Big Data and Smart Data
At present, there is a growing trend on data generation, collection and processing in many different application areas, namely health, economics, and mainly IoT. In this context, Data Science and Big Data Analytics have emerged as complementary areas with the aim of extracting knowledge from this vast amount of information. In addition, the establishment of somme recent innovative frameworks such as Hadoop and Spark, motivate data scientists to develop new algorithms intentionally for Big Data problems.
However, design of new scalable algorithms to handle large amounts of data is not a straightforward task, and may bring out complex systems. In this sense, any data scientist or data engineer must address different issues in order to transform raw data into actionable knowledge. It is well-known having that more data does not guarantee enabling better insights, but a good data quality does.
Taking the former into account, the design and / or application of several data preprocessing mechanisms is mandatory in order to ensure that the input information can be defined as Smart Data. Second, the latest tools and methods from the field of Machine Learning must be implemented and executed for the sake of obtaining accurate and interpretable models in a tolerable elapsed time. As an example, the use of transparent and simpler models such as Fuzzy Rule-based Systems is advisable for this purpose. Also, new algorithms need to be developed that can provide a fast and adaptive response to the changing nature of the data, especially for IoT devices. Finally, results must be reported using the proper visualisation and summarisation tools, with aims at presenting the main conclusions and to take useful decisions on the problem under study.
Contact: Alberto Fernández Hilario
Related Researchers:
Letra:
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Name |
Email |
Area |
Cat. |
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Benítez Sánchez, José Manuel |
J.M.Benitez@decsai.ugr.apRsjbnes |
Data Science and Big Data Area, Computational Intelligence Area |
PhD |
|
Casillas Barranquero, Jorge |
casillasrEvjWsQ3q@decsai.ugr.es |
Data Science and Big Data Area |
PhD |
|
Del Jesus Díaz, María José |
mjjesus@ujaen34jq2OUd4tH.es |
Data Science and Big Data Area |
PhD |
|
Fernández Hilario, Alberto |
alberto@decs54nq8n3ai.ugr.es |
Data Science and Big Data Area |
PhD |
|
García Gil, Diego Jesús |
djgarcia@decRMDQrSOsai.ugr.es |
Data Science and Big Data Area, Computational Intelligence Area |
PhD |
|
García López, Salvador |
salvagl@decsai2iigb3DYtEPM.ugr.es |
Data Science and Big Data Area |
PhD |
|
García Vico, Miguel Angel |
agvico@0o4UXVxugr.es |
Data Science and Big Data Area |
PhD |
|
González García, Pedro |
pglez@u.ZEHYaxjaen.es |
Data Science and Big Data Area |
PhD |
|
Herrera Triguero, Francisco |
herrera@decsaisnfekP4mtn.ugr.es |
DaSCI Technology Applications Area, Data Science and Big Data Area, Computational Intelligence Area |
PhD |
|
Herrera Viedma, Enrique |
viedma@decsai.ugrIpVrA1Oi6W1Y.es |
Computational Intelligence Area |
PhD |
|
Lastra Leidinger, Miguel |
mlastral@ugvMG8dUmNL32_r.es |
Data Science and Big Data Area, Computational Intelligence Area |
PhD |
|
Luengo Martín, Julián |
julianlm@decsai.u8@UAkVKyRagr.es |
Data Science and Big Data Area |
PhD |
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Peregrin Rubio, Antonio |
peregrin@dti.uhuwoOYNf.es |
Computational Intelligence Area |
PhD |
|
Peregrin Rubio, Antonio |
peregrin@dti.uhufJeaSj.es |
Computational Intelligence Area |
PhD |
|
Pérez Godoy, María Dolores |
lperez@ujaenWwrHEATqFW.es |
Data Science and Big Data Area |
PhD |
|
Pérez Godoy, María Dolores |
lperez@ujaSH7Gf_YKCcZen.es |
Data Science and Big Data Area |
PhD |
|
Rivera Rivas, Antonio Jesús |
arivera@uja8k4Q08en.es |
Data Science and Big Data Area |
PhD |
|
Rivera Rivas, Antonio Jesús |
arivera@ujaen.9Litk6pEMBes |
Data Science and Big Data Area |
PhD |
|
Triguero Velázquez, Isaac |
triguero@decsaI.m5Wsi.ugr.es |
Data Science and Big Data Area |
– |
|
Ventura Soto, Sebastián |
sventura@ucoeIk1NM_LdqKl.es |
Data Science and Big Data Area |
PhD |