Intelligent Simulation Tools for Mining Large Scientific Data Sets

16 Pages·1999·0.274 MB·English

by  Zhao F., Bailey-Kellogg C., Huang X.

IntelligentSimulationToolsforMiningLargeScienti(cid:12)cDataSets 1
Intelligent Simulation Tools for Mining
Large Scienti(cid:12)c Data Sets
Feng ZHAO
Xerox Palo Alto Research Center
3333 Coyote Hill Road, Palo Alto, CA 94304
[email protected]
Chris BAILEY-KELLOGG
Dartmouth College
6211 Sudiko(cid:11) Laboratory, Hanover, NH 03755
[email protected]
Xingang HUANG and Iv(cid:19)an ORDO(cid:19)N~EZ
The Ohio State University
2015 Neil Avenue, Columbus, OH 43210
fhuang,[email protected]
Received 24 February 1999
Abstract
This paper describes problems, challenges, and opportunities for in-
telligent simulation of physical systems. Prototype intelligent simulation
toolshavebeen constructed forinterpreting massivedatasets fromphys-
ical(cid:12)elds and for designingengineering systems. Weidentify the charac-
teristicsofintelligentsimulationanddescribe severalconcreteapplication
examples. These applications,whichincludeweatherdatainterpretation,
distributed control optimization, and spatio-temporal di(cid:11)usion-reaction
pattern analysis, demonstrate that intelligent simulationtools are indis-
pensable forthe rapidprototypingofapplicationprogramsinmanychal-
lenging scienti(cid:12)c and engineering domains.
Keywords Intelligent simulation, Scienti(cid:12)c data mining, Qualitative
reasoning,Reasoningaboutphysicalsystems,Programmingenvironments.
2 FengZHAO,ChrisBAILEY-KELLOGG, XingangHUANG, andIva(cid:19)nORDO(cid:19)N~EZ
x1 Introduction
Informationtechnology has fundamentallychanged the waywe conduct
scienti(cid:12)c experiments and synthesize engineering artifacts. For instance, power-
ful computers have routinely been used to extract interesting features in satel-
lite images, diagnose abnormalitiesin nuclear reactors, allocate resources in air
tra(cid:14)c control, and discern subtle trends in stock markets, to name just a few
applications.
This paper describes a coherent body of theories and techniques, col-
lectively known as intelligent simulation, that have recently been developed by
researchers in Arti(cid:12)cial Intelligence and Computational Science and Engineer-
ing to process massive spatio-temporal data sets arising from many scienti(cid:12)c
and engineering applications. Intelligentsimulationcombinesthe reasoningand
representational powerofcomputationalintelligencewithanarsenalofnumeric,
symbolic, and geometric tools to solve problems not amenable to traditional
1)
numericorsymbolicmethodsalone . Suitabletaskdomainsforintelligentsim-
ulation include miningscienti(cid:12)c data and designing and controllingengineering
systems. Instead of attemptingto survey the huge body of existing work in the
(cid:12)eld, we will introduce and focus on concrete instances from our own work to
demonstrate that intelligent simulation tools are essential for the rapid proto-
typingand execution ofapplicationprogramsinmanychallengingscienti(cid:12)c and
engineering domains. We willdescribe future research directions to developand
realize the full potential of intelligent simulation.
x2 Physical Fields are Ubiquitous
Amajorclassofphysicalproblemsforwhichintelligentsimulationtech-
niques are particularly well-suited are the so-called (cid:12)eld problems. Continuous,
distributedparameter(cid:12)eldsarecommonphysicalphenomena: considerthetem-
perature (cid:12)eld in a building, the air (cid:13)ow around an airplane wing, or the noise
from a copy machine. Figure 1 gives examples of (cid:12)elds | a (cid:13)uid (cid:12)eld and a
weather (cid:12)eld (map). Other examples of (cid:12)elds are phase spaces for describing
3)
behaviors of dynamical systems and con(cid:12)guration spaces for motion of me-
18)
chanical systems . Yet another example is the force (cid:12)eld synthesized from a
distributed micro-electro-mechanicalsystem(MEMS) actuatorarray;B(cid:127)ohringer
and Donalddeveloped force vector (cid:12)elds suitable fortransporting and orienting
8)
smallparts such as semiconductor wafers .
