Process monitoring using maximum sequence divergence

2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)

Hung

et al. 2020

Self Cite

Researchers have been overwhelmed by the explosion of research articles published by various research communities. Many research scholarly websites, search engines, and digital libraries have been created to help researchers identify potential research topics and keep up with recent progress on research of interests. However, it is still difficult for researchers to keep track of the research topic diffusion and evolution without spending a large amount of time reviewing numerous relevant and irrelevant articles. In this paper, we consider a novel topic diffusion discovery technique. Specifically, we propose using a Deep Non-negative Autoencoder with information divergence measurement that monitors evolutionary distance of the topic diffusion to understand how research topics change with time. The experimental results show that the proposed approach is able to identify the evolution of research topics as well as to discover topic diffusions in online fashions.

Section: Topic Diffusion Discovery Based On Deep Non-mentioning

confidence: 99%

Topic Diffusion Discovery based on Deep Non-negative Autoencoder

Huang

2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)

Hung

et al. 2020

Self Cite

“…Note that, by "associated" here, we mean those elements with P(document n |topic k )>0 in W and P(term i |topic k )>0 in Ĥ respectively. Next, to further evaluate the magnitude of the topic diffusions, we consider monitoring the aforementioned D GJS [9][10], which is here defined as:…”

Section: ≈Whmentioning

confidence: 99%

“…The D GJS is a symmetric measure that ranges between 0 and 1. Also defined in [9][10], a statistically significant threshold of the generalized Jensen-Shannon divergence D GJS|k,t can be asymptotically approximated and represented by Chi-square statistic χ 2 , as…”

Section: Topic Diffusion Discovery Based On Sparseness-constrained Nmfmentioning

confidence: 99%

“…In this paper, we introduce a novel topic diffusion discovery technique that combines a modified sparsenessconstrained NMF [8] and generalized Jensen-Shannon divergence (D GJS ) [9][10] aiming at building topic models with more prominent topics and detecting topic diffusion by monitoring the changes of probability distributions of given terms among multiple topics. That is, for example, suppose we have built topic models from thousands of news articles.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Topic Diffusion Discovery Based on Sparseness-Constrained Non-Negative Matrix Factorization

2018 IEEE International Conference on Information Reuse and Integration (IRI)

Lin

Cheng

2018

Self Cite

Due to recent explosion of text data, researchers have been overwhelmed by ever-increasing volume of articles produced by different research communities. Various scholarly search websites, citation recommendation engines, and research databases have been created to simplify the text search tasks. However, it is still difficult for researchers to be able to identify potential research topics without doing intensive reviews on a tremendous number of articles published by journals, conferences, meetings, and workshops. In this paper, we consider a novel topic diffusion discovery technique that incorporates sparseness-constrained Non-negative Matrix Factorization with generalized Jensen-Shannon divergence to help understand term-topic evolutions and identify topic diffusions. Our experimental result shows that this approach can extract more prominent topics from large article databases, visualize relationships between terms of interest and abstract topics, and further help researchers understand whether given terms/topics have been widely explored or whether new topics are emerging from literature.

“…We define the process as "a series of activities or state transitions of a dynamic system that produce some specific, either deterministic or probabilistic, outcomes." [2]. The process discovery refers to a series of actions that collect activities of instances (e.g.…”

Section: Introductionmentioning

confidence: 99%

Process Discovery Using Classification Tree Hidden Semi-Markov Model

2016 IEEE 17th International Conference on Information Reuse and Integration (IRI)

Zadorozhny

2016

Self Cite

Various and ubiquitous information systems are being used in monitoring, exchanging, and collecting information. These systems are generating massive amount of event sequence logs that may help us understand underlying phenomenon. By analyzing these logs, we can learn process models that describe system procedures, predict the development of the system, or check whether the changes are expected. In this paper, we consider a novel technique that models these sequences of events in temporal-probabilistic manners. Specifically, we propose a probabilistic process model that combines hidden semi-Markov model and classification trees learning. Our experimental result shows that the proposed approach can answer a kind of question-"what are the most frequent sequence of system dynamics relevant to a given sequence of observable events?". For example, "Given a series of medical treatments, what are the most relevant patients' health condition pattern changes at different times?"