The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business proc…
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48 results for “process discovery”
Survey shows users value usability over functionality in process discovery tools.
problem Users prioritize usability over functional aspects in process discovery tools.
method A survey was conducted with 66 respondents to gather feedback on process discovery tools.
result Users prefer usability over functionality in process discovery tools.
Automated process discovery is a class of process mining methods that allow analysts to extract business process models from event logs. Traditional process discovery methods extract process models from a snapshot of an event log stored in its entirety. In some scenarios, however, events keep coming with a high arrival…
The paper examines how timing of observations affects causal discovery methods.
problem The sensitivity of causal discovery methods to mismatched observation timing.
method Empirical and theoretical analysis of classical and recent causal discovery methods.
result Causal discovery methods are sensitive to sampling rate and window length.
Visualizes deep generative models for drug design.
problem Limited visualization tools for deep generative models in drug discovery.
method Proposes a visualization framework for deep graph generative models.
result Interactive visualization and molecular optimization tools.
New framework uses background knowledge to speed up causal discovery.
problem Scalable causal discovery for large datasets.
method Utilizes background knowledge during causal discovery process.
result Background knowledge reduces computational requirements and improves structure quality.
NLP techniques improve drug discovery by analyzing chemical and protein text.
problem Improving drug discovery through better analysis of chemical and protein text.
method Natural language processing techniques applied to biochemical entities.
result Enhanced prediction of molecular properties and design of novel molecules.
A new method distills material models from noisy data without prior selection.
problem Uncertainty in material model discovery from noisy data.
method Augmenting data with Gaussian process, approximating parameter distribution with normalizing flow, distilling by matching stress-deformation functions, performing sensitivity analysis.
result Sparse and interpretable material models discovered from experimental data.
DisCoveR efficiently discovers declarative process models from event logs.
problem Mining declarative process models from event logs efficiently and accurately.
method DisCoveR precisely formalizes an algorithm, uses a bit vector implementation, and rigorously evaluates performance.
result DisCoveR outperforms other declarative miners in accuracy and runtime.
Review of automation's role in chemical discovery, emphasizing future challenges.
problem Improving automation's contribution to chemical discovery.
method Analysis of exemplary studies and open research directions.
result Future autonomous systems need improvement in data handling, model building, and experiment automation.
RAMBO optimizes multi-regime problems by discovering and modeling distinct energy basins.
problem Multi-regime problems in molecular conformation and drug discovery.
method Dirichlet Process Mixture of Gaussian Processes with adaptive hyperparameters and concentration parameters.
result Consistent improvements over state-of-the-art on multi-regime objectives.
Equation discovery method reconstructs model structure and parameters from data.
problem Nonlinear system identification challenges.
method Two interlaced parts: model structure identification and parameter estimation.
result Equation discovery method successfully reconstructs model structure and parameters from data.
New method uncovers hidden causal connections in multivariate point process networks.
problem Unobserved hidden variables confound causal discovery in high-dimensional point process networks.
method Proposes a deconfounding procedure to estimate causal interactions among observed nodes with unknown unobserved processes.
result The method accurately identifies causal interactions among observed processes, even with hidden variables.
The paper develops CI tests for causal discovery in SDEs.
problem Inferring causal structure from stochastic dynamical systems.
method Developed CI constraints and a CI test for SDEs.
result Proposed CI test outperforms existing methods.
Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernelsphysics.comp-ph
GPR enhances materials discovery by automating parameter space exploration.
problem Automating exploration of large, high-dimensional parameter spaces in materials science.
method Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels.
result Importance and benefits of tuning GPR for materials science experiments.
Interpretable framework evaluates structure learning methods for causal discovery from observational data.
problem Evaluation of structure learning methods under assumption violations in causal discovery.
method Six-dimensional evaluation metric (DOS) tailored for causal discovery.
result Amortized causal discovery delivers results with high proximity to the optimal solution.
New methods for Markov Blanket discovery using MML outperform existing approaches.
problem Causal discovery from large datasets.
method Developed three new methods of Markov Blanket discovery using Minimum Message Length.
result Our best MML method is consistently competitive and has advantageous features.
