Mining and UDlizing Dataset Relevancy from Oceanographic ... › forum › estf2017 ›...

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EarthScienceTechnologyForum(ESTF2017),June13-15,2017Pasadena,CA

MiningandUDlizingDatasetRelevancyfromOceanographicDataset(MUDROD)Metadata,UsageMetrics,andUser

FeedbacktoImproveDataDiscoveryandAccess NASAAIST(NNX15AM85G)

Dr.Chaowei(Phil)Yang,Mr.YongyaoJiang,Ms.YunLi,

GeographyandGeoInformaDonScience,GeorgeMasonUniversity

Mr.EdwardMArmstrong,Mr.ThomasHuang,Mr.DavidMoroni,Mr.ChrisFinch,Dr.LewisJ.McGibbney,Mr.FrankGreguska,Mr.GaryChen

JetPropulsionLaboratory,NASA

•  ProjectBackground–  Problems–  ObjecDves–  FuncDons

•  System–  Logmining–  QuerySemanDcs–  Ranking–  RecommendaDon

•  Results•  Nextstep

Agenda

Data Discovery Problems

•  Keyword-based matching (traditional search engines) –  User query: ocean wind –  Final query: ocean AND wind

•  Reveal the real intent of user query –  ocean wind = “ocean wind” OR “greco” OR “surface wind” OR “mackerel breeze” …

•  PO.DAAC UWG Recommendation 2014-07

•  NASA ESDSWG Search Relevance Recommendations 2016 & 2017

•  Analyzeweblogstodiscoveruserknowledge(queryanddatarelaDonships)•  ConstructknowledgebasebycombiningsemanDcsandprofileanalyzer•  Improvedatadiscoveryby1)be^erranking;2)recommendaDon;3)

ontologynavigaDon

ObjecDves

•  Weblogpreprocessing•  SemanDcanalysisofuserqueries&NavigaDon

•  Machinelearningbasedsearchranking•  DataRecommendaDon

FuncDons/Modules

Weblogprocessing

•  Requestssentfromcliente.g.browser,cmdlinetool,etc.recordedbyserver

•  LogfilesprovidedbyPO.DAAC(HTTP(S),FTP)

ClientIP:68.180.228.99Requestdate/Dme:[31/Jan/2015:23:59:13-0800]Request:"GET/datasetlist/...HTTP/1.1"HTTPCode:200Bytesreturned:84779Referrer/previouspage:“/ghrsst/"Useragent/browser:"Mozilla/5.0...

68.180.228.99--[31/Jan/2015:23:59:13-0800]"GET/datasetlist/...HTTP/1.1"20084779"/ghrsst/""Mozilla/5.0..."

Weblogs

Goal: reconstruct user browsing pattern (search history & clickstream) from a set of raw logs

Weblogs

UseridenDficaDon

CrawlerdetecDon

StructurereconstrucDon

SessionidenDficaDon Searchhistory

Clickstream

AddiDonalstepsinclude:wordnormalizaDon,stopwordsremoval,andstemming

Datapreprocess

Reconstructedsessionstructure

Datapreprocessresults

1.Usersearchhistory2.Clickstream

Jiang,Y.,Y.Li,C.Yang,E.M.Armstrong,T.Huang&D.Moroni(2016)“ReconstrucDngSessionsfromDataDiscoveryandAccessLogstoBuildaSemanDcKnowledgeBaseforImprovingDataDiscovery”ISPRSInternaDonalJournalofGeo-InformaDon,5,54.

SemanDcsimilarity

ExisDngontology(SWEET)•  SWEET(RaskinandPan2003)•  FocusononlytworelaDons•  Thecloser,themoresimilar

Usersearchhistory•  Createquery–usermatrix•  Calculatebinarycosinesimilarity

Conceptualexample

Clickstream•  Hypothesis:similarqueriescanresultinsimilarclickingbehavior•  Iftwoqueriesaresimilar,thedatathatgetclickedaoertheyaresearched

wouldbemorelikelytobesimilar

Queryb

Querya

Similar?

Metadata•  Hypothesis:semanDcallyrelatedtermstendtoappearinthesame

metadatamorefrequently•  EssenDallythesameastheclickstreamanalysis•  PerformLatentSemanDcAnalyses(LSA)overtheterm–metadata

matrix

Queryb

Metadata

Querya

Similar?

