Abstract Attribute-Based Prediction of File Properties(10)

a learning algorithm.For the third,we must choose how

to evaluate the success of the predictions.We may con-sider different types of errors to have different degrees of importance–for example,if the?le system treats short-lived?les in a special manner,then incorrectly predicting that a?le will be short-lived may be worse than incor-rectly predicting that a?le will be long-lived.

5.1Obtaining Training Data

There are two basic ways to obtain a sample of?les: from a running system or from traces.ABLE currently uses the latter approach,using the NFS traces described in Section3.

Determining some of the attributes of a?le(gid,uid, mode)is a simple matter of scanning the traces and cap-turing any command(e.g.,lookup or getattr)that get or set attributes.To capture?le names,ABLE simu-lates each of the directory operations(such as create, symlink,link,and rename)in order to infer the?le names and connect them to the underlying?les.

Table2shows samples take from DEAS03.The spe-ci?c property for this table is the write-only property;

each?le is classi?ed as write-only or not.For this prop-erty,ABLE classi?es each?le by tracking it through the trace and observing whether it is ever read.

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