However,the CAMPUS and EECS03results tell us that,in some situations,ABLE does worse than MABLE or NABLE.In these traces,some multi-way associations existed on Monday that did not generalize to new?les on Tuesday.This is a common problem of over-?tting the data with too many attributes,although the differences are not severe in our evaluation.
There are two important points to take away from our analysis of MABLE and NABLE.First,more attributes are not always better.We can fall into a trap known as the curse of dimensionality in which each attribute adds a new dimension to the sample space[6].Unless we see a suf?cient number of?les,our decision trees may get clouded by transient multi-way associations that do not apply in the long run.Second,NABLE and MABLE offer predictions roughly equivalent to ABLE.This is somewhat surprising,particularly in the case of MABLE, because it means that we can make accurate predictions even if we do not consider?le names at all.
Given enough training data,ABLE always outper-forms MABLE and NABLE.For the results presented in the paper,ABLE required an extra week of training to detect the false attribute associations,due in part to the small number of attributes.We anticipate that more training will be required for systems with larger attribute spaces,such as object-based storage with extended at-tributes[18]and non-UNIX?le systems such as CIFS or NTFS[29].Furthermore,irrelevant attributes may need to be pre-?ltered before induction of the decision tree[6] to prevent over-?tting.The automation of ABLE’s train-ing policies,including attribute?ltering,is an area for future work.
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Figure4:Comparing the prediction accuracy of ABLE, NABLE,and MABLE for the properties size=0,write-only,and lifetime1second.Prediction accuracy is measured as percentage correct.
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