IntegraDon•  Allfourresultscouldbeconvertedto

•  Problem:–  Noneofthemareperfect(uncertaintyindata,hypothesisandmethod)

–  Metadataandontologymighthaveunknowntermstosearchengineendusers

–  SomeDmes,similarityvaluesfromdifferentmethodsareinconsistent

ConceptA

ConceptB

Similarity

IntegraDon

•  Themaximumsimilarityofallofthecomponents(largesimilarityappearstobemorereliable)

•  Theadjustmentincrementbecomeslargerwhenthesimilarityexistsinmoresources

SemanDcSimilarityCalculaDonWorkflow

ResultsandevaluaDonQuery Searchhistory Clickstream Metadata SWEET Integratedlist

oceantemperature

seasurface

temperature(0.66),sea

surfacetopography(0.56),

oceanwind(0.56),

aqua(0.49)

seasurface

temperature(0.94),

sst(0.94),grouphigh

resoluDonseasurface

temperaturedataset(0.89),

ghrsst(0.87)

sst(0.96),ghrsst(0.77),sea

surfacetemperature(0.72),

surfacetemperature(0.63),

reynolds(0.58)

sst(1.0),seasurfacetemperature(1.0),

ghrsst(1.0),grouphighresoluDonsea

surfacetemperaturedataset(0.99),

reynoldsseasurfacetemperature(0.74)

Samplegroup Overallaccuracy

Mostpopular10queries 88%

Leastpopular10queries 61%

Randomlyselected10queries 83%

Bydomainexperts

Jiang,Y.,Y.Li,C.Yang,K.Liu,E.M.Armstrong,T.Huang&D.Moroni(2017)AComprehensiveApproachtoDeterminingtheLinkageWeightsamongGeospaDalVocabularies-AnExamplewithOceanographicDataDiscovery.InternaDonalJournalofGeographicalInformaDonScience(minorrevision)

•  QuerysuggesDon•  QuerymodificaDon

Whatcanweuseitfor?

Searchranking

Background

•  Rankingisalong-standingproblemingeospaDaldatadiscovery•  Typically,hundreds,eventhousandsofmatches•  CangetlargerasmoreEarthobservaDondataisbeingcollected

ObjecDveandMethods

•  Putthemostdesireddatatothetopoftheresultlist•  Whatfeaturescanrepresentusers’searchpreferencesforgeospaDal

data?•  HowcantherankingfuncDonreachabalanceofallthesefeatures?

•  IdenDfiedelevenfeaturesfrom–  GeospaDalmetadataa^ributes–  Query– metadatacontentoverlap–  Userbehaviorfromweblogs

Rankingfeatures– Metadataa^ributesFeatures Description

Release date The date when the data was published

Processing level (PL) The processing level of image products, ranging from level 0 to level 4.

Version number The publish version of the data

Spatial resolution The spatial resolution of the data

Temporal resolution The temporal resolution of the data

•  Fivemetadatafeatures•  Verifiedbydomainsexperts•  Query-independent:staDc,dependsonthedataitself,won’tchangewiththequery

SpaDalquery-metadataoverlap

•  SpaDalsimilaritybetweenqueryareaandthecoverageofaparDculardata

•  Overlapareanormalizedbytheoriginalareaofqueryanddata

Rankingfeatures– Userbehavior•  All-Dme,monthly,userpopularity,andsemanDcpopularity(retrieved

fromweblogs)•  SemanDcpopularity:thenumberofDmesthatthedatahasbeen

clickedaoersearchingaparDcularqueryanditshighlyrelatedones(query-dependent)

oceantemperature

seasurfacetemperature

RankSVM•  Oneofthewell-recognizedMLrankingalgorithm•  ConvertarankingproblemintoaclassificaDonproblemthataregular

SVMalgorithmcansolve•  3mainsteps

Ø  1)Standardize:mean=0,std=1o  SVMisnotscaleinvarianto  Over-opDmizedo  Longertotrain

