Q. What is the main purpose of filtering in data analysis?

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Q. Why is it challenging to have a one-size-fits-all filter for big heterogeneous data?

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Q. What is a potential benefit of using smart filtering in data analysis?

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Q. How does the evolutionary approach address the challenge of smart filtering?

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Q. What property of knowledge organisms in the evolutionary approach helps in filtering out noise?

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Q. Why is it challenging to decide which part of a potentially useful collection of data should be sacrificed or "forgotten"?

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Q. What is the drawback of following straightforward policies like fixed lifetime for keeping records in Big Data management?

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Q. What is one potentially viable approach to forgetting in data management?

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Q. What does the approach of "forgetting before storing" propose?

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Q. What is data contextualization primarily concerned with in the context of the Big Data?

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Q. What does data contextualization involve transforming data from and into?

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Q. What is the primary benefit of using dynamic contextualization in knowledge discovery?

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Q. What is the primary purpose of data compression in the context of Big Data?

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Q. When is lossy compression typically applied to data?

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Q. What are the four major functional areas of autonomic computing according to IBM?

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Q. What is the primary benefit of sharing knowledge within a group?

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Q. How does knowledge evolve in a social context?

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Q. What is the role of ontologies in understanding Big Data?

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Q. Why is it important to treat Big Data processing as an ecosystem of evolving processing entities?

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Q. What is the primary challenge faced by traditional relational database management technologies when dealing with big data analytics?

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