Κλιμάκωση αναλυτικής επεξεργασίας συνεχών χρονοσειρών χρησιμοποιώντας την πλατφόρμα Storm
(EL)
Scaling out streaming time series analytics on Storm
(EN)
Παυλακης Νικολαος
(EL)
Pavlakis Nikolaos
(EN)
Δεληγιαννακης Αντωνιος
(EL)
Λαγουδακης Μιχαηλ
(EL)
Πολυτεχνείο Κρήτης
(EL)
Γαροφαλακης Μινως
(EL)
Lagoudakis Michael
(EN)
Technical University of Crete
(EN)
Garofalakis Minos
(EN)
Deligiannakis Antonios
(EN)
Data can provide meaningful insights, if we are able to process it. We live in a time where
the rate with which data is being generated grows exponentially, and extracting useful
information from all this data, becomes harder and harder, thus mandating efficient and
scalable data analytics solutions. Oftentimes, the input data to analytics applications
is in the form of massive, continuous data streams. Consider the example of the global
stock markets: An interesting piece of information for traders, portfolio managers, and
so on, are the correlation/dependence patterns between different market players (e.g.,
equities, indexes, etc.); yet, such patterns typically change very rapidly over time, and
the information is only valuable if it becomes available in real time (e.g., for algorithmic
trading). This implies that stock market data needs to be processed in a streaming
fashion, typically focusing only on a sliding window of recent readings (e.g., “monitor
all correlations during the last hour”). In addition, data stream processing solutions need
to be scalable as there are thousands of market players, implying millions of possible
correlation/dependence pairs that need to be tracked in real time. This thesis introduces
efficient algorithms and architectures for tackling the problem of monitoring the pair-
wise dependence among thousands of data streams, and introduces a generic stream
processing framework, T-Storm, which can be used in order to easily and efficiently
develop, scale-out, and deploy large-scale stream analytics applications.
(EN)
