50,852 research outputs found
Artifact Lifecycle Discovery
Artifact-centric modeling is a promising approach for modeling business
processes based on the so-called business artifacts - key entities driving the
company's operations and whose lifecycles define the overall business process.
While artifact-centric modeling shows significant advantages, the overwhelming
majority of existing process mining methods cannot be applied (directly) as
they are tailored to discover monolithic process models. This paper addresses
the problem by proposing a chain of methods that can be applied to discover
artifact lifecycle models in Guard-Stage-Milestone notation. We decompose the
problem in such a way that a wide range of existing (non-artifact-centric)
process discovery and analysis methods can be reused in a flexible manner. The
methods presented in this paper are implemented as software plug-ins for ProM,
a generic open-source framework and architecture for implementing process
mining tools
Counterexample-Guided Data Augmentation
We present a novel framework for augmenting data sets for machine learning
based on counterexamples. Counterexamples are misclassified examples that have
important properties for retraining and improving the model. Key components of
our framework include a counterexample generator, which produces data items
that are misclassified by the model and error tables, a novel data structure
that stores information pertaining to misclassifications. Error tables can be
used to explain the model's vulnerabilities and are used to efficiently
generate counterexamples for augmentation. We show the efficacy of the proposed
framework by comparing it to classical augmentation techniques on a case study
of object detection in autonomous driving based on deep neural networks
DNA-inspired online behavioral modeling and its application to spambot detection
We propose a strikingly novel, simple, and effective approach to model online
user behavior: we extract and analyze digital DNA sequences from user online
actions and we use Twitter as a benchmark to test our proposal. We obtain an
incisive and compact DNA-inspired characterization of user actions. Then, we
apply standard DNA analysis techniques to discriminate between genuine and
spambot accounts on Twitter. An experimental campaign supports our proposal,
showing its effectiveness and viability. To the best of our knowledge, we are
the first ones to identify and adapt DNA-inspired techniques to online user
behavioral modeling. While Twitter spambot detection is a specific use case on
a specific social media, our proposed methodology is platform and technology
agnostic, hence paving the way for diverse behavioral characterization tasks
On mining complex sequential data by means of FCA and pattern structures
Nowadays data sets are available in very complex and heterogeneous ways.
Mining of such data collections is essential to support many real-world
applications ranging from healthcare to marketing. In this work, we focus on
the analysis of "complex" sequential data by means of interesting sequential
patterns. We approach the problem using the elegant mathematical framework of
Formal Concept Analysis (FCA) and its extension based on "pattern structures".
Pattern structures are used for mining complex data (such as sequences or
graphs) and are based on a subsumption operation, which in our case is defined
with respect to the partial order on sequences. We show how pattern structures
along with projections (i.e., a data reduction of sequential structures), are
able to enumerate more meaningful patterns and increase the computing
efficiency of the approach. Finally, we show the applicability of the presented
method for discovering and analyzing interesting patient patterns from a French
healthcare data set on cancer. The quantitative and qualitative results (with
annotations and analysis from a physician) are reported in this use case which
is the main motivation for this work.
Keywords: data mining; formal concept analysis; pattern structures;
projections; sequences; sequential data.Comment: An accepted publication in International Journal of General Systems.
The paper is created in the wake of the conference on Concept Lattice and
their Applications (CLA'2013). 27 pages, 9 figures, 3 table
The Bases of Association Rules of High Confidence
We develop a new approach for distributed computing of the association rules
of high confidence in a binary table. It is derived from the D-basis algorithm
in K. Adaricheva and J.B. Nation (TCS 2017), which is performed on multiple
sub-tables of a table given by removing several rows at a time. The set of
rules is then aggregated using the same approach as the D-basis is retrieved
from a larger set of implications. This allows to obtain a basis of association
rules of high confidence, which can be used for ranking all attributes of the
table with respect to a given fixed attribute using the relevance parameter
introduced in K. Adaricheva et al. (Proceedings of ICFCA-2015). This paper
focuses on the technical implementation of the new algorithm. Some testing
results are performed on transaction data and medical data.Comment: Presented at DTMN, Sydney, Australia, July 28, 201
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