Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey

Shiguihara, Pedro; Lopes, Alneu de Andrade; Mauricio, David

doi:10.1109/access.2021.3105520

Cited by 19 publications

(7 citation statements)

References 55 publications

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“…Given that the number of events collected per session varied from one another, we decided to consider all the available data as a unique session and replicate this data to represent the different instants. For this, we used the R package dbnR [64]. We only need to specify the number of temporal measurements and the package performs the data replication as many times as we specify.…”

Section: Results and Interpretationmentioning

confidence: 99%

“…Next, transform all character values to factors and all integers to numeric using, for example, the dplyr [62] package. Finally, use the discretise function from the bnlearn [63] package to discretise all numeric values. Step 3 (needed for temporal models): To simulate a temporal model, use the fold_dt function from the dbnR [64] package. However, when using this function, keep in mind that to correctly simulate the sequence of actions we need to previously have the data ordered in a descending action order. Step 4: Learn the network structure from data, using the function hc from the bnlearn package, and then use the bn.fit function to estimate the conditional distributions corresponding to the learnt structure.…”

Section: Methodsmentioning

confidence: 99%

“…Step 3 (needed for temporal models): To simulate a temporal model, use the fold_dt function from the dbnR [64] package. However, when using this function, keep in mind that to correctly simulate the sequence of actions we need to previously have the data ordered in a descending action order.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian Network analysis of software logs for data‐driven software maintenance

2023

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Software organisations aim to develop and maintain high-quality software systems. Due to large amounts of behaviour data available, software organisations can conduct datadriven software maintenance. Indeed, software quality assurance and improvement programs have attracted many researchers' attention. Bayesian Networks (BNs) are proposed as a log analysis technique to discover poor performance indicators in a system and to explore usage patterns that usually require temporal analysis. For this, an action research study is designed and conducted to improve the software quality and the user experience of a web application using BNs as a technique to analyse software logs. To this aim, three models with BNs are created. As a result, multiple enhancement points have been identified within the application ranging from performance issues and errors to recurring user usage patterns. These enhancement points enable the creation of cards in the Scrum process of the web application, contributing to its data-driven software maintenance. Finally, the authors consider that BNs within quality-aware and data-driven software maintenance have great potential as a software log analysis technique and encourage the community to deepen its possible applications. For this, the applied methodology and a replication package are shared. K E Y W O R D SBayes methods, software maintenance, software quality | INTRODUCTIONSoftware quality management is a key activity for the maintenance of software systems. Indeed, the budget that companies invest in software quality management is estimated to increase in the next years [1]. Software systems can generate large amounts of data both from its internal state and from how it is being used. The maintenance of such systems can benefit from the analysis of the available data to get better insights of the system, thus, performing data-driven software maintenance. For instance, companies can analyse the user experiences of their software systems [2] in order to detect possible defects, such as performance downgrades or unexpected errors. To improve the user experience, it is key to understand the users' usage patterns when navigating a software system. Understanding these patterns enables companies the ability to enhance the design of their software systems [3] as well as provide personalised information depending on the user [4]. In this regard, software logs are a popular resource to monitor.Developers place Application Programming Interface calls to a logging framework (e.g., SLF4J [5] or Log4J [6]) to record events that describe the internal state of the system as it runs in production. The analysis of software logs has been proved useful in improving system comprehension [7,8], performance analysis [9, 10], finding usage patterns [11,12], and anomaly detection [13,14]. Nevertheless, this analysis is not free from its own challenges [15].Regarding software logs analysis, the introduction of Bayesian Networks (BNs) based on behaviour data to improve performance and users' experience has not been ...

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Section: Results and Interpretationmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Bayesian Network analysis of software logs for data‐driven software maintenance

2023

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“…The DBN is a temporal extension of the BBN, serving as a dynamic model that exhibits changes over time [110]. A DBN is a graphical model that portrays a system by capturing its state at different points in time, from the initial time to the final time.…”

Section: Dbn Model Buildingmentioning

confidence: 99%

A Quantitative Analysis of Decision-Making Risk Factors for Mega Infrastructure Projects in China

Wang,

Luo,

et al. 2023

Sustainability

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The “trillion-dollar era” of megaprojects has increased the demand for the scope of mega infrastructure. To address the requirement for high-quality “investment, construction, and operation” integration, the EPC and PPP models must be combined. The complexity of megaprojects has resulted in the complexity of project risk variables under the new model. However, few existing studies have undertaken in-depth studies on the risk of EPC + PPP megaprojects. The interplay and dynamic evolution of risk factors, in particular, have not been taken into account. This research intends to fill this gap by systematically identifying and modeling the risk variables associated with the adoption of the EPC + PPP model for mega infrastructure projects. In this study, the Bayesian network is used to detect decision-making risk variables for large infrastructure projects in China. The findings indicate that (i) 22 influencing factors of megaproject decision making are identified, including organizational decision making, PPP investment and financing, EPC construction, operations management, and policy and law. (ii) Considering the real project decision-making process, a model based on a dynamic Bayesian network is built, and associated governance techniques and early warning protection mechanisms are designed for the decision-making process. (iii) Using the Yiwu Mall Avenue project as an example, the Bayesian simulation model of decision-making risks is applied to a typical case to validate its feasibility and correctness. These findings have significant theoretical and practical implications for research on the identification and governance of decision-making risks in megaprojects using the EPC + PPP model in China.

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“…Dynamic Bayesian networks (DBNs) are powerful algorithmic tools that integrate the structure of static BNs with time-related information and are employed for dynamic uncertainty inference and temporal data analysis. DBNs have applications in various fields, including artificial intelligence, machine learning, and automatic control 2 . Furthermore, DBNs have a broad range of engineering applications, such as managing transcriptional regulatory relationships between cancer genes 3 , identifying connectivity issues between human brain regions through high-order DBNs using functional magnetic resonance imaging time series data 4 , and analyzing the vascularization in the formation process of engineered tissues, aiming to enhance the accuracy of predicting future time steps and ensuring an acceptable uncertainty in forecasting the future progress of the organization 5 .…”

Section: Introductionmentioning

confidence: 99%

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

Meng,

Cong,

et al. 2024

Sci Rep

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With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.

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Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey

Cited by 19 publications

References 55 publications

Bayesian Network analysis of software logs for data‐driven software maintenance

Bayesian Network analysis of software logs for data‐driven software maintenance

A Quantitative Analysis of Decision-Making Risk Factors for Mega Infrastructure Projects in China

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

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