@article{manco17,
author = {Giuseppe Manco and Ettore Ritacco and Pasquale Rullo
and Lorenzo Gallucci and Will Astill and Dianne
Kimber and Marco Antonelli},
title = {Fault detection and explanation through big data
analysis on sensor streams},
journal = {Expert Systems With Applications},
year = 2017,
url = {http://communications.elsevier.com/r/?id=h454a7a34,185d8b0b,185de8d2&p1=authors.elsevier.com/a/1VDsN3PiGT3sWA},
volume = 87,
pages = {141-156},
keywords = {Fault detection; Anomaly detection; Outlier
explanation; Big data; Sensor data},
abstract = {Fault prediction is an important topic for the
industry as, by providing effective methods for
predictive maintenance, allows companies to perform
important time and cost savings. In this paper we
describe an application developed to predict and
explain door failures on metro trains. To this end,
the aim was twofold: first, devising prediction
techniques capable of early detecting door failures
from diagnostic data; second, describing failures in
terms of properties distinguishing them from normal
behavior. Data pre-processing was a complex task
aimed at overcoming a number of issues with the
dataset, like size, sparsity, bias, burst effect and
trust. Since failure premonitory signals did not
share common patterns, but were only characterized
as non-normal device signals, fault prediction was
performed by using outlier detection. Fault
explanation was finally achieved by exhibiting
device features showing abnormal values. An
experimental evaluation was performed to assess the
quality of the proposed approach. Results show that
high-degree outliers are effective indicators of
incipient failures. Also, explanation in terms of
abnormal feature values (responsible for
outlierness) seems to be quite expressive.There are
some aspects in the proposed approach that deserve
particular attention. We introduce a general
framework for the failure detection problem based on
an abstract model of diagnostic data, along with a
formal problem statement. They both provide the
basis for the definition of an effective data
pre-processing technique where the behavior of a
device, in a given time frame, is summarized through
a number of suitable statistics. This approach
strongly mitigates the issues related to data
errors/noise, thus enabling to perform an effective
outlier detection. All this, in our view, provides
the grounds of a general methodology for advanced
prognostic systems.}
}
@article{AngiulliFMP17,
author = {Fabrizio Angiulli and Fabio Fassetti and Giuseppe
Manco and Luigi Palopoli},
title = {Outlying property detection with numerical
attributes},
journal = {Data Min. Knowl. Discov.},
volume = {31},
number = {1},
pages = {134--163},
year = {2017},
keywords = {Outlier detection Outlying properties Kernel density
estimation Clustering },
abstract = {The outlying property detection problem (OPDP) is
the problem of discovering the properties
distinguishing a given object, known in advance to
be an outlier in a database, from the other database
objects. This problem has been recently analyzed
focusing on categorical attributes only. However,
numerical attributes are very relevant and widely
used in databases. Therefore, in this paper, we
analyze the OPDP within a context where also
numerical attributes are taken into account, which
represents a relevant case left open in the
literature. As major contributions, we present an
efficient parameter-free algorithm to compute the
measure of object exceptionality we introduce, and
propose a unified framework for mining exceptional
properties in the presence of both categorical and
numerical attributes.},
url = {https://www.dropbox.com/s/mfy4vuzi9u7ltt6/dami_final.pdf?dl=0},
doi = {10.1007/s10618-016-0458-x}
}
@article{Barbieri:2016,
author = {Barbieri, Nicola and Bonchi, Francesco and Manco,
Giuseppe},
title = {Efficient Methods for Influence-Based
Network-Oblivious Community Detection},
journal = {ACM Trans. Intell. Syst. Technol.},
issue_date = {2017},
volume = {8},
number = {2},
url = {https://www.dropbox.com/s/x730nf3ljtxj534/TIST-cwn-final_uploaded.pdf?dl=0},
year = {2016},
issn = {2157-6904},
pages = {32:1--32:31},
articleno = {32},
numpages = {31},
url = {http://doi.acm.org/10.1145/2979682},
doi = {10.1145/2979682},
acmid = {2979682},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {Social influence, information diffusion,
network-oblivious community detection, social
network analysis},
abstract = {The study of influence-driven propagations in social
networks and its exploitation for viral marketing
purposes has recently received a large deal of
attention. However, regardless of the fact that
users authoritativeness, expertise, trust and
influence are evidently topic-dependent, the
research on social influence has surprisingly
