A research overview Professor Philip Sallis Auckland University of Technology New Zealand.

A research overviewA research overview

Professor Philip SallisProfessor Philip Sallis

Auckland University of TechnologyAuckland University of Technology

New ZealandNew Zealand

Greetings from Aotearoa-New ZealandGreetings from Aotearoa-New Zealand(the official bi-cultural name)(the official bi-cultural name)

Maori and EnglishMaori and English

IntroductionsIntroductions

Dr Philip Sallis (Computer Science)NLP and Computational LinguisticsProfessor and Senior University AcademicThe University Deputy Vice Chancellor (Vice Rector or Provost)

Dr Kathy Garden (Electrical Engineering)Computer Tomography and Signal ProcessingDean, Faculty of Design and Creative Technologies and Regional Pro Vice Chancellor

Brief Curriculum VitaeBrief Curriculum Vitae

KathyKathy PhD (NZ) and Post Doc Fellow (USA) (Electrical Engineering)PhD (NZ) and Post Doc Fellow (USA) (Electrical Engineering) Univ teaching, research & supervision (NZ)Univ teaching, research & supervision (NZ) Government science policy advisorGovernment science policy advisor Industry and Regional Govt strategic advisorIndustry and Regional Govt strategic advisor AUT – Dean and Pro Vice ChancellorAUT – Dean and Pro Vice Chancellor

PhilipPhilip PhD (Computer Science) (England)PhD (Computer Science) (England) Univ teaching, research & supervision (UK, Aust, NZ)Univ teaching, research & supervision (UK, Aust, NZ) Visiting research professor (UK, USA, HK...and Chile!)Visiting research professor (UK, USA, HK...and Chile!) Industry consulting and government commissionsIndustry consulting and government commissions Full Professor since 1987 and HoD three times since 1979Full Professor since 1987 and HoD three times since 1979 Deputy Vice Chancellor at AUT since 1999 Deputy Vice Chancellor at AUT since 1999

Auckland University of TechnologyAuckland University of Technology

26,00026,000 students students (full and part-time)(full and part-time) 10% 10% post-graduate post-graduate (Masters and PhD)(Masters and PhD) 23% 23% International students International students (70% in degrees, 30% short courses)(70% in degrees, 30% short courses)

Faculties:Faculties: Design and Creative TechnologiesDesign and Creative Technologies Business and LawBusiness and Law Health and Environmental SciencesHealth and Environmental Sciences HumanitiesHumanities Maori DevelopmentMaori Development

About this PresentationAbout this Presentation

Research in generalResearch in general

Overview of my own researchOverview of my own research

Description of two areas of work:Description of two areas of work: Software forensicsSoftware forensics Digital LibrariesDigital Libraries

Publications and further informationPublications and further information

www.aut.ac.nz/serlwww.aut.ac.nz/serl

Enquiries re PhD supervisionEnquiries re PhD [email protected]

[email protected]

Leopoldo Leoncio

Research Mix (Research Mix (‘Typical’‘Typical’))

Papers & Confs

Reports

Products

Alone

In teams

(Ideas + People + Funding + Work) = Results

Funding sources:• university funds• government grants• international grants• industry contracts

To choose one aspect of research that is effort To choose one aspect of research that is effort and cost effective - and cost effective - ClusteringClustering

P

P

P

Usually more appealing for grant providers too!

New one has recently emerged at UCM

‘‘Teams are best’ – why?Teams are best’ – why?

Sharing and testing ideasSharing and testing ideas Mix of expertise for multi dimension and Mix of expertise for multi dimension and

inter disciplinary researchinter disciplinary research Division of labour (efficient and effective)Division of labour (efficient and effective) Peer pressure to reach conclusions and Peer pressure to reach conclusions and

achieve outcomes – publish papers etcachieve outcomes – publish papers etc Writing grant applicationsWriting grant applications Using more names to strengthen proposalsUsing more names to strengthen proposals Demonstrating collaborationDemonstrating collaboration Inter colleague, inter institution, inter nationalInter colleague, inter institution, inter national

A team at work and play!A team at work and play!

My Research MapMy Research Map

Program and data structures. Compilers .