Manypractical applicationsrelyonthe abilitytoreason aboutandcon-
IntelligentSimulationToolsforMiningLargeScienti(cid:12)cDataSets 3
(a) (b)
Fig. 1 Examplesof (cid:12)elds: (a) A (cid:13)uid (cid:13)ow. The (cid:13)uid (cid:12)eld describes how objectssuch as
high density regions and large vortex structures are spatially distributed(shown here) and
temporallyevolving(notshown). (b)A300mbweathermapoverNorthAmerica. Thedatain
atypicalmeteorologicalmapincludespressure,temperature,andwindvelocitiesonaspatial
grid. An experiencedmeteorologist could identify qualitativelyimportantweather features
such as the location of a cold front and the direction of its movement, by extracting and
correlatinggeometricfeaturessuchaspressuretroughsandthermalpacking.
trol these processes and systems. For instance, the drag on an airplane can be
reducedbyanalyzingandcontrollingtheair(cid:13)owaroundthewings. Temperature
ina\smart"buildingcanberegulatedtomaximizeoccupantcomfortwhilemin-
imizingenergyconsumption. Because ofthe rapidadvances inmicro-fabrication
technology that can integrate and produce MEMS devices on a massive scale,
wearebecomingincreasinglyreliantonlargenetworksofsensors,actuators,and
computationalelements to augment our abilityto interact with and control the
26)
physical environment .
However, the challenges one faces in interpreting data from physical
(cid:12)elds and controlling the behaviors of the (cid:12)elds are enormous. The di(cid:14)culties
arise from three sources. First, a distributed parameter (cid:12)eld is conceptually
harder to reason about and model than a lumped parameter system such as a
circuit. In addition to the combinatorial structures, spatial topology, metric,
material properties and physical laws all come into play. The underlying phys-
ical processes might be nonlinear and defy analytic, closed-form solution. Sec-
ond, numerical methods developed for designing and controlling physical (cid:12)elds
require solving large systems of equations and hence are prohibitively expen-
sive for large, irregular geometric domains and highly non-uniform, nonlinear
phenomena. Third, many control applications rely on networks of sensors and
controllers to interact with a physical (cid:12)eld. The physical laws constrain the
abilityof spatiallydistributed, localagents tosense and a(cid:11)ect the environment.
4 FengZHAO,ChrisBAILEY-KELLOGG, XingangHUANG, andIva(cid:19)nORDO(cid:19)N~EZ
Local sensors and control elements measure and interact with small neighbor-
hoods around them. Macroscopic consequences are aggregated from local ac-
tions. Consequently, the design, programming,and coordination of distributed
computational agents immersed in physical media require abstraction mecha-
nisms, inference methods, and programminglanguages di(cid:11)erent from those for
reasoning about and controllingcentralized, lumpedparameter models.
x3 Interpreting Fields: An example of intelli-
gent simulation
Much of the (cid:12)eld information amassed by sensors is in an analogue,
data-rich form such as images, videos, or spatially distributed and continuous
measurements of physical processes. For many reasoning and control tasks,
we need to extract large-scale structures and behaviors from small-scale data
descriptions.
Asanexampleofhowintelligentsimulationprovidesorganizationalprin-
ciples and building blocks to facilitate the development of programs for engi-
neering problems,consider an interpretation task in dynamicalsystemanalysis,
27)
simpli(cid:12)ed fromYip’s KAM program for analysis of dynamicalsystems . The
input is a (cid:12)eld of sampled states as points in phase space | a vector (cid:12)eld |
showninFigure2(a). The objective istogroupstates intotrajectories andthen
trajectories into trajectory bundles that share the same qualitative behaviors,
as shown in Figure 2(d) and Figure 2(f) respectively. We use the vocabulary
29)
of Spatial Aggregation (SA) to describe the computational patterns in this
example. SA provides a uni(cid:12)ed description for a number of problem solvers
27,23,21,30) 9)
for interpreting dynamical systems , synthesizing controllers , and
16)
analyzing mechanical mechanisms . SA introduces the concepts of (cid:12)eld on-
tology,multi-layerspatialaggregates mediatedbyneighborhood structures, and
a uniform vocabulary for constructing and transformingthe spatial aggregates.
