  Modern Internet of Things (IoT) applications generate massive amounts of
data, much of it in the form of objects/items of readings, events, and log
entries. Specifically, most of the objects in these IoT data contain rich
embedded information (e.g., frequency and uncertainty) and different level of
importance (e.g., unit utility of items, interestingness, cost, risk, or
weight). Many existing approaches in data mining and analytics have limitations
such as only the binary attribute is considered within a transaction, as well
as all the objects/items having equal weights or importance. To solve these
drawbacks, a novel utility-driven data analytics algorithm named HUPNU is
presented, to extract High-Utility patterns by considering both Positive and
Negative unit utilities from Uncertain data. The qualified high-utility
patterns can be effectively discovered for risk prediction, manufacturing
management, decision-making, among others. By using the developed vertical
Probability-Utility list with the Positive-and-Negative utilities structure, as
well as several effective pruning strategies. Experiments showed that the
developed HUPNU approach performed great in mining the qualified patterns
efficiently and effectively.
