omation mbers - Society for Laboratory Automation and Screening
omation mbers - Society for Laboratory Automation and Screening
omation mbers - Society for Laboratory Automation and Screening
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3:00 pm Tuesday, February 3 Emerging Technologies – Engineering Applications Room A3<br />
Reinhold Schäfer<br />
Fachhochschule Wiesbaden<br />
Kurt-Schumacher-Ring 18<br />
Wiesbaden, D-65197 Germany<br />
schaefer@in<strong>for</strong>matik.fh-wiesbaden.de<br />
Dynamic Scheduling in the <strong>Laboratory</strong> – Problems <strong>and</strong> Solutions<br />
Analytical laboratories per<strong>for</strong>m manifold analyses in parallel. Different analytical procedures have to be optimized<br />
towards throughput or other parameters. Primarily workflows with well-defined functionality have to be <strong>for</strong>malized<br />
in order to be computable. They describe the testing including processing the sample on various instruments,<br />
sensor control, data acquisition, result calculations <strong>and</strong> storage etc. with all timing <strong>and</strong> conditional constraints.<br />
A working plan <strong>for</strong> different samples is calculated consisting of all activities per<strong>for</strong>med on different resources.<br />
This presentation discusses the need <strong>for</strong> different workflow elements, their interaction <strong>and</strong> effects of dynamic<br />
execution. In addition hidden transport implications are discussed as well as a scenario of dynamic recovery <strong>and</strong><br />
re-scheduling in case of errors.<br />
3:30 pm Tuesday, February 3 Emerging Technologies – Engineering Applications Room A3<br />
Mathew Hahn<br />
Scitegic<br />
9665 Chesapeake Drive, Suite 401<br />
San Diego, Cali<strong>for</strong>nia 92123<br />
mhahn@scitegic.com<br />
The Data Pipelining Approach to In<strong>for</strong>matics Aut<strong>omation</strong> <strong>and</strong> Integration<br />
The processing <strong>and</strong> interpretation of large volumes of automated laboratory data is burdened with numerous<br />
challenges. The Data Pipelining approach provides a software computing environment powerful enough to keep<br />
pace with data generation, yet flexible enough to adapt quickly <strong>and</strong> easily to changing processes. By streaming<br />
data between any of hundreds of available modular data processing steps, virtually unlimited functionality can<br />
be assembled. Further, with open st<strong>and</strong>ards <strong>for</strong> integrating 3rd party software as re-useable modular steps in the<br />
system, a Data Pipelining system can include existing resources as well as adapt to future needs as they arise. We<br />
will discuss the approach as well as the technologies employed to permit the easy integration of disparate data<br />
<strong>and</strong> external applications.<br />
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