Text Parsing Algorithms

Data modelling & DBMS

Software development process models (CMM) etc

Measurement and improvement of effort, activity and product

Computational linguistics(stylometrics)

Software Metrics

SoftwareMetrics

Software Forensics

PhD researchNLP /NLU

A journey with computingA journey with computing

•Elliott 503•PdP1100 & 1125•B6700 & 2700•HP2100A & 3000•ICL1902T & 1905E•IBM 1401,360, 6000•Prime 710•VAX 700 series•Onyx, Sun, Mac, PC

InspirationInspiration

Performance Analysis and Improvement milieuPerformance Analysis and Improvement milieu

The computer

Developers Users

Data&

schema

Program code&

structure

Outputs

URS

Unplanned input

Stress Testing using value changes to parameters and variables in all aspects of the system. Simulation.

An early interest in NLPAn early interest in NLP

Program code meta languages, Compilers, S-Grammars, parsing algorithms for text proc

Command and Edit languages and parsiing

NLP and symbol processing – symbolic AI methods

Full text, narrative and discourse analysis

1972-6

1972-6

1976-9

1980-n

TwoTwo research areasresearch areasemergedemerged and then and then merged merged asas

Software ForensicsSoftware Forensics

SoftwareEngineering

Computational Linguistics

A fascination with the delta!

Software Engineering researchSoftware Engineering research

Algorithms, program and data structures, programming style

Measuring aspects of the process such as programmer productivity (4GLs)

User and use profiling for system optimisation using simulation and other methods.

‘Programming in the large’ and the software system development process

Process and URS improvement . Data modelling and DB design. Time & Cost estimation. CASE.

Mathematics and Computer Science Prog Lang, Operating Systems, Compilers

System integration, blended data applications (GIS) and their usability measurement.

Computational Linguistics researchComputational Linguistics research

String handling Algs, Command Editors, Text processing, Bibliometrics (NLP)

Transformational Grammars, meta-information and ‘deep’ structures

• Authorship authentication

• Topic clustering depictions

Symbolic AI. Formal representation of meaning & semantics (NLU). Epistomology.

PhD - A domain grammar and parser for generating abstracts from journal articles

Mathematics and Computer Science Prog Lang, Operating Systems, Compilers

Stylometric parsers for thematic analysis, topic clustering, etc

Forensics - convergence and Forensics - convergence and incorporation of new technologiesincorporation of new technologies

Programming languages, interpreters, CASE, etcProgramming languages, interpreters, CASE, etc Geographic Information Systems (GIS)Geographic Information Systems (GIS) Global Positioning Systems (GPS)Global Positioning Systems (GPS) Voice over IP (VoIP)Voice over IP (VoIP) Voice RecognitionVoice Recognition Wireless and GPRS...now RFIDWireless and GPRS...now RFID Computational Neural NetworksComputational Neural Networks

Algorithms, data structures, pattern matchingAlgorithms, data structures, pattern matching Connectionist alternatives for clustering etcConnectionist alternatives for clustering etc RISC technologiesRISC technologies

Bio-informatics (first NZ course as PG Dip)Bio-informatics (first NZ course as PG Dip) (bio-medical data [text, image and telemetry] and technologies)(bio-medical data [text, image and telemetry] and technologies)

Fingerprint parsing algorithmsFingerprint parsing algorithms

Count everythingCompare everything A lot

of data

Program, data and image names File extensions and Temporary files Variable, parameter and label names Expressions and data structures (arrays etc) Structure – iteration, recursion, formulae, etc Algorithm characteristics Sub routines, case statements, DB calls, etc

Word , sentence & paragraph count Length of words, sentences, etc Word frequencies Phrases and adjacent word pairs Nouns and pronouns, adjectives, etc Prepositions, positive/negative exp Compare with Canon Corpora Differences in expression

Some Forensics ToolsSome Forensics Tools

IdentifyIdentify (program and data structure comparisons) (program and data structure comparisons) www.aut.ac.nz/serlwww.aut.ac.nz/serl

Beyond Compare Beyond Compare (file, variable, labels, line match)(file, variable, labels, line match) www.scootersoftware.comwww.scootersoftware.com

SignatureSignature (stylometric comparisons) (stylometric comparisons) www.signature.comwww.signature.com

Viscovery Viscovery (Data and results visualisation)(Data and results visualisation) www.www. eudaptics.com eudaptics.com

Improve English Improve English (readability & comprehension tests)(readability & comprehension tests) (www.improve-english.com)(www.improve-english.com)

Processing Forensics DataProcessing Forensics Data Programming LanguagesProgramming Languages - - SNOBOL, LISP, Prolog, C++, PerlSNOBOL, LISP, Prolog, C++, Perl