Even thoughthis probleminvolvesa2Dspace, the frameworkappliestohigher-
dimensionalspaces as well.
1. Points to trajectory curves
a. Given an input point (cid:12)eld (Figure 2(a)).
b. Aggregate the points into a minimal spanning tree neighborhood
graph (Figure 2(b)). Aggregation explicates a task-speci(cid:12)c neigh-
borhood relation (e.g. MST, Voronoi diagram, or nearness criteria)
IntelligentSimulationToolsforMiningLargeScienti(cid:12)cDataSets 5
on a set of spatial objects.
c. Classifyconnected points into the sameequivalence class if the edge
connectingthemisn’ttoolongrelativetonearbyedges(Figure2(c)).
Classi(cid:12)cation identi(cid:12)es equivalence classes of objects in a neighbor-
hood graph, according to a task-speci(cid:12)c clustering mechanism and
equivalence relation.
d. Redescribe equivalence classes of points as trajectory curves (Fig-
ure 2(d)). Redescription shifts the level of abstraction so that the
aggregation process can repeat at a higher level.
2. Trajectory curves to trajectory bundles
a. Aggregate trajectory curves such that curves are adjacent if any of
their constituent points are neighbors in the underlying minimal
spanning tree (Figure 2(e)).
b. Classify connected curves into the same equivalence class if their
shape is relatively similar(Figure 2(f)).
c. Redescribeequivalenceclasses oftrajectory curves astrajectory bun-
dles.
As the example illustrates, SA programs are modular,using a common
data structure (neighborhood graph) and an identical set of generic operators
reusable for multiplelevels of problem abstraction. They are concise and make
explicit the importantcharacteristics of the problem: neighborhood and equiv-
alence relations.
In the following,we (cid:12)rst describe an implementedlanguage, SAL, sup-
porting a subset of the desired capabilities for intelligent simulation. We then
overviewthreechallengingapplicationsdevelopedusingSAL:weatherdatainter-
pretation,distributedcontroloptimization,anddi(cid:11)usion-reactionspatio-temporal
pattern analysis.
x4 The Spatial Aggregation Language (SAL): A
rapid prototyping environment
6)
We have developed the SAL programming environment to provide
commonlyused data types and generic Spatial Aggregationoperators for appli-
cation development. Applicationprogramsare written by mixingand matching
components from prefabricated libraries. The SAL components explicate and
localize important domain speci(cid:12)c knowledge so that the resulting programs
6 FengZHAO,ChrisBAILEY-KELLOGG, XingangHUANG, andIva(cid:19)nORDO(cid:19)N~EZ
(a) (b) (c)
nbr nbr equiv
nbr equiv
nbr
nbr equiv
nbr nbr equiv
(d) (e) (f)
Fig. 2 Examplestepsintrajectorybundlingapplication.(a)Inputpoints. (b)Pointsaggre-
gatedintoa minimalspanningtree. (c)Equivalenceclassesofpointsjoinedbyshort-enough
edges. (d)Equivalenceclassesredescribedastrajectories.(e)Trajectoriesaggregatedbasedon
adjacenciesof constituentpoints. (f)Equivalenceclassesof trajectorieswith similar-enough
limitbehavior.
are more modularand easier to maintainthan traditional numericalsimulation
software. They allow a programmer to focus on the knowledge relevant to an
application,rather than on implementationdetails.
The SAL implementationcomprises a C++ library and an interpreted,
interactive environmentlayered over the library. The librarysupports construc-
tionofe(cid:14)cientC++programswithaccess toalargesetofdatatypeimplemen-
tations and operations supporting the Spatial Aggregation programming style.
