A survey towards an integration of big data analytics to big insights for value-creation

Saggi, Mandeep Kaur; Jain, Sushma

doi:10.1016/j.ipm.2018.01.010

Cited by 302 publications

(161 citation statements)

References 117 publications

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“…Big Data characteristics have been originally defined by the 3Vs criteria (Volume, Variety and Veracity), then this vision has been extended over years and now often described using 7 criteria [32,33] that we highlight in the Table 1.…”

Section: Big Data Characteristicsmentioning

confidence: 99%

Big data monetization throughout Big Data Value Chain: a comprehensive review

et al. 2020

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Value creation is the rational goal of any organization. However, in a changing world, in terms of data, it is very difficult to find strategies to sustainably generate new value and improve operations. In this respect, generating value may mean revisiting some deeplyheld conceptions of different processes. For this, each organization has to ensure an efficient and updated management of value creation processes for both inter-organization and partners. Value chain (VC) is one of the key solutions to such dilemmas.

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Section: Big Data Characteristicsmentioning

confidence: 99%

Big data monetization throughout Big Data Value Chain: a comprehensive review

et al. 2020

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“…The category includes deep learning. Hence, it is a subset of AI in which machine they get improved kind of decision making experience depending upon the training or the data they have and it is based upon deep learning [36]. The main challenges of machine learning are: generative vs discriminative learning, beyond classification and regression, learning from non-vectorial data, machine learning bottlenecks, intelligible models, combining learning methods, distributed data mining, unsupervised learning comes of age, and more informed information access.…”

Section: Challenges and Opportunities Of Big Data And Machine Learninmentioning

confidence: 99%

A survey of big data and machine learning

Salkuti¹

2020

IJECE

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This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.

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“…According to literature review, ICT is an essential component in smart city governance to gain necessary intelligence that provides basis for evidence‐based decision‐making. At the core of ICT based solutions are three main components: (i) data collection through sensors, Internet‐of‐Things, smart phones, remote sensing (e.g., satellite or in‐situ) and city databases; (ii) data processing and/or pre‐processing (e.g., filtering, data quality and format translations); (iii) data analysis using machine learning, data mining and other statistical algorithms to generate new knowledge in various cross‐thematic applications such as mobility (Docherty, Marsden, & Anable, ; Peters‐Anders et al, ; Rathore et al,), energy (Antonić, Marjanović, Pripužić, & Žarko, ; Carli, Albino, Dotoli, Mummolo, & Savino, ; Silva, Khan, & Han, ), health (Anisetti et al, ; Farahani et al, ), environment (Antonić et al, ), public services (Pérez‐González & Díaz‐Díaz, ; Zhang et al, ), economy (Chatfield & Reddick, ; Saggi & Jain, ; Zaman et al, ), waste management (Digiesi, Facchini, Mossa, Mummolo, & Verriello, ), social analysis (Kousiouris et al, ; Terroso‐Saenz, Gonzalez‐Vidal, Cuenca‐Jara, & Skarmeta, ), waste water management (Edmondson et al, ), urban planning (Eirinaki et al, ; Pettit et al, ; Rathore, Ahmad, Paul, & Rho, ), tourism and cultural heritage (Sun, Song, Jara, & Bie, ), buildings (Linder, Vionnet, Bacher, & Hennebert, ), agriculture (Kamilaris, Gao, Prenafeta‐Boldu, & Ali, ), emergency response (Abu‐Elkheir, Hassanein, & Oteafy, ), etc. The above three components—with some variations—are common among the most of smart city data analytics literature for example, Zhang et al (); Khan et al (); Rathore et al ().…”

Section: Big Data Analytics In Smart Citiesmentioning

confidence: 99%

Smart city big data analytics: An advanced review

Soomro

Bhutta

Khan

et al. 2019

WIREs Data Min & Knowl

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With the increasing role of ICT in enabling and supporting smart cities, the demand for big data analytics solutions is increasing. Various artificial intelligence, data mining, machine learning and statistical analysis-based solutions have been successfully applied in thematic domains like climate science, energy management, transport, air quality management and weather pattern analysis. In this paper, we present a systematic review of the literature on smart city big data analytics. We have searched a number of different repositories using specific keywords and followed a structured data mining methodology for selecting material for the review. We have also performed a technological and thematic analysis of the shortlisted literature, identified various data mining/machine learning techniques and presented the results. Based on this analysis we also present a classification model that studies four aspects of research in this domain. These include data models, computing models, security and privacy aspects and major market drivers in the smart cities domain. Moreover, we present a gap analysis and identify future directions for research. For the thematic analysis we identified the themes smart city governance, economy, environment, transport and energy. We present the major challenges in these themes, the major research work done in the field of data analytics to address these challenges and future research directions.

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A survey towards an integration of big data analytics to big insights for value-creation

Cited by 302 publications

References 117 publications

Big data monetization throughout Big Data Value Chain: a comprehensive review

Big data monetization throughout Big Data Value Chain: a comprehensive review

A survey of big data and machine learning

Smart city big data analytics: An advanced review

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