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In fact, big data refers to large datasets that combine the following characteristics<ref name="ChenBig14">{{cite journal |title=Big Data: A Survey |journal=Mobile Networks and Applications |author=Chen, M.; Mao, S.; Liu, Y. |volume=19 |issue=2 |pages=171–209 |year=2014 |doi=10.1007/s11036-013-0489-0}}</ref> : volume, which refers to high amounts of data; velocity, which means that data is generated at a rapid pace; variety, which emphasizes that data comes under different formats; and veracity, which means that data originates from a trustworthy sources. | In fact, big data refers to large datasets that combine the following characteristics<ref name="ChenBig14">{{cite journal |title=Big Data: A Survey |journal=Mobile Networks and Applications |author=Chen, M.; Mao, S.; Liu, Y. |volume=19 |issue=2 |pages=171–209 |year=2014 |doi=10.1007/s11036-013-0489-0}}</ref>: volume, which refers to high amounts of data; velocity, which means that data is generated at a rapid pace; variety, which emphasizes that data comes under different formats; and veracity, which means that data originates from a trustworthy sources. | ||
Another characteristic of big data is variability. It indicates variations that occur in the data flow rates. Indeed, velocity does not provide a consistent description of the data due to its periodic peaks and troughs. Another important aspect of big data is complexity; it arises from the fact that big data is often produced through many sources, which implies to perform many operations over the data, these operations include identifying relationships and cleansing and transforming data flowing from different origins. | Another characteristic of big data is variability. It indicates variations that occur in the data flow rates. Indeed, velocity does not provide a consistent description of the data due to its periodic peaks and troughs. Another important aspect of big data is complexity; it arises from the fact that big data is often produced through many sources, which implies to perform many operations over the data, these operations include identifying relationships and cleansing and transforming data flowing from different origins. | ||
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The paper has been organized as follows: In the next section, a background of big data computing approaches and big data platforms is provided. Recent contributions on big data for healthcare systems are reviewed in the section after. Then, in the section "An extensible big data architecture for healthcare," the components of the proposed big data architecture for healthcare are described. The implementation process is reported in the penultimate section, followed by conclusions, along with recommendations for future research. | The paper has been organized as follows: In the next section, a background of big data computing approaches and big data platforms is provided. Recent contributions on big data for healthcare systems are reviewed in the section after. Then, in the section "An extensible big data architecture for healthcare," the components of the proposed big data architecture for healthcare are described. The implementation process is reported in the penultimate section, followed by conclusions, along with recommendations for future research. | ||
==Background== | |||
===An overview of big data approaches=== | |||
Big data technologies have received great attention due to their successful handling of high volume data compared to traditional approaches. A big data framework supports all kinds of data—including structured, semistructured, and unstructured data—while providing several features. Those features include predictive model design and big data mining tools that allow better decision-making processes through the selection of relevant information. | |||
Big data processing can be performed through two manners: batch processing and stream processing.<ref name="ShahrivariBeyond14">{{cite journal |title=Beyond Batch Processing: Towards Real-Time and Streaming Big Data |journal=Computers |author=Shahrivari, S. |volume=3 |issue=4 |pages=117-129 |year=2014 |doi=10.3390/computers3040117}}</ref> The first method is based on analyzing data over a specified period of time; it is adopted when there are no constraints regarding the response time. On the other hand, stream processing is suitable for applications requiring real-time feedback. Batch processing aims to process a high volume of data by collecting and storing batches to be analyzed in order to generate results. | |||
