scholarly journals Big Data Analytics from a Wastewater Treatment Plant

2021 ◽  
Vol 13 (22) ◽  
pp. 12383
Author(s):  
Praewa Wongburi ◽  
Jae K. Park
Keyword(s):  
Big Data ◽  
Wastewater Treatment ◽  
Data Analytics ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Big Data Analytics ◽  
Operational Performance ◽  
Clear Understanding ◽  
Large Dataset ◽  
Regulatory Limits

Wastewater treatment plants (WWTPs) use considerable workforces and resources to meet the regulatory limits without mistakes. The advancement of information technology allowed for collecting large amounts of data from various sources using sophisticated sensors. Due to the lack of specialized tools and knowledge, operators and engineers cannot effectively extract meaningful and valuable information from large datasets. Unfortunately, the data are often stored digitally and then underutilized. Various data analytics techniques have been developed in the past few years. The methods are efficient for analyzing vast datasets. However, there is no wholly developed study in applying these techniques to assist wastewater treatment operation. Data analytics processes can immensely transform a large dataset into informative knowledge, such as hidden information, operational problems, or even a predictive model. The use of big data analytics will allow operators to have a much clear understanding of the operational status while saving the operation and maintenance costs and reducing the human resources required. Ultimately, the method can be applied to enhance the operational performance of the wastewater treatment infrastructure.

Big Data Predictive and Prescriptive Analytics

2014 ◽  
pp. 370-391 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Action Plan ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Future Planning ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

Big Data Predictive and Prescriptive Analytics

Big Data ◽  
2016 ◽  
pp. 30-55 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Action Plan ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Future Planning ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

The Strengths, Weaknesses, Opportunities, and Threats Analysis of Big Data Analytics in Healthcare

2022 ◽  
pp. 1703-1718
Author(s):  
Chaojie Wang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Success Factors ◽  
Swot Analysis ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Healthcare Organizations ◽  
Effective Use ◽  
The Cost

Improving the performance and reducing the cost of healthcare have been a great concern and a huge challenge for healthcare organizations and governments at every level in the US. Measures taken have included laws, regulations, policies, and initiatives that aim to improve quality of care, reduce costs of care, and increase access to care. Central to these measures is the meaningful and effective use of Big Data analytics. To reap the benefits of big data analytics and align expectations with results, researchers, practitioners, and policymakers must have a clear understanding of the unique circumstances of healthcare including the strengths, weaknesses, opportunities, and threats (SWOT) associated with the use of this emerging technology. Through descriptive SWOT analysis, this article helps healthcare stakeholders gain awareness of both success factors and issues, pitfalls, and barriers in the adoption of big data analytics in healthcare.

scholarly journals High genomic diversity of multi-drug resistant wastewater Escherichia coli

2017 ◽  
Author(s):  
Norhan Mahfouz ◽  
Serena Caucci ◽  
Eric Achatz ◽  
Torsten Semmler ◽  
Sebastian Guenther ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Genotypic Variation ◽  
Treatment Plant ◽  
Multidrug Resistant ◽  
Genomic Diversity ◽  
Drug Resistant ◽  
Large Dataset ◽  
Whole Genomes

AbstractWastewater treatment plants play an important role in the release of antibiotic resistance into the environment. It has been shown that wastewater contains multi-drug resistant Escherichia coli, but information on strain diversity is surprisingly scarce. Here we present an exceptionally large dataset on multidrug resistant Escherichia coli, originating from wastewater, over a thousand isolates were phenotypically characterized for twenty antibiotics and for 103 isolates whole genomes were sequenced. To our knowledge this is the first study documenting such a comprehensive diversity of multi-drug resistant Escherichia coli in wastewater. The genomic diversity of the isolates was unexpectedly high and contained a high number of resistance and virulence genes. To illustrate the genomic diversity of the isolates we calculated the pan genome of the wastewater Escherichia coli and found it to contain over sixteen thousand genes. To analyse this diverse dataset, we devised a computational approach correlating genotypic variation and resistance phenotype, this way we were able to identify not only known, but also candidate resistance genes. Finally, we could verify that the effluent of a wastewater treatment plant will contain multi-drug resistant Escherichia coli belonging to clinically important clonal groups.

