The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.
Data Governance Talking Points: Simple Lessons From the TrenchesHealth Catalyst
About 7 months ago, one of Health Catalyst's clients asked for a 90-minute cram course on data governance, including time for questions and answers. They were struggling, like so many other healthcare organizations, caught in the swing of extremes from too much to too little, while equilibrium eluded them. With a last-minute rush, Dale Sanders (President of Technology, Health Catalyst) fell back on his time in the Air Force and threw together a talking points paper to facilitate the conversation. At the end of the meeting, the client was effusive with their appreciation, using words like “incredibly insightful,” “brilliant,” and “hugely valuable.” Dale didn’t think it was that good, but their data governance function was “dramatically better,” and they were happy, so something worked.
Since then, Dale has used the same talking points in two other similar meetings, with similar feedback and results. It still doesn’t feel that great or insightful to him, but he's glad to flow with the feedback and share the same style in this webinar in the hope that it’s useful.
After viewing this webinar, Dale hopes that you will have some tactical ideas to assess your organization’s data governance strategy. Are you leveraging the data you have? What could improve?
Big data is generating a lot of hype in every industry including healthcare. As my colleagues and I talk to leaders at health systems, we’ve learned that they’re looking for answers about big data. They’ve heard that it’s something important and that they need to be thinking about it. But they don’t really know what they’re supposed to do with it.
Benefits of Big Data in Health Care A Revolutionijtsrd
Lifespan of a normal human is increasing with the world population and it produces new challenge in health care. big data change the method of data management ,leverage data and analyzing data.with the help of big data we can reduces the costs of treatment, reducing medication and provide better treatment with predictive analytics. Health related data collected from various sources like electronic health record EHR ,medical imaging system, genomic sequencing, pay of records, pharmaceutical research , and medical devices, etc. are refers to as big data in healthcare. Dr. Ritushree Narayan ""Benefits of Big Data in Health Care: A Revolution"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22974.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22974/benefits-of-big-data-in-health-care-a-revolution/dr-ritushree-narayan
Healthcare is changing rapidly. It is clear that humans need mechanisms to automate some parts of data processing and help humans in decision making. This talk will concentrate on how to improve the machine understanding of unstructured data.
This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
By leveraging Big Data, the healthcare industry has an incredible potential to improve lives. This session will give examples of how data volume, velocity and variety is transforming the “art” of a doctor to the science of care. It will describe how the use of machine learning and massive amount of data will drive the new Consumer Drive healthcare movement.
Data Governance Talking Points: Simple Lessons From the TrenchesHealth Catalyst
About 7 months ago, one of Health Catalyst's clients asked for a 90-minute cram course on data governance, including time for questions and answers. They were struggling, like so many other healthcare organizations, caught in the swing of extremes from too much to too little, while equilibrium eluded them. With a last-minute rush, Dale Sanders (President of Technology, Health Catalyst) fell back on his time in the Air Force and threw together a talking points paper to facilitate the conversation. At the end of the meeting, the client was effusive with their appreciation, using words like “incredibly insightful,” “brilliant,” and “hugely valuable.” Dale didn’t think it was that good, but their data governance function was “dramatically better,” and they were happy, so something worked.
Since then, Dale has used the same talking points in two other similar meetings, with similar feedback and results. It still doesn’t feel that great or insightful to him, but he's glad to flow with the feedback and share the same style in this webinar in the hope that it’s useful.
After viewing this webinar, Dale hopes that you will have some tactical ideas to assess your organization’s data governance strategy. Are you leveraging the data you have? What could improve?
Big data is generating a lot of hype in every industry including healthcare. As my colleagues and I talk to leaders at health systems, we’ve learned that they’re looking for answers about big data. They’ve heard that it’s something important and that they need to be thinking about it. But they don’t really know what they’re supposed to do with it.
Benefits of Big Data in Health Care A Revolutionijtsrd
Lifespan of a normal human is increasing with the world population and it produces new challenge in health care. big data change the method of data management ,leverage data and analyzing data.with the help of big data we can reduces the costs of treatment, reducing medication and provide better treatment with predictive analytics. Health related data collected from various sources like electronic health record EHR ,medical imaging system, genomic sequencing, pay of records, pharmaceutical research , and medical devices, etc. are refers to as big data in healthcare. Dr. Ritushree Narayan ""Benefits of Big Data in Health Care: A Revolution"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22974.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22974/benefits-of-big-data-in-health-care-a-revolution/dr-ritushree-narayan
Healthcare is changing rapidly. It is clear that humans need mechanisms to automate some parts of data processing and help humans in decision making. This talk will concentrate on how to improve the machine understanding of unstructured data.
This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
By leveraging Big Data, the healthcare industry has an incredible potential to improve lives. This session will give examples of how data volume, velocity and variety is transforming the “art” of a doctor to the science of care. It will describe how the use of machine learning and massive amount of data will drive the new Consumer Drive healthcare movement.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
Big Data Analytics for Smart Health CareEshan Bhuiyan
Healthcare big data refers to the vast quantities of data that is now available to healthcare providers.
As a response to the digitization of healthcare information and the rise of value-based care, the industry has taken advantage of big data and analytics to make strategic business decisions.
This presentation is about basics of Big data Analytics along with Characteristics,Challenges,Structures,Differences between Traditional and Big data,How Big data is getting benefited in Healthcare Industry,Big data in Real time
Data-Driven Healthcare for Manufacturers Amit Mishra
Data-driven healthcare empowers the providers with a common data platform to discover untapped data-driven opportunities. Healthcare data and its impact on the patient care decision process via accurate, real-time, reliable data from disparate sources is creating a digital health revolution. Physician groups, nursing facilities, hospitals, pharmaceutical companies, clinical researchers, and medical equipment manufacturers are all churning out vast amounts of data during their daily operations. This data has tremendous value and can revolutionize patient care, diagnosis, real-time decisions and help deliver new, unimagined innovations with quality of patient care. Know more about data-driven healthcare at https://www.solix.com/solutions/data-driven-solutions/healthcare/
Application of Big Data in Medical Science brings revolution in managing heal...IJEEE
Big Data can be combined with new technology to bring about positive conversion in the health care segment. A technology aimed at making Big Data analytics a certainty will act as a key element in transforming the way the health care industry operates today. The study and analysis of Big Data can be used for tracking and managing population health care effectively and efficiently. In ten years, eighty percent of the work people do in medicine will be replaced by technology. And medicine will not look anything like what it does today. Healthcare will change enormously as it becomes a data-driven industry. But the magnitude of the data, the speed at which it’s growing and the threat it could pose to individual privacy mean mastering "big data" is one of biomedicine's most pressing challenges. Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. This also plays a vital role in delivering preventive care. Health care will change a great deal as it becomes a data- driven industry. But the size of the data, the speed at which it’s growing and the threat it could cause to individual privacy mean mastering it is one of biomedicine's most critical challenges. In this research paper we will discuss problems faced by big data, obstacles in using big data in the health industry, how big Data analytics can take health care to a new level by enhancing the overall quality of patient care.
