Another difficulty with print is the size of data collection.
This makes the complexity of resource allocation problem increases with the size of data center.
Essentially the derived solution is related to an existing weighted least-squares method with class-specific weights set according to the size of data set.
Accordingly, the size of data pool grows from one sort of molecule to the number of solution assignments.
One way to make big-data analysis computationally practical is to reduce the size of data tables — or matrices, to use the mathematical term — by leaving out a bunch of rows.
Considering the size of data, the local microcontroller memory does not have enough space to store such a big file, therefore an external storing device, like a memory flash card, must be used.
The proposed approach inquires a cost of only two extra bits regardless of the size of data word used, hence provides a cost effective approach to the problem.
Because no exact labels are provided, these data exploration and investigation processes are iterative and time-consuming, especially when the size of data is huge.
Existing algorithms can only handle, however, the static database and unsuitable for the dynamic environment since the size of data is frequently changed in real-life situations.
Many scientific applications have large I/O requirements, in terms of both the size of data and the number of files or data sets.
As computing and data storage needs grow exponentially in HDS, increasing the size of data centers brings important diseconomies of scale.
