Kontaki Eirini: Master Thesis Presentation: “Big Data Analytics for Smarter Cities: Method – Architecture – Algorithmic Apparatus”

A smart city is a concept which integrates multiple information and communication technology (ICT) solutions to efficiently manage a city’s assets. City’s assets include, but are not limited to, transportation systems, waste management, water management, safety systems, local departments information systems and other community services. The concept of the smart city is to make traditional networks and services more efficient with the use of technologies, including Internet of Things (IoT) and Big Data, to improve the quality of people’s life and to boost the operation of city’s businesses. A smart city is an inherently distributed setting composed of a number of sites. Sites may range from conventional or modern data gathering devices (such as sensors, RFIDs, smartphones, etc.), which collect streams of relevant data, to intermediate routers that convey these data to local departments’ systems and finally to city authorities’ data centers. Valuable information hidden in the streaming data gathered by the distributed setting should be provided in real-time and in a continuous fashion to timely support decision making procedures. Therefore, extracting value, in the form of analytics, out of the massive flows of data that stream-in such a distributed setting is an intriguing task.

This thesis focuses on highlighting the importance of big data analysis in a smart city context by reviewing existing technologies that enable city authorities to produce valuable insights and facilitate the decision-making process both in the short and in the long run. Towards that direction, this work: (a) emphasizes on specific smart city focus areas (i.e., transportation, resource and environmental management) presenting the benefits of the provision of ICTs, (b) presents usual problems related to the specific focus areas, (c) reviews existing technological solutions with a reference to existing sensing technologies, (d) analyzes and translates usual problems into business objectives and key performance indicators, (e) underlines the technological challenges of utilizing large amounts of data and interprets them in technical requirements, (f) provides an in-depth analysis of existing technologies for big data processing, (g) incorporates the technical requirements into a full-fledged architecture of a smart city in refer to existing technologies for big data analysis and (h) summarizes algorithmic suites that can be implemented with the suggested technologies for big data analysis. The aforementioned are pursued through a real smart city scenario of Heraklion city, according to which we design big data processing pipelines for a number of core services such as parking, water and air quality monitoring, reporting future benefits for the stakeholders.