The interpreter (Figure 3) supports rapid prototyping of application programs
by providing a high-level interface to the main data type implementationsand
operators of the SAL library. Programmers can conveniently explore trade-
o(cid:11)s in the speci(cid:12)cation of domain knowledge such as neighborhood relations
and equivalence predicates, interactively examining and modifying the results
IntelligentSimulationToolsforMiningLargeScienti(cid:12)cDataSets 7
Fig. 3 The SAL interpreterinaction: interactivecodeevaluationandgraphicalinspection
ofresults.
without having to recompile a program. Graphical inspection tools support
manipulationand exploration of the structures in physical data.
x5 Weather Feature Analysis: Extracting salient
structures from large spatial data sets
In analyzingspatial data sets such as weather data or (cid:13)uid motion,ex-
perts often perceive and reason about these physical (cid:12)elds in terms of abstract
spatial objects, also called features or patterns, that evolve and interact with
each other. For example, meteorologists identify and explicitly label aggregate
weather features such as high/low pressure centers, pressure troughs, thermal
packings, fronts, and jet streams. The experts then use weather rules to cor-
relate these features and establish prediction patterns. Most of these rules are
qualitative and have rich geometric interpretation: \At 850mb, the polar front
is located parallel to and on the warm side of the thermal packing." \Major
and minor 500mb troughs are good indicators of existing or potential adverse
2)
weather."
Wehavedeveloped astructure (cid:12)ndingalgorithmfor extractingabstract
objects fromspatialdatasets. Itis unique inthat: (1) It classi(cid:12)es neighborhood
relationsintostrongandweakadjacencies. (2)Itbuildsinternalstructures ofag-
gregateobjectsfromstrongadjacenciesamongconstituentobjects,andrelations
between aggregateobjects fromweakadjacencies. Theinternalstructures ofob-
jects formaricher description thanafeature-valuebased representation andare
8 FengZHAO,ChrisBAILEY-KELLOGG, XingangHUANG, andIva(cid:19)nORDO(cid:19)N~EZ
(a) (b)
Fig. 4 Labelingweatherchart: (a)The high-altitudetrough(dashedline)detectedbythe
spatialaggregationalgorithm. (b)The correspondingtrough(dashedline)drawnbymeteo-
rologistsforthenationalweatherforecastmapforroughlythesameareaastheboxin(a).
essential for many identi(cid:12)cation and correspondence tasks in interpreting large
scienti(cid:12)c data sets.
We illustrate the algorithmwith an application to (cid:12)nding troughs and
ridges in weather data. Troughs and ridges are important features in weather
analysis: high-altitude troughs correlate with bendings of jet streams and are
useful for extended weather forecast; surface troughs are usually closely related
to fronts and are important for locating the fronts. What are troughs and
ridges? Visually, troughs and ridges are sequences of iso-bar segments bending
consistently to one direction, with troughs bending from low iso-bars to high
iso-bars and ridges from high iso-bars to low iso-bars. Though the extraction
of troughs seem e(cid:11)ortless and immediateto humaneyes, it is only qualitatively
de(cid:12)ned.
Ouralgorithmextractstroughfeaturesfromweatherdatausingamulti-
level approach. First, the algorithm extracts salient iso-bar segments using an
iterative thresholding technique. It buildsa neighborhood structure forthe seg-
mentsfromaDelaunaytriangulationofiso-points. Itthenclassi(cid:12)estheneighbor-
hood relations and uses the strong adjacencies to extract the linear structures
among the segments to obtain troughs. Figure 4 compares the high altitude
trough detected by ouralgorithmwiththat drawn bythe meteorologistsfor the
nationalweather forecast map,usingthe rawdata(pressure, temperature, wind
streamlines, air density, etc.) for Friday,Jan. 15, 1999.
The rich structures extracted by the algorithm can be used to identify
causal relations among weather events and to generate high-level explanations
for predictions.
