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Identifier |
000438731 |
Title |
Exploring real-time data analytics using distributed stream processing systems |
Alternative Title |
Διερεύνηση τεχνικών ανάλυσης δεδομένων σε πραγματικό χρόνο με χρήση κατανεμημένων συστημάτων επεξεργασίας ροών |
Author
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Μποφίλη Αρβανίτη, Ιωάννα Μαρία
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Thesis advisor
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Μαγκούτης, Κωνσταντίνος
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Reviewer
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Πλεξουσάκης, Δημήτρης
Πρατικάκης, Πολύβιος
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Abstract |
Processing high-volume streaming data is an important enabling technology in IoTdriven, social networking, and other e-services, having given rise to a new generation of
stream-processing systems (SPS). In this thesis, we apply modern SPS technologies to
improve the state of the art in two application areas involving real-time position tracking
in physical space: maintaining profiles of visitor movement in exhibit spaces and
predicting service-level objective violations in mass transit systems. To ensure that
scalable SPSs are able to seamlessly adapt to varying levels of load by adjusting their
resources, in this thesis we implement a mechanism by which SPSs can scale dynamically
even when such capability is not natively supported by the SPS and the underlying
resource management platform.
Our first application of SPS technologies to real-time data analytics is on the
development of dynamic behavioral profiles of visitors in exhibit spaces based on their
movements in physical space. While related approaches have been explored in the past,
this thesis applies for the first time stream-processing technologies to materialize
behavioral theories developed in social sciences and to collect richer information about
visitors' interests. Such profiles can be used to produce recommendations for the
visitors about exhibits they should visit, to decide the best content to present to them,
or to design personalized questionnaires. Our second application of SPS technologies to
real-time data analytics is on training appropriate models for predicting mass-transit
vehicles that are likely to violate service-level objectives in their route duration. In this
thesis we extend previous delay prediction techniques with the ability to apply
predictions in a real-time fashion.
In this thesis we also address the necessary tuning and support for scalable, adaptive
data analytics by examining various parameters of the SPS and its ingest engine. Even in
cases where dynamic scaling is not explicitly supported by the SPS platform, we
demonstrate a technique that achieves scale-out with low downtime.
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Language |
English |
Subject |
Big data |
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Data analysis |
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Spark structured streaming |
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Streaming data |
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Δεδομένα μεγάλου όγκου |
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Ροές δεδομένων |
Issue date |
2021-03-26 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/f/b/4/metadata-dlib-1617177147-797487-27024.tkl
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Views |
766 |