Q. What is the main problem with the set of potential indices in database systems?

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Q. How has the complexity of index selection in database systems changed over time?

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Q. What is required for offline indexing to make tuning choices and perform tuning actions?

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Q. In dynamic environments with Big Data, what are described as scarce resources?

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Q. What approach was introduced to address the physical design problem in databases?

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Q. How does database cracking differ from traditional indexing in terms of index creation?

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Q. How is every query treated in the context of database cracking?

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Q. What is the primary data storage representation used in column-store databases?

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Q. How does a column store's data access during query processing differ from traditional row stores?

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Q. What kind of processing do column stores typically rely on?

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Q. What does the term "cracking" in the context of database cracking reflect?

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Q. In the context of database cracking, what does the system do with the base column after the first query on a column?

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Q. What data structure is used to maintain partitioning information in cracking?

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Q. What is the benefit of continuously reorganizing columns through cracking in response to queries?

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Q. Why does cracking stop cracking a column for pieces smaller than L1 cache?

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Q. In column-store systems, what allows for efficient query processing when requesting multiple columns of the same table?

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Q. When a column is cracked in a column-store system, what happens to the alignment of columns within the same table?

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Q. How does sideways cracking address the problem of misalignment in column-store systems when working with multiple columns of the same table?

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Q. How does sideways cracking ensure alignment of column-pairs with the same head attribute?

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Q. What is the benefit of sideways cracking in column-store systems when multiple columns of the same table are used in a query?

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