Summarisation and Relevance Evaluation Techniques for Big Data Exploration: The Smart Factory Case Study

Bagozi, Ada; Bianchini, Devis; Antonellis, Valeria De; Marini, Alessandro; Ragazzi, Davide

doi:10.1007/978-3-319-59536-8_17

Cited by 19 publications

(7 citation statements)

References 11 publications

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“…In the physical world, operational information of manufacturing such as speed and energy consumption of production machines, temperature and humidity of products processing and location and speed of logistics vehicles for manufacturing and services is sent to the digital twin via data and control bus. In the virtual world, the digital twin based on big data contains four main layers (adapted from Ghita et al [13]): (1) data interaction layer deals with data collection, transmission and preprocessing; (2) data analysis layer addresses data modelling, simulation, and prediction; (3) data visualisation layer enables to visualise the results of the data analysis; and (4) data application layer concerns operational instructions and decision making of manufacturing guided by the BDA products (see Figure 1). As shown in Figure 1, functions of the virtual world of manufacturing integrated with the physical world can be enhanced by the digital twin that help achieve symmetries in SF.…”

Section: Digital Symmetrymentioning

confidence: 99%

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Data Quality Affecting Big Data Analytics in Smart Factories: Research Themes, Issues and Methods

et al. 2021

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Recent years have seen a growing call for use of big data analytics techniques to support the realisation of symmetries and simulations in digital twins and smart factories, in which data quality plays an important role in determining the quality of big data analytics products. Although data quality affecting big data analytics has received attention in the smart factory research field, to date a systematic review of the topic of interest for understanding the present state of the art is not available, which could help reveal the trends and gaps in this area. This paper therefore presents a systematic literature review of research articles about data quality affecting big data analytics in smart factories that have been published up to 2020. We examined 31 empirical studies from our selection of papers to identify the research themes in this field. The analysis of these studies links data quality issues toward big data analytics with data quality dimensions and methods used to address these issues in the smart factory context. The findings of this systematic review also provide implications for practitioners in addressing data quality issues to better use big data analytics products to support digital symmetry in the context of smart factory.

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Section: Digital Symmetrymentioning

confidence: 99%

“…The increasing connections of systems produce massive amount of data that support manufacturing decision-making in smart factories (SF) [1]. Furthermore, the interaction and convergence of both physical and virtual manufacturing worlds to achieve symmetry by using digital twins is an inevitable trend in SF, also boosting on big data [2].…”

Section: Introductionmentioning

confidence: 99%

Data Quality Affecting Big Data Analytics in Smart Factories: Research Themes, Issues and Methods

et al. 2021

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“…Due to the continuous generation of large size and multiple formats data, big data applications require fast processing at the same time in order to meet the deadlines. 1,[3][4][5]10,36,[39][40][41][42][43][44] Big data is present if, for a given size and format, the data generation rate becomes greater than the processing rate . It means the processing deadlines start getting missed because the processing time is greater than the generation time.…”

Section: Big Datamentioning

confidence: 99%

Toward a novel engine for compiler optimization space exploration of big data workloads

Ahmed

Ismail

2021

Softw Pract Exp

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Recently, big data specific technologies have been emerging, including domain-specific languages, software frameworks, databases, third-party libraries, and so forth. These techniques are successful in concealing the low-level details by producing high-level code, which is passed through the conventional compilation cycle for generating hardware operable code. Several optimization opportunities exist in the compiler which can assist in meeting the processing deadlines of big data workloads, through optimized machine code. However, the existing iterative compilation techniques are not enough for the exploration of big data applications. In this regard, a novel engine has been presented for exploiting the compiler optimization space of big data workloads. The engine is comprised of training and testing phases. During the training stage, the big data application is optimized with Mitigates the Compiler Phase-ordering (MiCOMP) and genetic algorithm (GA) optimization sequences, which are executed with train datasets. In the testing stage, the test datasets are executed only for the best 300 optimization sequences discovered at the training stage. The proposed engine has been tested with graph mining, machine learning, and text search categories of big data applications using a wide range of real-world and synthetic datasets. Overall, the engine is 56.8×, 47×, and 9.8× faster than Iterative Optimization for the Data Center (IODC), MiCOMP, and GA respectively in exploiting the compiler search space for big data workloads. Further, the integration of best-10 and best-3 techniques with the engine brings a speedup of 5.9× and 7.8×. The compiler level exploitation of general-purpose machines incurs no extra overhead, no heavy computing, and no personnel cost. Also, the overall performance of big data specialized software solutions can be enhanced by compiling their high-level code with suitable compiler optimizations.

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“…Nonetheless, the application of big data solution in smart factory will not be a straightforward task and can in fact be fraught with challenges. The most frequently mentioned challenge is related with technical ability to process huge amount of real-time data, derive findings from it and change machine behaviours accordingly (Bagozi et al , 2017). In addition, information security and trust had been highlighted as other key problems occurred when applying big data in smart factories (Sadeghi et al , 2015).…”

Section: Related Research On Smart Factory and Big Datamentioning

confidence: 99%

Barriers of embedding big data solutions in smart factories: insights from SAP consultants

Peng

Xing

2019

IMDS

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Purpose Big data is a key component to realise the vision of smart factories, but the implementation and usage of big data analytical tools in the smart factory context can be fraught with challenges and difficulties. The purpose of this paper is to identify potential barriers that hinder organisations from applying big data solutions in their smart factory initiatives, as well as to explore causal relationships between these barriers. Design/methodology/approach The study followed an inductive and exploratory nature. Ten in-depth semi-structured interviews were conducted with a group of highly experienced SAP consultants and project managers. The qualitative data collected were then systematically analysed by using a thematic analysis approach. Findings A comprehensive set of barriers affecting the implementation of big data solutions in smart factories had been identified and divided into individual, organisational and technological categories. An empirical framework was also developed to highlight the emerged inter-relationships between these barriers. Originality/value This study built on and extended existing knowledge and theories on smart factory, big data and information systems research. Its findings can also raise awareness of business managers regarding the complexity and difficulties for embedding big data tools in smart factories, and so assist them in strategic planning and decision making.

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Summarisation and Relevance Evaluation Techniques for Big Data Exploration: The Smart Factory Case Study

Cited by 19 publications

References 11 publications

Data Quality Affecting Big Data Analytics in Smart Factories: Research Themes, Issues and Methods

Data Quality Affecting Big Data Analytics in Smart Factories: Research Themes, Issues and Methods

Toward a novel engine for compiler optimization space exploration of big data workloads

Barriers of embedding big data solutions in smart factories: insights from SAP consultants

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