Batch processing mode requires ingesting all data before processing it in a specified time. MapReduce represents a widely adopted solution in the field of batch computing<ref name="DeanMap08">{{cite journal |title=MapReduce: Simplified data processing on large clusters |journal=Communications of the ACM |author=Dean, J.; Ghemawat, S. |volume=51 |issue=1 |pages=107-113 |year=2008 |doi=10.1145/1327452.1327492}}</ref>; it operates by splitting data into small pieces that are distributed to multiple nodes in order to obtain intermediate results. Once data processing by nodes is terminated, outcomes will be aggregated in order to generate the final results. Seeking to optimize computational resources use, MapReduce allocates processing tasks to nodes close to data location. This model has encountered a lot of success in many applications, especially in the field of [[bioinformatics]] and healthcare. Batch processing framework has many characteristics such as the ability to access all data and to perform many complex computation operations, and its latency is measured by minutes or more. | |||
Stream processing offers another methodology to analysts. In real applications such as healthcare, intelligent transportation, and finance, a high amount of data is produced in a continuous manner. When the need of processing such data streams in real time arises, data analysis takes into consideration the continuous evolution of data and permanent change regarding statistical characteristics of data streams, referred to as concept drift.<ref name="TatbulStreaming10">{{cite journal |title=Streaming data integration: Challenges and opportunities |journal=Proceedings from the 26th IEEE International Conference on Data Engineering Workshops |author=Tatbul, N. |pages=155-158 |year=2010 |doi=10.1109/ICDEW.2010.5452751}}</ref> Indeed, storing a large amount of data for further processing may be challenging in terms of memory resources. Moreover, real applications tend to produce noisy data containing missing values and contain redundant features, making data analysis complicated, as it requires important computational time. Stream processing reduces this computational burden by performing simple and fast computations for one data element or for a window of recent data, and such computations take seconds at most. | |||
Big data stream mining methods—including classification, frequent pattern mining, and clustering—relieve computational effort through rapid extraction of the most relevant information; this objective is often achieved by mining data in a distributed manner. Those methods belong to one of the two following classes: data-based techniques and task-based techniques.<ref name="SinghASurvey15">{{cite journal |title=A survey on platforms for big data analytics |journal=Journal of Big Data |author=Singh, D.; Reddy, C.K. |volume=2 |page=8 |year=2015 |doi=10.1186/s40537-014-0008-6}}</ref> Data-based techniques allow summarizing the entire dataset or selecting a subset of the continuous flow of streaming data to be processed. Sampling is one of these techniques; it consists of choosing a small subset of data to be processed according to a statistical criterion. Another data-based method is load shedding which drops a part from the entire data, while the sketching technique establishes a random projection on a feature set. The synopsis data structures method and aggregation method belong also to the family of data-based techniques, the first one summarizing data streams and the latter representing a number of elements in one element by using a statistical measure. | |||
==References== | ==References== | ||
Revision as of 16:59, 18 August 2018
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| Full article title | Big data management for healthcare systems: Architecture, requirements, and implementation |
|---|---|
| Journal | Advances in Bioinformatics |
| Author(s) | El aboudi, Naoual; Benhilma, Laila |
| Author affiliation(s) | Mohammed V University |
| Primary contact | Email: nawal dot elaboudi at gmail dot com |
| Editors | Fdez-Riverola, Florentino |
| Year published | 2018 |
| Volume and issue | 2018 |
| Page(s) | 4059018 |
| DOI | 10.1155/2018/4059018 |
| ISSN | 1687-8035 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.hindawi.com/journals/abi/2018/4059018/ |
| Download | http://downloads.hindawi.com/journals/abi/2018/4059018.pdf (PDF) |
 |
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Abstract
The growing amount of data in the healthcare industry has made inevitable the adoption of big data techniques in order to improve the quality of healthcare delivery. Despite the integration of big data processing approaches and platforms in existing data management architectures for healthcare systems, these architectures face difficulties in preventing emergency cases. The main contribution of this paper is proposing an extensible big data architecture based on both stream computing and batch computing in order to enhance further the reliability of healthcare systems by generating real-time alerts and making accurate predictions on patient health condition. Based on the proposed architecture, a prototype implementation has been built for healthcare systems in order to generate real-time alerts. The suggested prototype is based on Spark and MongoDB tools.
