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Figure 9 depicts the IL values corresponding to different sizes of data for different (beta) and (gamma).
However, traditional anonymization methods lack scalability and cannot, in general, cope with large sizes of data, as their performance degrades.
For all three strategies, we consider two alternative types of capacity constraints on index sizes of data centers.
Firstly, we retrieve the average number of blocks to be transferred as shown in Fig. 6a for different sizes of data.
For typical sizes of data sets, the new algorithm is 1.5 2.5 times faster than using direct summation in the space domain.
This phenomenon is considerably changing the way science and research is being conducted in many disciplines as they are dealing with unprecedented sizes of data that needs massive computing capacities to handle it.
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Moreover, a growing dependency on large-scale IT and the internet has generated strong growth in the number and size of data centres, despite the global recession.
Plus, people over estimate the size of data they have.
Linear O(N) dependency on the size of data.
The size of data is not critical factor.
Figure 9 Different variance on size of data chunk.
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