All Classes Interface Summary Class Summary Enum Summary Exception Summary
| Class |
Description |
| AbstractAllocation |
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| AbstractEntropyCalculator |
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| AbstractionLevel |
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| AbstractMeasure<T extends java.lang.Number> |
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| AbstractMiner |
Created by andreas on 9/15/16.
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| AbstractPNMLElement |
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| AbstractTimePredictor |
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| AccurateNoiseFilter |
This noise filter adds noise to an event log by randomly removing events and adding events.
|
| Activity |
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| AdvancedSimulator |
Event based simulator.
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| Aggregate |
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| Aggregate.Function<T,R> |
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| Allocatable |
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| Allocation |
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| Allocation.AllocType |
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| AllocationBasedNetGenerator |
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| AllocationBasedNetGenerator.ObsType |
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| AllocationConfig |
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| AllocationDistribution |
A distribution of possible allocation options for some activity/task in a process.
|
| AnomalousIntervalsComputer |
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| AnomalousIntervalsComputerPlugin |
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| AnomalyIntervals |
Simple container class to store temporal anomaly regions (intervals),
which correspond to duration outliers of transitions in a StochasticNet |
| AnotherAbstractRealDistribution |
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| ApproximateDensityDistribution |
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| ARMATimeSeries |
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| AutoArimaTimeSeries |
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| AverageMethodTimeSeries |
The simplest form of model to fit to a data set is the average.
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| BellmanFordDistance<V,E> |
Simple implementation of the Bellman Ford distance computation.
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| BernsteinExponentialApproximation |
Implementation of the expolynomial distribution class presented in:
|
| Building |
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| CaseStatistics |
Case statistics for an individual case in a log.
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| CaseStatisticsAnalyzer |
Provides statistical analysis for outliers using non-parametric density estimations
with foundations in:
|
| CaseStatisticsConnection |
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| CaseStatisticsList |
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| CaseTimeSeries |
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| ComparablePair<K extends java.lang.Comparable<K>,V> |
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| Compartment |
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| ComputedMeasures |
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| ConstrainedHistogramDistribution |
Constrains a histogram to only use the part that is consistent with the constraint.
|
| ConvertDistributionsPlugin |
Converts all timed distributions of a StochasticNet to a given distribution type (except for immediate and deterministic transitions) |
| ConvolutionHelper |
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| CostFunction |
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| DiagonalDistribution |
A diagonal parallel to the line y = -x is defined by the sum of x and y.
|
| DiracDeltaDistribution |
This is the distribution for a random variable emitting a deterministic value.
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| DirectAllocation |
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| Distance |
The score of a candidate Log-Model pair vs.
|
| DistanceFunction |
|
| DistributionUtils |
|
| DriftMethodTimeSeries |
The drift method extrapolates a line through the first observation and the last observation
for computing the next forecast.
|
| EfficientDiscreteStochasticNetSemanticsImpl |
Stores a marking encoding temporal states in a discrete domain.
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| EfficientStochasticNetSemanticsImpl |
Hopefully more efficient implementation of the semantics...
|
| EfficientTimedMarking |
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| Entity |
General superclass of everything that can be related to process models.
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| EntropyCalculatorApproximate |
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| EntropyCalculatorExact |
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| EntropyCalculatorQuantile |
Created by andreas on 6/7/17.
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| EntropyMeasure |
A class that captures the entropy of a net.
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| ExportLogLikelihoodPlugin |
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| FastDensityFunction |
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| GaussianKernelDistribution |
Simple gaussian kernel estimator.
|
| GaussianReflectionKernelDistribution |
Very plain boundary reflection kernel estimator.
|
| Generator |
Generates block-structured Petri nets with an iterative mechanism.
|
| GeneratorConfig |
Configuration settings for the stochastic Petri net generator Generator. |
| GeneratorConfigPanel |
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| GeneratorPlugin |
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| GradientDescent |
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| GreedyAnomalyResolver |
Greedily resolves anomalies in Sensor data.
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| Interaction |
An interaction candidate that is created by looking at different combinations of SensorInterval s.
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| JaccardSimilarity |
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| LastObservationTimeSeries |
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| LikelihoodAnalyzer |
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| LikelihoodAnalyzerPlugin |
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| LimitedQueue<K> |
A data structure that is limited in size and otherwise works as a queue
(but can also access the latest/youngest element at the tail)
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| LimitedTreeMap<K,V> |
A TreeMap limited in upper size...
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| LimitedTreeMap<K,V> |
A TreeMap limited in upper size...
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| LimitedTreeSet<E> |
A TreeSet with an upper bound on size (if an element will be inserted, the lowest value will be removed)
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| LimitedTreeSet<E> |
A TreeSet with an upper bound on size (if an element will be inserted, the lowest value will be removed)
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| ListAbstractionLevel |
Not really abstracting.
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| LoadAnnotationPlugin |
This is very basic.
