Comparing those three sources related to the usage of big data, intuitively, I can see the academic source is longest and comprehensive, non-academic primary source is medium and non-academic secondary source is the shortest and abridged. Academic source tells the usage of big data with the topic definition, detailed explanations and test results. Non-academic primary source tells the usage of big data with it's mechanism. "The more unrelated the datasets we are using in a model, the more likely they are to reveal unexpected correlations between customers’ behaviors in different settings." Non-academic secondary source tells the usage of big data without explanation. "Some hospitals use big data to detect blood infections in premature babies."
Fraud detection also utilizes big data usage. I've found academic source to be very comprehensive as well.
ReplyDeleteExcellent find and comparison on a source for each of the three types specified in 1.5.7 Activity: Identify Sources ^_^
ReplyDeleteGreat job Tsz!
I can see the use of big data and also the misuse if it as well. I also find the non academic side of things can sometimes give the lay person a better definition and application for some complex information.
ReplyDeleteHi, TszChoi! I really found the sources you used extremely insightful. They highlight the importance of evaluating the reliability and depth of information sources when researching a topic like big data.
ReplyDeleteHello Tszchoi, Well written comparison between the three sources. The scholarly sources always tend to be composed of sophisticated and complex structure with an introduction, abstract body paragraphs and conclusions, these data are also often cited sources from credible professionals and studies. The statement you have taken from the primary source explaining the correlation between unrelated data sets to consumer behavior shows how one can easily discover information about human behavior using models from "Big Data". I can also see how the secondary source makes a claim on the benefits of Big Data without any context but I did find it to be an interesting and very useful information if in fact hospitals are able to use Big Data to detect blood infections on premature babies.
ReplyDeleteAt the end of the day, I think it all comes down to what the topic at hand is about. I'm starting to see a pattern here where the more academic the topic is, the less secondary sources are available. Nonetheless, you did a great job showing us the comparison between your sources!
ReplyDeleteThank you for putting the links too.
ReplyDeleteBig data is also used in Machine Learning and Deep Learning models to train A.I. Data is immensely important and I definitely have a lot of interest in data analysis! Thank you for sharing!
ReplyDeletei like how you added the links.
ReplyDeleteHi, Tszchoi thank you for your insight on big data. Academic sources are important and the links provided are very refreshing.
ReplyDeleteThank You
Kim M
Since the early 2000s, the Internet and the Web began to offer unique data collections and data analysis opportunities. With the expansion of web traffic and online stores, companies such as Yahoo, Amazon and eBay started to analyze customer behavior by analyzing click-rates, IP-specific location data and search logs. This opened a whole new world of possibilities. From a data analysis, data analytics, and Big Data point of view, HTTP-based web traffic introduced a massive increase in semi-structured and unstructured data. Besides the standard structured data types, organizations now needed to find new approaches and storage solutions to deal with these new data types in order to analyze them effectively. The arrival and growth of social media data greatly aggravated the need for tools, technologies and analytics techniques that were able to extract meaningful information out of this unstructured data.
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