Publications

Persistent memory (PMem) - also known as non-volatile memory (NVM) - offers new opportunities not only for the design of data structures and system architectures but also for failure recovery in databases. However, instant recovery can mean not only to bring the system up as fast as possible but also to continue long-running queries which have been interrupted by a system failure. In this work, we discuss how PMem can be utilized to implement query recovery for analytical graph queries. Furthermore, we investigate the trade-off between the overhead of managing the query state in PMem at query runtime as well as the recovery and restart costs.

Modern big data applications integrate data from various sources. As a result, these datasets may not satisfy perfect constraints, leading to sparse schema information and non-optimal query performance. The existing approach of PatchIndexes enable the definition of approximate constraints and improve query performance by exploiting the materialized constraint information. As real world data warehouse workloads are often not limited to read-only queries, we enhance the PatchIndex structure towards an update-conscious design in this paper. Therefore, we present a sharded bitmap as the underlying data structure which offers efficient update operations, and describe approaches to maintain approximate constraints under updates, avoiding index recomputations and full table scans. In our evaluation, we prove that PatchIndexes provide more lightweight update support than traditional materialization approaches.

Compiling database queries into compact and efficient machine code has proven to be a great technique to improve query performance and to exploit characteristics of modern hardware. Furthermore, compilation frameworks like LLVM provide powerful optimization techniques and support different backends. However, the time for generating machine code becomes an issue for short-running queries or queries which could produce early results quickly. In this work, we present an adaptive approach integrating graph query interpretation and compilation. While query compilation and code generation are running in the background, the query execution starts using the interpreter. As soon as the code generation is finished, the execution switches to the compiled code. Our evaluation shows that autonomously switching execution modes helps to hide compilation times.

Over the past thirty years, database management systems have been established as one of the most successful software concepts. In todays business environment they constitute the centerpiece of almost all critical IT systems. The reasons for this success are manyfold. On the one hand, such systems provide abstractions hiding the details of underlying hardware or operating systems layers. On the other hand, database management systems are ACID compliant, which enables them to represent an accurate picture of a real world scenario, and ensures correctness of the managed data.However, the currently used database concepts and systems are not well prepared to support emerging application domains such as eSciences, Industry 4.0, Internet of Things or Digital Humanities. Furthermore, volume, variety, veracity as well as velocity of data caused by ubiquitous sensors have to be mastered by massive scalability and online processing by providing traditional qualities of database systems like consistency, isolation and descriptive query languages. At the same time, current and future hardware trends provide new opportunities such as many-core CPUs, co-processors like GPU and FPGA, novel storage technologies like NVRAM and SSD as well as high-speed networks provide new opportunities.In this talk we present our research results for the use of modern hardware architectures for data management. We discuss the design of data structures for persistent memory and the use of accelerators like GPU and FPGA for database operations.

Intro -- I Grundlegende Konzepte -- Vorbemerkungen und Überblick -- Informatik, Algorithmen und Datenstrukturen -- Historischer Überblick: Algorithmen -- Historie von Programmiersprachen und Java -- Grundkonzepte der Programmierung in Java -- Algorithmische Grundkonzepte -- Intuitiver Algorithmusbegriff -- Beispiele für Algorithmen -- Bausteine für Algorithmen -- Pseudocode-Notation für Algorithmen -- Struktogramme -- Rekursion -- Sprachen und Grammatiken -- Begriffsbildung -- Reguläre Ausdrücke -- Backus-Naur-Form (BNF) -- Elementare Datentypen -- Datentypen als Algebren -- Signaturen von Datentypen -- Der Datentyp bool -- Der Datentyp integer -- Felder und Zeichenketten -- Terme -- Bildung von Termen -- Algorithmus zur Termauswertung -- Datentypen in Java -- Primitive Datentypen -- Referenzdatentypen -- Operatoren -- Algorithmenparadigmen -- Überblick über Algorithmenparadigmen -- Applikative Algorithmen -- Terme mit Unbestimmten -- Funktionsdefinitionen -- Auswertung von Funktionen -- Erweiterung der Funktionsdefinition -- Applikative Algorithmen -- Beispiele für applikative Algorithmen -- Imperative Algorithmen -- Grundlagen imperativer Algorithmen -- Komplexe Anweisungen -- Beispiele für imperative Algorithmen -- Das logische Paradigma -- Logik der Fakten und Regeln -- Deduktive Algorithmen -- Weitere Paradigmen -- Genetische Algorithmen -- Neuronale Netze -- Umsetzung in Java -- Ausdrücke und Anweisungen -- Methoden -- Applikative Algorithmen und Rekursion -- Literaturhinweise zum Teil I -- II Algorithmen -- Ausgewählte Algorithmen -- Suchen in sortierten Folgen -- Sequenzielle Suche -- Binäre Suche -- Sortieren -- Sortieren: Grundbegriffe -- Sortieren durch Einfügen -- Sortieren durch Selektion -- Sortieren durch Vertauschen: BubbleSort -- Sortieren durch Mischen: MergeSort -- QuickSort -- Sortieren durch Verteilen: RadixSort.