Big data and predictive analytics: A systematic review of applications

Abstract Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data...
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Autor*in:	Jamarani, Amirhossein [verfasserIn] Haddadi, Saeid [verfasserIn] Sarvizadeh, Raheleh [verfasserIn] Haghi Kashani, Mostafa [verfasserIn] Akbari, Mohammad [verfasserIn] Moradi, Saeed [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Big data Predictive analytics Big data applications Systematic review

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Artificial intelligence review - Springer Netherlands, 1986, 57(2024), 7 vom: 17. Juni
Übergeordnetes Werk:	volume:57 ; year:2024 ; number:7 ; day:17 ; month:06

Links:	Volltext

DOI / URN:	10.1007/s10462-024-10811-5

Katalog-ID:	SPR056266952

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spelling	10.1007/s10462-024-10811-5 doi (DE-627)SPR056266952 (SPR)s10462-024-10811-5-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 77.31 bkl Jamarani, Amirhossein verfasserin aut Big data and predictive analytics: A systematic review of applications 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled. Big data (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Big data applications (dpeaa)DE-He213 Systematic review (dpeaa)DE-He213 Haddadi, Saeid verfasserin aut Sarvizadeh, Raheleh verfasserin aut Haghi Kashani, Mostafa verfasserin aut Akbari, Mohammad verfasserin aut Moradi, Saeed verfasserin aut Enthalten in Artificial intelligence review Springer Netherlands, 1986 57(2024), 7 vom: 17. Juni (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:7 day:17 month:06 https://dx.doi.org/10.1007/s10462-024-10811-5 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.72 VZ 77.31 VZ AR 57 2024 7 17 06
allfields_unstemmed	10.1007/s10462-024-10811-5 doi (DE-627)SPR056266952 (SPR)s10462-024-10811-5-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 77.31 bkl Jamarani, Amirhossein verfasserin aut Big data and predictive analytics: A systematic review of applications 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled. Big data (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Big data applications (dpeaa)DE-He213 Systematic review (dpeaa)DE-He213 Haddadi, Saeid verfasserin aut Sarvizadeh, Raheleh verfasserin aut Haghi Kashani, Mostafa verfasserin aut Akbari, Mohammad verfasserin aut Moradi, Saeed verfasserin aut Enthalten in Artificial intelligence review Springer Netherlands, 1986 57(2024), 7 vom: 17. Juni (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:7 day:17 month:06 https://dx.doi.org/10.1007/s10462-024-10811-5 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.72 VZ 77.31 VZ AR 57 2024 7 17 06
allfieldsGer	10.1007/s10462-024-10811-5 doi (DE-627)SPR056266952 (SPR)s10462-024-10811-5-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 77.31 bkl Jamarani, Amirhossein verfasserin aut Big data and predictive analytics: A systematic review of applications 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled. Big data (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Big data applications (dpeaa)DE-He213 Systematic review (dpeaa)DE-He213 Haddadi, Saeid verfasserin aut Sarvizadeh, Raheleh verfasserin aut Haghi Kashani, Mostafa verfasserin aut Akbari, Mohammad verfasserin aut Moradi, Saeed verfasserin aut Enthalten in Artificial intelligence review Springer Netherlands, 1986 57(2024), 7 vom: 17. Juni (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:7 day:17 month:06 https://dx.doi.org/10.1007/s10462-024-10811-5 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.72 VZ 77.31 VZ AR 57 2024 7 17 06
allfieldsSound	10.1007/s10462-024-10811-5 doi (DE-627)SPR056266952 (SPR)s10462-024-10811-5-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 77.31 bkl Jamarani, Amirhossein verfasserin aut Big data and predictive analytics: A systematic review of applications 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled. Big data (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Big data applications (dpeaa)DE-He213 Systematic review (dpeaa)DE-He213 Haddadi, Saeid verfasserin aut Sarvizadeh, Raheleh verfasserin aut Haghi Kashani, Mostafa verfasserin aut Akbari, Mohammad verfasserin aut Moradi, Saeed verfasserin aut Enthalten in Artificial intelligence review Springer Netherlands, 1986 57(2024), 7 vom: 17. Juni (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:7 day:17 month:06 https://dx.doi.org/10.1007/s10462-024-10811-5 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.72 VZ 77.31 VZ AR 57 2024 7 17 06
language	English
source	Enthalten in Artificial intelligence review 57(2024), 7 vom: 17. Juni volume:57 year:2024 number:7 day:17 month:06
sourceStr	Enthalten in Artificial intelligence review 57(2024), 7 vom: 17. Juni volume:57 year:2024 number:7 day:17 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Big data Predictive analytics Big data applications Systematic review
dewey-raw	004
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container_title	Artificial intelligence review
authorswithroles_txt_mv	Jamarani, Amirhossein @@aut@@ Haddadi, Saeid @@aut@@ Sarvizadeh, Raheleh @@aut@@ Haghi Kashani, Mostafa @@aut@@ Akbari, Mohammad @@aut@@ Moradi, Saeed @@aut@@
publishDateDaySort_date	2024-06-17T00:00:00Z
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author	Jamarani, Amirhossein
spellingShingle	Jamarani, Amirhossein ddc 004 bkl 54.72 bkl 77.31 misc Big data misc Predictive analytics misc Big data applications misc Systematic review Big data and predictive analytics: A systematic review of applications
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title	Big data and predictive analytics: A systematic review of applications
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title_sort	big data and predictive analytics: a systematic review of applications
title_auth	Big data and predictive analytics: A systematic review of applications
abstract	Abstract Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled. © The Author(s) 2024
abstractGer	Abstract Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled. © The Author(s) 2024
abstract_unstemmed	Abstract Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled. © The Author(s) 2024
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title_short	Big data and predictive analytics: A systematic review of applications
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