largely overlooked this aspect. In this article, we
study social influence from a topic modeling
perspective. We introduce novel topic-aware
influence-driven propagation models that, as we show
in our experiments, are more accurate in describing
real-world cascades than the standard (i.e.,
topic-blind) propagation models studied in the
literature. In particular, we first propose simple
We study the problem of detecting social communities
when the social graph is not available but instead
we have access to a log of user activity, that is, a
dataset of tuples (u, i, t) recording the fact that
user u “adopted” item i at time t. We propose a
stochastic framework that assumes that the adoption
of items is governed by an underlying diffusion
process over the unobserved social network and that
such a diffusion model is based on community-level
influence. That is, we aim at modeling communities
through the lenses of social contagion. By fitting
the model parameters to the user activity log, we
learn the community membership and the level of
influence of each user in each community. The
general framework is instantiated with two different
diffusion models, one with discrete time and one
with continuous time, and we show that the
computational complexity of both approaches is
linear in the number of users and in the size of the
propagation log. Experiments on synthetic data with
planted community structure show that our methods
outperform non-trivial baselines. The effectiveness
of the proposed techniques is further validated on
real-word data, on which our methods are able to
detect high-quality communities.}
}
@article{Manco:2016,
author = {Manco, Giuseppe and Rullo, Pasquale and Gallucci,
Lorenzo and Paturzo, Mirko},
title = {Rialto: A Knowledge Discovery suite for data
analysis},
journal = {Expert Syst. Appl.},
issue_date = {October 2016},
volume = {59},
number = {C},
url = {https://www.dropbox.com/s/h52bbs3qpldazyh/ESWA_10642_final.pdf?dl=0},
month = oct,
year = {2016},
issn = {0957-4174},
pages = {145--164},
numpages = {20},
url = {http://dx.doi.org/10.1016/j.eswa.2016.04.022},
doi = {10.1016/j.eswa.2016.04.022},
acmid = {2950691},
publisher = {Pergamon Press, Inc.},
address = {Tarrytown, NY, USA},
keywords = {Business analytics platforms, Data mining, Knowledge
Discovery process},
abstract = {A Knowledge Discovery (KD) process is a complex
inter-disciplinary task, where di↵erent types of
techniques coexist and cooperate for the purpose of
extract- ing useful knowledge from large amounts of
data. So, it is desirable having a unifying
environment, built on a formal basis, where to
design and perform the overall process. In this
paper we propose a general framework which for-
malizes a KD process as an algebraic expression,
that is, as a composition of operators representing
elementary operations on two worlds: the data and
the model worlds. Then, we describe a KD platform,
named Rialto, based on such a framework. In
particular, we provide the design principles of the
underlying architecture, highlight the basic
features, and provide a number of experimental
results aimed at assessing the e↵ectiveness of the
design choices.}
}
@article{QRE:QRE2008,
author = {Coleman, Shirley and Göb, Rainer and Manco, Giuseppe
and Pievatolo, Antonio and Tort-Martorell, Xavier
and Reis, Marco Seabra},
title = {How Can SMEs Benefit from Big Data? Challenges and a
Path Forward},
journal = {Quality and Reliability Engineering International},
volume = {32},
number = {6},
issn = {1099-1638},
url = {http://www.enbis.org/dl/9313_0081943714.pdf/as/Coleman_et_al-How%20Can%20SMEs%20Benefit%20from%20Big%20Data.pdf?_ts=519&_ts=519},
doi = {10.1002/qre.2008},
pages = {2151--2164},
keywords = {predictive analytics, maturity model, data science,
skills shortage},
year = {2016},
note = {QRE-15-0533.R1},
abstract = {Big data is big news, and large companies in all
sectors are making significant advances in their
customer relations, product selection and
development and consequent profitability through
using this valuable commodity. Small and medium
enterprises (SMEs) have proved themselves to be slow
adopters of the new technology of big data analytics
and are in danger of being left behind. In Europe,
SMEs are a vital part of the economy, and the
challenges they encounter need to be addressed as a
matter of urgency. This paper identifies barriers to
SME uptake of big data analytics and recognises
their complex challenge to all stakeholders,
including national and international policy makers,
IT, business management and data science
communities. The paper proposes a big data maturity
model for SMEs as a first step towards an SME
roadmap to data analytics. It considers the
‘state-of-the-art’ of IT with respect to usability
and usefulness for SMEs and discusses how SMEs can
