EDAWS: A distributed framework with efficient data analytics workspace towards discriminative services for critical infrastructures

Critical infrastructure systems which are interrelated with people's daily life perform functions in multiple domains. However, with the explosion of specialized textual information in such systems, providing discriminative services for users through potential knowledge discovery becomes an essential and technical concern. Once massive data analytics is conducted in standalone server, the performance will degenerate tremendously. Alternatively, people cannot conveniently get such discriminative (self-caring) services. To address these concerns, we propose the general solution of EDAWS: a Novel Distributed Framework with Efficient Data Analytics Workspace towards Discriminative Service for Critical Infrastructures, through leveraging the state-of-the-art software technologies and computing paradigms. We argue it from the following aspects: Firstly, the server-side platform facilitates native data capture, storage, index and data mining with a systematic organization. Secondly, a text-mining approach with index building in parallel is conducted for various functional business, by exploiting the potential of Lucene-based distributed cluster. Thirdly, with the widespread usage of tiny but powerful mobile devices, the server-side platform could be accessed by mobile-side clients remotely in a more convenient way. To demonstrate our solution, a case study of smart residence prototype towards discriminative services in terms of information retrieval, personalized information push, and hot topic discovery is thoroughly discussed. The extensively experimental studies are conducted for the prototype over various real-world datasets. Experimental results indicate that, data processing which runs on computing nodes has good scalability with data sizes and computing nodes, and the prototype passes from data to discriminative services successfully. (C) 2017 Elsevier B.V. All rights reserved.