Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/802769EF-66BD-411D-9E6E-BA00A61F2B6F | - |
| Identifier | https://doi.org/10.26233/heallink.tuc.67653 | - |
| Language | en | - |
| Extent | 74 pages | en |
| Title | Scaling out streaming time series analytics on Storm | en |
| Title | Κλιμάκωση αναλυτικής επεξεργασίας συνεχών χρονοσειρών χρησιμοποιώντας την πλατφόρμα Storm | el |
| Creator | Pavlakis Nikolaos | en |
| Creator | Παυλακης Νικολαος | el |
| Contributor [Thesis Supervisor] | Garofalakis Minos | en |
| Contributor [Thesis Supervisor] | Γαροφαλακης Μινως | el |
| Contributor [Committee Member] | Deligiannakis Antonios | en |
| Contributor [Committee Member] | Δεληγιαννακης Αντωνιος | el |
| Contributor [Committee Member] | Lagoudakis Michael | en |
| Contributor [Committee Member] | Λαγουδακης Μιχαηλ | el |
| Publisher | Πολυτεχνείο Κρήτης | el |
| Publisher | Technical University of Crete | en |
| Academic Unit | Technical University of Crete::School of Electrical and Computer Engineering | en |
| Academic Unit | Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών | el |
| Content Summary | 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 |
| Type of Item | Μεταπτυχιακή Διατριβή | el |
| Type of Item | Master Thesis | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2017-03-27 | - |
| Date of Publication | 2017 | - |
| Subject | Streaming time series analytics | en |
| Subject | Big data | en |
| Bibliographic Citation | Nikolaos Pavlakis, "Scaling out streaming time series analytics on Storm", Master Thesis, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2017 | en |
| Bibliographic Citation | Νικόλαος Παυλάκης, "Κλιμάκωση αναλυτικής επεξεργασίας συνεχών χρονοσειρών χρησιμοποιώντας την πλατφόρμα Storm", Μεταπτυχιακή Διατριβή, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2017 | el |
Copyright © DIAS 2013
