Εντοπίστηκε ένα σφάλμα στη λειτουργία της ΠΥΞΙΔΑΣ όταν χρησιμοποιείται μέσω του προγράμματος περιήγησης Safari. Μέχρι να αποκατασταθεί το πρόβλημα, προτείνουμε τη χρήση εναλλακτικού browser όπως ο Chrome ή ο Firefox. A bug has been identified in the operation of the PYXIDA platform when accessed via the Safari browser. Until the problem is resolved, we recommend using an alternative browser such as Chrome or Firefox.
Λογότυπο αποθετηρίου

Διδακτορικές διατριβές

Μόνιμο URI για αυτήν τη συλλογήhttps://pyxida.aueb.gr/handle/123456789/53

Περιήγηση

Πλοήγηση Διδακτορικές διατριβές ανά Θέμα "Analytics"

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Α Β Γ Δ Ε Ζ Η Θ Ι Κ Λ Μ Ν Ξ Ο Π Ρ Σ Τ Υ Φ Χ Ψ Ω
Φιλτράρετε τα αποτελέσματα πληκτρολογώντας τα πρώτα γράμματα
Τώρα δείχνει 1 - 1 από 1
  • Μικρογραφία εικόνας
    Τεκμήριο
    Combining relational and stream data for real-time analytics
    (23-08-2013) Sotiropoulos, Yannis; Athens University of Economics and Business, Department of Management Science and Marketing; Πραματάρη, Αικατερίνη; Σπινέλλης, Διομήδης; Βασιλειάδης, Παναγιώτης; Γιαγλής, Γεώργιος; Κωτίδης, Ιωάννης; Θεοδωρίδης, Ιωάννης; Χατζηαντωνίου, Δαμιανός
    Nowadays, more and more organizations realize the importance of analyzing available data. While most of the times data is stored, over the last years there is a growing amount of stream data. Such data arrives on-line from multiple sources in a continuous, rapid and time-varying fashion. Currently, most stream management applications and systems exploit stream data with the objective to answer monitoring queries. However, the real potential of stream data lies in the possibility to capture new types of information in (near) real-time and support decisions. To support this kind of analysis we need analytics queries that can support multiple and correlated stream aggregates over stream data coming from multiple and heterogeneous stream sources. Moreover a wide range of analytics applications need to combine already available data (e.g. stored data) and stream data to empower business with (near) real-time insights that can be used for improved decision making. As relational databases are extremely widespread our research focuses on how relation data can support relational-stream analytics applications. Overall, this thesis provides query formulation methods and tools that combine relational and stream data to support (near) real-time data analysis. In this thesis we introduce stream variables to support analytics over stream data. This kind of analytics queries can contain multiple stream aggregates, correlated stream aggregates and use data from multiple and heterogeneous stream sources. We provide SQL language extensions to support this kind of queries. Moreover we provide a spreadsheet-like approach to perform stream analytics. The intuition is that stream queries can by defined in a column-by-column fashion. The columns can contain either relational data or stream aggregates. The thesis studies how to extend current Relational Database Management Systems (RDBMSs) to handle stream data for (near) real-time decision making. We present a relational-based integration framework that sits atop any RDBMS and mix RDBMS’ data and stream aggregates managed by different stream systems. A SQL extension is provided to define relational-stream views and an API is developed to carry out the required communication between the relational and the stream systems. The proposed framework can serve as a standard for relational-stream interoperability.