SAL(ID:6177/sal008)

for Spatial Aggregation Language 

Spatial Aggregation Language
References:
  • Bailey-Kellogg, C.; Zhao, F. and K. Yip, "Spatial aggregation: language and applications." Proc. AAAI, 1996, pp. 517-522. view details Abstract: Spatial aggregation is a framework for organizing computations around image-like, analogue representations of physical processes in data interpretation and control tasks. It conceptualizes common computational structures in a class of implemented problem solvers for difficult scientific and engineering problems. It comprises a mechanism, a language, and a programming style. The spatial aggregation mechanism transforms a numerical input field to successively higher-level descriptions by applying a small, identical set of operators to each layer given a metric, neighborhood relation and equivalence relation. This paper describes the spatial aggregation language and its applications.

    The spatial aggregation language provides two abstract data types -- neighborhood graph and field -- and a set of interface operators for constructing the transformations of the field, together with a library of component implementations from which a user can mix-and-match and specialize for a particular application. The language allows users to isolate and express important computational ideas in different problem domains while hiding low-level details. We illustrate the use of the language with examples ranging from trajectory grouping in dynamics interpretation to region growing in image analysis. Programs for these different task domains can be written in a modular, concise fashion in the spatial aggregation language.

  • Bailey-Kellogg C. and F. Zhao, "The SAL Interpreter for Large-Scale Optimization in Distributed Control Systems." view details
          in Proc. IEEE Symposium on Computer-Aided Control Systems Design, 1999 view details
  • Bailey-Kellogg, C. "The Spatial Aggregation Language for Modeling and Controlling Distributed Physical Systems" Ph.D. diss., Dept. of Computer and Information Science, The Ohio State Univ. 1999 view details Abstract: Many important science and engineering applications, such as
    predicting weather patterns, controlling the temperature distribution over a semiconductor wafer, and controlling the noise of a photocopy machine, require interpreting data and designing decentralized controllers for spatially distributed systems. This thesis describes
    the Spatial Aggregation Language (SAL), a novel programming language
    and environment supporting data interpretation and control tasks for distributed physical systems. SAL provides a set of powerful, high-level components that make explicit use of domain-speci c physical knowledge, such as metrics, adjacency relations, and equivalence predicates, in order to uncover and exploit structures in distributed physical data at multiple levels of abstraction. The language data types and operators manipulate structured representations of spatial objects in distributed physical systems at multiple levels of abstraction.
          in Proc. IEEE Symposium on Computer-Aided Control Systems Design, 1999 view details
  • Bailey-Kellogg, Chris; Zhao, F. and Ordonez, I. "The Spatial Aggregation Language" April 2001, p68 view details Abstract: The Spatial Aggregation Language (SAL) is a programming environment and C++ library supporting rapid prototyping of data analysis and control applications for distributed physical systems. Weather prediction is an example of analysis of a distributed physical system. The weather charts you see on TV result from computation-intensive analysis on massive amounts of weather data, including pressure, temperature, humidity, and wind velocity measurements collected from weather sensors across the continent. Other examples might be the tasks of describing mixtures of chemical species in a reaction tank, understanding the phenomenon of glycolysis in living organisms, or predicting population variations in a predator-prey environment. Interestingly, diffusion-reaction models from nonlinear dynamics can describe all these phenomena. Analysis of these models results in rich spatio-temporal data exhibiting striking patterns, some resembling zebra stripes and others polka dots (see Figure 1). In all these applications, the core programming tasks involve representing and abstracting spatio-temporal data into concise descriptions suitable for explanation, design, prediction, and other analyses.
          in Proc. IEEE Symposium on Computer-Aided Control Systems Design, 1999 view details
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