A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

Alshreef Abed,Jingling Yuan,Lin Li

doi:10.14569/ijacsa.2017.080804

Abstract

The rapid growth of biomedical informatics has drawn increasing popularity and attention. The reason behind this are the advances in genomic, new molecular, biomedical approaches and various applications like protein identification, patient medical records, genome sequencing, medical imaging and a huge set of biomedical research data are being generated day to day. The increase of biomedical data consists of both structured and unstructured data. Subsequently, in a traditional database system (structured data), managing and extracting useful information from unstructured-biomedical data is a tedious job. Hence, mechanisms, tools, processes, and methods are necessary to apply on unstructured biomedical data (text) to get the useful business data. The fast development of these accumulations makes it progressively troublesome for people to get to the required information in an advantageous and viable way. Text mining can help us mine information and knowledge from a mountain of text, and is now widely applied in biomedical research. Text mining is not a new technology, but it has recently received spotlight attention due to the emergence of Big Data. The applications of text mining are diverse and span to multiple disciplines, ranging from biomedicine to legal, business intelligence and security. In this survey paper, the researcher identifies and discusses biomedical data (text) mining issues, and recommends a possible technique to cope with possible future growth.

Highlights

The field of biomedical research is booming, a lot of biomedical knowledge is in unstructured form in the form of text file, and the field has witnessed exponential trend increase; there is a need to solve the contradictions between massive growth of information and knowledge of text slowly and in a credible manner to identify useful patterns in the text which is still a challenge
Text mining techniques which involve the process of information retrieval, information extraction and data mining provide a means of solving this by Ananiadou et al [5]
The researcher discussed and analyzed text mining techniques for biomedical data retrieving from the pool of documents on the web

Summary

Introduction

The field of biomedical research is booming, a lot of biomedical knowledge is in unstructured form in the form of text file, and the field has witnessed exponential trend increase; there is a need to solve the contradictions between massive growth of information and knowledge of text slowly and in a credible manner to identify useful patterns in the text which is still a challenge. To enable data mining and knowledge discovery techniques, documents should be in the structured format [2]. The problem faced by the biological researchers is on how to effectively find out the useful and needed documents in such an information-overload environment. Text mining techniques which involve the process of information retrieval, information extraction and data mining provide a means of solving this by Ananiadou et al [5]

Methods

Results

Conclusion