Advanced harmonization and sensor fusion to transform data readiness and resolution for big data analytics

2020 ◽  
Author(s):  
Rasmus Houborg ◽  
Giovanni Marchisio
Keyword(s):  
Big Data ◽  
Land Surface ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Economic Benefits ◽  
Sensor Data ◽  
Full Potential ◽  
Spatiotemporal Resolution ◽  
Geospatial Information ◽  
Recent Emergence

<p>Access to data is no longer a problem. The recent emergence of new observational paradigms combined with advances in conventional spaceborne sensing has resulted in a proliferation of satellite sensor data. This geospatial information revolution constitutes a game changer in the ability to derive time-critical and location-specific insights into dynamic land surface processes. </p><p>However, it’s not easy to integrate all of the data that is available. Sensor interoperability issues and cross-calibration challenges present obstacles in realizing the full potential of these rich geospatial datasets.</p><p>The production of analysis ready, sensor-agnostic, and very high spatiotemporal resolution information feeds has an obvious role in advancing geospatial data analytics and machine learning applications at broad scales with potentially far reaching societal and economic benefits. </p><p>At Planet, our mission is to make the world visible, accessible, and actionable. We are pioneering a methodology--the CubeSat-Enabled Spatio-Temporal Enhancement Method (CESTEM)--to enhance, harmonize, inter-calibrate, and fuse cross-sensor data streams leveraging rigorously calibrated ‘gold standard’ satellites (i.e., Sentinel, Landsat, MODIS) in synergy with superior resolution CubeSats from Planet. The result is next generation analysis ready data, delivering clean (i.e. free from clouds and shadows), gap-filled (i.e., daily, 3 m), temporally consistent, radiometrically robust, and sensor agnostic surface reflectance feeds featuring and synergizing inputs from both public and private sensor sources. The enhanced data readiness, interoperability, and resolution offer unique opportunities for advancing big data analytics and positioning remote sensing as a trustworthy source for delivering usable and actionable insights.</p>

Big Data Analytics

2019 ◽  
pp. 67-81
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Big Data ◽  
Open Problem ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Capture ◽  
Data Sets ◽  
Full Potential ◽  
Processing Application ◽  
Nosql Databases ◽  
The World

The world is increasingly driven by huge amounts of data. Big data refers to data sets that are so large or complex that traditional data processing application software are inadequate to deal with them. Healthcare analytics is a prominent area of big data analytics. It has led to significant reduction in morbidity and mortality associated with a disease. In order to harness full potential of big data, various tools like Apache Sentry, BigQuery, NoSQL databases, Hadoop, JethroData, etc. are available for its processing. However, with such enormous amounts of information comes the complexity of data management, other big data challenges occur during data capture, storage, analysis, search, transfer, information privacy, visualization, querying, and update. The chapter focuses on understanding the meaning and concept of big data, analytics of big data, its role in healthcare, various application areas, trends and tools used to process big data along with open problem challenges.

scholarly journals Containing COVID-19 Among 627,386 Persons in Contact With the Diamond Princess Cruise Ship Passengers Who Disembarked in Taiwan: Big Data Analytics

10.2196/19540 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e19540 ◽  
Author(s):  
Chi-Mai Chen ◽  
Hong-Wei Jyan ◽  
Shih-Chieh Chien ◽  
Hsiao-Hsuan Jen ◽  
Chen-Yang Hsu ◽  
...  
Keyword(s):  
Big Data ◽  
Health Insurance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Case Fatality ◽  
Sensor Data ◽  
Contact Tracing ◽  
Cruise Ship ◽  
Health Insurance Data

Background Low infection and case-fatality rates have been thus far observed in Taiwan. One of the reasons for this major success is better use of big data analytics in efficient contact tracing and management and surveillance of those who require quarantine and isolation. Objective We present here a unique application of big data analytics among Taiwanese people who had contact with more than 3000 passengers that disembarked at Keelung harbor in Taiwan for a 1-day tour on January 31, 2020, 5 days before the outbreak of coronavirus disease (COVID-19) on the Diamond Princess cruise ship on February 5, 2020, after an index case was identified on January 20, 2020. Methods The smart contact tracing–based mobile sensor data, cross-validated by other big sensor surveillance data, were analyzed by the mobile geopositioning method and rapid analysis to identify 627,386 potential contact-persons. Information on self-monitoring and self-quarantine was provided via SMS, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests were offered for symptomatic contacts. National Health Insurance claims big data were linked, to follow-up on the outcome related to COVID-19 among those who were hospitalized due to pneumonia and advised to undergo screening for SARS-CoV-2. Results As of February 29, a total of 67 contacts who were tested by reverse transcription–polymerase chain reaction were all negative and no confirmed COVID-19 cases were found. Less cases of respiratory syndrome and pneumonia were found after the follow-up of the contact population compared with the general population until March 10, 2020. Conclusions Big data analytics with smart contact tracing, automated alert messaging for self-restriction, and follow-up of the outcome related to COVID-19 using health insurance data could curtail the resources required for conventional epidemiological contact tracing.

scholarly journals Big Data Analytics and Security in Healthcare Industry

2019 ◽  
Vol 8 (S3) ◽  
pp. 90-93
Author(s):  
K. Rohitha ◽  
V. Bhagyasree ◽  
K. Kusuma ◽  
S. Kokila
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Security ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Industry ◽  
Daily Basis ◽  
Sensor Data ◽  
Record Keeping ◽  
Care Industry

