Special caution must be taken at this stage with memory consumption, as it reaches the maximum memory requirements of the McGM algorithm.
However, the maximum memory requirements for prefix/suffix trees (or even arrays) increase quadratically with the length of the reference sequence.
We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.
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For example, replicating communication and computation to achieve availability, or including a shadow removal component to achieve high accuracy for event recognition, might conflict with performance requirements (e.g., low response time) or resource restrictions (e.g., maximum memory consumption).
This allows drastically reduce the memory requirement.
Its applicability, however, is limited by its exponential memory requirement.
Two kinds of physical memory requirements will be utilised 4 GB (typical physical memory requirement) and 1.5 GB (low requirement for physical memory) (experiment reference is given in [64]).
Thus, the memory requirement needs to be reduced.
Therefore, the memory requirement also grows exponentially as n increases.
