Verbundoperationen über Datenströmen und deren Optimierung unter Verwendung dynamischer Metadaten

Technischer Fortschritt und Vernetzung haben zu einem enormen Zuwachs an kontinuierlich anfallenden Datenströmen geführt, aus denen immer schneller wertvolle Informationen abgeleitet werden sollen. Zur Verarbeitung von kontinuierlichen Anfragen über Datenströme etablieren sich daher Datenstrommanage...

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1. Verfasser: Cammert, Michael
Beteiligte: Seeger, Bernhard (Prof. Dr) (BetreuerIn (Doktorarbeit))
Format: Dissertation
Sprache:Deutsch
Veröffentlicht: Philipps-Universität Marburg 2014
Datenbanksysteme
Ausgabe:http://dx.doi.org/10.17192/z2014.0500
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topic Optimization
Optimierung
Data Stream Management System
Join-Operation
Informatik
Metadata
Datenstrom
Datenstrommanagementsystem
Join
Metadaten
Data Stream
spellingShingle Optimization
Optimierung
Data Stream Management System
Join-Operation
Informatik
Metadata
Datenstrom
Datenstrommanagementsystem
Join
Metadaten
Data Stream
Cammert, Michael
Due to technological progress there has been an enormous increase of the number of continuous data streams from which valuable information has to be derived as fast as possible. Therefore, data stream management systems have emerged as a new technology to process continuous queries over data streams. In contrast to databases they primarily operate in memory and are optimized for processing continuous queries over data streams. During the development of this new kind of systems taking over the concept of using relational operator graphs from database theory has proven to be of value if executing them data driven instead of demand driven. Assigning validity intervals to the elements of the data streams solves the problem of processing potentially unbounded data streams while using bounded resources. The capabilities of such systems considerably depend on the availability of efficient and well defined techniques for combining information from different data streams. The objective of this thesis therefore is to transfer the proven concept of the relational join to the data driven data stream processing using validity intervals. For this purpose the semantic of the join operation for data streams is derived from the one of the extended relational algebra using the concept of snapshot-reducibility. Several join algorithms are presented and proven to comply with this semantic. The consequent usage of parameterization of the techniques with respect to the data structures used for storing the status allows supporting a large variety of different join predicates. Well known techniques of join processing using nested loops, hashing or indexing are adapted for data stream processing. Additionally, the Temporal Progressive-Merge-Join is introduced as an algorithm which allows to derive the join by using value based sorting of the data stream elements. Additionally, several optimizations are proposed, including the generalization of all presented algorithms for more than two input streams. To enable the automated choice and parameterization of the concrete implementations with respect to a forecast of their resource usage the techniques are embedded in a detailed cost model. Often, several kinds of metadata concerning the data streams to process are not available at registration time of the continuous queries and additionally may change during their long-lasting execution time. Therefore, it is important to be able to gather detailed information regarding the data streams and the system state at any time and to be able to adapt the processing strategy accordingly if necessary. A main part of the thesis therefore deals with the question how to provide dynamic metadata within a data stream management system. For that purpose a user-friendly framework is presented which allows to efficiently obtain consistent dynamic metadata. This process is investigated in several experiments and also used to evaluate the presented join techniques. Additionally, an approach allowing to restructure continuous queries at runtime is presented and it is shown that this approach is applicable for join processing.
