Applicability of Big Data Techniques to Smart Cities Deployments

Moreno, María Ángeles Esteban; Terroso-Sáenz, Fernando; González-Vidal, Aurora; Valdés-Vela, Mercedes; Skarmeta, Antonio F.; Zamora, Miguel A.; Chang, Victor

doi:10.1109/tii.2016.2605581

Cited by 134 publications

(57 citation statements)

References 26 publications

(27 reference statements)

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“…Also, we are extending the proposed techniques to larger cluster infrastmctw·es equipped with ente1prise inachines. Finally we explore the integration extending results given in [26] and [44][45][46][47] for distributed stream processing as a building block for IBIDAE.…”

Section: Discussionmentioning

confidence: 99%

An Efficient Industrial Big-Data Engine

Val

2018

IEEE Trans. Ind. Inf.

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Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Abstract-Current trends in industrial systems opt for the use of different big-data engines as a mean to process huge amounts of data that cannot be processed with an ordinary infrastructure. The number of issues an industrial infrastructure has to face is large and includes challenges such as the definition of different efficient architecture setups for different applications, and the definition of specific models for industrial analytics. In this context, the article explores the development of a medium size big-data engine (i.e. implementation) able to improve performance in map-reduce computing by splitting the analytic into different segments that may be processed by the engine in parallel using a hierarchical model. This type of facility reduces end-to-end computation time for all segments with their results then merged with other information from other segments after their processing in parallel. This type of setup increases performance of current clusters improving I/O operations remarkably as empirical results revealed.

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Section: Discussionmentioning

confidence: 99%

An Efficient Industrial Big-Data Engine

Val

2018

IEEE Trans. Ind. Inf.

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“…The storage and analysis of Smart Building data is challenging in several ways. First, due to the diversity of systems [31] and technologies, the building automation technique faces a long relation with interoperability, leading to data integration concerns [28]. Secondly, for a better perception and control of instruments, the density of sensors, promptly increases, generating a vast amount of data.…”

Section: B Data Managementmentioning

confidence: 99%

A Reference Architecture for Smart and Software-Defined Buildings

Mazzara

Afanasyev

Sarangi

et al. 2019

2019 IEEE International Conference on Smart Computing (SMARTCOMP)

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The vision encompassing Smart and Softwaredefined Buildings (SSDB) is becoming more and more popular and its implementation is now more accessible due to the widespread adoption of the IoT infrastructure. Some of the most important applications sustaining this vision are energy management, environmental comfort, safety and surveillance. This paper surveys IoT and SSB technologies and their cooperation towards the realization of Smart Spaces. We propose a fourlayer reference architecture and we organize related concepts around it. This conceptual frame is useful to identify the current literature on the topic and to connect the dots into a coherent vision of the future of residential and commercial buildings.

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“…Clustering has tackled tasks such as the assignment of genes with similar expression trajectories to the same group [63]. The creation of profiles of the trips carried out by tram users [64] or the acquisition of energy consumption predictions by clustering houses [65] are among examples of using clustering methods.…”

Section: Clusteringmentioning

confidence: 99%

BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation

González-Vidal

Barnaghi

Skarmeta

2018

IEEE Trans. Knowl. Data Eng.

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Abstract-The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to reduce the observations in the time series analysis domain. The IoT time series require aggregation methods that can preserve and represent the key characteristics of the data. In this paper, we propose a segmentation algorithm that adapts to unannounced mutations of the data (i.e. data drifts). The algorithm splits the data streams into blocks and groups them in square matrices, computes the Discrete Cosine Transform (DCT) and quantizes them. The key information is contained in the upper-left part of the resulting matrix. We extract this sub-matrix, compute the modulus of its eigenvalues and remove duplicates. The algorithm, called BEATS, is designed to tackle dynamic IoT streams, whose distribution changes over time. We implement experiments with six datasets combining real, synthetic, real-world data, and data with drifts. Compared to other segmentation methods like Symbolic Aggregate approXimation (SAX), BEATS shows significant improvements. Trying it with classification and clustering algorithms it provides efficient results. BEATS is an effective mechanism to work with dynamic and multi-variate data, making it suitable for IoT data sources. The datasets, code of the algorithm and the analysis results can be accessed publicly at: https://github.com/auroragonzalez/BEATS.

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Applicability of Big Data Techniques to Smart Cities Deployments

Cited by 134 publications

References 26 publications

An Efficient Industrial Big-Data Engine

An Efficient Industrial Big-Data Engine

A Reference Architecture for Smart and Software-Defined Buildings

BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation

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