Q. What is the primary characteristic of the proposed bottom-up path in the Big Data processing stack?

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Q. What is a way to reduce the overheads of the top-down path in Big Data processing?

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Q. What is the high-level approach for treating Big Data along the proposed path?

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Q. What does the "3F" in the formula "3F + 3Co" primarily refer to in the context of the Big Data?

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Q. What is one of the key purposes of the proposed bottom-up path in Big Data processing?

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Q. Why is the order of focusing considered important when processing Big Data or its semantics?

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Q. What is a potential advantage of using smart focusing when processing data tokens in a persistent storage?

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Q. What is a challenge associated with smart focusing in data processing?

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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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