Revisiting the dataflow principle for chemical information processing

Revisiting the dataflow principle for chemical information processing

Dataflow systems, such as Pipeline Pilot or KNIME have become important mainstream tools for data processing in chemistry. These established systems are all implemented relying on a data model emphasizing a strict row/column-centric data table view which does not facilitate interaction with individual chemistry objects, or non-uniform data contents. Resuming our pioneering work which resulted i...

Revisiting Tversky's diagnosticity principle

Similarity is a fundamental concept in cognition. In 1977, Amos Tversky published a highly influential feature-based model of how people judge the similarity between objects. The model highlights the context-dependence of similarity judgments, and challenged geometric models of similarity. One of the context-dependent effects Tversky describes is the diagnosticity principle. The diagnosticity p...

Compiling Dataflow Programs for Digital Signal Processing

∆-dataflow Networks for Event Stream Processing

We present a design of a new class of dataflow-like networks suitable for detecting complex conditions in systems in which parameters change rapidly. Such networks are helpful for detecting conditions that signal threats or opportunities in areas such as logistics, finance, and public health. Examples of such applications are detection of money laundering, epidemics, and unauthorized intrusion ...

Flexible Scheduling for DataFlow Audio Processing

The notions of audio and control rate have been a pervasive feature of audio programming languages and environments. Real-time computer music systems depend on schedulers to coordinate and order the execution of many tasks over the course of time. In this paper we describe the scheduling infrastructure of Marsyas-0.2, an open source framework for audio analysis and synthesis. We describe how to...