Both of big data and KDD is used in term of quality, the main different of these two frameworks is how to extract knowledge from both.
So I suggest to make condition to exploration and exploitation the data for both by applying parallel machine learning and statistical analysis to achieve coherence and reduce error.
Your question is not completely understood. Nevertheless, I am jotting down some sources which you may look for:
Sagiroglu, S., & Sinanc, D. (2013, May). Big data: A review. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.
Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A. Y., ... & Bouras, A. (2014). A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE transactions on emerging topics in computing, 2(3), 267-279.
Big Data facing challenges more than KDD through quality term. in simple statement big data treat with different type of data collection as capture data , generate data , and traditional. Moreover the forward and backward process.
your opinion make sense but need to go to experiment
Your question is not completely understood. Nevertheless, I am jotting down some sources which you may look for:
Sagiroglu, S., & Sinanc, D. (2013, May). Big data: A review. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.
Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A. Y., ... & Bouras, A. (2014). A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE transactions on emerging topics in computing, 2(3), 267-279.
Sobhan Sarkar My Idea is how is to employ KDD to big data to improve analysis and big data process , KDD and big data used for decision making and we can use them both to improve the quality in term of characteristic of big data. to achieve the ideal transcription from volume in KDD to other Vs in big data.
Please try to clarify your question by adding more details... Thanks
Emad Kamil Hussein the purposeof the KDD process to a particular big data analysis methods. For example, summarization, classification, regression, clustering, and so on, as well as The KDD process can involve significant iteration and may contain loops between any two steps So how this process with huge volume of big data can applyed to improve quality and extract valued data.
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