Big data analytics and internet of things for personalised healthcare: opportunities and challenges

With the increasing use of technologies and digitally driven healthcare systems worldwide, there will be several opportunities for the use of big data in personalized healthcare. In addition, With the advancements and availability of internet of things (IoT) based point-of-care (POC) technologies, big data analytics and artificial intelligence (AI) can provide useful methods and solutions in monitoring, diagnosis, and self-management of health issues for a better personalized healthcare. In this paper, we identify the current personalized healthcare trends and challenges. Then, propose an architecture to support big data analytics using POC test results of an individual. The proposed architecture can facilitate an integrated and self-managed healthcare as well as remote patient care by adapting three popular machine learning algorithms to leverage the current trends in IoT, big data infrastructures and data analytics for advancing personalized healthcare of the future

The impact of big data and business analytics on supply chain management

Background: Change is inevitable and as supply chain managers prepare for the future they face many challenges. Two major trends over the last few years are the growing importance of ‘big data’ and analysing these data though ‘analytics’. The data contain much value and companies need to capitalise on the variety of data sources by in-depth and proper analysis through the use of ‘big data’ analytics. Objective: This article endeavours to highlight the evolving nature of the supply chain management (SCM) environment, to identify how the two major trends (‘big data’ and analytics) will impact SCM in future, to show the benefits that can be derived if these trends are embraced and to make recommendations to supply chain managers. Method: The importance of extracting value from the huge amounts of data available in the SCM area is stated. ‘Big data’ and analytics are defined and the impact of these in various SCM applications clearly illustrated. Results: It is shown, through examples, how the SCM area can be impacted by these new trends and developments. In these examples ‘big data’ analytics have already been embraced, used and implemented successfully. Big data is a reality and using analytics to extract value from the data has the potential to make a huge impact. Conclusion: It is strongly recommended that supply chain managers take note of these two trends, since better use of ‘big data’ analytics can ensure that they keep abreast with developments and changes which can assist in enhancing business competitiveness

Diversified Usages of Big Data : Scope and Challenges

During the last few decades, novel trends in terms of advancement in computational power, increase in storage capacity, growing usage of Web and mass availability of smart devices are leading towards the collection and storage of enormous amount of Data. Big Data Analytics are applied to derive gainful insights and knowledge from this data. The present paper aims to characterize Big Data and then delves in various application domains where Big Data techniques can potentially be applied. The researchers in computer science and allied areas have several research challenges. To address these challenges, new tools and techniques need to be developed and leveraged to solve problems for people, industry and the world at large. DOI: 10.17762/ijritcc2321-8169.150517

An Architecture for Big Data Analytics

Big Data is the new experience curve in the new economy driven by data with high volume, velocity, variety, and veracity. They come from various sources that include the Internet, mobile devices, social media, geospatial devices, sensors, and other machine-generated data. Unlocking the value of Big Data allows business to better sense and respond to the environment, and is becoming a key to creating competitive advantages in a complex and rapidly changing market. Government is also taking notice of the Big Data phenomenon and has created initiatives to exploit Big Data in many areas such as science and engineering, healthcare and national security. Traditional data processing and analysis of structured data using RDBMS and data warehousing no longer satisfy the challenges of Big Data. Technology trends for Big Data embrace open source software, commodity servers, and massively parallel-distributed processing platforms. Analytics is at the core of exploiting values from Big Data to create consumable insights for business and government. This paper presents architecture for Big Data Analytics and explores Big Data technologies that include NoSQL databases, Hadoop Distributed File System and MapReduce

DEVELOPMENT OF CONCEPTUAL MODEL FOR SOCIAL COMMERCE RESEARCH THROUGH INTEGRATION WITH BIG DATA ANALYSIS

Information systems designers face great opportunities and challenges in developing a holistic big data research approach for the new analytics savvy generation. In addition business intelligence is largely utilized in the business community and thus can leverage the opportunities from the abundant data and domain-specific analytics in many critical areas. The aim of this paper is to assess the relevance of these trends in the current business context through evidence-based documentation of current and emerging applications as well as their wider business implications. In this paper, we use BigML to examine how the two social information channels (i.e., friends-based opinion leaders-based social information) influence consumer purchase decisions on social commerce sites. We undertake an empirical study in which we integrate a framework and a theoretical model for big data analysis. We conduct an empirical study to demonstrate that big data analytics can be successfully combined with a theoretical model to produce more robust and effective consumer purchase decisions. The results offer important and interesting insights into IS research and practice