RT journal article
T1 MEdit4CEP-SP: A model-driven solution to improve decision-making through user-friendly management and real-time processing of heterogeneous data streams
A1 Corral Plaza, David José
A1 Ortiz Bellot, Guadalupe
A1 Medina Bulo, María Inmaculada
A1 Boubeta Puig, Juan
A2 Ingeniería Informática
K1 Model-Driven Development
K1 Stream Processing
K1 Complex Event Processing
K1 Heterogeneous data
K1 Decision-making
AB Organisations today are constantly consuming and processing huge amounts of data. Such datasets are often heterogeneous, making it difficult to work with them quickly and easily due to their format constraints or their disparate data structures. Therefore, being able to efficiently and intuitively work with such data to analyse them in real time to detect situations of interest as quickly as possible is a great competitive advantage for companies. Existing approaches have tried to address this issue by providing users with analytics or modelling tools in an isolated way, but not combining them as a onein- all solution. In order to fill this gap, we present MEdit4CEP-SP, a model-driven system that integrates Stream Processing (SP) and Complex Event Processing (CEP) technologies for consuming, processing and analysing heterogeneous data in real time. It provides domain experts with a graphical editor that allows them to infer and define heterogeneous data domains, while also modelling, in a user-friendly way, the situations of interest to be detected in such domains. These graphical definitions are then automatically transformed into code, which is deployed in the processing system at runtime. The alerts detected by the system, in real-time, allow users to react as quickly as possible, thus improving the decision-making process. Additionally, MEdit4CEP-SP provides persistence, storing these definitions in a NoSQL database to permit their reuse by other instances of the system. Further benefits of this system are evaluated and compared with other existing approaches in this paper.
PB Elsevier
SN 0950-7051
YR 2021
FD 2021-02-15
LK http://hdl.handle.net/10498/29802
UL http://hdl.handle.net/10498/29802
LA eng
NO This work was partly supported by the Spanish Ministry of Science and Innovation and the European Regional Development Fund (ERDF) under project FAME (RTI2018-093608-B-C33), and also by the pre-doctoral program of the University of Cádiz, Spain (2017-020/PU/EPIF-FPI-CT/CP).
DS Repositorio Institucional de la Universidad de Cádiz
RD 06-nov-2026