Model Checking of Stream Processing Pipelines

Event stream processing (ESP) is the application of a computation to a set of input sequences of arbitrary data objects, called "events", in order to produce other sequences of data objects. In recent years, a large number of ESP systems have been developed; however, none of them is easily amenable to a formal verification of properties on their execution. In this paper, we show how stream processing pipelines built with an existing ESP library called BeepBeep 3 can be exported as a Kripke structure for the NuXmv model checker. This makes it possible to formally verify properties on these pipelines, and opens the way to the use of such pipelines directly within a model checker as an extension of its specification language

Weiterentwicklung analytischer Datenbanksysteme

This thesis contributes to the state of the art in analytical database systems. First, we identify and explore extensions to better support analytics on event streams. Second, we propose a novel polygon index to enable efficient geospatial data processing in main memory. Third, we contribute a new deep learning approach to cardinality estimation, which is the core problem in cost-based query optimization.Diese Arbeit trägt zum aktuellen Forschungsstand von analytischen Datenbanksystemen bei. Wir identifizieren und explorieren Erweiterungen um Analysen auf Eventströmen besser zu unterstützen. Wir stellen eine neue Indexstruktur für Polygone vor, die eine effiziente Verarbeitung von Geodaten im Hauptspeicher ermöglicht. Zudem präsentieren wir einen neuen Ansatz für Kardinalitätsschätzungen mittels maschinellen Lernens

The TXM Portal Software giving access to Old French Manuscripts Online

Texte intégral en ligne : http://www.lrec-conf.org/proceedings/lrec2012/workshops/13.ProceedingsCultHeritage.pdfInternational audiencehttp://www.lrec-conf.org/proceedings/lrec2012/workshops/13.ProceedingsCultHeritage.pdf This paper presents the new TXM software platform giving online access to Old French Text Manuscripts images and tagged transcriptions for concordancing and text mining. This platform is able to import medieval sources encoded in XML according to the TEI Guidelines for linking manuscript images to transcriptions, encode several diplomatic levels of transcription including abbreviations and word level corrections. It includes a sophisticated tokenizer able to deal with TEI tags at different levels of linguistic hierarchy. Words are tagged on the fly during the import process using IMS TreeTagger tool with a specific language model. Synoptic editions displaying side by side manuscript images and text transcriptions are automatically produced during the import process. Texts are organized in a corpus with their own metadata (title, author, date, genre, etc.) and several word properties indexes are produced for the CQP search engine to allow efficient word patterns search to build different type of frequency lists or concordances. For syntactically annotated texts, special indexes are produced for the Tiger Search engine to allow efficient syntactic concordances building. The platform has also been tested on classical Latin, ancient Greek, Old Slavonic and Old Hieroglyphic Egyptian corpora (including various types of encoding and annotations)

Domain-specific languages for modeling and simulation

Simulation models and simulation experiments are increasingly complex. One way to handle this complexity is developing software languages tailored to specific application domains, so-called domain-specific languages (DSLs). This thesis explores the potential of employing DSLs in modeling and simulation. We study different DSL design and implementation techniques and illustrate their benefits for expressing simulation models as well as simulation experiments with several examples.Simulationsmodelle und -experimente werden immer komplexer. Eine Möglichkeit, dieser Komplexität zu begegnen, ist, auf bestimmte Anwendungsgebiete spezialisierte Softwaresprachen, sogenannte domänenspezifische Sprachen (\emph{DSLs, domain-specific languages}), zu entwickeln. Die vorliegende Arbeit untersucht, wie DSLs in der Modellierung und Simulation eingesetzt werden können. Wir betrachten verschiedene Techniken für Entwicklung und Implementierung von DSLs und illustrieren ihren Nutzen für das Ausdrücken von Simulationsmodellen und -experimenten anhand einiger Beispiele