overcome the barriers preventing them from adopting
existing solutions. The paper then considers
management perspectives and the role of maturity
models in enhancing and structuring the adoption of
data analytics in an organisation. The history of
total quality management is reviewed to inform the
core aspects of implanting a new paradigm. The paper
concludes with recommendations to help SMEs develop
their big data capability and enable them to
continue as the engines of European industrial and
business success.}
}
@article{BBM2013,
year = {2013},
issn = {0219-1377},
journal = {Knowledge and Information Systems},
doi = {10.1007/s10115-013-0646-6},
title = {Topic-aware social influence propagation models},
url = {https://www.dropbox.com/s/hvcb12oqc3dr8j6/influence-2012.pdf?dl=0},
publisher = {Springer-Verlag},
keywords = {Social influence; Topic modeling; Topic-aware
propagation model; Viral marketing},
author = {Barbieri, Nicola and Bonchi, Francesco and Manco,
Giuseppe},
pages = {1-30},
abstract = {The study of influence-driven propagations in social
networks and its exploitation for viral marketing
purposes has recently received a large deal of
attention. However, regardless of the fact that
users authoritativeness, expertise, trust and
influence are evidently topic-dependent, the
research on social influence has surprisingly
largely overlooked this aspect. In this article, we
study social influence from a topic modeling
perspective. We introduce novel topic-aware
influence-driven propagation models that, as we show
in our experiments, are more accurate in describing
real-world cascades than the standard (i.e.,
topic-blind) propagation models studied in the
literature. In particular, we first propose simple
topic-aware extensions of the well-known Independent
Cascade and Linear Threshold models. However, these
propagation models have a very large number of
parameters which could lead to
overfitting. Therefore, we propose a different
approach explicitly modeling authoritativeness,
influence and relevance under a topic-aware
perspective. Instead of considering user-to-user
influence, the proposed model focuses on user
authoritativeness and interests in a topic, leading
to a drastic reduction in the number of parameters
of the model. We devise methods to learn the
parameters of the models from a data set of past
propagations. Our experimentation confirms the high
accuracy of the proposed models and learning
schemes.}
}
@article{BarbieriMRCB13,
author = {Nicola Barbieri and Giuseppe Manco and Ettore
Ritacco and Marco Carnuccio and Antonio Bevacqua},
title = {Probabilistic topic models for sequence data},
journal = {Machine Learning},
volume = {93},
number = {1},
year = {2013},
pages = {5-29},
doi = {http://dx.doi.org/10.1007/s10994-013-5391-2},
url = {https://www.dropbox.com/s/hvf9rnorqbntqge/ECMLPKDD2013MLJ.pdf?dl=0},
abstract = {Probabilistic topic models are widely used in
different contexts to uncover the hidden structure
in large text corpora. One of the main (and perhaps
strong) assumption of these models is that
generative process follows a bag-of-words
assumption, i.e. each token is independent from the
previous one. We extend the popular Latent Dirichlet
Allocation model by exploiting three different
conditional Markovian assumptions: (i) the token
generation depends on the current topic and on the
previous token; (ii) the topic associated with each
observation depends on topic associated with the
previous one; (iii) the token generation depends on
the current and previous topic. For each of these
modeling assumptions we present a Gibbs Sampling
procedure for parameter estimation. Experimental
evaluation over real-word data shows the performance
advantages, in terms of recall and precision, of the
sequence-modeling approaches.}
}
@article{Costa201326,
author = {Gianni Costa and Giuseppe Manco and Riccardo Ortale
and Ettore Ritacco},
title = {Hierarchical clustering of XML documents focused on
structural components},
journal = {Data & Knowledge Engineering},
volume = {84},
number = {0},
pages = {26 - 46},
year = {2013},
doi = {10.1016/j.datak.2012.12.002},
url = {https://www.dropbox.com/s/e7mxy6zrezque94/XML_Clustering_DKE.pdf?dl=0},
abstract = {Clustering XML documents by structure is the task of
grouping them by common structural
components. Hitherto, this has been accomplished by
looking at the occurrence of one preestablished type
of structural components in the structures of the
XML documents. However, the a-priori chosen
structural components may not be the most
appropriate for effective clustering. Moreover, it
is likely that the resulting clusters exhibit a
certain extent of inner structural inhomogeneity,
because of uncaught differences in the structures of
the XML documents, due to further neglected forms of
