4,492 research outputs found
Real-time and distributed applications for dictionary-based data compression
The greedy approach to dictionary-based static text compression can be executed by a finite state machine.
When it is applied in parallel to different blocks of data independently, there is no lack of robustness
even on standard large scale distributed systems with input files of arbitrary size. Beyond standard large
scale, a negative effect on the compression effectiveness is caused by the very small size of the data blocks.
A robust approach for extreme distributed systems is presented in this paper, where this problem is fixed by
overlapping adjacent blocks and preprocessing the neighborhoods of the boundaries.
Moreover, we introduce the notion of pseudo-prefix dictionary, which allows optimal compression by means
of a real-time semi-greedy procedure and a slight improvement on the compression ratio obtained by the
distributed implementations
Connectionist natural language parsing
The key developments of two decades of connectionist parsing are reviewed. Connectionist parsers are assessed according to their ability to learn to represent syntactic structures from examples automatically, without being presented with symbolic grammar rules. This review also considers the extent to which connectionist parsers offer computational models of human sentence processing and provide plausible accounts of psycholinguistic data. In considering these issues, special attention is paid to the level of realism, the nature of the modularity, and the type of processing that is to be found in a wide range of parsers
Massiv-Parallele Algorithmen zum Laden von Daten auf Moderner Hardware
While systems face an ever-growing amount of data that needs to be ingested, queried and analysed, processors are seeing only moderate improvements in sequential processing performance. This thesis addresses the fundamental shift towards increasingly parallel processors and contributes multiple massively parallel algorithms to accelerate different stages of the ingestion pipeline, such as data parsing and sorting.Systeme sehen sich mit einer stetig anwachsenden Menge an Daten konfrontiert, die geladen und analysiert, sowie Anfragen darauf bearbeitet werden müssen. Gleichzeitig nimmt die sequentielle Verarbeitungsgeschwindigkeit von Prozessoren nur noch moderat zu. Diese Arbeit adressiert den Wandel hin zu zunehmend parallelen Prozessoren und leistet mit mehreren massiv-parallelen Algorithmen einen Beitrag um unterschiedliche Phasen der Datenverarbeitung wie zum Beispiel Parsing und Sortierung zu beschleunigen
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