Search Results for author:
Found 11 papers, 1 papers with code
Prompting LLMs with content plans to enhance the summarization of scientific articles
This paper presents novel prompting techniques to improve the performance of automatic summarization systems for scientific articles.
Process-To-Text: A Framework for the Quantitative Description of Processes in Natural Language
In this paper we present the Process-To-Text (P2T) framework for the automatic generation of textual descriptive explanations of processes.
Fuzzy Temporal Protoforms for the Quantitative Description of Processes in Natural Language
In this paper, we propose a series of fuzzy temporal protoforms in the framework of the automatic generation of quantitative and qualitative natural language descriptions of processes.
Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring
Predictive monitoring is a subfield of process mining that aims to predict how a running case will unfold in the future.
Gradual Drift Detection in Process Models Using Conformance Metrics
Changes, planned or unexpected, are common during the execution of real-life processes.
Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring
Predictive monitoring of business processes is a subfield of process mining that aims to predict, among other things, the characteristics of the next event or the sequence of next events.
Deep Learning for Predictive Business Process Monitoring: Review and Benchmark
Predictive monitoring of business processes is concerned with the prediction of ongoing cases on a business process.
A Conformance Checking-based Approach for Drift Detection in Business Processes
These changes over time are called concept drift and its detection is a big challenge in process mining since the inherent complexity of the data makes difficult distinguishing between a change and an anomalous execution.
Mining Frequent Patterns in Process Models
Experiments have validated that WoMine can find all types of patterns, extracting information that cannot be mined with the state of the art techniques.
Hybrid Optimization Algorithm for Large-Scale QoS-Aware Service Composition
In this paper we present a hybrid approach for automatic composition of Web services that generates semantic input-output based compositions with optimal end-to-end QoS, minimizing the number of services of the resulting composition.
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.
