Utility-Driven Data Analytics on Uncertain Data

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 levels 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 (PU ± -list) 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.

Impact of Trust and Privacy Concerns on Technology Acceptance in Healthcare: An Indian Perspective

“…Emerging technologies such as big data, cloud computing, block chain and health sensing are revolutionizing healthcare operations and delivery [128], [119] and [40]. [120] enumerated a number of capabilities of big data analytics in healthcare sector, particularly for decision support capability, analytical capability for pattern of care, predictive capability, unstructured data analysis capability and traceability.…”

TOPIC: Top-k High-Utility Itemset Discovering

“…Therefore, the real utility of an itemset X in a transaction or database is given as U(X) = pUtil(X) + nUtil(X). It can be inferred that pUtil(X) ≥ U (X) ≥ nUtil(X) [26], [44].…”

A Multi-Core Approach to Efficiently Mining High-Utility Itemsets in Dynamic Profit Databases

“…To prune the search space, several data structures were introduced as a global and partially downward closure properties, such as a novel frequency-utility tree, utility-occupancy list and frequency-utility table. With the data obtained from the IoT devices, which contain positive, negative unit utilities and uncertain, Gan et al proposed an algorithm namely HUPNU [27] to discover high-utility pattern having these characteristics. The discovered patterns can be used in many applications, such as intrusion detection, risk prediction, decision-making, etc.…”