Verbundoperationen über Datenströmen und deren Optimierung unter Verwendung dynamischer Metadaten
url http://archiv.ub.uni-marburg.de/diss/z2014/0500/pdf/dmc.pdf
institution Datenbanksysteme
title_alt Join Queries over Data Streams and Their Optimization using Dynamic Metadata
author Cammert, Michael
format Dissertation
oai_set_str_mv doc-type:doctoralThesis
ddc:004
open_access
xMetaDissPlus
publisher Philipps-Universität Marburg
description Technischer Fortschritt und Vernetzung haben zu einem enormen Zuwachs an kontinuierlich anfallenden Datenströmen geführt, aus denen immer schneller wertvolle Informationen abgeleitet werden sollen. Zur Verarbeitung von kontinuierlichen Anfragen über Datenströme etablieren sich daher Datenstrommanagementsysteme, die hauptsächlich im Hauptspeicher operieren und im Gegensatz zu Datenbanksystemen auf die Verarbeitung kontinuierlicher Anfragen über Datenströmen zugeschnitten sind. Bei der Implementierung dieser neuen Systeme hat sich die Übernahme des Konzeptes der relationalen Operatorgraphen aus der Welt der Datenbanken bewährt, wobei diese allerdings statt bedarfsgesteuert nun kontinuierlich und datengetrieben ausgewertet werden. Durch Zuweisen von Gültigkeitsintervallen zu den Datenstromelementen kann dabei das Problem gelöst werden, mit endlichen Ressourcen potentiell unbegrenzte Datenströme zu verarbeiten. Die Mächtigkeit solcher Systeme hängt wesentlich von der Verfügbarkeit effizienter wohldefinierter Techniken zur Verknüpfung von Informationen aus verschiedenen Datenströmen ab. Ziel dieser Arbeit ist es daher, das für Datenbanken bewährte Konzept des relationalen Verbundes auf die datengetriebene Datenstromverarbeitung mit Gültigkeitsintervallen zu übertragen. Dazu wird die Semantik der Verbundoperation für Datenströme mittels der Schnappschuss-Reduzierung formal auf die des Verbundes in der erweiterten relationalen Algebra zurückgeführt. Es werden mehrere Verbundalgorithmen vorgestellt und gezeigt, dass diese die gewünschte Semantik haben. Durch eine konsequente Parametrisierung der Verfahren bezüglich der Datenstrukturen zur Statusverwaltung werden verschiedenste Typen von Verbundprädikaten effizient unterstützt. Bewährte Techniken der Verbundberechnung mittels verschachtelter Schleifen, Hashing und Indexierung werden dabei für die Datenstromverarbeitung adaptiert. Mit dem temporalen Progressive-Merge-Join wird zudem ein Verfahren vorgestellt, dass den Verbund über Datenströmen mittels einer wertbasierten Sortierung berechnet. Zudem werden für die Verfahren verschiedenste Optimierungen vorgeschlagen, darunter für alle Verfahren die Verallgemeinerung auf mehr als zwei Datenströme. Zur Ermöglichung der automatischen Auswahl und Parametrisierung der Implementierungen anhand ihres prognostizierten Ressourcenverbrauchs werden diese in ein detailliertes Kostenmodell eingebettet. Da bestimmte Metadaten bezüglich der zu verarbeitenden Datenströmen bei der Registrierung kontinuierlicher Anfragen oftmals nicht vorliegen und sich zudem während der langen Laufzeit der Anfragen ändern können, ist es wichtig, jederzeit detaillierte Informationen bezüglich der Datenströme und des Systemverhaltens erheben und gegebenenfalls durch Anpassungen an der Verarbeitungsstrategie darauf reagieren können. Ein wesentlicher Teil der Arbeit ist daher der Fragestellung gewidmet, wie in einem Datenstrommanagementsystem dynamische Metadaten erhoben werden können. Dazu wird ein benutzerfreundliches Rahmenwerk vorgestellt, das es ermöglicht, dynamische Metadaten konsistent und effizient zu erheben. In Experimenten wird die dynamische Metadatenerhebung untersucht und auch für die Evaluation der vorgestellten Verbundoperationen eingesetzt. Zudem wird eine Technik vorgestellt, mit der kontinuierliche Anfragen zur Laufzeit restrukturiert werden können, und deren Anwendbarkeit für die Verbundoptimierung aufgezeigt.
building Fachbereich Mathematik und Informatik
dewey-raw 004
dewey-search 004
genre Data processing, Computer science
genre_facet Data processing, Computer science
topic_facet Informatik
last_indexed 2015-04-23T23:59:59Z
contents Due to technological progress there has been an enormous increase of the number of continuous data streams from which valuable information has to be derived as fast as possible. Therefore, data stream management systems have emerged as a new technology to process continuous queries over data streams. In contrast to databases they primarily operate in memory and are optimized for processing continuous queries over data streams. During the development of this new kind of systems taking over the concept of using relational operator graphs from database theory has proven to be of value if executing them data driven instead of demand driven. Assigning validity intervals to the elements of the data streams solves the problem of processing potentially unbounded data streams while using bounded resources. The capabilities of such systems considerably depend on the availability of efficient and well defined techniques for combining information from different data streams. The objective of this thesis therefore is to transfer the proven concept of the relational join to the data driven data stream processing using validity intervals. For this purpose the semantic of the join operation for data streams is derived from the one of the extended relational algebra using the concept of snapshot-reducibility. Several join algorithms are presented and proven to comply with this semantic. The consequent usage of parameterization of the techniques with respect to the data structures used for storing the status allows supporting a large variety of different join predicates. Well known techniques of join processing using nested loops, hashing or indexing are adapted for data stream processing. Additionally, the Temporal Progressive-Merge-Join is introduced as an algorithm which allows to derive the join by using value based sorting of the data stream elements. Additionally, several optimizations are proposed, including the generalization of all presented algorithms for more than two input streams. To enable the automated choice and parameterization of the concrete implementations with respect to a forecast of their resource usage the techniques are embedded in a detailed cost model. Often, several kinds of metadata concerning the data streams to process are not available at registration time of the continuous queries and additionally may change during their long-lasting execution time. Therefore, it is important to be able to gather detailed information regarding the data streams and the system state at any time and to be able to adapt the processing strategy accordingly if necessary. A main part of the thesis therefore deals with the question how to provide dynamic metadata within a data stream management system. For that purpose a user-friendly framework is presented which allows to efficiently obtain consistent dynamic metadata. This process is investigated in several experiments and also used to evaluate the presented join techniques. Additionally, an approach allowing to restructure continuous queries at runtime is presented and it is shown that this approach is applicable for join processing.