Review of automation's role in chemical discoveries.
problem Improving autonomous discovery in chemistry.
method Classification of discovery types, assessment of autonomy, case studies.
result Rapid advancements in automation and machine learning are transforming experimentation and modeling.
Existing methods for structure discovery in time series data construct interpretable, compositional kernels for Gaussian process regression models. While the learned Gaussian process model provides posterior mean and variance estimates, typically the structure is learned via a greedy optimization procedure. This restri…
In nonlinear latent variable models or dynamic models, if we consider the latent variables as confounders (common causes), the noise dependencies imply further relations between the observed variables. Such models are then closely related to causal discovery in the presence of nonlinear confounders, which is a challeng…
REDS improves scenario discovery from few simulations, reducing costs by 50-75%.
problem Discovering scenarios in data spaces resulting from simulations with limited computational resources.
method Uses an intermediate machine learning model to label data for subgroup discovery methods.
result Reduces the number of simulations required by 50-75% on average.
Unified kernel-based methods improve nonlinear causal discovery.
problem Identifying nonlinear causal relationships between time series variables.
method Unified Kernel Principal Component Regression (KPCR) and Gaussian Process score-based model with Smooth Information Criterion.
result Improved performance in time series nonlinear causal discovery.
Generates synthetic manufacturing data for causal discovery benchmarking.
problem Lack of suitable real data for validating causal discovery algorithms.
method Distributional random forests for estimating conditional distributions.
result Semisynthetic manufacturing data adheres to a causal model.
Bayesian model selection improves multivariate causal discovery without restrictive assumptions.
problem Real-world causal discovery requires flexible assumptions to avoid restrictive model assumptions.
method Continuous relaxation of discrete model selection problem, using Causal Gaussian Process Conditional Density Estimator (CGP-CDE).
result Bayesian approach outperforms traditional methods in multivariate causal discovery.
CDFM aims to unify causal discovery across diverse datasets.
problem Fragmented, test-driven causal discovery approaches struggle with modern data heterogeneity.
method CDFM is a unified, general-purpose framework using a variational decomposition of causal mechanisms.
result CDFM outperforms traditional algorithms across diverse datasets.
Machine discovers PDEs from spatiotemporal data without prior knowledge.
problem Discovering PDEs from complex spatiotemporal data without prior knowledge.
method Sparse Spatiotemporal System Discovery () using Sparse Bayesian Learning.
result Automatically discovers ten types of PDEs from simulation data.
Interpretable ML helps discover insights from big data.
problem Validating data-driven discoveries from complex datasets.
method Statistical and machine learning techniques for interpretable models.
result Challenges in validating data-driven discoveries remain.
Novel process model for metabolomics data analysis.
problem Analyzing complex metabolomics data.
method Data-driven and hypothesis-driven data mining approaches using various techniques.
result Demonstrated applicability and strengths of MeKDDaM model.
KEEL improves causal discovery with fuzzy knowledge and complex data.
problem Challenges in causal discovery due to prior knowledge, domain inconsistencies, and small sample sizes.
method Weakly-supervised fuzzy knowledge and data co-driven causal discovery method (KEEL).
result KEEL outperforms state-of-the-art methods in accuracy, robustness, and computational efficiency.
New algorithm improves materials discovery using max K-Armed Bandit.
problem Maximizing material breakthroughs in materials discovery.
method Proposed a search algorithm based on max K-Armed Bandit (MKB) for materials discovery.
result Stable performance in late search stages, outperforming other bandit algorithms.
Method estimates bivariate causal models using normalising flows and variational Gaussian process regression.
problem Lack of explainability in AI models, especially in causal mechanisms.
method Combination of normalising flows for density estimation and variational Gaussian process regression for post-nonlinear models.
result Method better explains cause-effect pairs than simple additive noise models.
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functionsq-bio.BM
Q-SAVI model improves drug discovery accuracy with prior knowledge of chemical space.
problem Challenges in drug discovery due to covariate shift and limited labeled data.
method Probabilistic model with domain-informed prior distributions over functions.
result Q-SAVI outperforms state-of-the-art techniques in predictive accuracy and calibration.
