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Identifier 000429157
Title Incremental evaluation of continuous analytic queries in a high-level query language
Alternative Title Αυξητική αποτίμηση συνεχών αναλυτικών επερωτήσεων βασιζόμενοι σε μια γλώσσα επερωτήσεων υψηλού επιπέδου
Author Ζερβουδάκης, Πέτρος Ν.
Thesis advisor Πλεξουσάκης, Δημήτριος
Reviewer Τζίτζικας, Γιάννης
Σπυράτος, Νικόλαος
Abstract Data analytics have received a significant attention in recent years, as huge amounts of data is generated each day from various sources. Analysis of these massive data poses an interesting but challenging task and requires new forms of processing to enable enhanced decision making, insight discovery and process optimization. In addition, besides their ever increasing volume, data sets change frequently, and as such, results to continuous queries have to be updated at short intervals. In this thesis, we address the problem of evaluating continuous queries over big data streams that are frequently updated. To this end, we adopt HIFUN, a high-level query language, proposed for expressing analytic queries over big data sets. HIFUN offers a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer where queries are evaluated, by encoding them as map-reduce jobs or as SQL group-by queries, thus supporting different types of data set formats. Using HIFUN, we design an algorithm for incremental evaluation of continuous queries, processing only the most recent data batch, and exploiting already computed information, without requiring the evaluation of the query over the complete data set. Subsequently, we translate the generic algorithm to both SQL and MapReduce using SPARK, exploiting the query rewriting methods provided by HIFUN. Using a synthetic data set, we demonstrate the effectiveness of our approach in achieving query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization in our implementation.
Language English
Subject Big data
Data analytics
Incremental processing
Query language
Ανάλυση δεδομένων
Αυξητική αποτίμηση
Γλώσσα επερώτησης
Μεγάλα δεδομένα
Issue date 2020-03-27
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work
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