Unit:
Speciality Endovascular TechniquesLibrary of the School of Health Sciences
Author:
Λουίζος Αλέξανδρος Λουίζος
Supervisors info:
Κωνσταντίνος Κατσένης Αναπληρωτής Καθηγητής
Original Title:
Mining big data for knowledge extraction
Translated title:
Ανάλυση μεγάλου όγκου δεδομένων για την εξαγωγή γνώσης
Summary:
Lately we observe an exponential increase in knowledge pool where data are
abundant. Few years ago computational power and lack of data where
bottlenecks in knowledge creation. Today data and computational power are
abundant and algorithms and tools for knowledge extraction have become more
prominent. Big data science has emerged through these facts and has been very
fruitful in astrophysics, material science and social networks. In this thesis
we
present a big data approach in vascular surgery through the analysis of all a
big
dataset, that of all english books as delivered by google N-gram project. We
find
insights inside this dataset regarding the evolution and future of vascular
surgery through time. Big data tools like Python natural language processing,
python Sci-Kit and Hadoop clusters have been used for this thesis.
Keywords:
Vascular surgery, Endovascular surgery, Big data analysis, Knowledge extraction, Parallel processing