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
This is the next evolution in health information exchanges and data warehouses, specifically designed to support analytics, transaction processing, and third party application development, in one platform, the Data Operating System.
www.panorama.com
Panorama Necto uncovers the hidden insights in your data and presents them in beautiful dashboards powered with KPI Alerts, which is managed by a the most secure, centralized & state of the art BI solution.
Healthcare Analytics Summit Keynote Fall 2017Dale Sanders
The Data Operating System. Changing the Digital Trajectory of Healthcare. Why do we need to change the current digital trajectory? What’s the business case for a Data Operating System? What is a Data Operating System and how did we get here? What difference will DOS make? What should we do with it and what should we expect?
Outline
Value Based Healthcare System – How it is seen today
Healthcare Challenge & IoT as a Solution
IoT – Big Data Structure
Recent Trends in IoT Big Data Analytics
Challenges & Our Future
In-depth Knowledge of
What causes the most premature death?
Distribution of Disease burden from 1990 - 2020
Challenges in Healthcare
Future Healthcare
IoT Machine Talking to Machine
Prediction of IoT Usage
About PEPGRA HEALTHCARE,
A leading healthcare communication firm with years of excellence serving clients with a dedicated team of Medical, Regulatory and Scientific writers specialized in all therapeutic areas.
Contact us at :
UK: +44-1143520021
US/Canada: +1-972-502-9262
India: +91-8754446690
info@pepgra.com
www.pepgra.com
Healthcare and Life Sciences organizations are leveraging Big Data technology to capture data in order to get a better insight into patient centric and research centric information. Combining these two requires extreme computing power. We will discuss use cases where Big Data technology was instrumental ; Merging Genomic and Clinical Data in order to advance personalized Medicine
Revenue opportunities in the management of healthcare data delugeShahid Shah
Healthcare data is hard to deal with and getting even harder and more expensive. In this presentation, Shahid Shah covers why:
* Healthcare data is going from hard to nearly impossible to manage.
* Applications come and go, data lives forever.
* Data integration is notoriously difficult, even in the best of circumstances, and requires sophisticated tools and attention to detail.
And, then talks about how new techniques are needed to store and manage healthcare data.
The Hive Think Tank: Unpacking AI for Healthcare The Hive
In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.
A BIG DATA REVOLUTION IN HEALTH CARE SECTOR: OPPORTUNITIES, CHALLENGES AND TE...ijistjournal
Health care sector grows tremendously in last few decades. The health care sector has generated huge amounts of data that has huge volume, enormous velocity and vast variety. Also it comes from a variety of new sources as hospitals are now tend to implemented electronic health record (EHR) systems. These sources have strained the existing capabilities of existing conventional relational database management systems. In such scenario, Big data solutions offer to harness these massive, heterogeneous and complex data sets to obtain more meaningful and knowledgeable information.
This paper basically studies the impact of implementing the big data solutions on the healthcare sector, the potential opportunities, challenges and available platform and tools to implement Big data analytics in health care sector.
Big data is to be implemented in as full way in real-time; it is still in a research. People
need to know what to do with enormous data. Insurance agencies are actively participating for the
analysis of patient's data which could be used to extract some useful information. Analysis is done in
term of discharge summary, drug & pharma, diagnostics details, doctor’s report, medical history,
allergies & insurance policies which are made by the application of map reduce and useful data is
extracted. We are analysing more number of factors like disease Types with its agreeing reasons,
insurance policy details along with sanctioned amount, family grade wise segregation.
Keywords: Big data, Stemming, Map reduce Policy and Hadoop.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
Big Data Analytics for Smart Health CareEshan Bhuiyan
Healthcare big data refers to the vast quantities of data that is now available to healthcare providers.
As a response to the digitization of healthcare information and the rise of value-based care, the industry has taken advantage of big data and analytics to make strategic business decisions.
This presentation is about basics of Big data Analytics along with Characteristics,Challenges,Structures,Differences between Traditional and Big data,How Big data is getting benefited in Healthcare Industry,Big data in Real time
Data-Driven Healthcare for Manufacturers Amit Mishra
Data-driven healthcare empowers the providers with a common data platform to discover untapped data-driven opportunities. Healthcare data and its impact on the patient care decision process via accurate, real-time, reliable data from disparate sources is creating a digital health revolution. Physician groups, nursing facilities, hospitals, pharmaceutical companies, clinical researchers, and medical equipment manufacturers are all churning out vast amounts of data during their daily operations. This data has tremendous value and can revolutionize patient care, diagnosis, real-time decisions and help deliver new, unimagined innovations with quality of patient care. Know more about data-driven healthcare at https://www.solix.com/solutions/data-driven-solutions/healthcare/
Application of Big Data in Medical Science brings revolution in managing heal...IJEEE
Big Data can be combined with new technology to bring about positive conversion in the health care segment. A technology aimed at making Big Data analytics a certainty will act as a key element in transforming the way the health care industry operates today. The study and analysis of Big Data can be used for tracking and managing population health care effectively and efficiently. In ten years, eighty percent of the work people do in medicine will be replaced by technology. And medicine will not look anything like what it does today. Healthcare will change enormously as it becomes a data-driven industry. But the magnitude of the data, the speed at which it’s growing and the threat it could pose to individual privacy mean mastering "big data" is one of biomedicine's most pressing challenges. Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. This also plays a vital role in delivering preventive care. Health care will change a great deal as it becomes a data- driven industry. But the size of the data, the speed at which it’s growing and the threat it could cause to individual privacy mean mastering it is one of biomedicine's most critical challenges. In this research paper we will discuss problems faced by big data, obstacles in using big data in the health industry, how big Data analytics can take health care to a new level by enhancing the overall quality of patient care.
