The MUSE Machine -- an Architecture for Structured Data Flow Computation

Computers employing some degree of data flow organisation are now well established as providing a possible vehicle for concurrent computation. Although data-driven computation frees the architecture from the constraints of the single program counter, processor and global memory, inherent in the classic von Neumann computer, there can still be problems with the unconstrained generation of fresh result tokens if a pure data flow approach is adopted. The advantages of allowing serial processing for those parts of a program which are inherently serial, and of permitting a demand-driven, as well as data-driven, mode of operation are identified and described. The MUSE machine described here is a structured architecture supporting both serial and parallel processing which allows the abstract structure of a program to be mapped onto the machine in a logical way.

Extracting reusable document components for variable data printing

Variable Data Printing (VDP) has brought new flexibility and dynamism to the printed page. Each printed instance of a specific class of document can now have different degrees of customized content within the document template. This flexibility comes at a cost. If every printed page is potentially different from all others it must be rasterized separately, which is a time-consuming process. Technologies such as PPML (Personalized Print Markup Language) attempt to address this problem by dividing the bitmapped page into components that can be cached at the raster level, thereby speeding up the generation of page instances. A large number of documents are stored in Page Description Languages at a higher level of abstraction than the bitmapped page. Much of this content could be reused within a VDP environment provided that separable document components can be identified and extracted. These components then need to be individually rasterisable so that each high-level component can be related to its low-level (bitmap) equivalent. Unfortunately, the unstructured nature of most Page Description Languages makes it difficult to extract content easily. This paper outlines the problems encountered in extracting component-based content from existing page description formats, such as PostScript, PDF and SVG, and how the differences between the formats affects the ease with which content can be extracted. The techniques are illustrated with reference to a tool called COG Extractor, which extracts content from PDF and SVG and prepares it for reuse.