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T HI NGWORX PL AT F ORM T E CHNI CAL OVE RVI E W June 2018 T E AR OF F S L I DE T his pre se ntatio n pro vide s a te c hnic al o ve rvie w o f the T hing Wo rx platfo rm I t is g e ne rally to be use d by te c hnic

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SLIDE 1

T HI NGWORX PL AT F ORM

T E CHNI CAL OVE RVI E W

June 2018

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SLIDE 2

2

T his pre se ntatio n pro vide s a te c hnic al o ve rvie w o f the T hing Wo rx platfo rm I t is g e ne rally to be use d by te c hnic al pre -sale s and S E te ams T he inte nde d audie nc e are te c hnic al buye rs, g e ne rally mid-to -hig h le ve l me mbe rs o f the I T func tio n o r me mbe rs o f o pe rating func tio ns with te c hnic al ac ume n T his pre se ntatio n is no t spe c ific ally inte nde d fo r de ve lo pe rs

T E AR OF F S L I DE

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SLIDE 3

3

Today’s presentation and Q&A session will include forward-lo o king statements regarding PTC’s future financial performance, strategic

  • utlo o k a nd e xpe c ta tio ns, a ntic ipa te d future o pe ra tio ns, a nd pro duc ts

a nd ma rke ts. Be c a use suc h sta te me nts de a l with future e ve nts, a c tua l re sults ma y diffe r ma te ria lly fro m tho se pro je c te d in the fo rwa rd -lo o king sta te me nts. I nfo rma tio n c o nc e rning fa c to rs tha t c o uld c a use a c tua l re sults to diffe r ma te ria lly fro m tho se in the fo rwa rd -lo o king sta te me nts

can be found in PTC’s Annual Report on Form 10-K

, F

  • rm 10-Q a nd o the r

filing s with the U.S. Se c uritie s a nd E xc ha ng e Co mmissio n. During the pre se nta tio n PT C will disc uss no n-GAAP fina nc ia l me a sure s. T he se no n-GAAP me a sure s a re no t pre pa re d in a c c o rda nc e with g e ne ra lly a c c e pte d a c c o unting princ iple s. A re c o nc ilia tio n o f the no n- GAAP fina nc ia l me a sure s to the mo st dire c tly c o mpa ra b le GAAP me a sure s c a n b e fo und o n o ur we b site .

SAF E HARBOR ST AT E ME NT

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SLIDE 4

4

WHY T HI NGWORX?

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SLIDE 5

5

  • I

I

  • T

is a b o ut applying dig ital te c hnology to the

physic al wor ld and c r e ating value

– I

I

  • T

is a b o ut muc h mo re tha n a c q uiring da ta fro m ma c hine s

– T

his physic a l dig ita l c o nve rg e nc e re q uire s

  • rc he stra tio n o f pro duc t de sig n c o nte nt,
  • pe ra tio na l da ta , suppo rting b usine ss syste ms, a nd

the pe o ple who o pe ra tio na lize pro c e sse s

  • While the re a re c o mmo n, re c urring so lutio n

pa tte rns, all e nte rprise solutions r

e quir e c ustomization to me e t c omple x use c ase s

  • So lutio ns de ma nd a g ility – this me a ns e na b ling

do ma in e xpe rts, pa rtne rs, a nd c usto me rs the mse lve s to pa rtic ipa te in the so lutio n de sig n, c re a tio n, a nd e vo lutio n

T HE E SSE NCE OF I NDUST RI AL I NT E RNE T OF T HI NGS SOL UT I ONS

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SLIDE 6

6

  • A syste m o f inte g ra l c a pa b ilitie s fo r c re a ting

so lutio ns with physic a l a nd dig ita l c o nve rg e nc e

  • An e c o syste m o f pa rtne rs a nd de ve lo pe rs

T HI NGWORX I S…

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SLIDE 7

7

ADDRESSING THE NEEDS FOR “LONG TAIL” APPLICATIONS

L e ve l o f Usa g e

(Billio ns)

Numb e r

  • f Apps

(T ho usa nds)

  • Hig h-Vo lume
  • Me dium c o mple xity
  • F

a st – Ra pid a pplic a tio n de ve lo pme nt

  • Se tting -pe rfe c t

App # 1 App # N

L

  • ng T

ail of B2B Apps

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SLIDE 8

8

Purpo se -Built fo r I ndustria l I nno va tio n Quic kly Cre a te a nd De plo y I ndustria l Apps & AR E xpe rie nc e s Wra p & E xte nd E xisting T e c hno lo g y Ag ile & F le xib le E xpa nsive E c o syste m