Data Management Data Management - flat and I-S files, RDBMS, - flat and I-S files, RDBMS, MySQLMySQL, , php, ASP etc php, ASP etc

Statistical methodsStatistical methods - probability, inference, prediction - probability, inference, prediction SPSSSPSS and and ExcelExcel

Connectionist alternatives Connectionist alternatives for dependency analysis for dependency analysis (FNN) (FNN) - - KEDRIKEDRI

Cluster analysis Cluster analysis MatLabMatLab and Visualisation alternatives and Visualisation alternatives (Viscovery)(Viscovery)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

A lot of data to analyse, represent and reach conclusions aboutA lot of data to analyse, represent and reach conclusions about

Not an exact science (closeness of fit but also human interpretation)Not an exact science (closeness of fit but also human interpretation)

Example formatted results after program code Example formatted results after program code comparisoncomparison

Titles Beyond Compare Software Computer Comparison report:

DJFruits Terms of Trade 60 Lines match13 match on left side

only49 match on right side

only25 lines with important

differences0 lines with only

unimportant differences17 sections different

Plaintiff Terms of Trade

DJ Fruits Personal Hygiene Policy3 Lines match 7 match on left side only

93 match on right side only

31 lines with important differences

0 lines with only unimportant differences

4 sections different

Plaintiff Personal Hygiene Policy

DJ Fruits Process Policy 1 Lines match 2 match on left side only21 match on right side




Plaintiff Process Policy

DJ Fruits Trace back and Recalls 1 Lines match 0 match on left side only34 match on right side




Plaintiff Trace back and Recalls

DJ Fruits Control1 Lines match 1 match on left side only





Plaintiff Control

DJ Fruits Validation2 Lines match 0 match on left side only





Plaintiff Validation

Example formatted results after documentation Example formatted results after documentation comparisoncomparison

Date Title Grade Level Reading Ease Readability # of words

Average syllables per

word # of sentencesAverage words per sentence

DJFruits Terms of Trade 19.2 23.16 23.79 1218 1.73 33 36.91

Plaintiff Terms of Trade 15.72 31.82 19.85 1249 1.74 45 27.76

DJ Fruits Personal Hygiene Policy 22.57 17.22 26.37 330 1.68 7 47.14

Plaintiff Personal Hygiene Policy 32.39 -9.79 36.43 286 1.7 4 71.5

DJ Fruits Process Policy 23.2 17.32 26.73 199 1.64 4 49.75

Plaintiff Process Policy 14.63 31.1 19.39 161 1.8 7 23

DJ Fruits Trace back and Recalls 10.86 42.64 13.97 286 1.77 20 14.3

Plaintiff Trace back and Recalls 17.61 26.23 21.03 290 1.75 9 32.22

DJ Fruits Control 14.49 31.88 18.39 160 1.79 7 22.86

Plaintiff Control 12.51 38 15.33 165 1.78 9 18.33

DJ Fruits Validation 35.01 -43.77 39.8 63 2.21 1 63

Plaintiff Validation 37.61 -44.57 42.35 73 2.1 1 73

English Language Comparisons of files: using www.improve-english.com

Raw Data CountingRaw Data Countingbegin to build a ‘fingerprint picture’begin to build a ‘fingerprint picture’

‘‘stylometrics’stylometrics’filename characters_incl_blanks characters word_count uniq_words

CasketLetterEight.txt 1303 1014 255 143

CasketLetterFive.txt 1405 1092 280 140

CasketLetterFour.txt 2663 2070 527 239

CasketLetterOne.txt 1439 1117 285 149

CasketLetterSeven.txt 1337 1045 259 145

CasketLetterSix.txt 2243 1731 443 213

CasketLetterThree.txt 3638 2867 698 292

CasketLetterTwo.txt 18550 14382 3631 900

Letter1.txt 1381 1089 263 142

Letter2.txt 1271 1000 230 132

Letter3.txt 2808 2223 521 251

Letter4.txt 3020 2368 573 270

Totals 41058 31998 7965 3016

Writing/Readability TestsWriting/Readability Teststhe picture becomes more complexthe picture becomes more complex

filename Fog Flesch FleschKincaid

CasketLetterEight.txt 14.4706 63.6178 11.4247

CasketLetterFive.txt 18.7143 49.91 16.235

CasketLetterFour.txt 15.2843 58.3427 12.823

CasketLetterOne.txt 13.786 63.9201 11.4239

CasketLetterSeven.txt 12.507 69.9186 9.3564

CasketLetterSix.txt 10.6954 70.65 8.6457

CasketLetterThree.txt 24.5142 32.5883 22.0526

CasketLetterTwo.txt 11.7655 68.1661 9.3893

Letter1.txt 14.4744 61.4432 11.2229

Letter2.txt 16.1957 49.6503 13.4764

Letter3.txt 25.1394 30.0575 22.01

Letter4.txt 17.7456 49.2696 15.284

Word and sentence frequencies, Word and sentence frequencies, length, etc – visualisationlength, etc – visualisation