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
This is the next evolution in health information exchanges and data warehouses, specifically designed to support analytics, transaction processing, and third party application development, in one platform, the Data Operating System.
www.panorama.com
Panorama Necto uncovers the hidden insights in your data and presents them in beautiful dashboards powered with KPI Alerts, which is managed by a the most secure, centralized & state of the art BI solution.
Healthcare Analytics Summit Keynote Fall 2017Dale Sanders
The Data Operating System. Changing the Digital Trajectory of Healthcare. Why do we need to change the current digital trajectory? What’s the business case for a Data Operating System? What is a Data Operating System and how did we get here? What difference will DOS make? What should we do with it and what should we expect?
Outline
Value Based Healthcare System – How it is seen today
Healthcare Challenge & IoT as a Solution
IoT – Big Data Structure
Recent Trends in IoT Big Data Analytics
Challenges & Our Future
In-depth Knowledge of
What causes the most premature death?
Distribution of Disease burden from 1990 - 2020
Challenges in Healthcare
Future Healthcare
IoT Machine Talking to Machine
Prediction of IoT Usage
About PEPGRA HEALTHCARE,
A leading healthcare communication firm with years of excellence serving clients with a dedicated team of Medical, Regulatory and Scientific writers specialized in all therapeutic areas.
Contact us at :
UK: +44-1143520021
US/Canada: +1-972-502-9262
India: +91-8754446690
info@pepgra.com
www.pepgra.com
Healthcare and Life Sciences organizations are leveraging Big Data technology to capture data in order to get a better insight into patient centric and research centric information. Combining these two requires extreme computing power. We will discuss use cases where Big Data technology was instrumental ; Merging Genomic and Clinical Data in order to advance personalized Medicine
Revenue opportunities in the management of healthcare data delugeShahid Shah
Healthcare data is hard to deal with and getting even harder and more expensive. In this presentation, Shahid Shah covers why:
* Healthcare data is going from hard to nearly impossible to manage.
* Applications come and go, data lives forever.
* Data integration is notoriously difficult, even in the best of circumstances, and requires sophisticated tools and attention to detail.
And, then talks about how new techniques are needed to store and manage healthcare data.
The Hive Think Tank: Unpacking AI for Healthcare The Hive
In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.
A BIG DATA REVOLUTION IN HEALTH CARE SECTOR: OPPORTUNITIES, CHALLENGES AND TE...ijistjournal
Health care sector grows tremendously in last few decades. The health care sector has generated huge amounts of data that has huge volume, enormous velocity and vast variety. Also it comes from a variety of new sources as hospitals are now tend to implemented electronic health record (EHR) systems. These sources have strained the existing capabilities of existing conventional relational database management systems. In such scenario, Big data solutions offer to harness these massive, heterogeneous and complex data sets to obtain more meaningful and knowledgeable information.
This paper basically studies the impact of implementing the big data solutions on the healthcare sector, the potential opportunities, challenges and available platform and tools to implement Big data analytics in health care sector.
Big data is to be implemented in as full way in real-time; it is still in a research. People
need to know what to do with enormous data. Insurance agencies are actively participating for the
analysis of patient's data which could be used to extract some useful information. Analysis is done in
term of discharge summary, drug & pharma, diagnostics details, doctor’s report, medical history,
allergies & insurance policies which are made by the application of map reduce and useful data is
extracted. We are analysing more number of factors like disease Types with its agreeing reasons,
insurance policy details along with sanctioned amount, family grade wise segregation.
Keywords: Big data, Stemming, Map reduce Policy and Hadoop.
A Case Analysis on Involvement of Big Data during Natural Disaster and Pandem...YogeshIJTSRD
Big data is an upcoming technology and requires utmost care for an efficient and smooth implementation of the technology. In case of healthcare the most challenging part of big data is the privacy, data security, handling large volume of medical imaging data and data leakage. It can be useful to this sector when big data is made structured, relevant, smart and accessible and the managers should give importance to the strategic and business value of big data technology rather than only concentrating at the technological aspect of the implementation. The use of big data in natural disasters and pandemics helps to understand and make better decision with fast processing of the data that are collected through various sources such as social media, sensors and other internet activities. This paper tries to focus on effective involvement of Big Data in natural disaster and pandemic and also identify the current and future use of Big Data in health care sector. The paper identifies the critical aspects which are used for Big data implementation and describe ways to handle the challenges related to it. Mr. Bibin Mathew | Dr. Swati John "A Case Analysis on Involvement of Big Data during Natural Disaster and Pandemics and its Uses in the Health Care Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45049.pdf Paper URL: https://www.ijtsrd.com/management/other/45049/a-case-analysis-on-involvement-of-big-data-during-natural-disaster-and-pandemics-and-its-uses-in-the-health-care-sector/mr-bibin-mathew
Big Data, CEP and IoT : Redefining Holistic Healthcare Information Systems an...Tauseef Naquishbandi
Healthcare industry has been a significant area for innovative application of various technologies over decades. Being an area of social relevance governmental spending on healthcare have always been on the rise over the years. Event Processing (CEP) has been in use for many years for situational awareness and response generation. Computing technologies have played an important role in improvising several aspects of healthcare. Recently emergent technology paradigms of Big Data, Internet of Things (IoT) and Complex Event Processing (CEP) have the potential not only to deal with pain areas of healthcare domain but also to redefine healthcare offerings. This paper aims to lay the groundwork for a healthcare system which builds upon integration of Big Data, CEP and IoT.