Ø  2)Foranypairoftrainingdata,calculatethedifferenceØ  3)ArankingproblembecomesabinaryclassificaDonproblem,whereSVMis

appliedtofindtheopDmaldecisionboundary

ArchitectureSec.15.4.2

•  All of these (except for training) can be finished within 2 seconds

•  None of the open source mainstream ML library provide any ranking algorithm

•  Implemented it by ourselves with the aid of Spark MLlib Index

User query

Semantic query

Top K retrieval

Ranking model

Learning algorithm

Training data

Re-ranked results

Feature extractor

Weblogs

User clicks Knowledge base

NDCG(K)forfivedifferentrankingmethodsatvaryingK(1-40)

Jiang,Y.,Y.Li,C.Yang,K.Liu,E.M.Armstrong,T.Huang,D.Moroni&L.J.McGibbney(2017)TowardsintelligentgeospaDaldiscovery:amachinelearningrankingframework.InternaDonalJournalofDigitalEarth(minorrevision)

DatarecommendaDon

HowtorecommendgeospaDaldata?•  UsegeospaDalmetadataforcontent-basedrecommendaDon

-MetadataspaDotemporalsimilarity-Metadataa^ributesimilarity-Metadatacontentsimilarity

•  LeverageuserbehaviorsdataforCFrecommendaDon

-Session-basedco-occurrenceofdata

AFributetype AFributename AFributedescripHon

SpaHotemporalaFributes DatasetCoverage-EastLon TheEastlongitudeoftheboundingrectangle

DatasetCoverage-WestLon TheWestlongitudeoftheboundingrectangle

DatasetCoverage-NorthLat TheNorthlaDtudeoftheboundingrectangle

DatasetCoverage-SouthLat TheSouthlaDtudeoftheboundingrectangle

DatasetCoverage-StartTimeLong ThestartDmeofthedata

DatasetCoverage-StopTimeLong TheendDmeofthedata

Categoricalgeographic

aFributes

DatasetRegion-Region Regionofdataset.Suchasglobal,AtlanDc

Dataset-ProjecDonType Projecttypelikecylindricallat-lon

Dataset-ProcessingLevel Dataprocessinglevel

DatasetPolicy-DataFormat Dataformate.g.HDF,NetCDF

DatasetSource-Sensor-ShortName Shortnameofsensor

OrdinalgeographicaFributes Dataset-TemporalResoluDon TemporalresoluDonofdataset

Dataset-TemporalRepeat TemporalresoluDonofdataset

Dataset-SpaDalResoluDon SpaDalresoluDonofdataset

Descriptive attributes Dataset-description Describe the content of the dataset

Geographicmetadata

•  SpaDalvariables:NorthLat,SouthLat,WestLon,EastLon•  Temporalvariables:DatasetCoverage-StartTimeLong,StopTimeLong

•  UsevolumeoverlapraDotocalculatesimilarity

SpaDotemporalsimilarity

•  Fixednumberofvalues•  Nointrinsicordering•  sensor-name:"AMSR-E","MODIS","AVHRR-3"and

"WindSat"

𝑐𝑎𝑡𝑒𝑔𝑜𝑟𝑖𝑐𝑎𝑙_𝑣𝑎𝑟_𝑠𝑖𝑚(𝑣↓𝑖 , 𝑣↓𝑗 )= 𝑣↓𝑖  ∩ 𝑣↓𝑗 /𝑣↓𝑖  ∪ 𝑣↓𝑗  

Categoricalsimilarity

Ordinala^ributeissimilartocategoricala^ributebutitsvalueshasaclearorder,e.g.spaDalresoluDon•  Convertedintorankfrom1toR•  NominalizetheseranksforsimilaritycalculaDon