Average Word Length

3.6 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4

CasketLetterEight.txt

CasketLetterFive.txt

CasketLetterFour.txt

CasketLetterOne.txt

CasketLetterSeven.txt

CasketLetterSix.txt

CasketLetterThree.txt

CasketLetterTwo.txt

Letter1.txt

Letter2.txt

Letter3.txt

Letter4.txt

Ch

arac

ters

Typical Histogram VisualisationTypical Histogram Visualisation

0

10

20

30

40

50

60

70

80

Fog

Flesch

FleschKincaid

Typical Clustering VisualisationTypical Clustering Visualisation

Conventional line graph visualisation Conventional line graph visualisation all assist interpretationall assist interpretation

0

10

20

30

40

50

60

70

C 1:CasketLetterOne.txt,CasketLetterFour.txt,CasketLetterEight.txt,CasketLetterTwo.txt,CasketLetterSix.txt,

CasketLetterSeven.txt

C 2: Letter4.txt,Letter1.txt

C 3:CasketLetterThree.txt,

Letter3.txt

C 4:CasketLetterFive.txt

C 5: Letter2.txt

average_word_length

avg_syls_per_word

percent_complex_words

avg_words_per_sentence

Fog

Flesch

FleschKincaid

Typical co-efficient vector linkage Typical co-efficient vector linkage visualisationvisualisation

Transposed co-efficients for greater Transposed co-efficients for greater granularity (more precision)granularity (more precision)

0

10

20

30

40

50

60

70

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

106

111

116

121

126

131

136

141

146

151

156

161

166

171

176

C 1: Letter five, Letter six, Letter four, Lettertwo, Letter one, Letter eight, Letter seven,Letter threeC 2: Madison, Jay, Hammad, Hamilton,Unknown

C 3: Letter1, Letter3, letter4, Letter2

C4: Lord Rutheaven

Greater the data comparison set, Greater the data comparison set, more the need for claritymore the need for clarity

0

5

10

15

20

25

30

1 7

13

19

25

31

37

43

49

55

61

67

73

79

85

91

97

10

3

10

9

11

5

12

1

12

7

13

3

13

9

14

5

C 1: Jay, Hamilton, Hammad, Madison, Unknown C 2: Letter one, Letter eight, Letter two

C 3: Letter six, Letter four, letter4 C 4: Letter1, Letter2

C 5: Letter seven C 6: Letter3

C 7: Lord Ruthven C 8: Letter three

C 9:Letter five

Alternative cluster depictionAlternative cluster depictionSOM SOM (Kohonan methods) (Kohonan methods) ViscoveryViscovery

average_word_lengthCasketLetterOne.txtCasketLetterFour.txt CasketLetterFive.txtCasketLetterThree.txt

CasketLetterE ight.txt Letter3.txt

Letter4.txt

CasketLetterTwo.txt

Letter2.txtCasketLetterS ix.txtCasketLetterS even.txt Letter1.txt

3.9 4.0 4.1 4.2 4.3

avg_syls_per_wordCasketLetterOne.txtCasketLetterFour.txt CasketLetterFive.txtCasketLetterThree.txt


Letter4.txt

CasketLetterTwo.txt


1.3 1.4 1.4 1.5 1.5

percent_complex_wordsCasketLetterOne.txtCasketLetterFour.txt CasketLetterFive.txtCasketLetterThree.txt


Letter4.txt

CasketLetterTwo.txt


6 7 9 10 12

avg_words_per_sentenceCasketLetterOne.txtCasketLetterFour.txt CasketLetterFive.txtCasketLetterThree.txt


Letter4.txt

CasketLetterTwo.txt


21 29 37 46 54

FogCasketLetterOne.txtCasketLetterFour.txt CasketLetterFive.txtCasketLetterThree.txt