Gain insights from data analytics and take action! Learn why everyone is making a big deal about big data in healthcare and how data analytics creates action.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today
communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data, accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters, automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions of individuals around the world to contribute to the amount of data available. In addition, the extremely increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security & Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources along with their characteristics in each domain. Later, it presents the highly productive and competitive big data applications with innovative big data technologies. Subsequently, the study showcases the impact of big data on each domain to capture value addition in its services. Finally, the study put forwards many more research opportunities as all these domains differ in their complexity and development in the usage of big data analytics
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The
advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,
automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In
fact, as they go about their business and interact with individuals, they are producing an incredible amount
of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data
and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation
factor for the present researchers.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics
in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security &
Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources
along with their characteristics in each domain. Later, it presents the highly productive and competitive big
data applications with innovative big data technologies. Subsequently, the study showcases the impact of
big data on each domain to capture value addition in its services. Finally, the study put forwards many
more research opportunities as all these domains differ in their complexity and development in the usage of
big data analytics.
Due to the arrival of new technologies, devices, and communication means, the amount of data produced by mankind is growing rapidly every year. This gives rise to the era of big data. The term big data comes with the new challenges to input, process and output the data. The paper focuses on limitation of traditional approach to manage the data and the components that are useful in handling big data. One of the approaches used in processing big data is Hadoop framework, the paper presents the major components of the framework and working process within the framework.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
White paper examines the unstructured data management challenges healthcare organizations face and how the Hitachi Data Systems solution employs metadata to address the data storm.
Similar to Big Data Analytics in Health Care: A Review Paper (20)
In the era of data-driven warfare, the integration of big data and machine learning (ML) techniques has
become paramount for enhancing defence capabilities. This research report delves into the applications of
big data and ML in the defence sector, exploring their potential to revolutionize intelligence gathering,
strategic decision-making, and operational efficiency. By leveraging vast amounts of data and advanced
algorithms, these technologies offer unprecedented opportunities for threat detection, predictive analysis,
and optimized resource allocation. However, their adoption also raises critical concerns regarding data
privacy, ethical implications, and the potential for misuse. This report aims to provide a comprehensive
understanding of the current state of big data and ML in defence, while examining the challenges and
ethical considerations that must be addressed to ensure responsible and effective implementation.
Cloud Computing, being one of the most recent innovative developments of the IT world, has been
instrumental not just to the success of SMEs but, through their productivity and innovative contribution to
the economy, has even made a remarkable contribution to the economic growth of the United States. To
this end, the study focuses on how cloud computing technology has impacted economic growth through
SMEs in the United States. Relevant literature connected to the variables of interest in this study was
reviewed, and secondary data was generated and utilized in the analysis section of this paper. The findings
of this paper revealed that there have been meaningful contributions that the usage of virtualization has
made in the commercial dealings of small firms in the United States, and this has also been reflected in the
economic growth of the country. This paper further revealed that as important as cloud-based software is,
some SMEs are still skeptical about how it can help improve their business and increase their bottom line
and hence have failed to adopt it. Apart from the SMEs, some notable large firms in different industries,
including information and educational services, have adopted cloud computing technology and hence
contributed to the economic growth of the United States. Lastly, findings from our inferential statistics
revealed that no discernible change has occurred in innovation between small and big businesses in the
adoption of cloud computing. Both categories of businesses adopt cloud computing in the same way, and
their contribution to the American economy has no significant difference in the usage of virtualization.
Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in
recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized
application of MIMO technology within WSNs. This approach operates effectively, especially in
challenging and resource-constrained environments. By facilitating collaboration among sensor nodes,
Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments.
Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring,
industrial automation, and healthcare applications.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication. IJCSIT publishes original research papers and review papers, as well as auxiliary material such as: research papers, case studies, technical reports etc.
With growing, Car parking increases with the number of car users. With the increased use of smartphones
and their applications, users prefer mobile phone-based solutions. This paper proposes the Smart Parking
Management System (SPMS) that depends on Arduino parts, Android applications, and based on IoT. This
gave the client the ability to check available parking spaces and reserve a parking spot. IR sensors are
utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the
server and are recovered by the mobile application which offers many options attractively and with no cost
to users and lets the user check reservation details. With IoT technology, the smart parking system can be
connected wirelessly to easily track available locations.
Welcome to AIRCC's International Journal of Computer Science and Information Technology (IJCSIT), your gateway to the latest advancements in the dynamic fields of Computer Science and Information Systems.
Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with
linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language
Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such
systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic
language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the
fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer)
which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different
questions that deal with the different concepts and have different difficulty levels. Constraint-based student
modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain
level the different grammar skills through the defined skill structures. The main contribution of this paper
is the hierarchal representation of the system's basic grammar skills as domain knowledge. That
representation is used as a mechanism for efficiently checking constraints to model the student knowledge
and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number
of trails the student takes for answering each question and fuzzy logic decision system are used to
determine the student learning level for each lesson as a long-term model. The results of the evaluation
showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its
features and abilities.
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This research aims to further understanding in the field of continuous authentication using behavioural
biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing
Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed
machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and
Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust
model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch
dynamics can effectively distinguish users. However, further studies are needed to make it viable option
for authentication systems. You can access our dataset at the following
link:https://github.com/AuthenTech2023/authentech-repo
This paper discusses the capabilities and limitations of GPT-3 (0), a state-of-the-art language model, in the
context of text understanding. We begin by describing the architecture and training process of GPT-3, and
provide an overview of its impressive performance across a wide range of natural language processing
tasks, such as language translation, question-answering, and text completion. Throughout this research
project, a summarizing tool was also created to help us retrieve content from any types of document,
specifically IELTS (0) Reading Test data in this project. We also aimed to improve the accuracy of the
summarizing, as well as question-answering capabilities of GPT-3 (0) via long text
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
The security of Electric Vehicle (EV) charging has gained momentum after the increase in the EV adoption
in the past few years. Mobile applications have been integrated into EV charging systems that mainly use a
cloud-based platform to host their services and data. Like many complex systems, cloud systems are
susceptible to cyberattacks if proper measures are not taken by the organization to secure them. In this
paper, we explore the security of key components in the EV charging infrastructure, including the mobile
application and its cloud service. We conducted an experiment that initiated a Man in the Middle attack
between an EV app and its cloud services. Our results showed that it is possible to launch attacks against
the connected infrastructure by taking advantage of vulnerabilities that may have substantial economic and
operational ramifications on the EV charging ecosystem. We conclude by providing mitigation suggestions
and future research directions.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This paper describes the outcome of an attempt to implement the same transitive closure (TC) algorithm
for Apache MapReduce running on different Apache Hadoop distributions. Apache MapReduce is a
software framework used with Apache Hadoop, which has become the de facto standard platform for
processing and storing large amounts of data in a distributed computing environment. The research
presented here focuses on the variations observed among the results of an efficient iterative transitive
closure algorithm when run against different distributed environments. The results from these comparisons
were validated against the benchmark results from OYSTER, an open source Entity Resolution system. The
experiment results highlighted the inconsistencies that can occur when using the same codebase with
different implementations of Map Reduce.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
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The family offers a choice of engines
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Halogenation process of chemical process industries
Big Data Analytics in Health Care: A Review Paper
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 13, No 2, April 2021
10.5121/ijcsit.2021.13202 17
BIG DATA ANALYTICS IN HEALTH CARE: A
REVIEW PAPER
Maria Mohammad Yousef
Department of Computer Science, Al-albayt University, Jordan
ABSTRACT
The application of big data in health care is a fast-growing field, with many discoveries and methodologies
published in the last five years. Big data refers to datasets that are not only big but also high in variety and
velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical
data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients
themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques
in order to handle and extract value and knowledge from these datasets to improve the quality of patient
care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which
is the application of advanced analytics techniques on big data. This paper presents an overview of big data
content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies
to overcome the challenges.