Big data analytics plays a major role in today’s industry which insisted to use big data analytics for the analysis of previous data. Patient record keeping is very much important to track the history of the patient. According to the patient previous records, decision is made. Large volumes of data are created on a daily basis and this data is used in decision making process. But, health care industry has not sensed the potential benefits from big data analytics. To address this need, four big data analytics capabilities were identified. In addition to four, five capabilities were proposed which provides practical insights for administrator. On the other way, data security plays a key role in health care industry. In order to overcome this, a new architecture is proposed for the implementation to IOT and process scalable sensor data for health care systems. This paper focuses on data security so that we can make use of potential capabilities and benefits of big data analytics in a better way.

scholarly journals Automated Transverse Crack Mapping System with Optical Sensors and Big Data Analytics

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1838
Author(s):  
Kwanghee Won ◽  
Chungwook Sim
Keyword(s):  
Big Data ◽  
Optical Sensors ◽  
Data Analytics ◽  
Crack Detection ◽  
Big Data Analytics ◽  
Sensor Data ◽  
Detection Methods ◽  
Transverse Cracks ◽  
Data Pipeline ◽  
Localization Strategy

Transverse cracks on bridge decks provide the path for chloride penetration and are the major reason for deck deterioration. For such reasons, collecting information related to the crack widths and spacing of transverse cracks are important. In this study, we focused on developing a data pipeline for automated crack detection using non-contact optical sensors. We developed a data acquisition system that is able to acquire data in a fast and simple way without obstructing traffic. Understanding that GPS is not always available and odometer sensor data can only provide relative positions along the direction of traffic, we focused on providing an alternative localization strategy only using optical sensors. In addition, to improve existing crack detection methods which mostly rely on the low-intensity and localized line-segment characteristics of cracks, we considered the direction and shape of the cracks to make our machine learning approach smarter. The proposed system may serve as a useful inspection tool for big data analytics because the system is easy to deploy and provides multiple properties of cracks. Progression of crack deterioration, if any, both in spatial and temporal scale, can be checked and compared if the system is deployed multiple times.

scholarly journals Containing COVID-19 Among 627,386 Persons in Contact With the Diamond Princess Cruise Ship Passengers Who Disembarked in Taiwan: Big Data Analytics (Preprint)

2020 ◽  
Author(s):  
Chi-Mai Chen ◽  
Hong-Wei Jyan ◽  
Shih-Chieh Chien ◽  
Hsiao-Hsuan Jen ◽  
Chen-Yang Hsu ◽  
...  
Keyword(s):  
Big Data ◽  
Health Insurance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Case Fatality ◽  
Sensor Data ◽  
Contact Tracing ◽  
Cruise Ship ◽  
Health Insurance Data

BACKGROUND Low infection and case-fatality rates have been thus far observed in Taiwan. One of the reasons for this major success is better use of big data analytics in efficient contact tracing and management and surveillance of those who require quarantine and isolation. OBJECTIVE We present here a unique application of big data analytics among Taiwanese people who had contact with more than 3000 passengers that disembarked at Keelung harbor in Taiwan for a 1-day tour on January 31, 2020, 5 days before the outbreak of coronavirus disease (COVID-19) on the Diamond Princess cruise ship on February 5, 2020, after an index case was identified on January 20, 2020. METHODS The smart contact tracing–based mobile sensor data, cross-validated by other big sensor surveillance data, were analyzed by the mobile geopositioning method and rapid analysis to identify 627,386 potential contact-persons. Information on self-monitoring and self-quarantine was provided via SMS, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests were offered for symptomatic contacts. National Health Insurance claims big data were linked, to follow-up on the outcome related to COVID-19 among those who were hospitalized due to pneumonia and advised to undergo screening for SARS-CoV-2. RESULTS As of February 29, a total of 67 contacts who were tested by reverse transcription–polymerase chain reaction were all negative and no confirmed COVID-19 cases were found. Less cases of respiratory syndrome and pneumonia were found after the follow-up of the contact population compared with the general population until March 10, 2020. CONCLUSIONS Big data analytics with smart contact tracing, automated alert messaging for self-restriction, and follow-up of the outcome related to COVID-19 using health insurance data could curtail the resources required for conventional epidemiological contact tracing.

A Survey of Big Data Analytics Systems

2014 ◽  
pp. 392-418 ◽  
Author(s):  
M. Baby Nirmala
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Processing System ◽  
Full Potential ◽  
Analytical Processing ◽  
Tools And Techniques ◽  
New Generation ◽  
Analytical Platforms

In this emerging era of analytics 3.0, where big data is the heart of talk in all sectors, achieving and extracting the full potential from this vast data is accomplished by many vendors through their new generation analytical processing systems. This chapter deals with a brief introduction of the categories of analytical processing system, followed by some prominent analytical platforms, appliances, frameworks, engines, fabrics, solutions, tools, and products of the big data vendors. Finally, it deals with big data analytics in the network, its security, WAN optimization tools, and techniques for cloud-based big data analytics.

SMSS: does social, mobile, spatial and sensor data have high impact on big data analytics

2020 ◽  
Vol 7 (1/2/3) ◽  
pp. 215
Author(s):  
Chemmalar Selvi Govardanan ◽  
Lakshmi Priya Gopalsamy Gnanapandithan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
High Impact ◽  
Sensor Data
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