𝑜𝑟𝑑𝑖𝑛𝑎𝑙↓𝑣𝑎𝑟↓𝑠𝑖𝑚(𝑣↓𝑖 , 𝑣↓𝑗 )  =1− |𝑛𝑜𝑟𝑚↓𝑟𝑎𝑛𝑘(𝑣↓𝑖 ) −𝑛𝑜𝑟𝑚↓𝑟𝑎𝑛𝑘(𝑣↓𝑗 ) |

𝑛𝑜𝑟𝑚_𝑟𝑎𝑛𝑘(𝑣↓𝑖 )= 𝑅𝑎𝑛𝑘𝑣↓𝑖 +1/𝑅+1 

Ordinalsimilarity

OriginaltextAquariusLevel3seasurfacesalinity(SSS)standardmappedimagedatacontainsgridded1degreespaDalresoluDonSSSaveragedoverdaily,7day,monthly,andseasonalDmescales.ThisparDculardatasetistheseasonalclimatology,Ascendingseasurfacesalinityproductforversion4.0oftheAquariusdataset,whichistheofficialendofprimemissionpublicdatareleasefromtheAQUARIUS/SAC-Dmission.OnlyretrievedvaluesforAscendingpasseshavebeenusedtocreatethisproduct.TheAquariusinstrumentisonboardtheAQUARIUS/SAC-Dsatellite,acollaboraDveeffortbetweenNASAandtheArgenDnianSpaceAgencyComisionNacionaldeAcDvidadesEspaciales(CONAE).Theinstrumentconsistsofthreeradiometersinpushbroomalignmentatincidenceanglesof29,38,and46degreesincidenceanglesrelaDvetotheshadowsideoftheorbit.Footprintsforthebeamsare:76km(along-track)x94km(cross-track),84kmx120kmand96kmx156km,yieldingatotalcross-trackswathof370km.Theradiometersmeasurebrightnesstemperatureat1.413GHzintheirrespecDvehorizontalandverDcalpolarizaDons(THandTV).AscaêrometeroperaDngat1.26GHzmeasuresoceanbackscaêrineachfootprintthatisusedforsurfaceroughnesscorrecDonsintheesDmaDonofsalinity.Thescaêrometerhasanapproximate390kmswath.

ExtractedtermsRadiometersMeasureBrightnessTemperature,AQUARIUS/SACMission,ImageData,BroomAlignment,ResoluDonSSS,AQUARIUS/SACSatellite,Sca^erometer,Sca^erometer,AquariusData,ArgenDnianSpaceAgencyComisionNacional,IncidenceAngles,TimeScales,AcDvidadesEspaciales,Cross-trackSwath,OfficialEnd,AquariusInstrument,ShadowSide,AscendingSeaSurfaceSalinityProduct,Level,Level,SurfaceRoughnessCorrecDons,DataRelease,Salinity,Density,Salinity,Density,AQUARIUS,L3,SSS,SMIA,SEASONAL-CLIMATOLOGY,V4

Step1:PhraseextracDon1.  ExtracttermcandidatesfrommetadatadescripDonwithPOS(partofspeech)Tagging2.  Introduce“occurrence”and“strength”tofilterouttermsfromcandidates.“occurrence”:occurrencesnumberofterms“strength”:thenumberofwordsinaterm

DescripDvesimilarity

Step2:Representmetadatainthephrasevectorspace(Thedimensionlowerthanwordfeaturespace)

Term1 Term2 Term3 Term4 Term5 Term6 Term7 … TermN

dataser1 1 0 1 0 0 0 0 1

dataser2

0 0 0 1 1 0 0 0

datasetk 1 0 1 0 0 0 0 1

Step3:Calculatecosinesimilarity

MetadataabstractsemanDcsimilarity

Session1 Session2 … SessionN

Data1 1 0 1Data2 0 0 0…

Datak 1 0 1

Calculatemetadatasimilaritybasedonsessionlevelco-occurrence

Similarity (i,j) = 𝑁(𝑖ᴖ𝑗)/√𝑁(𝑖) ∗√𝑁(𝑗)  

N(i):ThenumberofsessionsinwhichdatasetiwasviewedordownloadN(j):ThenumberofsessionsinwhichdatasetjwasviewedordownloadN(𝑖ᴖ𝑗):Thenumberofsessionsinwhichbothdatasetiandjwereviewedordownload

SessionbasedrecommendaDon

RecommendaHonmethod

Pros Cons Strategy

DescripDvesimilarity

1.NaturallanguageprocessingmethodscanbeadoptedtofindlatentsemanDcrelaDonship

1.  Manydatasetshasnearlysameabstractwithfewwords/valueschanged2.  Itishardtoextractdetaileda^ributesfromdescripDon

UsedasthebasicmethodofrecommendaDonalgorithm

A^ributesimilarity(spaDotemporal,ordinal,categorical)

1.Asstructureddata,geographicmetadatahavemanyvariables.