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| LocalSearchMiner<M> |
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| Location |
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| LocationChange |
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| LogLocationDelayInducer |
This class takes a location unaware log and transforms it into a location
aware log.
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| LogToSensorIntervalConverter |
Given a rich log with resources and locations for events (and start-and complete transitions),
we can generate intervals of where the resources are located.
|
| LoopsLinearizerPlugin |
Goes through all the traces in a log and numbers occurrences of repeated events,
such that repetitions get numbered and distinguished.
|
| LoopsNotSupportedException |
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| MarkingBasedSelectionWeightCostFunction |
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| MeanCorrectedReflectionKernelDistribution |
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| MeasureConfig |
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| MeasurePlugin |
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| MeasureProvider<T extends AbstractMeasure<?>> |
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| MemorylessTruncatedWrapper |
When truncating an exponential function left (setting a lower constraint),
the result is the same exponential function offset by the constraint
|
| ModelComparisonResult |
|
| MultiSetAbstractionLevel |
Abstracting from permutations, but still keeping count of executions
|
| NaiveMethodTimeSeries |
The naive predictor for a time series simply returns the latest value as the predictor for the next one.
|
| NoiseLogFilter |
Introduces noise into an event log by randomly removing items, or adding duplicate events.
|
| NoiseLogFilter.NoiseLogFilterParameters |
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| NoiseLogFilter.NoiseTypes |
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| NonConvergenceException |
It might be, that the EM-fitting algorithm used to fit densities to data values might not converge.
|
| NormalizedMarkingCache |
|
| NormalizedPositiveGradientDescent |
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| Observation<H> |
An individual historical observation on which to base future predictions.
|
| OnlineNormalEstimator |
An OnlineNormalEstimator provides an object that estimates means,
variances, and standard deviations for a stream of numbers presented one at a
time.
|
| OptimalMiner<M> |
|
| Outcome |
The outcome of an abstraction of a trace
|
| OutlierVisualizer |
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| OverfitChoiceMiner |
Assumes the log to have dedicated start and end events embracing each trace.
|
| OverfitMiner |
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| OverfitMinerPlugin |
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| PerformanceEnricher |
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| PerformanceEnricherConfig |
Configuration to be used for the net to be mined.
|
| PerformanceEnricherExperimentPlugin |
Performs a round-trip: model -> log -> model
1.
|
| PerformanceEnricherExperimentPlugin.ExperimentType |
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| PerformanceEnricherExperimentResult |
|
| PerformanceEnricherPlugin |
|
| PerformanceVisualization |
|
| PerformingResource |
|
| Person |
|
| PetrinetLSMiner |
Created by andreas on 3/22/17.
|
| PetrinetModelAllocations |
|
| PetrinetWithMapping |
|
| Plot |
Plot to display the probability functions.
|
| PlotPanelFreeChart |
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| PNMLArc |
|
| PNMLDimension |
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| PnmlExportStochasticNet |
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| PNMLFill |
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| PNMLFinalMarkings |
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| PNMLGraphics |
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| PnmlImportStochasticNet |
|
| PNMLMarking |
|
| PNMLModule |
|
| PNMLName |
|
| PNMLNet |
|
| PNMLPage |
|
| PNMLParameter |
The default way of Prom to import PNML files is to scale them by a factor of
2.0 (see PnmlPosition.SCALE) We do not want
to change this, so that we are compatible with importing and exporting PNML
files
|
| PNMLPlace |
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| PNMLPlaceRef |
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| PNMLPoint |
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| PNMLRoot |
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| PNMLText |
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| PNMLToolSpecific |
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| PNMLTransition |
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| PNSimulator |
Very plain simulator only used for evaluation of the evaluation of the mining of stochastic Petri nets
and the repair-log plug-in.
|
| PNSimulatorConfig |
Configuration parameters for the simple simulation of (stochastic) Petri Nets.
|
| PNSimulatorConfigUI |
A configuration window containing properties for the configuration
of the simple simulator PNSimulator. |
| PNSimulatorPlugin |
Deprecated.
|
| PNTimeSeriesSimulator |
A stochastic Petri net simulator that replaces each timed transition's distribution with a
time series predictor.
|
| PNUnfoldedSimulator |
Idea: Use one unfolded model for multiple traces.
|
| PNUnroller |
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| PoissonAllocation |
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| PrecisionAligner |
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| Prediction<T> |
|
| PredictionData |
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| PredictionExperimentConfig |
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| PredictionExperimentPlugin |
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| PredictionExperimentResult |
|
| PreprocessLogFilter |
Created by andreas on 2/15/17.