Introduction
The proportion of elderly people in society is growing worldwide[1]; this phenomenon—referred to by the World Health Organization as "humanity’s aging"[1]—has many implications on healthcare services, especially in terms of cost. In the face of such a situation, relying on classical systems may result in a life quality decline for millions of people. Seeking to overcome this problem, a variety of different healthcare systems have been designed. Their common principle is transferring, on a periodical basis, medical parameters like blood pressure, heart rate, glucose level, body temperature, and ECG signals to an automated system aimed at monitoring in real time patients' health condition. Such systems provide quick assistance when needed since data is analyzed continuously. Automating health monitoring favors a proactive approach that relieves medical facilities by saving costs related to hospitalization, and it also enhances healthcare services by improving waiting time for consultations. Recently, the number of data sources in the healthcare industry has grown rapidly as a result of widespread use of mobile and wearable sensor technologies, which have flooded the healthcare arena with a huge amount of data. Therefore, it becomes challenging to perform healthcare data analysis based on traditional methods which are unfit to handle the high volume of diversified medical data. In general, the healthcare domain has four categories of analytics: descriptive, diagnostic, predictive, and prescriptive analytics. A brief description of each one of them is given below.
Descriptive analytics refers to describing current situations and reporting on them. Several techniques are employed to perform this level of analytics. For instance, descriptive statistics tools like histograms and charts are among the techniques used in descriptive analytics.
Diagnostic analysis aims to explain why certain events occurred and what the factors that triggered them are. For example, diagnostic analysis attempts to understand the reasons behind the regular readmission of some patients by using several methods such as clustering and decision trees.
Predictive analytics reflects the ability to predict future events; it also helps in identifying trends and determining probabilities of uncertain outcomes. An illustration of its role is to predict whether or not a patient will have complications. Predictive models are often built using machine learning techniques.
Prescriptive analytics proposes suitable actions leading to optimal decision-making. For instance, prescriptive analysis may suggest rejecting a given treatment in the case of a harmful side effect's high probability. Decision trees and Monte Carlo simulation are examples of methods applied to perform prescriptive analytics. Figure 1 illustrates analytics phases for the healthcare domain.[2] The integration of big data technologies into healthcare analytics may lead to better performance of medical systems.
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In fact, big data refers to large datasets that combine the following characteristics[3]: volume, which refers to high amounts of data; velocity, which means that data is generated at a rapid pace; variety, which emphasizes that data comes under different formats; and veracity, which means that data originates from a trustworthy sources.
Another characteristic of big data is variability. It indicates variations that occur in the data flow rates. Indeed, velocity does not provide a consistent description of the data due to its periodic peaks and troughs. Another important aspect of big data is complexity; it arises from the fact that big data is often produced through many sources, which implies to perform many operations over the data, these operations include identifying relationships and cleansing and transforming data flowing from different origins.
Moreover, Oracle decided to introduce value as a key attribute of big data. According to Oracle, big data has a “low value density,” which means that raw data has a low value compared to its high volume. Nevertheless, analysis of important volumes of data may lead to obtaining a high value.
In the context of healthcare, high volumes of data are generated by multiple medical sources, and it includes, for example, biomedical images, lab test reports, physician written notes, and health condition parameters allowing real-time patient health monitoring. In addition to its huge volume and its diversity, healthcare data flows at high speed. As a result, big data approaches offer tremendous opportunities regarding healthcare systems efficiency.
The contribution of this research paper is to propose an extensible big data architecture for healthcare applications formed by several components capable of storing, processing, and analyzing the significant amount of data in real time and batch modes. This paper demonstrates the potential of using big data analytics in the healthcare domain to find useful information in highly valuable data.
The paper has been organized as follows: In the next section, a background of big data computing approaches and big data platforms is provided. Recent contributions on big data for healthcare systems are reviewed in the section after. Then, in the section "An extensible big data architecture for healthcare," the components of the proposed big data architecture for healthcare are described. The implementation process is reported in the penultimate section, followed by conclusions, along with recommendations for future research.
Background
An overview of big data approaches
Big data technologies have received great attention due to their successful handling of high volume data compared to traditional approaches. A big data framework supports all kinds of data—including structured, semistructured, and unstructured data—while providing several features. Those features include predictive model design and big data mining tools that allow better decision-making processes through the selection of relevant information.
