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Aggregate Index | Apache Kylin
Kylin is renowned for enhancing query performance by pre-calculated aggregate groups, which represent various combinations of all dimensions, also known as cuboids. However, it faces the well-known challenge of the curse of dimensionality—as the number of dimensions increases, the number of possible indices grows exponentially (2^n - 1). For example, with 3 dimensions, there are 7 possible indices, but with 10 dimensions, the number of cuboids surges to 1023.
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Aggregate Index | Apache Kylin
Kylin is renowned for enhancing query performance by pre-calculated aggregate groups, which represent various combinations of all dimensions, also known as cuboids. However, it faces the well-known challenge of the curse of dimensionality—as the number of dimensions increases, the number of possible indices grows exponentially (2^n - 1). For example, with 3 dimensions, there are 7 possible indices, but with 10 dimensions, the number of cuboids surges to 1023.
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Aggregate Index | Apache Kylin
Kylin is renowned for enhancing query performance by pre-calculated aggregate groups, which represent various combinations of all dimensions, also known as cuboids. However, it faces the well-known challenge of the curse of dimensionality—as the number of dimensions increases, the number of possible indices grows exponentially (2^n - 1). For example, with 3 dimensions, there are 7 possible indices, but with 10 dimensions, the number of cuboids surges to 1023.
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