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Benchmarking real-time vehicle data streaming models for a smart city

Artículo ya publicadoInformation Systems, Volume 72, December 2017, Pages 62-76https://doi.org/10.1016/j.is.2017.09.002Q2, (COMPUTER SCIENCE, INFORMATION SYSTEMS)—The information systems of smart cities offer project developers, institutions, industry and experts the possibility to handle massive incoming data from diverse information sources in order to produce new information services for citizens. Much of this information has to be processed as it arrives because a real-time response is often needed. Stream processing architectures solve this kind of problems, but sometimes it is not easy to benchmark the load capacity or the efficiency of a proposed architecture. This work presents a real case project in which an infrastructure was needed for gathering information from drivers in a big city, analyzing that information and sending real-time recommendations to improve driving efficiency and safety on roads. The challenge was to support the real-time recommendation service in a city with thousands of simultaneous drivers at the lowest possible cost. In addition, in order to estimate the ability of an infrastructure to handle load, a simulator that emulates the data produced by a given amount of simultaneous drivers was also developed. Experiments with the simulator show how recent stream processing platforms like Apache Kafka could replace custom-made streaming servers in a smart city to achieve a higher scalability and faster responses, together with cost reduction.

A First Approach towards Storage and Query Processing of Big Spatial Networks in Scalable and Distributed Systems

Due to the ubiquitous use of spatial data applications and the large amounts of spatial data that these applications generate, the processing of large-scale queries in distributed systems is becoming increasingly popular. Complex spatial systems are very often organized under the form of Spatial Networks, a type of graph where nodes and edges are embedded in space. Examples of these spatial networks are transportation and mobility networks, mobile phone networks, social and contact networks, etc. When these spatial networks are big enough that exceed the capacity of commonly-used spatial computing technologies, we have Big Spatial Networks, and to manage them is necessary the use of distributed graph-parallel systems. In this paper, we describe our emerging work concerning the design of new storage methods and query processing algorithms over big spatial networks in scalable and distributed systems, which is a very active research area in the past years.