Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

Rahmani, Amir Masoud; Azhir, Elham; Ali, Saqib; Mohammadi, Mokhtar; Ahmed, Omed Hassan; Ghafour, Marwan Yassin; Ahmed, Sarkar Hasan; Hosseinzadeh, Mehdi

doi:10.7717/peerj-cs.488

Cited by 73 publications

(34 citation statements)

References 61 publications

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“…The clustering method separates a collection of data into groupings. The similarity between two points in the same group is greater than the similarity between two points in different groups [11]. Previously, classification methods depended on great personal effort and personal scientific experience in a particular field in order to lead to accurate and reliable results, but the computer's invasion of broad fields of science made the process of controlling abundant and wide information easy, with little effort and little cost.…”

Section: Clusteringmentioning

confidence: 99%

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A Review Of Clustering Methods Based on Artificial Intelligent Techniques

Khaleel¹

2022

J.Edu.Sci.

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Due to the development in various areas of life, the development of the Internet, and the presence of many datasets, and in order to obtain useful information from the rapidly increasing volumes of digital data, there must be theories and computational tools to help humans extract the useful information they need from this data. Large data is collected from many different services and resources. Clustering is one of the most basic and well-known methods of data mining and extraction and obtaining useful information. The technique of recognizing natural groups or clusters within several datasets based on some measure of similarity is known as data clustering. Many researchers have introduced and developed many clustering algorithms based on the different methods of artificial intelligence techniques. Finding the right algorithms greatly helps in organizing information and extracting the correct answer from different database queries. This paper provides an overview of the different clustering methods using artificial intelligence and finding the appropriate clustering algorithm to process different data sets. We highlight the best-performing clustering algorithm that gives effective and correct clustering for each data set.

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Section: Clusteringmentioning

confidence: 99%

“…Clustering methods are commonly used not just to organize and classify data, but also for data compression and data model creation. These algorithms are called cluster analysis [11] [12].…”

Section: Clusteringmentioning

confidence: 99%

A Review Of Clustering Methods Based on Artificial Intelligent Techniques

Khaleel¹

2022

J.Edu.Sci.

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“…The performance of these methods is assessed by several factors such as the number of input parameters, data size, cluster shapes, and noise. Moreover, considering the fast growth of IT and the huge data generated daily by millions of people (Rahmani et al, 2021), there is no doubt that a single device may not be able to handle such quantities of data. So, novel technologies are needed to store and extract information.…”

Section: Overview and Backgroundmentioning

confidence: 99%

A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method

Azhir

Navimipour

Hosseinzadeh

et al. 2021

PeerJ Computer Science

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Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each with a corresponding execution cost. To produce an effective query plan thus requires examining a large number of alternative plans. Access plan recommendation is an alternative technique to database query optimization, which reuses the previously-generated QEPs to execute new queries. In this technique, the query optimizer uses clustering methods to identify groups of similar queries. However, clustering such large datasets is challenging for traditional clustering algorithms due to huge processing time. Numerous cloud-based platforms have been introduced that offer low-cost solutions for the processing of distributed queries such as Hadoop, Hive, Pig, etc. This paper has applied and tested a model for clustering variant sizes of large query datasets parallelly using MapReduce. The results demonstrate the effectiveness of the parallel implementation of query workloads clustering to achieve good scalability.

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“…The large-scale data accessibility powered a plethora of new analytical tools such as those commonly bundled under the generic denomination of artificial intelligence [4] , [5] , [6] , [7] , [8] .…”

Section: Introductionmentioning

confidence: 99%

Sharing data, sharing methods, sharing science.

Pantano

2022

MethodsX

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Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

Cited by 73 publications

References 61 publications

A Review Of Clustering Methods Based on Artificial Intelligent Techniques

A Review Of Clustering Methods Based on Artificial Intelligent Techniques

A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method

Sharing data, sharing methods, sharing science.

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