KEYWORDS
Big Data, Healthcare, Big data challenges, EHRs.
1. INTRODUCTION
Nowadays there is increasing in the details and data presented through the advancements in
technologies and the internet. Anything ranging from consumer names and addresses to products
available, to purchases made, to employees hired, etc. has become necessary for day-to-day
continuity. With the improvement in storage capacities and techniques of data collection, enormous
amounts of data have become easily available. Every second, more and more data is being
produced and needs to be stored and analyzed in order to obtain value. Furthermore, data have
become cheaper to store, so business companies and organizations need to get as much value as
possible from the huge amounts of data collected daily.
Data sets increase rapidly because they are frequently gathered by many information-sensing
devices such as mobile devices, aerial (remote sensing), software logs and records, cameras,
microphones, radio-frequency identification (RFID) readers, and wireless sensor networks [1].
Thus, big data is a field that explains methods to analyze, systematically obtain information from,
and how to deal with data sets that are too large or complex to be dealt with by traditional data
processing applications.
The healthcare industry is one of the most important industries. It is also one of the world's largest
and fastest-growing industries it can produce and handles data at a staggering speed, but different
electronic health records (EHRs) collect data in different structures: structured, unstructured, and
semi structured. This variety can pose a challenge when seeking veracity or quality assurance of
the data. The EHRs can provide a rich source of data, ready for analysis to improve our
understanding of disease mechanisms, as well as better and personalized health care, but the data
structures pose a problem to standard means of analysis. So, there is a need for converting the raw
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 13, No 2, April 2021
18
data into significant and actionable information by using big data analytics tools [2].
Big data in healthcare refers to electronic health data sets so large and complex that they are
difficult (or impossible) to manage with traditional software or popular tools and methods [3].
Accordingly, big data in healthcare is overwhelming not only because of its volume of data sets
but also because of the variety of data types and the speed at which it must be managed.
The purpose of this systematic review is to provide a summarize of big data analytics in healthcare.
First, we define and explain the definition of big data and the characteristics of big data analytics
in the healthcare domain. Then we describe the big data types in healthcare. Third, we provide
examples of big data analytics in healthcare. Fourth, we compile a list of challenges and
opportunities faced by big data analytics in health care. Finally, we offer conclusions and future
directions.
2. BACKGROUND
2.1. Defining Big Data
The concept of “big data” is not new, however, the way it is defined is continually changing. Many
authors have provided big data definitions such as Zulkarnain et al. [4] define Big Data as “datasets
whose size is beyond the ability of typical database software tools to capture, store, manage, and
analyze”. Likewise, Kaislere et al. [5] say “Big data is data too big to be handled and analyzed by
traditional database protocols such as SQL”. Moreover, the authors in [6] present big data as a
collection of data elements whose size, speed, type, and/or complexity require an attempt to use
and discover new hardware and software tools to successfully store, examine, and visualize the
data. Accordingly, Big Data points to large, complex datasets that are exceeding the capabilities
of the traditional data management system to store, manage and process them.
2.2. Big Data Characteristic
As with all big things, if we want to manage them, we need to characterize them to organize our
understanding. The three Vs (volume, velocity, and variety) are known as the main characteristics
of big data. These features are key to understanding how we can measure big data. The volume of
the data refers to its size, and how huge it is. While the velocity points to the rate with which data
is changing, or how often it is created. Finally, the variety involves several formats and types of
data, as well as the different kinds of uses and ways of analyzing the data [7]. The characteristics
are described below in Fig. 1.
Figure 1. The Big Data Characteristic.
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 13, No 2, April 2021
19
As shown in Figure.1. Big Data can be described by the following characteristic:
Data volume: This is the first and most important attribute of big data. Big data can be
quantified by size in TBs or PBs, as well as even the number of records, transactions, reports,
or files. The volume of data used to play important role in storage and processing. However,
many factors can contribute to the volume rise in data, it could amount to hundreds of
terabytes or even petabytes of information generated anywhere. As displayed in [8], the
number of data sources for an organization is growing day by day. And therefore, more data
sources consisting of enormous datasets increase the volume of data, which needs to be
analyzed. As noted in [8], Fig. 2 shows that the volume of data stored in the world would be
more than 40 zettabytes (〖10〗^21 Byte) by 2020.
Figure 2. Data volume growth by year in zettabytes
Data Velocity: Points to the speed at which new data is generated and the speed at which
data flows around, Hence, increasing speed in data processing, storage, and analysis by
relational databases. Moreover, Velocity assists organizations understand the relative growth
of their big data and how quickly that data reaches sourcing users, applications, and systems.
Some activities are very important and require immediate responses, which is why quick
processing maximizes effectiveness. For time-sensitive processes such as fraud detection,
Big Data flows must be analyzed and used as they stream into the organizations to maximize
the usefulness of the information. An illustration of data that is generated with great velocity
would be Twitter messages or Facebook posts.