Letter4.txt

CasketLetterTwo.txt


11 14 18 22 25

FleschCasketLetterOne.txtCasketLetterFour.txt CasketLetterFive.txtCasketLetterThree.txt


Letter4.txt

CasketLetterTwo.txt


30 40 50 61 71

FleschKincaidCasketLetterOne.txtCasketLetterFour.txt CasketLetterFive.txtCasketLetterThree.txt


Letter4.txt

CasketLetterTwo.txt


9 12 15 19 22

Example cluster dependency Example cluster dependency depiction for border coefficientsdepiction for border coefficients

CasketLetterOne.txtCasketLetterFour.txt CasketLetterFive.txtCasketLetterThree.txt

CasketLetterEight.txt Letter3.txt

Letter4.txt

CasketLetterTwo.txt

Letter2.txtCasketLetterSix.txtCasketLetterSeven.txt Letter1.txt

Still a need for conventional Still a need for conventional depictions to reach conclusionsdepictions to reach conclusions

34

12

23

-200

-150

-100

-50

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 21 2 2 2 2 2 2 2 2 3 31 3 3 3 3 3 3 3 3 4 41 4 4 4 4 4 4 4 4 5 51

1(1-11)

2(12-22)

3(23-33)

4(34-51)

Especially for multivariate clustersEspecially for multivariate clusters

12

33

5262

238

254

261

301

22

223

282294

-400

-300

-200

-100

0

100

200

300

400

1 12 23 34 45 56 67 78 89 100

111

122

133

144

155

166

177

188

199

210

221

232

243

254

265

276

287

298

O1

O2

O3

O4

C1

C2

C3

C4C5

C6

29C7

C8

In summaryIn summary

A blend of conventional statistics and A blend of conventional statistics and visualisation methods with new visualisation methods with new

alternative (connectionist) methods alternative (connectionist) methods brings more precision and greater brings more precision and greater

clarity to the mix of precise and clarity to the mix of precise and imprecise data!imprecise data!

Sample published research combining Sample published research combining software metrics and stylometricssoftware metrics and stylometrics

Semantic structures in empirical science text (from PhD)Semantic structures in empirical science text (from PhD) Generating abstracts from journal articles (from PhD)Generating abstracts from journal articles (from PhD) Railway fault report narrative analaysis (with GIS)Railway fault report narrative analaysis (with GIS) Emergency services events and resources (with GIS)Emergency services events and resources (with GIS) Case Law comparisons with Legislation PreamblesCase Law comparisons with Legislation Preambles Family Law topic clustering, Law and Action TakenFamily Law topic clustering, Law and Action Taken Dialogue topic clustering (email traffic project)Dialogue topic clustering (email traffic project) Text editing for the visually impaired (Voice Recognition)Text editing for the visually impaired (Voice Recognition) Semantic dependency depiction (CNNs and SOMs)Semantic dependency depiction (CNNs and SOMs) Canonical Scripture analysis (themes eg. “justice”)Canonical Scripture analysis (themes eg. “justice”) English Language expression/readability algorithmsEnglish Language expression/readability algorithms Letters of St. Ignatius of Antioch (authorship - extra)Letters of St. Ignatius of Antioch (authorship - extra) Letters of Mary Queen of Scots (authorship - intra)Letters of Mary Queen of Scots (authorship - intra) Litigation projects for copyright etc (Law Courts)Litigation projects for copyright etc (Law Courts)

Ongoing Forensics WorkOngoing Forensics Work

Conduct litigation work as it comesConduct litigation work as it comes

Authorship authentication, including plagiarismAuthorship authentication, including plagiarism

Narrative analysis (topics, themes, etc)Narrative analysis (topics, themes, etc)

Always interested in new approaches, methods Always interested in new approaches, methods and tools...also joint projects and PhD Students!and tools...also joint projects and PhD Students!

Life after DVC administration work Life after DVC administration work

End of Forensics PresentationEnd of Forensics Presentation

In 1994 a new and different projectIn 1994 a new and different project

Digital Libraries Digital Libraries

Alexandria Digital Library Project Alexandria Digital Library Project www.alexandria.eduwww.alexandria.edu

NZADLNZADL

www.nzadl.orgwww.nzadl.org

Alexandria Digital Library (1994) Alexandria Digital Library (1994) www.alexandria.ucsb.eduwww.alexandria.ucsb.edu

to to map the surface of the earth map the surface of the earth using land sat, radio using land sat, radio spectrometry and orthophoto imagery from NASA etcspectrometry and orthophoto imagery from NASA etc

US$US$99 million ( million (ADLADL). New US$). New US$1515 million ( million (NGDANGDA))

a distributed digital library a distributed digital library with collections of geo referenced with collections of geo referenced materials and services for accessing collections...materials and services for accessing collections...a super a super powerful GIS for researchpowerful GIS for research!!

