# Utility-driven Data Analytics on Uncertain Data

## TL;DR

This paper introduces HUPNU, a novel algorithm for extracting high-utility patterns from uncertain IoT data by considering both positive and negative utilities, improving risk prediction and decision-making.

## Contribution

The paper presents a new utility-driven data analytics algorithm, HUPNU, that effectively mines high-utility patterns from uncertain data considering both positive and negative utilities.

## Key findings

- HUPNU efficiently discovers high-utility patterns in uncertain data.
- The approach outperforms existing methods in mining quality and speed.
- Effective pruning strategies enhance computational performance.

## Abstract

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.