No abstract

Smart-cities are an emerging paradigm containing heterogeneous network infrastructure, ubiquitous sensing devices, big-data processing and intelligent control systems. Their primary aim is to improve the quality of life of the citizens by providing intelligent services in a wide variety of aspects like transportation, healthcare, environment, and energy. In order to provide such services, the role of big-data is important. In this article, we investigate the state-of-art research efforts directed towards big-data analytics in a smart-city context. First, we present a big-data centric taxonomy for the smart-cities to bring forth a generic overview of the big-data paradigm in a smart-city environment. Second, we present a top-level snapshot of the commonly used big-data analytical platforms. Due to the heterogeneity of data being collected by the smart-cities, often with conflicting processing requirements, suitable analytical techniques depending upon the data type are suggested. Additionally, a generic four-tier big-data framework comprising of the sensing hub, storage hub, processing hub and application hub is presented that can be applied in any smart-city context. This is complemented by providing the common big-data applications and presentation of ten selected case studies of smart-cities across the globe. Finally, open challenges are highlighted in order to give future research directions.

This paper proposes an unsupervised discrimination analysis for feature selection based on a property of the Fourier transform of the probability density distribution. Each feature is evaluated on the basis of a simple observation motivated by the concept of optical diffraction, which is invariant under feature scaling. The time complexity is O(mn), where m is number of features and n is number of instances when being applied directly to the given data. This approach is also extended to deal with data orientation, which is the direction of data alignment. Therefore, the discrimination score of any transformed space can be used for evaluating the original features. The experimental results on several real-world datasets demonstrate the effectiveness of the proposed method.

No abstract

No abstract