3 years ago

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

Navroop Kaur, Rajkumar Buyya, Rajinder Sandhu, Sandeep K. Sood


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 are 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 levels based on concept hierarchy of each attribute. Mathematical foundation of tensor formation and query processing are developed. The proposed method is successful in creating tensors of a diabetes dataset proving its applicability. The proposed system provides faster computation, low response time, better privacy and high relevancy as compared to baseline PARAFAC2 and CANDELINC 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.

Publisher URL: https://link.springer.com/article/10.1007/s11227-017-2163-y

DOI: 10.1007/s11227-017-2163-y

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.