Creating personalized documents: an optimization approach
Proceedings of the 2003 ACM symposium on Document engineering
High performance XSL-FO rendering for variable data printing
Proceedings of the 2006 ACM symposium on Applied computing
A study on information extraction from PDF files
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
A system for converting PDF documents into structured XML format
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
An improved parallel XSL-FO rendering for personalized documents
PVM/MPI'07 Proceedings of the 14th European conference on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Job profiling and queue management in high performance printing
Computer Science - Research and Development
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Digital presses have consistently improved their speed in the past ten years. Meanwhile, the need for document personalization and customization has increased. As a consequence of these two facts, the traditional RIP (Raster Image Processing) process has became a highly demanding computational step in the print workflow. Print Service Providers (PSP) are now using multiple RIP engines and parallelization strategies to speed up the whole ripping process which is currently based on a per-page base. Nevertheless, these strategies are not optimized in terms of assuring the best Return On Investment (ROI) for the RIP engines. Depending on the input document jobs characteristics, the ripping step may not achieve the print-engine speed creating a unwanted bottleneck. The aim of this paper is to present a way to improve the ROI of PSPs proposing a profiling strategy which enables the optimal usage of RIPs for specific jobs features ensuring that jobs are always consumed at least at engine speed. The profiling strategy is based on a per-page analysis of input PDF jobs identifying their key components. This work introduces a profiler tool to extract information from jobs and some metrics to predict a job ripping cost based on its profile. This information is extremely useful during the job splitting step, since jobs can be split in a clever way. This improves the load balance of the allocated RIPs engines and makes the overall process faster. Finally, experimental results are presented in order to evaluate both, the profiler and the proposed metrics.