Big data processing can be performed through two manners: batch processing and stream processing.[4] The first method is based on analyzing data over a specified period of time; it is adopted when there are no constraints regarding the response time. On the other hand, stream processing is suitable for applications requiring real-time feedback. Batch processing aims to process a high volume of data by collecting and storing batches to be analyzed in order to generate results.
Batch processing mode requires ingesting all data before processing it in a specified time. MapReduce represents a widely adopted solution in the field of batch computing[5]; it operates by splitting data into small pieces that are distributed to multiple nodes in order to obtain intermediate results. Once data processing by nodes is terminated, outcomes will be aggregated in order to generate the final results. Seeking to optimize computational resources use, MapReduce allocates processing tasks to nodes close to data location. This model has encountered a lot of success in many applications, especially in the field of bioinformatics and healthcare. Batch processing framework has many characteristics such as the ability to access all data and to perform many complex computation operations, and its latency is measured by minutes or more.
Stream processing offers another methodology to analysts. In real applications such as healthcare, intelligent transportation, and finance, a high amount of data is produced in a continuous manner. When the need of processing such data streams in real time arises, data analysis takes into consideration the continuous evolution of data and permanent change regarding statistical characteristics of data streams, referred to as concept drift.[6] Indeed, storing a large amount of data for further processing may be challenging in terms of memory resources. Moreover, real applications tend to produce noisy data containing missing values and contain redundant features, making data analysis complicated, as it requires important computational time. Stream processing reduces this computational burden by performing simple and fast computations for one data element or for a window of recent data, and such computations take seconds at most.
Big data stream mining methods—including classification, frequent pattern mining, and clustering—relieve computational effort through rapid extraction of the most relevant information; this objective is often achieved by mining data in a distributed manner. Those methods belong to one of the two following classes: data-based techniques and task-based techniques.[7] Data-based techniques allow summarizing the entire dataset or selecting a subset of the continuous flow of streaming data to be processed. Sampling is one of these techniques; it consists of choosing a small subset of data to be processed according to a statistical criterion. Another data-based method is load shedding which drops a part from the entire data, while the sketching technique establishes a random projection on a feature set. The synopsis data structures method and aggregation method belong also to the family of data-based techniques, the first one summarizing data streams and the latter representing a number of elements in one element by using a statistical measure.
References
- ↑ 1.0 1.1 World Health Organization; National Institute of Aging, ed. (October 2011). "Global Health and Aging". WHO. http://www.who.int/ageing/publications/global_health/en/.
- ↑ Gandomi, A.; Haider, M. (2015). "Beyond the hype: Big data concepts, methods, and analytics". International Journal of Information Management 35 (2): 137–44. doi:10.1016/j.ijinfomgt.2014.10.007.
- ↑ Chen, M.; Mao, S.; Liu, Y. (2014). "Big Data: A Survey". Mobile Networks and Applications 19 (2): 171–209. doi:10.1007/s11036-013-0489-0.
- ↑ Shahrivari, S. (2014). "Beyond Batch Processing: Towards Real-Time and Streaming Big Data". Computers 3 (4): 117-129. doi:10.3390/computers3040117.
- ↑ Dean, J.; Ghemawat, S. (2008). "MapReduce: Simplified data processing on large clusters". Communications of the ACM 51 (1): 107-113. doi:10.1145/1327452.1327492.
- ↑ Tatbul, N. (2010). "Streaming data integration: Challenges and opportunities". Proceedings from the 26th IEEE International Conference on Data Engineering Workshops: 155-158. doi:10.1109/ICDEW.2010.5452751.
- ↑ Singh, D.; Reddy, C.K. (2015). "A survey on platforms for big data analytics". Journal of Big Data 2: 8. doi:10.1186/s40537-014-0008-6.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. Grammar was cleaned up for smoother reading. In some cases important information was missing from the references, and that information was added.
