248 research outputs found
An Integrated Semantic Web Service Discovery and Composition Framework
In this paper we present a theoretical analysis of graph-based service
composition in terms of its dependency with service discovery. Driven by this
analysis we define a composition framework by means of integration with
fine-grained I/O service discovery that enables the generation of a graph-based
composition which contains the set of services that are semantically relevant
for an input-output request. The proposed framework also includes an optimal
composition search algorithm to extract the best composition from the graph
minimising the length and the number of services, and different graph
optimisations to improve the scalability of the system. A practical
implementation used for the empirical analysis is also provided. This analysis
proves the scalability and flexibility of our proposal and provides insights on
how integrated composition systems can be designed in order to achieve good
performance in real scenarios for the Web.Comment: Accepted to appear in IEEE Transactions on Services Computing 201
Eco‐Holonic 4.0 Circular Business Model to Conceptualize Sustainable Value Chain Towards Digital Transition
The purpose of this paper is to conceptualize a circular business model based on an Eco-Holonic Architecture, through the integration of circular economy and holonic principles. A conceptual model is developed to manage the complexity of integrating circular economy principles, digital transformation, and tools and frameworks for sustainability into business models. The proposed architecture is multilevel and multiscale in order to achieve the instantiation of the sustainable value chain in any territory. The architecture promotes the incorporation of circular economy and holonic principles into new circular business models. This integrated perspective of business model can support the design and upgrade of the manufacturing companies in their respective industrial sectors. The conceptual model proposed is based on activity theory that considers the interactions between technical and social systems and allows the mitigation of the metabolic rift that exists between natural and social metabolism. This study contributes to the existing literature on circular economy, circular business models and activity theory by considering holonic paradigm concerns, which have not been explored yet. This research also offers a unique holonic architecture of circular business model by considering different levels, relationships, dynamism and contextualization (territory) aspects
Gradual Drift Detection in Process Models Using Conformance Metrics
Changes, planned or unexpected, are common during the execution of real-life
processes. Detecting these changes is a must for optimizing the performance of
organizations running such processes. Most of the algorithms present in the
state-of-the-art focus on the detection of sudden changes, leaving aside other
types of changes. In this paper, we will focus on the automatic detection of
gradual drifts, a special type of change, in which the cases of two models
overlap during a period of time. The proposed algorithm relies on conformance
checking metrics to carry out the automatic detection of the changes,
performing also a fully automatic classification of these changes into sudden
or gradual. The approach has been validated with a synthetic dataset consisting
of 120 logs with different distributions of changes, getting better results in
terms of detection and classification accuracy, delay and change region
overlapping than the main state-of-the-art algorithms
Efficient edge filtering of directly-follows graphs for process mining
Automated process discovery is a process mining operation that takes as input an event log of a business process and generates a diagrammatic representation of the process. In this setting, a common diagrammatic representation generated by commercial tools is the directly-follows graph (DFG). In some real-life scenarios, the DFG of an event log contains hundreds of edges, hindering its understandability. To overcome this shortcoming, process mining tools generally offer the possibility of filtering the edges in the DFG. We study the problem of efficiently filtering the DFG extracted from an event log while retaining the most frequent relations. We formalize this problem as an optimization problem, specifically, the problem of finding a sound spanning subgraph of a DFG with a minimal number of edges and a maximal sum of edge frequencies. We show that this problem is an instance of an NP-hard problem and outline several polynomial-time heuristics to compute approximate solutions. Finally, we report on an evaluation of the efficiency and optimality of the proposed heuristics using 13 real-life event logsWe thank Luciano García-Baíuelos for proposing the idea of combining the results of Chu-Liu-Edmonds’ algorithm to filter a DFG. We also thank Adriano Augusto for providing us with the implementation of the Split Miner filtering technique. This research was funded by the Spanish Ministry of Economy and Competitiveness (TIN2017-84796-C2-1-R) and the Galician Ministry of Education, Culture and Universities (ED431G/08). These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program). D. Chapela-Campa is supported by the Spanish Ministry of Education, under the FPU national plan (FPU16/04428 and EST19/00135). This research is also funded by the Estonian Research Council (grant PRG1226)S
La calificación del insight en la práctica clínica psicodinámicamente orientada. Una investigación clínica
Repairing Alignments of Process Models
Process mining represents a collection of data driven techniques that support the analysis, understanding and improvement of business processes. A core branch of process mining is conformance checking, i.e., assessing to what extent a business process model conforms to observed business process execution data. Alignments are the de facto standard instrument to compute such conformance statistics. However, computing alignments is a combinatorial problem and hence extremely costly. At the same time, many process models share a similar structure and/or a great deal of behavior. For collections of such models, computing alignments from scratch is inefficient, since large parts of the alignments are likely to be the same. This paper presents a technique that exploits process model similarity and repairs existing alignments by updating those parts that do not fit a given process model. The technique effectively reduces the size of the combinatorial alignment problem, and hence decreases computation time significantly. Moreover, the potential loss of optimality is limited and stays within acceptable bounds
Extracción localizada: una solución efectiva para controlar los nuevos riesgos higiénicos emergentes
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