Data Variety: The next aspect of Big Data is its Variety. Which indicates the type of data
that big data can contain. Big data is not always structured data. That means Big Data
consists of any type of data, this data may be structured or unstructured such as text, sensor
data, speech recordings, video, click streams, log files, and so on. Because Big Data contains
data of different types and sources, Dealing with a variety of structured and unstructured
data increases the complexity of both analyzing and storing Big Data. One of the goals of
big data is to employ technology to take this unstructured data and obtain an understanding
of it.
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 13, No 2, April 2021
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2.3. Big Data in Health Care
In the healthcare field, the progress in information technology and the capability of storing more
data have driven countries and governmental institutions to computerize health records and
produced the Electronic Health Record (EHR) or Electronic Medical Record (EMR). Big data
analytics in medicine and healthcare allows analysis of the large datasets from thousands of
patients, identifying clusters and correlation between datasets, Moreover improving predictive
models using data mining techniques. As the healthcare industry focuses on improvements in order
to save patients' lives, Big Data Analytics can play an important role in improving the services
provided to healthcare by:
Managing hospital performance
Prevent epidemics, cure disease, and decrease costs.
Increase transparency and efficiency in early disease diagnosis
Enhancing clinical outcomes
Engaging patients and family
Give patients more personalized treatment and enhance the overall patient experience.
3. RESEARSH METHODOLGY
A systematic review was conducted for obtaining related literature from various sources,
focusing on the following aims.
1. Determine the different perspectives to defining big data and its applications in healthcare.
2. To explore the sources of Big Health Data.
3. Discover Big Data analytical techniques and technologies in healthcare.
4. Introduce approaches to reduce the challenges of implementing big data within healthcare.
By considering these goals in detail, this review will make a significant contribution to
understanding the overall meaning and the future applying of Big Data techniques and applications
in the healthcare domain.
3.1. Information Source
We searched four databases to find related research articles: (1), IEEE Xplore, (2) ScienceDirect,
(3) Springer, and (4) Scopus. In searching these databases, we used the main keywords “big data”
or “big data analytics”, and “healthcare” or “medicine” or “biomedicine”.
3.2. Selection Criteria
To select the literature for inclusion in the literature review, we depending on the following
inclusion criteria:
1. Only papers published in English and between 2013 and 2020
2. Papers that deal with Big Data analytics in healthcare.
3. Papers that discuss the design and use of a big data application in the biomedical and health-
care domains.
4. Research papers that discuss the challenges of big data in healthcare.
5. Research surveys that point to the benefit effectiveness of Big Data technologies in
the health care domain.
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 13, No 2, April 2021
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3.3. Exclusion Criteria
The following exclusion criteria were used to filter out irrelevant papers:
1. The paper was a tutorial or a course material.
2. The paper did not discuss any specific big data applications (e.g., general comments about
big data).
3. Papers that focus primarily on traditional analytics in healthcare.
3.4. Study Selection
The proposed procedure framework for search and select the research elements is presented in Fig.
3. Also, the selection, examination, and filtering process for studies are described in the next four
phases.
Figure 3. Research Process.
As shown in the previous figure, we depend on several consecutive steps to obtain the most useful
and relevant studies for this proposed review by the following steps:
Began the search process for publications on different databases based on the main keywords
such as: “big data” or “big data analytics”, and “healthcare” or “medicine” or “biomedicine”.
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 13, No 2, April 2021
22
All potentially related papers were collected by scrutiny of the title to identified papers and
selection of the significant articles based on selection criteria. This initial search resulted in
270 from 700 papers.
In the second search, we perusal the papers that were not eliminated in the previous phase
for the review, where we screened the papers based on the abstract, keywords, and the
abovementioned inclusion and exclusion criteria, and consequently selected 87 papers.
Finally, we evaluated the final selection by reading the content of the papers in more detail,
and consensus to review 58 papers for this study.
4. RESULT
The literature included in this study contains essentially descriptive papers and studies. Based on
the main research goals, the content from these studies was extracted and the papers were classified
into many groups: Big Data analytics definition and concepts, sources of Big Data in healthcare,
Big Data techniques for healthcare analytics, application and potential benefits of Big Data in
healthcare and challenges in Big Data analytics in healthcare. The next section summarizes the
conclusions in each of these categories.
4.1. Big Data Analytics Concept
With the evolving of technology and the increasing numbers of data flowing in and out of
organizations daily, there has become a demand for faster and more efficient ways of analyzing
such data.
The authors in [9] explained that Big Data is ineffective in a vacuum. So, its potential value is only
obtained when used in decision making. To enable an organization to acquire knowledge and use
it in decision-making, organizations need effective methods to apply large amounts of fast-moving
data of various types and forms to analyze and benefit from it. The analytics concept refers to
techniques used to analyze and acquire knowledge from big data. Thus, big data analytics can be
viewed as a sub-process in the overall process of ‘knowledge extraction’ from big data.
As discussed in [10], Big data analytics refers to using advanced techniques and tools for analyzing
and examining very large and various data sets that combine structured, semi-structured,
unstructured data from various sources and in different sizes from terabytes to zettabytes in aims
to obtain helpful information included within the data and will also help identify the data that is
most important to the business and future business decisions. Instead of: hidden patterns,
associations, market trends, and consumer preferences.
4.2. Source of Health Care Big Data
Data that is obtained, collected, and stored in the healthcare industry may be are disorganized and
distributed, coming from various sources and having different structures and forms. Healthcare
Big Data involves data on physiological, behavioral, clinical, environmental illness, medical
imaging, disease administration, medicine prescription records, nutrition, or exercise parameters
[11]. However, most of the studies reviewed agreed on common sources of big data in the
healthcare field, which are as follows:
Electronic Health Records (EHRs): An electronic copy of a patient's medical record that
is maintained by the service provider over time. The EHRs can be containing data related to
the results of clinical and administrative meetings between the service provider (doctor,
nurse, etc.) and the patient [12]. EHRs may include a range of data including demographics,
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol 13, No 2, April 2021
23
medical history, medication and allergies, immunization status, laboratory test results,
radiology images, vital signs, personal statistics like age and weight, billing information, and
active medical problems [13].
