Q. How does partial cracking manage storage space for cracking columns?

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Q. How does cracking handle updates to the data?

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Q. What is the purpose of the auxiliary delete and insert columns in cracking?

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Q. How does cracking manage merging pending updates into cracking columns?

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Q. What is the primary motivation for the introduction of adaptive merging as a complementary technique to cracking?

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Q. How are data handled in adaptive merging after they are sorted in memory with a quicksort action?

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Q. What happens if a query in adaptive merging is fully covered by the results column?

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Q. How does adaptive merging improve over plain cracking in terms of convergence speed?

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Q. What is the main advantage of the crack-sort hybrid compared to other hybrid algorithms?

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Q. How does the performance of cracking depend on the arrival order of queries?

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Q. What problem does stochastic cracking aim to solve?

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Q. How does stochastic cracking work to reduce the chances of leaving back big uncracked pieces?

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Q. Why is concurrent access to data challenging in the context of database cracking?

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Q. How does database cracking achieve concurrency control for adaptive indexing during read queries?

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Q. In the context of adaptive indexing during read queries, what remains intact even when data organization changes?

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Q. What is the advantage of using latching for concurrency control in adaptive indexing?

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Q. Why is data loading considered a necessary step when setting up a database system?

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Q. What is one of the significant costs associated with data loading in a database system?

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Q. In the context of Big Data, what challenge does data loading create?

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Q. What is the main challenge addressed by the adaptive loading direction in the NoDB project?

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