TDRM: tensor-based data representation and mining for healthcare data in cloud computing environments

Abstract

Big data analytics proved to be one of the most influential forces in today's competitive business environments due to its ability to generate new insights by processing a large volume and variety of data. Storing as well as mining these datasets is one of the primary challenges of the big data era. If data is stored in a well-defined pattern, then its updation mining and deletion processes become easy. In this paper, granular computing concept is used to store heterogeneous data in the format of tensor. A multi-dimensional matrix, also known as tensor, stores data in the raw format and then raw tensor is replicated to multiple tensors of different abstraction level based on concept hierarchy of each attribute. Mathematical foundation of tensor formation and query processing are developed. Proposed method is successful in creating tensors of a diabetes dataset proving its applicability. Proposed system provides faster computation, low response time, better privacy and high relevancy as compared to baseline PARAFAC2 and CANADLINC tensor analysis method when run on Microsoft Azure cloud infrastructure. Different levels of information granules in the form of tensors make data storage and its query processing effective.

A Decoupled Blockchain Approach for Edge-Envisioned IoT-Based Healthcare Monitoring

“…For instance, He et al [20] proposed a distributed, scalable, and sparse tensor factorization method to provide scalability and accuracy while performing healthcare analytics. Furthermore, Sandhu et al [21] showed the applicability of tensor-based data representation and storage approach on the healthcare diabetes data. The authors noted that their tensorbased system provides faster computations, low latency, and high relevance as compared to the considered baseline models.…”

Fog‐assisted virtual reality‐based learning framework to control panic

“…Sandhu and Sood (2015) presented an e‐learning system based on cloud computing and the Design Science Research method. Sandhu et al (2018) proposed a framework related to data mining that has an efficient method for storing information granules. Otoom et al (2020) proposed a framework based on IoT that retrieved real‐time data and analysed it to identify coronaviruses cases.…”

Profile-Free and Real-Time Task Recommendation in Mobile Crowdsensing

“…Matrices with dimensions greater than or equal to three are called tensors, and the decomposition of tensors has important applications in signal processing [27], [28], data mining [29], and graph analysis [30]. Existing tensor factorization methods include HOSVD [31], the pairwise interaction approach [32], and [33].…”