IntelligentSimulationToolsforMiningLargeScienti(cid:12)cDataSets 9
x6 Distributed Control Optimization: Divide-
and-conquer by exploiting structural information
in (cid:12)elds
Manysensor-rich controlapplicationsrequire decentralizedcontroltoat-
tainadaptivity,robustness, andscalability. Forexample,acontrollerforasmart
buildingregulates buildingtemperature withnetworks ofsensors andactuators;
decentralized control allows the network to tolerate failures in individual ele-
ments and to scale up more gracefully with the number of nodes. As another
example, rapid thermal processing in semiconductor curing employs separately
controlled lamp zones to maintaina uniform temperature pro(cid:12)le over semicon-
17)
ductor wafers in order to avoiddefects . Designing and optimizingdecentral-
izedcontrolsforadistributedsystemrequires achievingglobalcontrolobjectives
through appropriate combinations of local control actions. To determine the
placementofcontrolnodes andtheir parameters,onemustsearch alargedesign
space subject to structural, behavioral,and performance constraints.
WehaveemployedSALtomodeladistributed thermalregulationprob-
4,5)
lem and to design controls . The (cid:12)rst task is to model the heat (cid:13)ow in a
problem domain. Figure 5(a) overviews major computational patterns in this
process. A geometric description of a problem domain(e.g. a piece of material
forwhichthetemperatureistoberegulated)isdiscretized intoaspaceofspatial
objects and an associated neighborhood graph. Local interaction rules in the
neighborhoodgraph support the computationofheat(cid:13)ow. This process canoc-
cur at multiple levels of abstraction, with approximationsat coarse resolutions
driving re(cid:12)nements at (cid:12)ner resolutions. It can also occur separately in subre-
gionsoftheinitialspace,withresults iterativelycombined. Inthismanner,SAL
supports many traditional paradigms of engineering computation (e.g. domain
11) 10)
decomposition and multigrid ), but withaset ofconcise, generic operators
in a vocabulary natural for the domain.
The second task, overviewed in Figure 5(b), is to design both control
placements and control parameters to achieve a desired control objective. SAL
operators performcontrol design based on high-levelcharacteristics ofheat (cid:13)ow
ina(cid:12)eld. Thesecharacteristicsareextractedbyclassifyingthe(cid:12)eld’sresponseto
asetofcontrolprobes. Inthe example,thegeometricconstraintimposedbythe
narrow channel in the dumbbell-shaped piece of materialresults in similar(cid:12)eld
responses tothetwoprobesinthelefthalfofthedumbbellandsimilarresponses
to the two probes in the right half of the dumbbell. Based on the resulting
10 FengZHAO,ChrisBAILEY-KELLOGG, XingangHUANG, andIva(cid:19)nORDO(cid:19)N~EZ
Probe classes
Coarse discretization
Geometry
Fine discretization
Controls
Probes
Partition Field
(a) (b)
Fig. 5 OverviewofSAL-basedcomputationalpatternsfordecentralizedthermalregulation.
(a)Modelingheat(cid:13)ow. (b)Designingcontrollers.
classes, the (cid:12)eld is decomposed into regions to be separately controlled. In this
case, the left half of the dumbbell is decomposed from the right half. Controls
are placed in the regions and optimized by adjusting their outputs in response
totheire(cid:11)ects onthe (cid:12)eld. Thestructural informationalsosupports parametric
optimization by indicating the in(cid:13)uence of control nodes on (cid:12)eld nodes. This
allows e(cid:14)cient computation of changes in the (cid:12)eld due to actions of a control,
andthe abilitytofocus computationandcommunicationonthe partofthe(cid:12)eld
most strongly a(cid:11)ected by a control’s actions.
Theresultingcontrolplacementdesignsarecomparabletothoseachieved
20)
by traditional engineering optimization methods (e.g. simulated annealing ),
butuseasmall,(cid:12)xednumberoffunctionevaluationstoextractandexploitstruc-
5)
turaldescriptionsofthe(cid:12)eld . Basingthealgorithmsonstructuraldescriptions
allowsexplanationofandhigher-levelreasoningabouttheresultingdesigns. Use
of structural information in parametric optimization results in drastic (several
orders of magnitude) speed-ups in (cid:12)eld evaluation, and methods for explicitly
4)
trading o(cid:11) between computation,communication,and control quality .
x7 Di(cid:11)usion-Reaction Patterns Analysis: Mod-
eling spatio-temporal processes