structural components. To overcome these
limitations, a new hierarchical approach is
proposed, that allows to consider (if necessary)
multiple forms of structural components to isolate
structurally-homogeneous clusters of XML
documents. At each level of the resulting hierarchy,
clusters are divided by considering some type of
structural components (unaddressed at the preceding
levels), that still differentiate the structures of
the XML documents. Each cluster in the hierarchy is
summarized through a novel technique, that provides
a clear and differentiated understanding of its
structural properties. A comparative evaluation
over both real and synthetic XML data proves that
the devised approach outperforms established
competitors in effectiveness and
scalability. Cluster summarization is also shown to
be very representative.}
}
@article{DBLP:journals/kais/CostaMOR11,
author = {Gianni Costa and Giuseppe Manco and Riccardo Ortale
and Ettore Ritacco},
title = {From global to local and viceversa: uses of
associative rule learning for classification in
imprecise environments},
journal = {Knowl. Inf. Syst.},
volume = {33},
number = {1},
year = {2011},
pages = {137-169},
url = {https://www.dropbox.com/s/su4jqahygicwdwb/kais2011_final.pdf?dl=0},
abstract = {We propose two models for improving the performance
of rule-based classification under unbalanced and
highly imprecise domains. Both models are
probabilistic frameworks aimed to boost the
performance of basic rule-based classifiers. The
first model implements a global-to-local scheme,
where the response of a global rule-based classifier
is refined by performing a probabilistic analysis of
the coverage of its rules. In particular, the
coverage of the individual rules is used to learn
local probabilistic models, which ultimately refine
the predictions from the corresponding rules of the
global classifier. The second model implements a
dual local-to-global strategy, in which single
classification rules are combined within an
exponential probabilistic model in order to boost
the overall performance as a side effect of mutual
influence. Several variants of the basic ideas are
studied, and their perfor- mances are thoroughly
evaluated and compared with state-of-the-art
algorithms on standard benchmark datasets.}
}
@article{DBLP:journals/datamine/CostaMO10,
author = {Gianni Costa and Giuseppe Manco and Riccardo Ortale},
title = {An incremental clustering scheme for data
de-duplication},
journal = {Data Min. Knowl. Discov.},
volume = {20},
number = {1},
year = {2010},
pages = {152-187},
url = {http://dx.doi.org/10.1007/s10618-009-0155-0},
abstract = {We propose an incremental technique for discovering
duplicates in large databases of textual sequences,
i.e., syntactically different tuples, that refer to
the same real-world entity. The problem is
approached from a clustering perspective: given a
set of tuples, the objective is to partition them
into groups of duplicate tuples. Each newly arrived
tuple is assigned to an appropriate cluster via
nearest-neighbor classifi- cation. This is achieved
by means of a suitable hash-based index, that maps
any tuple to a set of indexing keys and assigns
tuples with high syntactic similarity to the same
buckets. Hence, the neighbors of a query tuple can
be efficiently identified by simply retrieving those
tuples that appear in the same buckets associated to
the query tuple itself, without completely scanning
the original database. Two alternative schemes for
computing indexing keys are discussed and
compared. An extensive experimental evaluation on
both synthetic and real data shows the effectiveness
of our approach.}
}
@article{DBLP:journals/kais/CesarioFLMO08,
author = {Eugenio Cesario and Francesco Folino and Antonio
Locane and Giuseppe Manco and Riccardo Ortale},
title = {Boosting text segmentation via progressive
classification},
journal = {Knowl. Inf. Syst.},
volume = {15},
number = {3},
year = {2008},
pages = {285-320},
url = {http://dx.doi.org/10.1007/s10115-007-0085-3},
abstract = {A novel approach for reconciling tuples stored as
free text into an existing attribute schema is
proposed. The basic idea is to subject the available
text to progressive classification, i.e., a
multi-stage classification scheme where, at each
intermediate stage, a classifier is learnt that
analyzes the textual fragments not reconciled at the
end of the previous steps. Classifica- tion is
accomplished by an ad hoc exploitation of
traditional association mining algorithms, and is
supported by a data transformation scheme which
takes advantage of domain-specific
dictionaries/ontologies. A key feature is the
capability of progressively enriching the avail-
able ontology with the results of the previous
stages of classification, thus significantly
