Event-Driven Continuous Time Bayesian Networks

Bhattacharjya, Debarun; Shanmugam, Karthikeyan; Gao, Tian; Mattei, Nicholas; Varshney, Kush R.; Subramanian, Dharmashankar

doi:10.1609/aaai.v34i04.5725

Cited by 14 publications

(5 citation statements)

References 11 publications

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“…Strong SCM effectiveness results in higher revenue margins and improved product pricing for SMEs; this is also essential since high investment ratios lead to reduced profit sensitivity to accumulate events. The supply curve may be reduced if a business identifies the fulfilment of its SCM commitments as a differentiating strategy, and the impact of economic shocks on the firm’s profitability will be lessens (Bhattacharjya et al 2020 ). Among other things, the COVID-19 epidemic has resulted in significant stock market losses.…”

Section: Research Model and Hypothesesmentioning

confidence: 99%

Does supply chain matter for environmental firm performance: mediating role of financial development in China

Zhao

Luo

Liu

2022

Econ Change Restruct

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The purpose of this research is to understand the impact of technological innovation on corporate social responsibility and corporate environmental performance, as well as the difference in the measurement of this impact between domestic and foreign companies. Supply chain management is an essential tool to promote environmentally sustainable manufacturing by owning green products, green supply chain management and green manufacturing processes, believes that companies should participate in inappropriate policies, assessments, practices and activities while achieving all components of economy, environment, charity and ethics. The hierarchical regression model is used to analyse 175 small and medium enterprises samples. The survey results show that technological innovation will affect environmental performance and will also have a positive impact on company performance. Efficient companies can bring greater financial success, which not only supports economic community projects but also supports social welfare. Innovating through the participation of management and employees in environmental protection practices can not only improve the company’s performance but also enhance the company’s image among stakeholders. The findings of this article strengthen existing theories and help establish sustainable practices in Chinese and other developing and developed countries.

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Section: Research Model and Hypothesesmentioning

confidence: 99%

Does supply chain matter for environmental firm performance: mediating role of financial development in China

Zhao

Luo

Liu

2022

Econ Change Restruct

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“…Those CTBNs have been extended in terms of more general transition times, explicit negative evidence (Gopalratnam et al 2005) or by adding static chance nodes of BNs (Portinale and Codetta-Raiteri 2009). To model event streams, in Bhattacharjya et al (2020) Event-Driven Continuous Time Bayesian Networks, where proposed that model event occurrences modeled as a multivariate point process and states as Markov processes. In further works, event streams are modeled with Poisson cascades (Simma and Jordan 2012) or graphical event models (Gunawardana and Meek 2016).…”

Section: Related Work: Existing Modelsmentioning

confidence: 99%

Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation

Mrowca

Gyrock

Günnemann

2021

Data Min Knowl Disc

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Many systems can be expressed as multivariate state sequences (MSS) in terms of entities and their states with evolving dependencies over time. In order to interpret the temporal dynamics in such data, it is essential to capture relationships between entities and their changes in state and dependence over time under uncertainty. Existing probabilistic models do not explicitly model the evolution of causality between dependent state sequences and mostly result in complex structures when representing complete causal dependencies between random variables. To solve this, Temporal State Change Bayesian Networks (TSCBN) are introduced to effectively model interval relations of MSSs under evolving uncertainty. Our model outperforms competing approaches in terms of parameter complexity and expressiveness. Further, an efficient structure discovery method for TSCBNs is presented, that improves classical approaches by exploiting temporal knowledge and multiple parameter estimation approaches for TSCBNs are introduced. Those are expectation maximization, variational inference and a sampling based maximum likelihood estimation that allow to learn parameters from partially observed MSSs. Lastly, we demonstrate how TSCBNs allow to interpret and infer patterns of captured sequences for specification mining in automotive.

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“…It estimates the influence of historical events or trajectories on upcoming events. Graphical models [10] and regression analysis [52] are widely used. We enhance the approach by Rossignon et al [48] which combines sequence clustering with sequence inference to estimate the time-varying impacts of multiple factors on the upcoming career performance.…”

Section: Sequence Mining and Visualizationmentioning

confidence: 99%

Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers

Wang¹,

Peng²,

Lu³

et al. 2021

Preprint

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How to achieve academic career success has been a long-standing research question in social science research. With the growing availability of large-scale well-documented academic profiles and career trajectories, scholarly interest in career success has been reinvigorated, which has emerged to be an active research domain called the Science of Science (i.e., SciSci). In this study, we adopt an innovative dynamic perspective to examine how individual and social factors will influence career success over time. We propose ACSeeker, an interactive visual analytics approach to explore the potential factors of success and how the influence of multiple factors changes at different stages of academic careers. We first applied a Multi-factor Impact Analysis framework to estimate the effect of different factors on academic career success over time. We then developed a visual analytics system to understand the dynamic effects interactively. A novel timeline is designed to reveal and compare the factor impacts based on the whole population. A customized career line showing the individual career development is provided to allow a detailed inspection. To validate the effectiveness and usability of ACSeeker, we report two case studies and interviews with a social scientist and general researchers.

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Event-Driven Continuous Time Bayesian Networks

Cited by 14 publications

References 11 publications

Does supply chain matter for environmental firm performance: mediating role of financial development in China

Does supply chain matter for environmental firm performance: mediating role of financial development in China

Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation

Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers

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