15,727 research outputs found
Unfolding-Based Process Discovery
This paper presents a novel technique for process discovery. In contrast to
the current trend, which only considers an event log for discovering a process
model, we assume two additional inputs: an independence relation on the set of
logged activities, and a collection of negative traces. After deriving an
intermediate net unfolding from them, we perform a controlled folding giving
rise to a Petri net which contains both the input log and all
independence-equivalent traces arising from it. Remarkably, the derived Petri
net cannot execute any trace from the negative collection. The entire chain of
transformations is fully automated. A tool has been developed and experimental
results are provided that witness the significance of the contribution of this
paper.Comment: This is the unabridged version of a paper with the same title
appearead at the proceedings of ATVA 201
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
Stacked Generalizations in Imbalanced Fraud Data Sets using Resampling Methods
This study uses stacked generalization, which is a two-step process of
combining machine learning methods, called meta or super learners, for
improving the performance of algorithms in step one (by minimizing the error
rate of each individual algorithm to reduce its bias in the learning set) and
then in step two inputting the results into the meta learner with its stacked
blended output (demonstrating improved performance with the weakest algorithms
learning better). The method is essentially an enhanced cross-validation
strategy. Although the process uses great computational resources, the
resulting performance metrics on resampled fraud data show that increased
system cost can be justified. A fundamental key to fraud data is that it is
inherently not systematic and, as of yet, the optimal resampling methodology
has not been identified. Building a test harness that accounts for all
permutations of algorithm sample set pairs demonstrates that the complex,
intrinsic data structures are all thoroughly tested. Using a comparative
analysis on fraud data that applies stacked generalizations provides useful
insight needed to find the optimal mathematical formula to be used for
imbalanced fraud data sets.Comment: 19 pages, 3 figures, 8 table
Neural networks in geophysical applications
Neural networks are increasingly popular in geophysics.
Because they are universal approximators, these
tools can approximate any continuous function with an
arbitrary precision. Hence, they may yield important
contributions to finding solutions to a variety of geophysical applications.
However, knowledge of many methods and techniques
recently developed to increase the performance
and to facilitate the use of neural networks does not seem
to be widespread in the geophysical community. Therefore,
the power of these tools has not yet been explored to
their full extent. In this paper, techniques are described
for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size
and architecture
Incorporating negative information to process discovery of complex systems
The discovery of a formal process model from event logs describing real process executions is a challenging problem that has been studied from several angles. Most of the contributions consider the extraction of a model as a one-class supervised learning problem where only a set of process instances is available. Moreover, the majority of techniques cannot generate complex models, a crucial feature in some areas like manufacturing. In this paper we present a fresh look at process discovery where undesired process behaviors can also be taken into account. This feature may be crucial for deriving process models which are less complex, fitting and precise, but also good on generalizing the right behavior underlying an event log. The technique is based on the theory of convex polyhedra and satisfiability modulo theory (SMT) and can be combined with other process discovery approach as a post processing step to further simplify complex models. We show in detail how to apply the proposed technique in combination with a recent method that uses numerical abstract domains. Experiments performed in a new prototype implementation show the effectiveness of the technique and the ability to be combined with other discovery techniques.Peer ReviewedPostprint (author's final draft
Conformance checking: A state-of-the-art literature review
Conformance checking is a set of process mining functions that compare
process instances with a given process model. It identifies deviations between
the process instances' actual behaviour ("as-is") and its modelled behaviour
("to-be"). Especially in the context of analyzing compliance in organizations,
it is currently gaining momentum -- e.g. for auditors. Researchers have
proposed a variety of conformance checking techniques that are geared towards
certain process model notations or specific applications such as process model
evaluation. This article reviews a set of conformance checking techniques
described in 37 scholarly publications. It classifies the techniques along the
dimensions "modelling language", "algorithm type", "quality metric", and
"perspective" using a concept matrix so that the techniques can be better
accessed by practitioners and researchers. The matrix highlights the dimensions
where extant research concentrates and where blind spots exist. For instance,
process miners use declarative process modelling languages often, but
applications in conformance checking are rare. Likewise, process mining can
investigate process roles or process metrics such as duration, but conformance
checking techniques narrow on analyzing control-flow. Future research may
construct techniques that support these neglected approaches to conformance
checking
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
There is often latent network structure in spatial and temporal data and the
tools of network analysis can yield fascinating insights into such data. In
this paper, we develop a nonparametric method for network reconstruction from
spatiotemporal data sets using multivariate Hawkes processes. In contrast to
prior work on network reconstruction with point-process models, which has often
focused on exclusively temporal information, our approach uses both temporal
and spatial information and does not assume a specific parametric form of
network dynamics. This leads to an effective way of recovering an underlying
network. We illustrate our approach using both synthetic networks and networks
constructed from real-world data sets (a location-based social media network, a
narrative of crime events, and violent gang crimes). Our results demonstrate
that, in comparison to using only temporal data, our spatiotemporal approach
yields improved network reconstruction, providing a basis for meaningful
subsequent analysis --- such as community structure and motif analysis --- of
the reconstructed networks
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