Knowledge extraction from big data using MapReduce-based Parallel-Reduct algorithm

Chowdhury, Tapan; Chakraborty, Susanta Kumar; Setua, S. K.

doi:10.1109/iccsnt.2016.8070156

Cited by 2 publications

(3 citation statements)

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“…In the proposed methodology, as one of the essential phases of the technique (after calculating of the stability of pixel categories in discrete intervals), they borrowed the degree of dependence among knowledge from the classical RST as the evaluation criterion of the discretization scheme. Then, each band was scanned in turn with the strategy Zhang et al (2012), Sachin and Shubhangi (2015), Cui and Huang (2015), Jing et al (2014), Chowdhury et al (2016), Pandu (2020) Apache Spark Dagdia et al (2017), Vluymans et al (2015) of splitting and merging to obtain an optimal discrete feature set. Remote sensing image features were the input of the algorithm, and discretized features were the output.…”

Section: 1mentioning

confidence: 99%

“…(2012),Sun et al (2019),Sachin and Shubhangi (2015),Jing et al (2014),Banerjee and Badr (2018), Hong-Wei and Xindi (2016),Narayanan et al (2017),Chowdhury et al (2016),Pal (2020),Li et al (2019), Vluymans et al (2015), Wang et al (2016b), Zhao et al (2020) Cloud computing Zhang et al (2012), Sun et al (2019), Kune (2014), Qu et al (2019), Li et al (2015), Wang et al (2016b), Grzegorowski et al (2017) Rule induction Zhou and Lin (2018), Wang et al (2016b) Data clustering Wan and Li (2019), Cui and Gao (2019), Xie (2018), Grzegorowski et al (2017), Li et al (2021), Janusz and Ślęzak (2014) RS-based approximate SQL Naouali and Missaoui (2005), Ślęzak et al (2012; 2018) Hybridizations Local rough sets Skowron et al (2018), Yang et al (2017), Qiana et al…”

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Applications of rough sets in big data analysis: An overview

Pięta¹,

Szmuc²

2021

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Big data, artificial intelligence and the Internet of things (IoT) are still very popular areas in current research and industrial applications. Processing massive amounts of data generated by the IoT and stored in distributed space is not a straightforward task and may cause many problems. During the last few decades, scientists have proposed many interesting approaches to extract information and discover knowledge from data collected in database systems or other sources. We observe a permanent development of machine learning algorithms that support each phase of the data mining process, ensuring achievement of better results than before. Rough set theory (RST) delivers a formal insight into information, knowledge, data reduction, uncertainty, and missing values. This formalism, formulated in the 1980s and developed by several researches, can serve as a theoretical basis and practical background for dealing with ambiguities, data reduction, building ontologies, etc. Moreover, as a mature theory, it has evolved into numerous extensions and has been transformed through various incarnations, which have enriched expressiveness and applicability of the related tools. The main aim of this article is to present an overview of selected applications of RST in big data analysis and processing. Thousands of publications on rough sets have been contributed; therefore, we focus on papers published in the last few years. The applications of RST are considered from two main perspectives: direct use of the RST concepts and tools, and jointly with other approaches, i.e., fuzzy sets, probabilistic concepts, and deep learning. The latter hybrid idea seems to be very promising for developing new methods and related tools as well as extensions of the application area.

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Section: 1mentioning

confidence: 99%

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confidence: 99%

Applications of rough sets in big data analysis: An overview

Pięta¹,

Szmuc²

2021

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“…In the scenario of big data, in multi-database integration environment, [1] give general algorithms and basic algorithms for different aspects of network paradox knowledge discovery. Based on rough set theory, [2] use parallel-reduction algorithm for knowledge extraction and it is suitable for large data sets with different roughness. In the process of constructing enterprise knowledge graphs, the inconsistency and knowledge conflicts are solved by [3] using the associated data paradigm algorithm.…”

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confidence: 99%

A Method of Ontology Evolution and Concept Evaluation Based on Knowledge Discovery in the Heavy Haul Railway Risk System

Cao

Montarnal

et al. 2019

Collaborative Networks and Digital Transformation

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The risk pre-control of heavy haul railways is a collaborative scenario with multi-department linkage and the risk analysis model relies on multiple data sources. As a tool for knowledge formal modeling, Ontology and knowledge graph can achieve knowledge discovery, reasoning and decision support based on multi-dimensional heterogeneous data. This paper restores unusual context with participant behavior data as the core, establishes a basic Scenario-Risk-Accident Chain (SRAC) ontology framework. Under collaborative relationships formed by reasoning rules between context and risk, this paper establishes evolution mechanism of SRAC to introduce new knowledge, such as knowledge extracted from device detection data. New entities are added to the risk concept tree through semantic similarity algorithms. In addition, researchers added weight attribute to the risk ontology. With quantitative representation of risk concepts, this paper uses risk relevance mining to establish associatedsubgraphs, establishes a new method for potential accident level assessment through maximum flow search mechanism.

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Knowledge extraction from big data using MapReduce-based Parallel-Reduct algorithm

Cited by 2 publications

References 9 publications

Applications of rough sets in big data analysis: An overview

Applications of rough sets in big data analysis: An overview

A Method of Ontology Evolution and Concept Evaluation Based on Knowledge Discovery in the Heavy Haul Railway Risk System

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