Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Kelly DataSciJourn22 21.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Nambiar BigDataCogComp22 6-4.png|240px]]</div>
'''"[[Journal:A critical literature review of historic scientific analog data: Uses, successes, and challenges|A critical literature review of historic scientific analog data: Uses, successes, and challenges]]"'''
'''"[[Journal:An overview of data warehouse and data lake in modern enterprise data management|An overview of data warehouse and data lake in modern enterprise data management]]"'''


For years, scientists in fields from climate change to biodiversity to hydrology have used older data to address contemporary issues. Since the 1960s, researchers, recognizing the value of this data, have expressed concern about its [[Information management|management]] and potential for loss. No widespread solutions have emerged to address the myriad issues around its storage, access, and findability. This paper summarizes observations and concerns of researchers in various disciplines who have articulated problems associated with analog data and highlights examples of projects that have used historical data. The authors also examined selected papers to discover how researchers located historical data and how they used it. While many researchers are not producing huge amounts of analog data today, there are still large volumes of it that are at risk. To address this concern, the authors recommend the development of best practices for managing historic data ... ('''[[Journal:A critical literature review of historic scientific analog data: Uses, successes, and challenges|Full article...]]''')<br />
Data is the lifeblood of any organization. In today’s world, organizations recognize the vital role of data in modern [[business intelligence]] systems for making meaningful decisions and staying competitive in the field. Efficient and optimal data analytics provides a competitive edge to its performance and services. Major organizations generate, collect, and process vast amounts of data, falling under the category of "big data." [[Information management|Managing]] and [[Data analysis|analyzing]] the sheer volume and variety of big data is a cumbersome process. At the same time, proper utilization of the vast collection of an organization’s [[information]] can generate meaningful insights into business tactics. In this regard, two of the more popular data management systems in the area of big data analytics—the [[data warehouse]] and [[data lake]]—act as platforms to accumulate the big data generated and used by organizations ... ('''[[Journal:An overview of data warehouse and data lake in modern enterprise data management|Full article...]]''')<br />
''Recently featured'':
''Recently featured'':
{{flowlist |
{{flowlist |
* [[Journal:A critical literature review of historic scientific analog data: Uses, successes, and challenges|A critical literature review of historic scientific analog data: Uses, successes, and challenges]]
* [[Journal:Data management of microscale reaction calorimeter using a modular open-source IoT platform|Data management of microscale reaction calorimeter using a modular open-source IoT platform]]
* [[Journal:Data management of microscale reaction calorimeter using a modular open-source IoT platform|Data management of microscale reaction calorimeter using a modular open-source IoT platform]]
* [[Journal:Integrative diagnostics: The time is now—a report from the International Society for Strategic Studies in Radiology|Integrative diagnostics: The time is now—a report from the International Society for Strategic Studies in Radiology]]
* [[Journal:Integrative diagnostics: The time is now—a report from the International Society for Strategic Studies in Radiology|Integrative diagnostics: The time is now—a report from the International Society for Strategic Studies in Radiology]]
* [[Journal:Ten simple rules for maximizing the recommendations of the NIH data management and sharing plan|Ten simple rules for maximizing the recommendations of the NIH data management and sharing plan]]
}}
}}

Revision as of 20:08, 28 August 2023

Fig1 Nambiar BigDataCogComp22 6-4.png

"An overview of data warehouse and data lake in modern enterprise data management"

Data is the lifeblood of any organization. In today’s world, organizations recognize the vital role of data in modern business intelligence systems for making meaningful decisions and staying competitive in the field. Efficient and optimal data analytics provides a competitive edge to its performance and services. Major organizations generate, collect, and process vast amounts of data, falling under the category of "big data." Managing and analyzing the sheer volume and variety of big data is a cumbersome process. At the same time, proper utilization of the vast collection of an organization’s information can generate meaningful insights into business tactics. In this regard, two of the more popular data management systems in the area of big data analytics—the data warehouse and data lake—act as platforms to accumulate the big data generated and used by organizations ... (Full article...)
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