improving the overall classification accuracy. An
extensive experimental evaluation shows the
effectiveness of our approach.}
}
@article{DBLP:journals/jiis/MancoMT08,
author = {Giuseppe Manco and Elio Masciari and Andrea
Tagarelli},
title = {Mining categories for emails via clustering and
pattern discovery},
journal = {J. Intell. Inf. Syst.},
volume = {30},
number = {2},
year = {2008},
pages = {153-181},
url = {http://dx.doi.org/10.1007/s10844-006-0024-x},
abstract = {The continuous exchange of information by means of
the popular email service has raised the problem of
managing the huge amounts of messages received from
users in an effective and efficient way. We deal
with the problem of email classification by
conceiving suitable strategies for: (1) organizing
messages into homogeneous groups, (2) redirecting
further incoming messages according to an initial
organization, and (3) building reliable descriptions
of the message groups discovered. We propose a
unified framework for handling and classifying email
messages. In our framework, messages sharing similar
features are clustered in a folder
organization. Clustering and pattern discovery
techniques for mining struc- tured and unstructured
information from email messages are the basis of an
overall process of folder creation/maintenance and
email redirection. Pattern discovery is also
exploited for generating suitable cluster
descriptions that play a leading role in cluster
updating. Experimental evaluation performed on
several personal mailboxes shows the effectiveness
of our approach.}
}
@article{DBLP:journals/dke/FlescaMMPP07,
author = {Sergio Flesca and Giuseppe Manco and Elio Masciari
and Luigi Pontieri and Andrea Pugliese},
title = {Exploiting structural similarity for effective Web
information extraction},
journal = {Data Knowl. Eng.},
volume = {60},
number = {1},
year = {2007},
pages = {222-234},
url = {http://dx.doi.org/10.1016/j.datak.2006.01.001},
abstract = {In this paper, we propose a classification technique
for Web pages, based on the detection of structural
similarities among semistructured documents, and
devise an architecture exploiting such technique for
the purpose of information extraction. The proposal
significantly differs from standard methods based on
graph-matching algorithms, and is based on the idea
of representing the structure of a document as a
time series in which each occurrence of a tag
corresponds to an impulse. The degree of similarity
between documents is then stated by analyzing the
frequencies of the corresponding Fourier
transform. Experiments on real data show the
effectiveness of the proposed technique.}
}
@article{DBLP:journals/tkde/CesarioMO07,
author = {Eugenio Cesario and Giuseppe Manco and Riccardo
Ortale},
title = {Top-Down Parameter-Free Clustering of
High-Dimensional Categorical Data},
journal = {IEEE Trans. Knowl. Data Eng.},
volume = {19},
number = {12},
year = {2007},
pages = {1607-1624},
url = {http://dx.doi.org/10.1109/TKDE.2007.190649},
abstract = {A parameter-free, fully-automatic approach to
clustering high-dimensional categorical data is
proposed. The technique is based on a two-phase
iterative procedure, which attempts to improve the
overall quality of the whole partition. In the first
phase, cluster assignments are given, and a new
cluster is added to the partition by identifying and
splitting a low-quality cluster. In the second
phase, the number of clusters is fixed, and an
attempt to optimize cluster assignments is done. On
the basis of such features, the algorithm attempts
to improve the overall quality of the whole
partition and finds clusters in the data, whose
number is naturally established on the basis of the
inherent features of the underlying data set rather
than being previously specified. Furthermore, the
approach is parametric to the notion of cluster
quality: Here, a cluster is defined as a set of
tuples exhibiting a sort of homogeneity. We show how
a suitable notion of cluster homogeneity can be
defined in the context of high-dimensional
categorical data, from which an effective instance
of the proposed clustering scheme immediately
follows. Experiments on both synthetic and real data
prove that the devised algorithm scales linearly and
achieves nearly optimal results in terms of
compactness and separation.}
}
@article{DBLP:journals/is/GrecoGMS07,
author = {Gianluigi Greco and Antonella Guzzo and Giuseppe
Manco and Domenico Sacc{\`a}},
title = {Mining unconnected patterns in workflows},
journal = {Inf. Syst.},
volume = {32},
number = {5},
year = {2007},
pages = {685-712},
url = {http://dx.doi.org/10.1016/j.is.2006.05.001},
abstract = {General patterns of execution that have been
frequently scheduled by a workflow management system
provide the administrator with previously unknown,
