Stream Processing in the Context of CTS

The recent development of innovative technologies related to mobile computing combined with smart city infrastructures is generating massive, heterogeneous data and creating opportunities for novel applications in transportational computation science. The heterogeneous data sources provide streams of information that can be used to create smart cities. The knowledge on stream analysis is thus crucial and requires collaboration of people working in logistics, city planning, transportation engineering and data science. We provide a list of materials for a course on stream processing for computational transportation science. The objectives of the course are: Motivate data stream and event processing, its model and challenges. Acquire basic knowledge about data stream processing systems. Understand and analyze their application in the transportation domain..

Software Defined Network-based control system for an efficient traffic management for emergency situations in smart cities

[EN] Smart cities provide new applications based on Internet of Things (loT) technology. Moreover, Software Defined Networks (SDNs) offer the possibility of controlling the network based on applications requirements. One of the main problems that arise when an emergency happens is minimizing the delay time in emergency resource forwarding so as to reduce both human and material damages. In this paper, a new control system based on the integration of SDN and loT in smart city environments is proposed. This control system actuates when an emergency happens and modifies dynamically the routes of normal and emergency urban traffic in order to reduce the time that the emergency resources need to get to the emergency area. The architecture is based on a set of loT networks composed by traffic lights, traffic cameras and an algorithm. The algorithm controls the request of resources and the modification of routes in order to ease the movement of emergency service units. Afterwards, the proposal is tested by emulating a Smart City as a SDN-utilizing Mininet. The experiments show that the delay of the emergency traffic improves in a 33% when the algorithm is running. Moreover, the energy consumed by the loT nodes is modeled and the obtained results display that it increases linearly with the number of nodes, therefore, the proposal is scalable. (C) 2018 Elsevier B.V. All rights reserved.This work has been partially supported by the " Ministerio de Educacion, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2015)". Grant number FPU15/06837, by the "Ministerio de Economia y Competitividad", through the "Convocatoria 2014. Proyectos I+D - P rograma Estatal de Investigacion Cientifica y Tecnica de Excelencia" in the "Subprograma Estatal de Generacion de Conocimiento", project TIN 2014-57991- C 3 - 1 - P and through the "Convocatoria 2016 - Proyectos I+D+I - P rograma Estatal De Investigacion, Desarrollo e Innovacion Orientada a los retos de la sociedad" (Project TEC 2016 - 76795 - C 6 - 4 - R). This work has also been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P.Rego Mañez, A.; García-García, L.; Sendra, S.; Lloret, J. (2018). Software Defined Network-based control system for an efficient traffic management for emergency situations in smart cities. Future Generation Computer Systems. 88:243-253. https://doi.org/10.1016/j.future.2018.05.054S2432538

A Big-Data based and process-oriented decision support system for traffic management

Data analysis and monitoring of road networks in terms of reliability and performance are valuable but hard to achieve, especially when the analytical information has to be available to decision makers on time. The gathering and analysis of the observable facts can be used to infer knowledge about traffic congestion over time and gain insights into the roads safety. However, the continuous monitoring of live traffic information produces a vast amount of data that makes it difficult for business intelligence (BI) tools to generate metrics and key performance indicators (KPI) in nearly real-time. In order to overcome these limitations, we propose the application of a big-data based and process-centric approach that integrates with operational traffic information systems to give insights into the road network's efficiency. This paper demonstrates how the adoption of an existent process-oriented DSS solution with big-data support can be leveraged to monitor and analyse live traffic data on an acceptable response time basis.publishedVersio

Computing Crowd Consensus with Partial Agreement

Crowdsourcing has been widely established as a means to enable human computation at large-scale, in particular for tasks that require manual labelling of large sets of data items. Answers obtained from heterogeneous crowd workers are aggregated to obtain a robust result. However, existing methods for answer aggregation are designed for \emph{discrete} tasks, where answers are given as a single label per item. In this paper, we consider \emph{partial-agreement} tasks that are common in many applications such as image tagging and document annotation, where items are assigned sets of labels. Common approaches for the aggregation of partial-agreement answers either (i) reduce the problem to several instances of an aggregation problem for discrete tasks or (ii) consider each label independently. Going beyond the state-of-the-art, we propose a novel Bayesian nonparametric model to aggregate the partial-agreement answers in a generic way. This model enables us to compute the consensus of partially-sound and partially-complete worker answers, while taking into account mutual relationships in labels and different answer sets. We also show how this model is instantiated for incremental learning, incorporating new answers from crowd workers as they arrive. An evaluation of our method using real-world datasets reveals that it consistently outperforms the state-of-the-art in terms of precision, recall, and robustness against faulty workers and data sparsity

Wiki-health: from quantified self to self-understanding

Today, healthcare providers are experiencing explosive growth in data, and medical imaging represents a significant portion of that data. Meanwhile, the pervasive use of mobile phones and the rising adoption of sensing devices, enabling people to collect data independently at any time or place is leading to a torrent of sensor data. The scale and richness of the sensor data currently being collected and analysed is rapidly growing. The key challenges that we will be facing are how to effectively manage and make use of this abundance of easily-generated and diverse health data. This thesis investigates the challenges posed by the explosive growth of available healthcare data and proposes a number of potential solutions to the problem. As a result, a big data service platform, named Wiki-Health, is presented to provide a unified solution for collecting, storing, tagging, retrieving, searching and analysing personal health sensor data. Additionally, it allows users to reuse and remix data, along with analysis results and analysis models, to make health-related knowledge discovery more available to individual users on a massive scale. To tackle the challenge of efficiently managing the high volume and diversity of big data, Wiki-Health introduces a hybrid data storage approach capable of storing structured, semi-structured and unstructured sensor data and sensor metadata separately. A multi-tier cloud storage system—CACSS has been developed and serves as a component for the Wiki-Health platform, allowing it to manage the storage of unstructured data and semi-structured data, such as medical imaging files. CACSS has enabled comprehensive features such as global data de-duplication, performance-awareness and data caching services. The design of such a hybrid approach allows Wiki-Health to potentially handle heterogeneous formats of sensor data. To evaluate the proposed approach, we have developed an ECG-based health monitoring service and a virtual sensing service on top of the Wiki-Health platform. The two services demonstrate the feasibility and potential of using the Wiki-Health framework to enable better utilisation and comprehension of the vast amounts of sensor data available from different sources, and both show significant potential for real-world applications.Open Acces