ICT is becoming an enabler for smart city applications by making effective use of various data resources generated daily in an urban environment. Mostly this data is utilised by city authorities for city planning purposes and often citizens become indirect beneficiaries of such applications. In this paper we present an algorithm for real-time processing of streaming data from multiple sources. We also present the design and proof of concept of an application that performs mining and analysis of open data available through city portals and social networks and generates an information service in real time for use by city administrations. The prototype utilises streaming data from Twitter and open data from Bristol to demonstrate a hypothetical scenario using Apache Storm. The output is presented in the form of visual maps using OpenStreetMaps as a backend and the prototype highlights various challenges which are discussed in detail.
Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on streaming in Big Data, a task referring to the processing of massive volumes of structured/unstructured streaming data. Recently proposed streaming frameworks for Big Data applications help to store, analyze and process the continuously captured data. In this paper, we discuss the challenges of Big Data and we survey existing streaming frameworks for Big Data. We also present an experimental evaluation and a comparative study of the most popular streaming platforms.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.