Parallel and Distributed Execution of Model Management Programs

The engineering process of complex systems involves many stakeholders and development artefacts. Model-Driven Engineering (MDE) is an approach to development which aims to help curtail and better manage this complexity by raising the level of abstraction. In MDE, models are first-class artefacts in the development process. Such models can be used to describe artefacts of arbitrary complexity at various levels of abstraction according to the requirements of their prospective stakeholders. These models come in various sizes and formats and can be thought of more broadly as structured data. Since models are the primary artefacts in MDE, and the goal is to enhance the efficiency of the development process, powerful tools are required to work with such models at an appropriate level of abstraction. Model management tasks – such as querying, validation, comparison, transformation and text generation – are often performed using dedicated languages, with declarative constructs used to improve expressiveness. Despite their semantically constrained nature, the execution engines of these languages rarely capitalize on the optimization opportunities afforded to them. Therefore, working with very large models often leads to poor performance when using MDE tools compared to general-purpose programming languages, which has a detrimental effect on productivity. Given the stagnant single-threaded performance of modern CPUs along with the ubiquity of distributed computing, parallelization of these model management program is a necessity to address some of the scalability concerns surrounding MDE. This thesis demonstrates efficient parallel and distributed execution algorithms for model validation, querying and text generation and evaluates their effectiveness. By fully utilizing the CPUs on 26 hexa-core systems, we were able to improve performance of a complex model validation language by 122x compared to its existing sequential implementation. Up to 11x speedup was achieved with 16 cores for model query and model-to-text transformation tasks

Performance Optimizations and Operator Semantics for Streaming Data Flow Programs

Unternehmen sammeln mehr Daten als je zuvor und müssen auf diese Informationen zeitnah reagieren. Relationale Datenbanken eignen sich nicht für die latenzfreie Verarbeitung dieser oft unstrukturierten Daten. Um diesen Anforderungen zu begegnen, haben sich in der Datenbankforschung seit dem Anfang der 2000er Jahre zwei neue Forschungsrichtungen etabliert: skalierbare Verarbeitung unstrukturierter Daten und latenzfreie Datenstromverarbeitung. Skalierbare Verarbeitung unstrukturierter Daten, auch bekannt unter dem Begriff "Big Data"-Verarbeitung, hat in der Industrie schnell Einzug erhalten. Gleichzeitig wurden in der Forschung Systeme zur latenzfreien Datenstromverarbeitung entwickelt, die auf eine verteilte Architektur, Skalierbarkeit und datenparallele Verarbeitung setzen. Obwohl diese Systeme in der Industrie vermehrt zum Einsatz kommen, gibt es immer noch große Herausforderungen im praktischen Einsatz. Diese Dissertation verfolgt zwei Hauptziele: Zuerst wird das Laufzeitverhalten von hochskalierbaren datenparallelen Datenstromverarbeitungssystemen untersucht. Im zweiten Hauptteil wird das "Dual Streaming Model" eingeführt, das eine Semantik zur gleichzeitigen Verarbeitung von Datenströmen und Tabellen beschreibt. Das Ziel unserer Untersuchung ist ein besseres Verständnis über das Laufzeitverhalten dieser Systeme zu erhalten und dieses Wissen zu nutzen um Anfragen automatisch ausreichende Rechenkapazität zuzuweisen. Dazu werden ein Kostenmodell und darauf aufbauende Optimierungsalgorithmen für Datenstromanfragen eingeführt, die Datengruppierung und Datenparallelität einbeziehen. Das vorgestellte Datenstromverarbeitungsmodell beschreibt das Ergebnis eines Operators als kontinuierlichen Strom von Veränderugen auf einer Ergebnistabelle. Dabei behandelt unser Modell die Diskrepanz der physikalischen und logischen Ordnung von Datenelementen inhärent und erreicht damit eine deterministische Semantik und eine minimale Verarbeitungslatenz.Modern companies are able to collect more data and require insights from it faster than ever before. Relational databases do not meet the requirements for processing the often unstructured data sets with reasonable performance. The database research community started to address these trends in the early 2000s. Two new research directions have attracted major interest since: large-scale non-relational data processing as well as low-latency data stream processing. Large-scale non-relational data processing, commonly known as "Big Data" processing, was quickly adopted in the industry. In parallel, low latency data stream processing was mainly driven by the research community developing new systems that embrace a distributed architecture, scalability, and exploits data parallelism. While these systems have gained more and more attention in the industry, there are still major challenges to operate them at large scale. The goal of this dissertation is two-fold: First, to investigate runtime characteristics of large scale data-parallel distributed streaming systems. And second, to propose the "Dual Streaming Model" to express semantics of continuous queries over data streams and tables. Our goal is to improve the understanding of system and query runtime behavior with the aim to provision queries automatically. We introduce a cost model for streaming data flow programs taking into account the two techniques of record batching and data parallelization. Additionally, we introduce optimization algorithms that leverage our model for cost-based query provisioning. The proposed Dual Streaming Model expresses the result of a streaming operator as a stream of successive updates to a result table, inducing a duality between streams and tables. Our model handles the inconsistency of the logical and the physical order of records within a data stream natively, which allows for deterministic semantics as well as low latency query execution