It doesn't look like the intent of this system is to detect price gauging, etc. but rather to detect service outages/disruptions. Agree that high variance makes 'jump-based' methods such as this fail a lot. The associated paper claims a 30% false positive rate. But in the case of a true positive, if you know which metric is failing, then that's a very good jump off point to respond to the disruption.
Having benchmark datasets could mean more semi-supervised approaches, compared to unsupervised approaches (such as this one). But - what would the end-result be? Everyone has different metrics, so unless you merely use the datasets as raw numbers to feed into an algorithm, you're unlikely to get meaningful results.
Other notes:
(1) It's hard to determine whether the proposed algorithm was successful without a feedback system which would tag true and false positives postmortem.
(2) I wish the raw data used for this project and similar studies were available, e.g. http://webscope.sandbox.yahoo.com/. Yahoo datasets are permission-only and are not available unless you're a researcher at .edu