and potentially useful information, e.g., about the
existence of unexpected causalities between
subprocesses of a given workflow. This paper
investigates the problem of mining unconnected
patterns on the basis of some execution traces,
i.e., of detecting sets of activities exhibiting no
explicit dependency relationships that are
frequently executed together. The problem is faced
in the paper by proposing and analyzing two
algorithms. One algorithm takes into account
information about the structure of the control-flow
graph only, while the other is a smart refinement
where the knowledge of the frequencies of edges and
activities in the traces at hand is also accounted
for, by means of a sophisticated graphical
analysis. Both algorithms have been implemented and
integrated into a system prototype, which may
profitably support the enactment phase of the
workflow. The correctness of the two algorithms is
formally proven, and several experiments are
reported to evidence the ability of the graphical
analysis to significantly improve the performances,
by dramatically pruning the search space of
candidate patterns.}
}
@article{DBLP:journals/tkde/FlescaMMPP05,
author = {Sergio Flesca and Giuseppe Manco and Elio Masciari
and Luigi Pontieri and Andrea Pugliese},
title = {Fast Detection of XML Structural Similarity},
journal = {IEEE Trans. Knowl. Data Eng.},
volume = {17},
number = {2},
year = {2005},
pages = {160-175},
url = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.27},
abstract = {Because of the widespread diffusion of
semistructured data in XML format, much research
effort is currently devoted to support the storage
and retrieval of large collections of such
documents. XML documents can be compared as to their
structural similarity, in order to group them into
clusters so that different storage, retrieval, and
processing techniques can be effectively
exploited. In this scenario, an efficient and
effective similarity function is the key of a
successful data management process. We present an
approach for detecting structural similarity between
XML documents which significantly differs from
standard methods based on graph-matching algorithms,
and allows a significant reduction of the required
computation costs. Our proposal roughly consists of
linearizing the structure of each XML document, by
representing it as a numerical sequence and, then,
comparing such sequences through the analysis of
their frequencies. First, some basic strategies for
encoding a document are proposed, which can focus on
diverse structural facets. Moreover, the theory of
Discrete Fourier Transform is exploited to
effectively and efficiently compare the encoded
documents (i.e., signals) in the domain of
frequencies. Experimental results reveal the
effectiveness of the approach, also in comparison
with standard methods.}
}
@article{DBLP:journals/tkde/GrecoGMS05,
author = {Gianluigi Greco and Antonella Guzzo and Giuseppe
Manco and Domenico Sacc{\`a}},
title = {Mining and Reasoning on Workflows},
journal = {IEEE Trans. Knowl. Data Eng.},
volume = {17},
number = {4},
year = {2005},
pages = {519-534},
url = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.63},
abstract = {Today’s workflow management systems represent a key
technological infrastructure for advanced
applications that is attracting a growing body of
research, mainly focused in developing tools for
workflow management, that allow users both to
specify the “static” aspects, like preconditions,
precedences among activities, and rules for
exception handling, and to control its execution by
scheduling the activities on the available
resources. This paper deals with an aspect of
workflows which has so far not received much
attention even though it is crucial for the
forthcoming scenarios of large scale applications on
the Web: Providing facilities for the human system
administrator for identifying the choices performed
more frequently in the past that had lead to a
desired final configuration. In this context, we
formalize the problem of discovering the most
frequent patterns of executions, i.e., the workflow
substructures that have been scheduled more
frequently by the system. We attacked the problem by
developing two data mining algorithms on the basis
of an intuitive and original graph formalization of
a workflow schema and its occurrences. The model is
used both to prove some intractability results that
strongly motivate the use of data mining techniques
and to derive interesting structural properties for
reducing the search space for frequent
patterns. Indeed, the experiments we have carried
out show that our algorithms outperform standard
data mining algorithms adapted to discover frequent
patterns of workflow executions.}
}
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