title Verbundoperationen über Datenströmen und deren Optimierung unter Verwendung dynamischer Metadaten
title_short Verbundoperationen über Datenströmen und deren Optimierung unter Verwendung dynamischer Metadaten
title_full Verbundoperationen über Datenströmen und deren Optimierung unter Verwendung dynamischer Metadaten
title_fullStr Verbundoperationen über Datenströmen und deren Optimierung unter Verwendung dynamischer Metadaten
title_full_unstemmed Verbundoperationen über Datenströmen und deren Optimierung unter Verwendung dynamischer Metadaten
title_sort Verbundoperationen über Datenströmen und deren Optimierung unter Verwendung dynamischer Metadaten
ref_str_mv references
license_str http://archiv.ub.uni-marburg.de/adm/urhg.html
language German
author2 Seeger, Bernhard (Prof. Dr)
author2_role ths
publishDate 2014
era_facet 2014
doi_str_mv http://dx.doi.org/10.17192/z2014.0500
edition http://dx.doi.org/10.17192/z2014.0500
first_indexed 2015-01-14T00:00:00Z
thumbnail http://archiv.ub.uni-marburg.de/diss/z2014/0500/cover.png
spelling diss/z2014/0500 2014-12-12 Join Queries over Data Streams and Their Optimization using Dynamic Metadata Technischer Fortschritt und Vernetzung haben zu einem enormen Zuwachs an kontinuierlich anfallenden Datenströmen geführt, aus denen immer schneller wertvolle Informationen abgeleitet werden sollen. Zur Verarbeitung von kontinuierlichen Anfragen über Datenströme etablieren sich daher Datenstrommanagementsysteme, die hauptsächlich im Hauptspeicher operieren und im Gegensatz zu Datenbanksystemen auf die Verarbeitung kontinuierlicher Anfragen über Datenströmen zugeschnitten sind. Bei der Implementierung dieser neuen Systeme hat sich die Übernahme des Konzeptes der relationalen Operatorgraphen aus der Welt der Datenbanken bewährt, wobei diese allerdings statt bedarfsgesteuert nun kontinuierlich und datengetrieben ausgewertet werden. Durch Zuweisen von Gültigkeitsintervallen zu den Datenstromelementen kann dabei das Problem gelöst werden, mit endlichen Ressourcen potentiell unbegrenzte Datenströme zu verarbeiten. Die Mächtigkeit solcher Systeme hängt wesentlich von der Verfügbarkeit effizienter wohldefinierter Techniken zur Verknüpfung von Informationen aus verschiedenen Datenströmen ab. Ziel dieser Arbeit ist es daher, das für Datenbanken bewährte Konzept des relationalen Verbundes auf die datengetriebene Datenstromverarbeitung mit Gültigkeitsintervallen zu übertragen. Dazu wird die Semantik der Verbundoperation für Datenströme mittels der Schnappschuss-Reduzierung formal auf die des Verbundes in der erweiterten relationalen Algebra zurückgeführt. Es werden mehrere Verbundalgorithmen vorgestellt und gezeigt, dass diese die gewünschte Semantik haben. Durch eine konsequente Parametrisierung der Verfahren bezüglich der Datenstrukturen zur Statusverwaltung werden verschiedenste Typen von Verbundprädikaten effizient unterstützt. Bewährte Techniken der Verbundberechnung mittels verschachtelter Schleifen, Hashing und Indexierung werden dabei für die Datenstromverarbeitung adaptiert. Mit dem temporalen Progressive-Merge-Join wird zudem ein Verfahren vorgestellt, dass den Verbund über Datenströmen mittels einer wertbasierten Sortierung berechnet. Zudem werden für die Verfahren verschiedenste Optimierungen vorgeschlagen, darunter für alle Verfahren die Verallgemeinerung auf mehr als zwei Datenströme. Zur Ermöglichung der automatischen Auswahl und Parametrisierung der Implementierungen anhand ihres prognostizierten Ressourcenverbrauchs werden diese in ein detailliertes Kostenmodell eingebettet. Da bestimmte Metadaten bezüglich der zu verarbeitenden Datenströmen bei der Registrierung kontinuierlicher Anfragen oftmals nicht vorliegen und sich zudem während der langen Laufzeit der Anfragen ändern können, ist es wichtig, jederzeit detaillierte Informationen bezüglich der Datenströme und des Systemverhaltens erheben und gegebenenfalls durch Anpassungen an der Verarbeitungsstrategie darauf reagieren können. Ein wesentlicher Teil der Arbeit ist daher der Fragestellung gewidmet, wie in einem Datenstrommanagementsystem dynamische Metadaten erhoben werden können. Dazu wird ein benutzerfreundliches Rahmenwerk vorgestellt, das