1.Variablevaluesmaybenullorwrong2.Thequalitydependsontheweightassignedtoeveryvariable

AssupplementtosemanDcsimilarity

Sessionconcurrence

1.Reflectusers’preference

1.Coldstartproblem:Newlypublisheddatadon’thaveusagedata

Fine-tunerecommendaDonlist

Recommend (i) =𝑊𝑠𝑠∗𝐷𝑒𝑠𝑐𝑟𝑖𝑝𝑡𝑖𝑣𝑒𝑐↓𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑖) + ∑𝑊𝑐𝑣∗𝐶𝑎𝑡𝑒𝑔𝑜𝑟𝑖𝑎𝑙↓𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑖) +∑𝑊𝑜𝑣∗𝑂𝑟𝑑𝑖𝑎𝑛𝑙↓𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑖) +𝑊𝑠𝑡𝑣 ∗𝑆𝑝𝑎𝑡𝑖𝑜𝑇𝑒𝑚𝑝𝑜𝑟𝑎𝑙𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦+ 𝑊𝑠𝑜∗𝑆𝑒𝑠𝑠𝑖𝑜𝑛𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦

HybridrecommendaDon

1 2 3 4 5 6 7 8 9 10

Precision

PosiDon

Hybridsimilarity Word-basedsimilarity A^ributesimilarity Session-basedsimilarity

QuanDtaDveEvaluaDon HybridsimilarityoutperformothersimilariDessinceitintegratesmetadataa^ributesanduserpreference.

Y.Li,Jiang,Y.,C.Yang,K.Liu,E.M.Armstrong,T.Huang,D.Moroni&L.J.McGibbney(2017)AGeospaDalDataRecommenderSystembasedonMetadataandUserBehaviour(revision)

Conclusion

•  LogminingenablesadataportalintegraDngimplicituserpreferences•  Wordsimilarityretrievedbydataminingtasksexpandsanygivenquery

toimprovesearchrecallandprecision.•  TherichsetofrankingfeaturesandtheMLalgorithmprovide

substanDaladvantagesoverusingotherrankingmethods•  TherecommendaDonalgorithmcandiscoverlatentdatarelevancy•  Theproposedarchitectureenablesthelooselycoupledsooware

structureofadataportalandavoidsthecostofreplacingtheexisDngsystem

•  PublicaDons•  Jiang,Y.,Y.Li,C.Yang,E.M.Armstrong,T.Huang&D.Moroni(2016)ReconstrucDngSessionsfromDataDiscoveryand

AccessLogstoBuildaSemanDcKnowledgeBaseforImprovingDataDiscovery.ISPRSInternaDonalJournalofGeo-InformaDon,5,54.

•  Y.Li,Jiang,Y.,C.Yang,K.Liu,E.M.Armstrong,T.Huang&D.Moroni(2016)LeveragecloudcompuDngtoimprovedataaccesslogmining.IEEEOceans2016.

•  Yang,C.,etal.,2017.BigDataandcloudcompuDng:innovaDonopportuniDesandchallenges.Interna>onalJournalofDigitalEarth,10(1),pp.13-53.(the2ndmostreadpaperofIJDEinit’sdecadalhistory)

•  Jiang,Y.,Y.Li,C.Yang,K.Liu,E.M.Armstrong,T.Huang&D.Moroni(2017)AComprehensiveApproachtoDeterminingtheLinkageWeightsamongGeospaDalVocabularies-AnExamplewithOceanographicDataDiscovery.InternaDonalJournalofGeographicalInformaDonScience(minorrevision)

•  Jiang,Y.,Y.Li,C.Yang,K.Liu,E.M.Armstrong,T.Huang,D.Moroni&L.J.McGibbney(2017)TowardsintelligentgeospaDaldiscovery:amachinelearningrankingframework.InternaDonalJournalofDigitalEarth(minorrevision)

•  Y.Li,Jiang,Y.,C.Yang,K.Liu,E.M.Armstrong,T.Huang,D.Moroni&L.J.McGibbney(2017)AGeospaDalDataRecommenderSystembasedonMetadataandUserBehaviour(revision)

•  Jiang,Y.,Y.Li,C.Yang,K.Liu,E.M.Armstrong,T.Huang,D.Moroni&L.J.McGibbney(2017)Asmartweb-baseddatadiscoverysystemforoceansciences.(ongoing)