Electronic Medical Records (EMRs): EMRs are similar to EHRs, they are digital records
of patient health information; it is a digital version of a patient's information maintained in
the form of a chart, and it contains the patient's medical and treatment history from one clinic.
Usually, this digital record stays in the doctor's office and does not get shared. If a patient
switches doctors, his or her EMR is unlikely to follow. However, this paper chart is stored
in clinician offices, clinics, and hospital databases [14].
Patient-Reported Outcomes (PROs): Defined as a report coming directly from patients
about their health condition and treatment which are based on a patient’s perception of a
disease and its treatment. This report includes a range of outcomes such as symptoms, health
status, and health-related quality-of-life [15].
Data collected from wearable sensors: The majority of wearable devices allow the
collection of biochemical, physiological, and motion-sensing data such as (Heart rate, Steps
walked, Blood pressure, etc.). So, it can collect patient health data and have data sharing
capabilities [16]. The analysis of this type of data, when integrated with electronic health
records, can support health monitoring and diagnosis for different chronic conditions.
Data extraction from social networking tools (social media): Patient posts on online social
media such as Facebook, Instagram, Twitter, etc. can be extracted to obtain information about
disease trends, patients' satisfaction, happiness, interests, and feelings. Twitter is a common
example where data analytics methods have been used for disease monitoring and health-
related trends (e.g. [17]).
4.3. Big Data Analytical Techniques and Tools in Healthcare
Different types of healthcare data are difficult to analyze due to their dynamicity and complexity,
such as medical images (X-ray, Magnetic Resonance Imaging MRI images), biomedical signals
(EEG, ECG, EMG, etc.), audio records, multi-dimensional healthcare data, written prescriptions
and structured data from EMRs and EHRs [18]. Moreover, there is a lack of analytical approaches
that can handle such unstructured data and help decision making [19]. In this review, we
summarize the literature that considers some of the analytical strategies and tools which can apply
to healthcare and medicine.
As reported by (Asante-Korang and Jacobs, 2016) [20], there are 4 types of Big Data Analytics:
Descriptive, Diagnostic, Predictive, and Prescriptive Analytics. According to the literature,
predictive analytics is the most popular in the healthcare industry as they are used to detect early
signs of patient deterioration, predict high-cost patients, re-admission, what might happen (when
the patient's condition worsens), adverse events, and treatment improvement for diseases affecting
the multi-organ system as discussed in [21, 22,23]. Moreover, Healthcare organizations have
observed improved quality of care after adopting several Big Data analytics techniques that helped
enhance the ability of the healthcare sectors to predict epidemics and treat disease. Table 1.
Summarizes some of the Big Data Analytical Techniques used in healthcare.
8. International Journal of Computer Science & Information Technology (IJCSIT) Vol 13, No 2, April 2021
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Table 1. Summarizes some of the Big Data analytical techniques used in healthcare.
Analytic
Technique
Description Healthcare Application Studies
By
Cluster
Analysis
(CA)
(CA) is a commonly used applied
statistical technique in Big Data, that
helps to reveal hidden structures and
“clusters” found in large data sets.
The CA aims to place objects into
groups or "clusters" that have been
suggested by the data and not
defined a priori, the objects in the
same cluster are similar to each other
in some attributes, while, objects in
different clusters tend to be different.
K-Mean method is considered to be
one example of clustering in big data
-Identify cost change patterns
of patients with end-stage
renal disease (ESRD) who
initiated hemodialysis (HD)
by applying different
clustering method.
ISMAIL
et al.,[21]
Data Mining
Is the ability to extract useful
knowledge hidden in the large
volume of data by applying new
techniques, for discovering
understandable patterns and
correlations from data and use it in
making decisions and Prediction of
likely outcomes such as Association
Rule Learning and Regression
Analysis.
-Determination of epidemics;
- detection some diseases
- management of healthcare
and measuring the
effectiveness of certain
treatments
Jothi et
al.,[22]
Graph
Analytics
Graph Analytics are analytic tools
used to define the strength and
discover the direction of
relationships between objects in a
graph. In big data, graph analytics
focus of understand, codify, and
visualize pairwise relationships that
exist between two objects at a time
and structural attributes of the graph
as a whole.
-Analysis of hospital
performance across various
quality measures
Nisar et
al.,[23]
Natural
Language
Processing
(NLP)
Is the subfield of artificial
intelligence (AI) that concerned with
analyzing, understanding, and
interpreting written text and spoken
language, as well as using natural
languages for communicating with
computers
-Extract clinical concept (e.g.
diagnosis, procedure, and
symptoms) from electronic
medical record, patient
discharge summaries, and lab
report.
Gudivada
et al.,
[24]
Neural
Networks
A neural network is a series of
algorithms that attempt to identify
underlying relationships in a large
amount of data through a process
that mimics the way that the human
brain works. The purpose of a neural
network is to learn how to discover
patterns from the data. Once the
neural network has been trained on
samples of data, it can make
predictions by detecting similar
patterns in future data.
-Prediction of patients future
disease
-Diagnosis of chronic
diseases;
Wang et
al.,[25]
9. International Journal of Computer Science & Information Technology (IJCSIT) Vol 13, No 2, April 2021
25
On the other hand, Hadoop and MapReduce are used as tools for processing big data. On the other
hand, Hadoop was proposed by Doug Cutting and Mike Cafarella in the year 2002 when they both
started to work on an open-source web search engine (Apache Nutch) [27]. Apache Nutch project
was the process of building a search engine system that can index 1 billion pages. Apache Nutch
producers recognized the limitation of nutch and the challenge to reach a very huge number of
webpages on the internet [28]. So, they understood that their project architecture will not be
capable enough to work around billions of pages on the web. So they were looking for a possible
solution that can reduce the implementation cost as well as the problem of storing and processing
huge datasets [29].