Big data analytics capability and organizational performance measures: The mediating role of business intelligence infrastructure

2021 ◽  
pp. 026638212110553
Author(s):  
Aboobucker Ilmudeen
Keyword(s):  
Big Data ◽  
Firm Performance ◽  
Organizational Performance ◽  
Performance Measures ◽  
Business Intelligence ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Operational Performance ◽  
Marketing Performance ◽  
Analytics Capability

The growing importance of big data has headed enterprises to advance their big data analytics capability to strengthen their firm performance. This study tests how big data capability impact on business intelligence infrastructure to achieve firm performance measures such as operational performance and marketing performance. This study is based on the recent literature on the knowledge-based view, big data capability, IT capability, and business intelligence. The primary survey of 272 responses from Chinese firms’ IT managers and big data analysts are used to uncover the relationship in the proposed model. The finding shows that the big data analytics capability significantly impacts on business intelligence infrastructure that in turn positively impact on operational performance and marketing performance. Further, the business intelligence infrastructure partially mediates between big data analytics capability and operational performance, and fully mediates between big data analytics capability and marketing performance. This research contributes to the information systems literature such as big data analytic capability, business intelligence, and firm performance measures, and thus offers grounds to extend more widespread studies in this field. This study adds to the literature on the theory and practical bases for big data capability and business intelligence infrastructure.

Big Data and Analytics

2018 ◽  
pp. 55-66 ◽  
Author(s):  
Misbahul Haque ◽  
Mohd Imran ◽  
Mohd Vasim Ahamad ◽  
Mohd Shoaib
Keyword(s):  
Big Data ◽  
Customer Satisfaction ◽  
Data Analytics ◽  
Care Delivery ◽  
Big Data Analytics ◽  
Cost Savings ◽  
Heterogeneous Data ◽  
Future Perspective ◽  
Clear Understanding ◽  
Health Organizations

In today's world, humungous and heterogeneous data are being generated from every action of researchers, health organizations, etc. This fast, voluminous, and heterogeneous generation leads to the evolution of the term big data. Big data can be computationally analyzed to uncover hidden trends and patterns that help in finding solutions to the problems arising in various fields. Analysis of big data for manufacturing operational acquaintance at an unparalleled specificity and scale is called big data analytics. Proper utilization of analytics can assist in making effective decisions, improved care delivery, and achieving cost savings. Recognizing hidden trends and useful patterns can lead us to have a clear understanding of the valuable information that these data holds. This chapter presents a quality overview of big data and analytics with its application in the field of healthcare industries as these industries requires their stream of data to be stored and analyzed efficiently in order to improve their future perspective and customer satisfaction.

The Strengths, Weaknesses, Opportunities, and Threats Analysis of Big Data Analytics in Healthcare

2019 ◽  
Vol 4 (1) ◽  
pp. 1-14
Author(s):  
Chaojie Wang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Success Factors ◽  
Swot Analysis ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Healthcare Organizations ◽  
The Us ◽  
Effective Use ◽  
The Cost

Improving the performance and reducing the cost of healthcare have been a great concern and a huge challenge for healthcare organizations and governments at every level in the US. Measures taken have included laws, regulations, policies, and initiatives that aim to improve quality of care, reduce costs of care, and increase access to care. Central to these measures is the meaningful and effective use of Big Data analytics. To reap the benefits of big data analytics and align expectations with results, researchers, practitioners, and policymakers must have a clear understanding of the unique circumstances of healthcare including the strengths, weaknesses, opportunities, and threats (SWOT) associated with the use of this emerging technology. Through descriptive SWOT analysis, this article helps healthcare stakeholders gain awareness of both success factors and issues, pitfalls, and barriers in the adoption of big data analytics in healthcare.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
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