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| ProbabilisticAlignmentPlugin |
|
| ProbabilisticAllocation |
|
| QualityCriterion |
|
| RAbstractDistribution |
|
| RCensoredLogSplineDistribution |
Wraps the logspline density estimation for data that is randomly right-censored.
|
| Record |
|
| RejectionWrapper |
A wrapper around a distribution that cuts off a part below a certain threshold RejectionWrapper.constraint
and rescales the rest of the distribution to become a valid one again. |
| ReliableInvisibleTransitionPerfCounter |
This class behaves as the ReliablePerfCounter class,
but allows invisible transitions in the model and assumes that these do not take time.
|
| ReplayStep |
A Replay Step with the duration and
probabilistic information like density p(A=a | Model)
according to the probabilistic Model used in replay.
|
| ReplayStepDensityFunction |
|
| Resource |
|
| RGaussianKernelDistribution |
|
| RLogSplineDistribution |
Logspline density fitting to data using the logspline package in R.
|
| Role |
|
| Room |
A representation of a room which can be allocated to activities.
|
| RProvider |
|
| RTimeSeries<H> |
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| ScheduleDrivenDiscoveryPlugin |
|
| SeasonalNaiveMethodTimeSeries |
A naive seasonal forecast (forecasts the same value as the one observed in the last season)
|
| SensorInterval |
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| SensorIntervalVisualization |
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| SensorIntervalVisualization.IntervalVisualizer |
|
| SetAbstractionLevel |
Abstracts from sequences and only retains the set of elements in the sequence, disregards permutations and also frequencies.
|
| ShiftedDistribution |
We shift the distribution downwards so that the outliers are below zero.
|
| SimpleDistanceFunction |
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| SimpleHeuristicFilter |
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| SimpleHistogramDistribution |
|
| SinusoidalSeries |
|
| SliceSampler |
|
| SortedPair<K extends java.lang.Comparable<K>,V> |
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| SortedSensorIntervals |
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| SortedSetComparator<E extends java.lang.Comparable<E>> |
|
| StatefulTimeseriesDistribution |
This is a "stateful" distribution that knows it's current time.
|
| StochasticManifestCollector |
Simple analyzer for a manifest based replay.
|
| StochasticMinerPlugin |
Not yet implemented.
|
| StochasticNet |
"stochastic Petri net" = Petri net with annotated stochastic information.
|
| StochasticNet.DistributionType |
Supported parametric and non-parametric distributions
|
| StochasticNet.ExecutionPolicy |
Execution policy of the network.
|
| StochasticNet.TimeUnit |
Enumeration specifying in which time unit the parameters of the net are given.
|
| StochasticNetDeserializer |
|
| StochasticNetImpl |
|
| StochasticNetSemantics |
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| StochasticNetSemanticsImpl |
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| StochasticNetSemanticsProvider |
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| StochasticNetToPNMLConverter |
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| StochasticNetUtils |
|
| StochasticNetUtils.DummyConnectionManager |
|
| StochasticNetUtils.DummyConsolePluginContext |
|
| StochasticNetUtils.FakePluginContext |
Created by andreas on 25/01/2017.
|
| StochasticNetUtils.Renamer |
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| TemporalAnalyzer |
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| TemporalConnection |
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| TemporalMiner |
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| TemporalMinerPlugin |
|
| TemporalModel |
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| TemporalModelPanel |
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| TemporalModelVizualizer |
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| TemporalNode |
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| TemporalProfile |
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| TemporalProfileOptimizer |
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| TemporalRelation |
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| TemporalRelations |
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| TemporalSample |
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| TemporalTraces |
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| TimeConstraints |
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| TimeConstraints.TimeAssumption |
|
| TimeConstraintsPanel |
|
| TimedTransition |
A timed transition to be used in
Stochastic Petri Nets
with arbitrary distributions
|
| TimePredictor |
|
| TimePredictorPlugin |
This is a simple time predictor based on a stochastic model.
|
| TimeSeries<H> |
A time series of values (might be durations, might be rates, might be anything)
|
| TimeSeriesAlignmentPlugin |
|
| TimeSeriesComparer |
|
| TimeSeriesConfiguration |
|
| TimeSeriesConfiguration.AvailableScripts |
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| TimeSeriesConfiguration.MissingDataHandling |
Different strategies to try for handling missing data (replacement almost always introduces a bias (mean replacement underestimates the variance)
It might be better to keep them as missing values and let R figure out how to deal with them...
|
| TimeSeriesConfiguration.TimeSeriesType |
|
| TimeSeriesConverter |
|
| TimeSeriesPanel |
|
| TimeseriesPredictor |
|
| TopologyLayout<V,E> |
|
| ToStochasticNet |
|
| Triple<K,L,M> |
|
| TruncatedDistributionFactory |
|
| TruncatedWrapper |
A wrapper around a distribution that cuts off a part below a certain threshold RejectionWrapper.constraint
and rescales the rest of the distribution to become a valid one again. |
| UniformAllocation |
|
| UniformSetAllocation |
|
| VisitState |
A State to visit during exploration
TODO: make more compact (e.g.
|
| VisualControls |
|
| WeightsOptimizer |
|
| WorldConfiguration |
|