2 research outputs found
Utility-driven Data Analytics on Uncertain Data
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.Comment: Under review in IEEE Internet of Things Journal since 2018, 11 page
Influence Maximization Based on Dynamic Personal Perception in Knowledge Graph
Viral marketing on social networks, also known as Influence Maximization
(IM), aims to select k users for the promotion of a target item by maximizing
the total spread of their influence. However, most previous works on IM do not
explore the dynamic user perception of promoted items in the process. In this
paper, by exploiting the knowledge graph (KG) to capture dynamic user
perception, we formulate the problem of Influence Maximization with Dynamic
Personal Perception (IMDPP) that considers user preferences and social
influence reflecting the impact of relevant item adoptions. We prove the
hardness of IMDPP and design an approximation algorithm, named Dynamic
perception for seeding in target markets (Dysim), by exploring the concepts of
dynamic reachability, target markets, and substantial influence to select and
promote a sequence of relevant items. We evaluate the performance of Dysim in
comparison with the state-of-the-art approaches using real social networks with
real KGs. The experimental results show that Dysim effectively achieves up to
6.7 times of influence spread in large datasets over the state-of-the-art
approaches