Sparsistent Model Discoverystat.ML
New method recovers PDEs from noisy data, even when conditions are violated.
problem Discovering PDEs from noisy, limited data.
method Randomized adaptive Lasso integrated into DeepMod.
result Recovery of PDEs with higher noise-to-sample ratios and single hyperparameters.
Simulates neuropathic pain to evaluate causal discovery algorithms.
problem Lack of benchmark datasets for evaluating causal discovery algorithms.
method Developed a neuropathic pain diagnosis simulator.
result Simulator produces data similar to real-world data.
New method identifies causal structure in exchangeable data.
problem Existing causal discovery methods struggle with i.i.d. data.
method Exchangeable data provides richer conditional independence structure.
result Exchangeable data allows for unique causal structure identification.
Study reveals how investor flows impact stock prices, especially during herding episodes.
problem Understanding how information transmits through prices and why it breaks down.
method Combining regularized deconvolution with Hawkes process analysis.
result Institutional price impact deteriorates sharply during herding episodes in small-cap stocks, while large-cap stocks maintain resilience.
Software automates metabolomics data analysis for reproducible results.
problem Automating reproducible metabolomics data analysis.
method Object-oriented software engineering, Java, XML database, GUI, version control system.
result MeKDDaM-SAGA successfully guides metabolomics applications.
Framework detects anomalies in industrial processes using deep learning.
problem Detect anomalies in complex industrial processes.
method Causal-based framework with unsupervised deep learning.
result Successfully validated abstract contexts of blast furnace assets.
Study shows informed traders harm market makers but price discovery benefits outweigh costs.
problem Informed traders' impact on market makers' profitability.
method Agent-based model with heterogeneous learning agents, multi-agent reinforcement learning.
result Informed market order flow is harmful when aggregate informedness is low but beneficial as it increases.
BEACON optimizes discovery by efficiently finding novel behaviors.
problem Discovering diverse system behaviors without a scalar objective.
method Bayesian optimization inspired strategy using multi-output Gaussian processes.
result BEACON discovers broader sets of distinct behaviors than competing methods.
CGD improves diffusion models' out-of-distribution generalization.
problem Reliable sampling from high-value regions beyond training data.
method Context-guided diffusion (CGD) using unlabeled data and smoothness constraints.
result Substantial performance gains across various diffusion processes.
A new method optimizes material discovery by balancing exploration and exploitation.
problem Substantial experimental costs and lengthy development periods in material discovery.
method Threshold-Driven UCB-EI Bayesian Optimization (TDUE-BO) method.
result TDUE-BO significantly outperforms traditional BO methods in material discovery.
A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Biasstat.ML
Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias. No constraint-based causal discovery algorithm can currently handle cycles, latent variables and selection bias (CLS) simultaneously. I therefore introduce an algorithm called Cyclic Causal…
ProEval efficiently estimates AI performance and discovers failures using pre-trained Gaussian Processes.
problem Resource-intensive evaluation of generative AI models.
method ProEval uses pre-trained Gaussian Processes and Bayesian quadrature to estimate performance and discover failures.
result ProEval requires significantly fewer samples to achieve accurate performance estimates and reveals more diverse failure cases.
Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. …
Python toolbox for causal structure learning from data.
problem Causal structure learning from data.
method Generates data from simulators or real-world datasets, learns causal structure, evaluates graphs, and includes gradient-based methods.
result Convenience and efficiency in causal discovery with GPU acceleration.
Study shows cognitive load impacts financial market efficiency, especially for less sophisticated investors.
problem Cognitive load's effect on financial market information processing.
method Developed a theoretical framework and tested it with exogenous disclosure complexity variation.
result Cognitive load significantly impairs price discovery, particularly for less sophisticated investors.
New methods identify concepts in trained embeddings reliably without human labels.
problem Identifying interpretable concepts in trained embedding spaces without human labels.
method Explicitly connecting concept discovery to PCA and ICA, proposing novel approaches for dependent concepts.
result Proven methods outperform competitors on a variety of experiments, achieving up to 29% better alignment with ground truth.