A Comprehensive Survey on Cloud Data Mining (CDM) Frameworks and Algorithms

Abstract

Data mining is used for finding meaningful information out of a vast expanse of data. With the advent of Big Data concept, data mining has come to much more prominence. Discovering knowledge out of a gigantic volume of data efficiently is a major concern as the resources are limited. Cloud computing plays a major role in such a situation. Cloud data mining fuses the applicability of classical data mining with the promises of cloud computing. This allows it to perform knowledge discovery out of huge volumes of data with efficiency. This article presents the existing frameworks, services, platforms, and algorithms for cloud data mining. The frameworks and platforms are compared among each other based on similarity, data mining task support, parallelism, distribution, streaming data processing support, fault tolerance, security, memory types, storage systems, and others. Similarly, the algorithms are grouped on the basis of parallelism type, scalability, streaming data mining support, and types of data managed. We have also provided taxonomies on the basis of data mining techniques such as clustering, classification, and association rule mining. We also have attempted to discuss and identify the major applications of cloud data mining. The various taxonomies for cloud data mining frameworks, platforms, and algorithms have been identified. This article aims at gaining better insight into the present research realm and directing the future research toward efficient cloud data mining in future cloud systems.

Decision support based on optimized data mining techniques: Application to mobile telecommunication companies

“…To this end, we will adapt our approach for big data context to mine ARs from very large datasets using well known frameworks, like MapReduce 40 and Spark 41 . With the advent of high‐performance computing, distributed systems, and cloud data processing, big DM has been easier than before 42 . Distributed computing using Hadoop and Spark frameworks became popular because of their parallel processing 42 .…”

“…With the advent of high‐performance computing, distributed systems, and cloud data processing, big DM has been easier than before 42 . Distributed computing using Hadoop and Spark frameworks became popular because of their parallel processing 42 . Several research works adopted the Hadoop with MapReduce programming engine for frequent itemset mining on big data 43‐45 .…”

“…42 Distributed computing using Hadoop and Spark frameworks became popular because of their parallel processing. 42 Several research works adopted the Hadoop with MapReduce programming engine for frequent itemset mining on big data. [43][44][45] Finally, on a practical level, the deployment of our solution in a real setting within the CRM service, would allow us to see the contribution of "optimized" ARs compared to other rules and to show the advantage of using our framework in the decision-making process.…”

Popularity and correlation aware data replication strategy based on half‐life concept and clustering in cloud system

“…The knowledge about data correlation can be extracted from historical data using several methods such as machine learning, data mining, 3,16,17 graph theory, 18‐20 statistics, and so on. In this respect, discovering knowledge out of huge volumes of data can be performed more efficiently, based on the high‐performance computing of cloud 21 . In addition, file correlations have become an increasingly important consideration for performance enhancement in various contexts, such as file systems, 22‐26 distributed systems, 27‐30 and cloud systems 3,18,31‐36 …”

Access Control Role Evolution Mechanism for Open Computing Environment

“…Open computing environments (including big data [1], internet of things [2] and cloud computing [3]) provide us convenient services such as data sharing and effective computing. It has been widely used in human's production and life.…”