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On big data predictive analytics-trends, perspectives, and challenges

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 15 No. 6 (2025)

DOI
10.11591/ijece.v15i6.pp5978-5985
ISSN
2722-2578, 2088-8708
Published
2025-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The world is experiencing explosive growth in numerous sectors such as healthcare, engineering, scientific studies, business, social networking. This growth is causing an immense surge in data generation too. And with the emergence of technologies like internet of things (IoT), Mobile, and cloud computing, the volume of data being produced is skyrocketing. However, making sense of this colossal amount of data is a daunting challenge. Enter big data computing, a new paradigm that blends large datasets with advanced analytical techniques. Big data is characterized by the three V's: Volume, velocity, and variety, and refers to massive datasets. By processing this data, we can uncover new opportunities and gain valuable insights into market trends. Traditional techniques are simply not equipped to handle the scale of Big Data. The purpose of this article is to gather reviews of various predictive analytics applications related to big data and the advantages of using big data analytics across various decision-making domains.

Collaboration network of the authors

8 authors · 11 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Université Moulay Ismail de Meknes
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Mariya OuaissaUniversité Moulay Ismail de Meknes1
Safaa LaqtibUniversité Moulay Ismail de Meknes1
Khalid El YassiniUniversité Moulay Ismail de Meknes1
Meriem HoumerAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>On big data predictive analytics-trends, perspectives, and challenges</dc:title><dc:creator>Benlachmi, Yassine</dc:creator><dc:creator>Yazidi, Abdelaziz El</dc:creator><dc:creator>Rhattoy, Abdallah</dc:creator><dc:creator>Hasnaoui, Moulay Lahcen</dc:creator><dc:description>&lt;jats:p&gt;The world is experiencing explosive growth in numerous sectors such as healthcare, engineering, scientific studies, business, social networking. This growth is causing an immense surge in data generation too. And with the emergence of technologies like internet of things (IoT), Mobile, and cloud computing, the volume of data being produced is skyrocketing. However, making sense of this colossal amount of data is a daunting challenge. Enter big data computing, a new paradigm that blends large datasets with advanced analytical techniques. Big data is characterized by the three V's: Volume, velocity, and variety, and refers to massive datasets. By processing this data, we can uncover new opportunities and gain valuable insights into market trends. Traditional techniques are simply not equipped to handle the scale of Big Data. The purpose of this article is to gather reviews of various predictive analytics applications related to big data and the advantages of using big data analytics across various decision-making domains.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2025-12-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v15i6.pp5978-5985</dc:identifier><dc:identifier>10.11591/ijece.v15i6.pp5978-5985</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 15 No 6 (2025); 5978</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/33369/18584</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>