Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1600193.1600218 | ACM Symposium on Document Engineering |
Keywords | Field | DocType |
profiling strategy,high performance printing,print-engine speed,overall process,rip engine,multiple rip engine,computational step,job splitting step,engine speed,ripping step,input document jobs characteristic,digital printing,service provider,return on investment,image processing,load balance,print,parallel processing | Bottleneck,Return on investment,Load balancing (computing),Profiling (computer programming),Computer science,Service provider,Digital printing,Workflow,Database,Personalization | Conference |
Citations | PageRank | References |
3 | 0.50 | 6 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thiago Nunes | 1 | 20 | 3.97 |
| Fabio Giannetti | 2 | 35 | 7.94 |
| Mariana Kolberg | 3 | 7 | 3.05 |
| Rafael Nemetz | 4 | 5 | 0.95 |
| Alexis Cabeda | 5 | 11 | 2.34 |
| Luiz Gustavo Fernandes | 6 | 130 | 23.10 |