Apache Hadoop is an open-source software framework for storing and processing huge clusters of
data. Hadoop consists of a two-component Hadoop Distributed File System (HDFS), and Map
Reduce framework [30]. The HDFS uses the cluster architecture to facilitate the partitioning of the
big data to multiple machines (nodes), such as PCs and servers, which gives the ability to store a
huge volume of data on thousands of nodes. The second component MapReduce is defined as a
programming model for processing the large data sets stored in the Hadoop File System (HDFS)
in a distributed fashion over several machines or nodes. There are two phases in MapReduce, the
“Map” phase and the “Reduce” phase [31]. The system splits the input data into multiple chunks,
each of which is assigned a map task that can process the data in parallel. Each map task reads the
input as a set of (key, value) pairs and produces a transformed set of (key, value) pairs as the output.
The framework shuffles and sorts outputs of the map tasks, sending the intermediate (key, value)
pairs to reduce task, which groups them into final results.
4.4. Big Data Analytics Challenges in Health Care
Big Data helps organizations, individuals, countries, and the world to create new growth
opportunities, but it also poses significant challenges that could offset any potential gains, such as
the loss of privacy and confidentiality, and the lack of appropriate IT infrastructure. Also, many of
the big data tools are open source and free to use, which could provide the opportunity for intrusive
operations, hackers, and data theft. Some literatures [32-38] discuss obstacles in the development
of big data in healthcare applications. The key challenges are listed as follows:
1. Privacy and Security: Privacy and security are a key concern for individuals and
corporations that hold information/data about people, products, activities, etc. Medical data
obtained by healthcare providers from individuals and their medical records may contain
private and confidential data [32]. Wherefore, protecting the patient's information must be
handled with enormous care from harm and hacker. When we use big data, many tools
applied to analytics and data processes are open source and do not include all security
measures [33]. Therefore, the primary justification for protecting personal privacy is to
protect the interests of individuals. In order to overcome these challenges, some approaches
are used to enhance the security level and obtain some confidentiality. First, Employing
Machine
Learning
Is a discipline within the area of
Artificial Intelligence Moreover, is a
field of research that formally
focuses on the theory, performance,
and properties of learning systems
and algorithms Which can provides
computers with the ability to
recognize patterns from raw data to
make predictions such as Decision
Tree, Naive Bayes and Random
Forest algorithems.
-Microsoft's InnerEye
application employs machine
learning to differentiate
between tumors and healthy
anatomy using 3D
radiological images that assist
medical experts in
radiotherapy and surgical
planning, among other things.
Qiu et
al., [26]
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security measures, including strong encryption of data, validation of the source of data,
access control, and authentication, where authentication is one of the measures for securing
the data and maintaining confidentiality.
2. Storage and Processing Issues: Doubtlessly, the most obvious challenge associated with
big data is simply storing and analyzing the huge amount of data. Nowadays, data grow
significantly whenever a new storage technology is invented due to the huge amount of data
collected and transferred by social media, healthcare providers, business transactions, and
stock markets [34]. Moreover, this data is not just high on volume, but it also includes data
of varied kinds that is generated periodically. With the rate of data explosion, the biggest
challenge in dealing with this big data is that the present or traditional systems are unable to
store and process data of this size and kind [35]. Therefore, the storage problem can be
solved by making use of cloud computing. This would enable small and medium-sized
hospitals and care organizations to eliminate cost and data storage issues.
3. Data Ownership: Data ownership represents a crucial and ongoing problem in big data
applications in healthcare and other areas. Though petabytes of medical records generally
belong to the healthcare providers, governmental healthcare systems, or hospital in which
they were created, but the information in it is not owned by them [36]. On the other hand,
patients believe that they own the data. This dispute may be ended in the legal system to
resolve the ownership issues unless healthcare providers receive written approval from
patients before using data for experiences or research objectives.
4. Skills Requirement: A data analyst is a professional whose work involves collecting,
cleaning, visualizing, and transforming or modeling raw data into the blocks of information
that are used by marketers, developers, and even healthcare providers [37]. One of the most
important challenges in dealing with big data is the skills required for individuals to works
in the big data field. A recent study [38] examined the required skills to deal with big data
and concluded that the skills you need to work with big data will involve analytical
capabilities.
5. CONCLUSION
The paper first defined what is meant by big data. We presented various definitions of big data,
highlighting the fact that size is only one dimension of big data. Other dimensions, such as velocity
and variety, are equally important. The studies reviewed showed that big data in the healthcare
industry is obtained from several sources such as results of medical examinations, hospital records,
medical devices, and records of patients. For better treat disease and diagnosis in medical, the role
of big data is one where it can construct better predictive models using tools with the ability to
analyze and process this vast amount of data. Finally, a discussion has been made of some
challenges that face individuals and organizations in the process of utilizing big data in healthcare,
such as data ownership, privacy and security, storage and processing issues, and skills
requirements.
6. LIMITATIONS
While the proposed Review covers details about Big Data analytics and its applications in
healthcare and medicine, however, we face a few limitations. First, the contents of this research
consist of a systematic review of the current state of Big Data technology in healthcare, but it does
not get into consideration the technical details concerning the implementation and outcomes
achieved in each of the studies reviewed. Second, there is heterogeneity in the documentation since
11. International Journal of Computer Science & Information Technology (IJCSIT) Vol 13, No 2, April 2021
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the literature includes various sources of information on the meaning of Big Data, methods of Big
Data analytic, and their techniques and challenges in healthcare. Finally, despite the use of a
systematic strategy for review, the inclusion of studies on big data analytics in ‘healthcare’ for this
review was based on personal experience and knowledge, hence the cross-reference literature were
also examined for this review.
7. FUTURE OUTLOOK
Big data analytics in medicine and healthcare is a very encouraging process of integrating,
examining, and analyzing enormous amounts of complex heterogeneous data with different types:
biomedical data, medical data, electronic health records data (EHRs), and experimental data. The
combination of such various data makes big data analytics weave many fields, such as
bioinformatics, medical imaging, sensor informatics, medical informatics, health informatics, and
computational biomedicine. As further work, we plan to study the various improvements in big
data analytic systems and databases. Also, we will attempt to produce a new high-performance
data management system by depending on open source platform such as Apache Hadoop
MapReduce, which can assist heterogeneous datasets and uses memory and other hardware
resources in a more efficient way to reveal hidden patterns and novel knowledge from the data in
a great execution speed.
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