Expectation to Expectation to build applications build applications by integrating by integrating environmental and other data with the imagesenvironmental and other data with the images

Researchers from 5 US univs, 4 other countries...and AUT Researchers from 5 US univs, 4 other countries...and AUT

The ADL ProjectThe ADL Project

Invitation to UCSBInvitation to UCSB Map and Imagery LaboratoryMap and Imagery Laboratory Alexandria Digital Library Project (NSF)Alexandria Digital Library Project (NSF)

Methods for measuring system performanceMethods for measuring system performance Profile system usersProfile system users Profile system useProfile system use Observe correlations and process dynamicsObserve correlations and process dynamics System optimisation & operation managementSystem optimisation & operation management

ResultResult = a sampling and simulation suite - = a sampling and simulation suite - metrics again!metrics again!

Numerous collections of ADL Numerous collections of ADL digital imagesdigital images

Topographical and terrain mapsTopographical and terrain maps Geospatial and geodetic imagesGeospatial and geodetic images Marine geodetic and composition Marine geodetic and composition Environmental and climatalogicalEnvironmental and climatalogical Demographic and land utilisationDemographic and land utilisation Object location mappingObject location mapping Sundry specific image collectionsSundry specific image collections

ApplicationsApplications

Forestry and crop managementForestry and crop management Land utilisation changesLand utilisation changes Environmental influence mappingEnvironmental influence mapping Tectonic displacement Tectonic displacement Topographical alterations post typhoonTopographical alterations post typhoon Marine pollution and fisheries managementMarine pollution and fisheries management Demographic density trendsDemographic density trends etcetc

Upon loading the NGDA collection browser Landsat Upon loading the NGDA collection browser Landsat imagery over the US is loaded by defaultimagery over the US is loaded by default

USA - The Great Lakes AreaUSA - The Great Lakes Area

NASA land sat of NZNASA land sat of NZ

Telephoto Terrain ProjectionTelephoto Terrain Projection

Stereoscopic (orthophoto) showing Stereoscopic (orthophoto) showing physical boundary features (NZ)physical boundary features (NZ)

Satellite image collection of Satellite image collection of the Maya Forest Mexicothe Maya Forest Mexico

Scripps Institute CollectionScripps Institute Collection

An orchid greenhouse in Hawaii

Wine ResearchWine ResearchAn example of using ADL and other technologies could be...An example of using ADL and other technologies could be...

Chile and NZ both have excellent wines!Chile and NZ both have excellent wines! What makes for a ‘good wine’?What makes for a ‘good wine’?

Four factors apparently:Four factors apparently: Soil, Climate, Variety, TerrainSoil, Climate, Variety, Terrain Personal taste of flavour, robustness, etcPersonal taste of flavour, robustness, etc

Land-sat images, historical data, telemetry devices and Land-sat images, historical data, telemetry devices and analytical methods to:analytical methods to: Identify the ‘good years’ in both countriesIdentify the ‘good years’ in both countries Compare the data values and develop a set of Compare the data values and develop a set of

correlation coefficientscorrelation coefficients Build a real-time system to predict the next ‘good Build a real-time system to predict the next ‘good

yearyear’...then buy up!!!’...then buy up!!!

GIS DB

•Soil {d...........n}•Climate {d...........n}•Variety {d...........n}•Terrain {d...........n}

Analytical software (CNN)

Information to growers and consumers

Telemetry Devices {d...........n}

Spatial data

Kept current by NASA

Real Time

Location related Historical data

Fuzzy ‘good year’Input Data

Fuzzy feedback

Chemical and marketing f’back

Project team undertaking researchProject team undertaking research

Perhaps you would like to join Perhaps you would like to join our team? our team?

Research is a serious matter

but it has to be fun too!

Thank you for listening

A research overview Professor Philip Sallis Auckland University of Technology New Zealand.

Documents

research supervision

general research

research professor uk

research description

presentation research

aspect of research

phd supervision enquiries

kathy kathy phd nz