es ermöglicht, dynamische Metadaten konsistent und effizient zu erheben. In Experimenten wird die dynamische Metadatenerhebung untersucht und auch für die Evaluation der vorgestellten Verbundoperationen eingesetzt. Zudem wird eine Technik vorgestellt, mit der kontinuierliche Anfragen zur Laufzeit restrukturiert werden können, und deren Anwendbarkeit für die Verbundoptimierung aufgezeigt. urn:nbn:de:hebis:04-z2014-05004 2015-04-23 Due to technological progress there has been an enormous increase of the number of continuous data streams from which valuable information has to be derived as fast as possible. Therefore, data stream management systems have emerged as a new technology to process continuous queries over data streams. In contrast to databases they primarily operate in memory and are optimized for processing continuous queries over data streams. During the development of this new kind of systems taking over the concept of using relational operator graphs from database theory has proven to be of value if executing them data driven instead of demand driven. Assigning validity intervals to the elements of the data streams solves the problem of processing potentially unbounded data streams while using bounded resources. The capabilities of such systems considerably depend on the availability of efficient and well defined techniques for combining information from different data streams. The objective of this thesis therefore is to transfer the proven concept of the relational join to the data driven data stream processing using validity intervals. For this purpose the semantic of the join operation for data streams is derived from the one of the extended relational algebra using the concept of snapshot-reducibility. Several join algorithms are presented and proven to comply with this semantic. The consequent usage of parameterization of the techniques with respect to the data structures used for storing the status allows supporting a large variety of different join predicates. Well known techniques of join processing using nested loops, hashing or indexing are adapted for data stream processing. Additionally, the Temporal Progressive-Merge-Join is introduced as an algorithm which allows to derive the join by using value based sorting of the data stream elements. Additionally, several optimizations are proposed, including the generalization of all presented algorithms for more than two input streams. To enable the automated choice and parameterization of the concrete implementations with respect to a forecast of their resource usage the techniques are embedded in a detailed cost model. Often, several kinds of metadata concerning the data streams to process are not available at registration time of the continuous queries and additionally may change during their long-lasting execution time. Therefore, it is important to be able to gather detailed information regarding the data streams and the system state at any time and to be able to adapt the processing strategy accordingly if necessary. A main part of the thesis therefore deals with the question how to provide dynamic metadata within a data stream management system. For that purpose a user-friendly framework is presented which allows to efficiently obtain consistent dynamic metadata. This process is investigated in several experiments and also used to evaluate the presented join techniques. Additionally, an approach allowing to restructure continuous queries at runtime is presented and it is shown that this approach is applicable for join processing. Verbundoperationen über Datenströmen und deren Optimierung unter Verwendung dynamischer Metadaten Raman, Vijayshankar ; Deshpande, Amol ; Hellerstein, Joseph M.: Using State Modules for Adaptive Query Processing. In: Proc. of the IEEE Int. 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In: Euro-Par, 2008, S. 632–641 2008 Efficient Processing of Continuous Join Queries Using Distributed Hash Tables opus:5892 2014 http://dx.doi.org/10.17192/z2014.0500 2015-01-14 Cammert, Michael Cammert Michael Philipps-Universität Marburg ths Prof. Dr Seeger Bernhard Seeger, Bernhard (Prof. Dr)
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