1,365 research outputs found
Information system support in construction industry with semantic web technologies and/or autonomous reasoning agents
Information technology support is hard to find for the early design phases of the architectural design process. Many of the existing issues in such design decision support tools appear to be caused by a mismatch between the ways in which designers think and the ways in which information systems aim to give support. We therefore started an investigation of existing theories of design thinking, compared to the way in which design decision support systems provide information to the designer. We identify two main strategies towards information system support in the early design phase: (1) applications for making design try-outs, and (2) applications as autonomous reasoning agents. We outline preview implementations for both approaches and indicate to what extent these strategies can be used to improve information system support for the architectural designer
Expressivity in Natural and Artificial Systems
Roboticists are trying to replicate animal behavior in artificial systems.
Yet, quantitative bounds on capacity of a moving platform (natural or
artificial) to express information in the environment are not known. This paper
presents a measure for the capacity of motion complexity -- the expressivity --
of articulated platforms (both natural and artificial) and shows that this
measure is stagnant and unexpectedly limited in extant robotic systems. This
analysis indicates trends in increasing capacity in both internal and external
complexity for natural systems while artificial, robotic systems have increased
significantly in the capacity of computational (internal) states but remained
more or less constant in mechanical (external) state capacity. This work
presents a way to analyze trends in animal behavior and shows that robots are
not capable of the same multi-faceted behavior in rich, dynamic environments as
natural systems.Comment: Rejected from Nature, after review and appeal, July 4, 2018
(submitted May 11, 2018
Petabyte Scale Data Mining: Dream or Reality?
Science is becoming very data intensive1. Today's astronomy datasets with
tens of millions of galaxies already present substantial challenges for data
mining. In less than 10 years the catalogs are expected to grow to billions of
objects, and image archives will reach Petabytes. Imagine having a 100GB
database in 1996, when disk scanning speeds were 30MB/s, and database tools
were immature. Such a task today is trivial, almost manageable with a laptop.
We think that the issue of a PB database will be very similar in six years. In
this paper we scale our current experiments in data archiving and analysis on
the Sloan Digital Sky Survey2,3 data six years into the future. We analyze
these projections and look at the requirements of performing data mining on
such data sets. We conclude that the task scales rather well: we could do the
job today, although it would be expensive. There do not seem to be any
show-stoppers that would prevent us from storing and using a Petabyte dataset
six years from today.Comment: originals at
http://research.microsoft.com/scripts/pubs/view.asp?TR_ID=MSR-TR-2002-8
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