Institut für Informatik und Computational Science
Refine
Has Fulltext
- no (121) (remove)
Year of publication
Document Type
- Doctoral Thesis (121) (remove)
Is part of the Bibliography
- yes (121)
Keywords
- Android Security (1)
- Answer Set Programming (1)
- Answer Set Solving modulo Theories (1)
- Artificial Neuronal Network (1)
- Business Process (1)
- Codierungstheorie (1)
- Declarative Problem Solving (1)
- Deep Learning (1)
- Enterprise Architecture (1)
- Erklärbarkeit (1)
- Explainability (1)
- Fehlererkennung (1)
- Geschäftsprozess (1)
- Grounding Theory (1)
- Industrie 4.0 (1)
- Industry 4.0 (1)
- Interpretability (1)
- Interpretierbarkeit (1)
- Knowledge (1)
- Knowledge Management (1)
- Künstliche Neuronale Netzwerke (1)
- Learning (1)
- Lernen (1)
- Machine Learning (1)
- Modeling (1)
- Modellierung (1)
- Optimierung (1)
- Optimization (1)
- Preference Handling (1)
- Privacy Protection (1)
- Process (1)
- Process Management (1)
- Prozess (1)
- Prozessmanagement (1)
- Simulation (1)
- Speicher (1)
- Static Analysis (1)
- Temporal Answer Set Solving (1)
- Wissen (1)
- Wissensmanagement (1)
- unidirektionale Fehler (1)
Institute
The introduction of columnar in-memory databases, along with hardware evolution, has made the execution of transactional and analytical enterprise application workloads on a single system both feasible and viable. Yet, we argue that executing analytical aggregate queries directly on the transactional data can decrease the overall system performance. Despite the aggregation capabilities of columnar in-memory databases, the direct access to records of a materialized aggregate is always more efficient than aggregating on the fly. The traditional approach to materialized aggregates, however, introduces significant overhead in terms of materialized view selection, maintenance, and exploitation. When this overhead is handled by the application, it increases the application complexity, and can slow down the transactional throughput of inserts, updates, and deletes.
In this thesis, we motivate, propose, and evaluate the aggregate cache, a materialized aggregate engine in the main-delta architecture of a columnar in-memory database that provides efficient means to handle costly aggregate queries of enterprise applications. For our design, we leverage the specifics of the main-delta architecture that separates a table into a main and delta partition. The central concept is to only cache the partial aggregate query result as defined on the main partition of a table, because the main partition is relatively stable as records are only inserted into the delta partition. We contribute by proposing incremental aggregate maintenance and query compensation techniques for mixed workloads of enterprise applications. In addition, we introduce aggregate profit metrics that increase the likelihood of persisting the most profitable aggregates in the aggregate cache.
Query compensation and maintenance of materialized aggregates based on joins of multiple tables is expensive due to the partitioned tables in the main-delta architecture. Our analysis of enterprise applications has revealed several data schema and workload patterns. This includes the observation that transactional data is persisted in header and item tables, whereas in many cases, the insertion of related header and item records is executed in a single database transaction. We contribute by proposing an approach to transport these application object semantics to the database system and optimize the query processing using the aggregate cache by applying partition pruning and predicate pushdown techniques.
For the experimental evaluation, we propose the FICO benchmark that is based on data from a productive ERP system with extracted mixed workloads. Our evaluation reveals that the aggregate cache can accelerate the execution of aggregate queries up to a factor of 60 whereas the speedup highly depends on the number of aggregated records in the main and delta partitions. In mixed workloads, the proposed aggregate maintenance and query compensation techniques perform up to an order of magnitude better than traditional materialized aggregate maintenance approaches. The introduced aggregate profit metrics outperform existing costbased metrics by up to 20%. Lastly, the join pruning and predicate pushdown techniques can accelerate query execution in the aggregate cache in the presence of multiple partitioned tables by up to an order of magnitude.
E-Learning-Anwendungen bieten Chancen für die gesetzlich vorgeschriebene Inklusion von Lernenden mit Beeinträchtigungen. Die gleichberechtigte Teilhabe von blinden Lernenden an Veranstaltungen in virtuellen Klassenzimmern ist jedoch durch den synchronen, multimedialen Charakter und den hohen Informationsumfang dieser Lösungen kaum möglich.
Die vorliegende Arbeit untersucht die Zugänglichkeit virtueller Klassenzimmer für blinde Nutzende, um eine möglichst gleichberechtigte Teilhabe an synchronen, kollaborativen Lernszenarien zu ermöglichen. Im Rahmen einer Produktanalyse werden dazu virtuelle Klassenzimmer auf ihre Zugänglichkeit und bestehende Barrieren untersucht und Richtlinien für die zugängliche Gestaltung von virtuellen Klassenzimmern definiert. Anschließend wird ein alternatives Benutzungskonzept zur Darstellung und Bedienung virtueller Klassenzimmer auf einem zweidimensionalen taktilen Braille-Display entwickelt, um eine möglichst gleichberechtigte Teilhabe blinder Lernender an synchronen Lehrveranstaltungen zu ermöglichen. Nach einer ersten Evaluation mit blinden Probanden erfolgt die prototypische Umsetzung des Benutzungskonzepts für ein Open-Source-Klassenzimmer. Die abschließende Evaluation der prototypischen Umsetzung zeigt die Verbesserung der Zugänglichkeit von virtuellen Klassenzimmern für blinde Lernende unter Verwendung eines taktilen Flächendisplays und bestätigt die Wirksamkeit der im Rahmen dieser Arbeit entwickelten Konzepte.