as crowdsourcing and crowdsensing have fostered the need for scalable energy-efficient platforms that can enable distributed data analytics. In this paper, we propose CARDAP, a (C)ontext (A)ware (R)eal-tim...

Emergence of uniquely addressable embeddable devices has raised the bar on Telematics capabilities. Though the technology itself is not new, its application has been quite limited until now. Sensor based telematics technologies generate volumes of data that are orders of magnitude larger than what operators have dealt with previously. Real-time big data computation capabilities have opened the ...

Today many analysts in information technology are facing challenges to work with large amount of data. Most analysts are smart enough to find their way out and write quires using appropriate table join conditions, data mining, index keys and hash object for quick data retrieval. Recently SAS system has collaborated with Hadoop data storage and started providing efficient processing power to SAS...

33 We witness a revolution that capitalizes on the prevalence of networked (embedded) devices ranging from simple ones, such as sensors, to complex Cyber-Physical Systems (CPS), in order to empower a new generation of innovative anytime-anywhere services. The CPS revolution enables system interconnection and fine-grained information acquisition at unprecedented scales. The analytics of CPS data...

The objective of this work is to propose the design and deployment of sensor networks and the Internet of Things (IoT) in the Guadalajara Smart City. Initially, all project development was conducted at the Living Lab in the Smart Cities Innovation Center at the CUCEA University of Guadalajara (UDG). Once the first sensor networks are functional, the goal is to transfer the experience to the fir...

In accordance with a "people centred" vision, this paper critically examines current approaches to smart cities benchmarking. In particular, by means of correlation analysis and Principal Component Analysis (PCA) we put in evidence current limitations of city rankings and, as well, the emergence of different perspectives for data interpretations. To follow, a possible redesign of the "Smart Cit...

Objective We developed geo-targeted social media application program interfaces (APIs) for Twitter and a web-based social media analytics and research testbed (SMART) dashboard to analyze “flu” related tweets. During the 2013-14 flu season, for 10 cities with active surveillance for influenza (ILI), we correlated weekly tweeting rates and visual patterns of flu tweeting rates. To facilitate wid...

Deep learning has recently achieved very promising results in a wide range of areas such as computer vision, speech recognition and natural language processing. It aims to learn hierarchical representations of data by using deep architecture models. In a smart city, a lot of data (e.g. videos captured from many distributed sensors) need to be automatically processed and analyzed. In this paper,...

Smart cities rely on dynamic and real-time data to enable smart urban applications such as intelligent transport and epidemics detection. However, the streaming of big data from IoT devices, especially from mobile platforms like pedestrians and cars, raises significant privacy concerns. Future autonomous vehicles will generate, collect and consume significant volumes of data to be utilized in d...