•  Sourcecode:h^ps://github.com/mudrod/mudrod•  PO.DAACLabs:h^p://mudrod.jpl.nasa.gov/•  PDLeverage:h^p://pd.cloud.gmu.edu/

Products

Component CurrentTRL ProjectendTRL DescripHonSemanHcsearchengine

SearchDispatcher 7 7 TranslaDngausersearchqueryintoasetofnewsemanDcqueries

Similaritycalculator 7 7 CalculaDngthesemanDcsimilarityfromweblogs,metadata,andontology

RecommendaDonmodule 7 7 Recommendingsimilardatasetstotheclickeddataset

Rankingmodule 7 7 Re-rankingthesearchresultsbasedonRankSVMMLalgorithm

Knowledgebase

Ontology 7 7 ExtensionsfromSWEETontologyforearthsciencedata

TripleStore 7 7 ESIPontologyrepositoryVocabularylinkagediscoveryengine

Profileanalyzer 7 7 ExtracDnguserbrowsingpa^ernfromrawweblogs

Webservices/GUI

Rankingservice/presenter 7 7 ProvidingandpresenDngtherankedresults

RecommendaDonservice/presenter 7 7 ProvidingandpresenDngtherelateddatasets

OntologynavigaDonservice/presenter 7 7 ProvidingandpresenDngrelatedsearches

Nextsteps•  Addmorefeatures(e.g.,temporalsimilarity)•  CreatetrainingdatafromweblogsforRankSVM•  Developaqueryunderstandingmoduletobe^erinterpretuser’ssearch

intent(e.g.“oceanwindlevel3”->“oceanwind”AND“level3”)•  SupportSolr•  Supportnearreal-DmedataingesDontodynamicallyupdateknowledge

base•  IntegraDonwithDOMSandOceanXtremesforanoceanscienceanalyDcs

center•  LeverageadvancedcompuDngtechniquestospeeduptheprocess

•  YangC.,JiangY.,LY.,ArmstrongE.,HuangT.,andMoroniD.,2015.“UDlizingAdvancedITTechnologiestoSupportMUDRODtoAdvanceDataDiscoveryandAccess”,AGU,SanFrancisco,CA.

•  YangC.,JiangY.,LY.,ArmstrongE.,HuangT.,andMoroniD.,2016.“MiningandUDlizingDatasetRelevancyfromOceanographicDataset(MUDROD)Metadata,UsageMetrics,andUserFeedbacktoImproveDataDiscoveryandAccess”,ESIPwintermeeDng2016,WashingtonD.C.

•  JiangY.,YangC.,LY.,ArmstrongE.,HuangT.,andMoroniD.,2016.“AComprehensiveApproachtoDeterminingtheLinkageWeightsamongGeospaDalVocabularies-AnExamplewithOceanographicDataDiscovery”,AAG2016,SanFrancisco,CA.

•  YangC.,JiangY.,LY.,ArmstrongE.,HuangT.,andMoroniD.,2016.“MiningandUDlizingDatasetRelevancyfromOceanographicDataset(MUDROD)Metadata,UsageMetrics,andUserFeedbacktoImproveDataDiscoveryandAccess”,PO.DAACUWG,Pasadena,CA.

•  LY.,YangC.,JiangY.,ArmstrongE.,HuangT.,andMoroniD.,2016.“LeveragingcloudcompuDngtospeedupuseraccesslogmining”,Oceans16MTSIEEE,Monterey,CA.

•  JiangY.,YangC.,LY.,ArmstrongE.,HuangT.,andMoroniD.,2017.“TowardsintelligentgeospaDaldiscovery:amachinelearningrankingframework”,AAG2017,Boston,MA.

•  LY.,YangC.,JiangY.,ArmstrongE.,HuangT.,andMoroniD.,2017.“Ageographicrecommendersystemusingmetadataanduserfeedbacks”,AAG2017,Boston,MA.

PresentaDons

1.  NASAAISTProgram(NNX15AM85G)2.  PO.DAACSWEETOntologyTeam(IniDallyfundedbyESTO)3.  HydrologyDAACRahulRamachandran(providingtheearlier

versionofNOESIS)4.  ESDISforprovidingtesDnglogsofCMR5.  AllteammembersatJPLandGMU

Acknowledgements

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