I OT PL AT F ORM VAL UE DRI VE RS

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SLIDE 9

Industrial Digital Innovation is complex…

▪ Highly dispersed device environments ▪ Constantly evolving platform architectures ▪ “Plumbing” battles with disparate technology frameworks ▪ Repurposed legacy technology stacks ▪ Volume, velocity and variety of data making data analysis challenging ▪ Project requirements outweigh current resources and development tools ▪ Complex Value Chain that needs to engage

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10

  • De mo c ra tize de ve lo pme nt o f I

ndustria l So lutio ns

  • Po we rful T
  • o ls fo r the rig ht te a m

me mb e rs

  • Ce nte r to o ls a ro und a T

hing Mo de l

  • Ope n, E

xte nsib le Arc hite c ture

  • Rig ht Ca pa b ilitie s in the Rig ht Pla c e
  • Po we re d b y the T

hing Wo rx

“ThingModel” Engine

T HE T HI NGWORX WAY

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11

I NDUST RI AL I NNOVAT I ON PL AT F ORM

Mo de l Dig itize via sc a n, pho to , o r vide o

SOURCE CONT E XT UAL IZE SYNT HE SIZE ORCHE ST RAT E E NGAGE

Mic ro so ft Azure AWS

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12

I NDUST RI AL I NNOVAT I ON PL AT F ORM

Mo de l Dig itize via sc a n, pho to , o r vide o

SOURCE CONT E XT UAL IZE SYNT HE SIZE ORCHE ST RAT E E NGAGE

Mic ro so ft Azure AWS

SOURCE

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SLIDE 13
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SLIDE 14

14

T HI NGWORX CONNE CT I VI T Y T O DAT A SOURCE S

ThingWorx Edge SDK’s

  • Build ro b ust, se c ure , full-

fe a ture d e dg e inte g ra tio ns a nd g a te wa ys fo r a ny pla tfo rm.

T hingWor x RE ST API

  • Bring the po we r o f the

T hing Wo rx pla tfo rm to e ve n the sma lle st o f de vic e s.

T hingWor x E dg e Mic r

  • Se r

ve r

  • Pre -b uilt I
  • T

Ga te wa y fo r e a sily c o nne c ting yo ur Windo ws, L inux, o r L inux ARM de vic e s a nd de vic e s o n lo c a l ne two rks.

L ua Sc r ipt Re sour c e

  • Ra pidly inte g ra te da ta so urc e s

via simple L ua sc ripts.

Co nne c tio n Se rve r

De vic e Clo uds

AWS I

  • T Azure I
  • T

Hub Build-Yo ur-Own

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SLIDE 15

15

I NDUST RI AL I NNOVAT I ON PL AT F ORM

Mo de l Dig itize via sc a n, pho to , o r vide o

SOURCE CONT E XT UAL IZE SYNT HE SIZE ORCHE ST RAT E E NGAGE

Mic ro so ft Azure AWS

CONT E XT UAL IZE

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SLIDE 16

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  • T

he T

hing Mode l is a c o lle c tio n o f

e ntitie s tha t re pre se nt yo ur pro c e ss, so lutio n o r a pplic a tio n.

  • T

hings pro vide c o nte xt into yo ur I

  • T

da ta a nd a re the b uilding b lo c ks fo r a pplic a tio n de ve lo pe rs

  • T

hings ha ve struc ture a nd re la tio nships

tha t re pre se nt yo ur re a l wo rld o b je c ts

T HE T HI NG MODE L

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SLIDE 17

17 17

T HI NGS AS APPL I CAT I ON BUI L DI NG BL OCK S

T hings

3c 90056 56a 897c 4jklzp0

E nte rprise Syste ms

Se rvic e Sa le s F ina nc e Ma nufa c turing E ng ine e ring Ope ra tio ns Pro pe rtie s

  • Running ho urs
  • Ave ra g e te mp
  • Wa rra nty
  • L
  • a d Size

Se rvic e s

  • Che c k Wa sh
  • Upda te firmwa re
  • Re po rt F

a ilure E ve nts

  • Wa sh Co mple te
  • Wa sh Sta rte d
  • Ma lfunc tio n

Applia nc e

T hing T e mpla te

Pro pe rtie s Se rvic e s E ve nts

Sub sc riptio ns

Sub sc riptio ns

  • Clo the s re a dy
  • De te rg e nt

a va ila b le

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COMPOSE R T HI NG MODE L I NG T OOL

Mode l Ke pSe r ve r T ags into the T hingMode l

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19

I NDUST RI AL I NNOVAT I ON PL AT F ORM

Mo de l Dig itize via sc a n, pho to , o r vide o

SOURCE CONT E XT UAL IZE SYNT HE SIZE ORCHE ST RAT E E NGAGE

Mic ro so ft Azure AWS

SYNT HE SIZE

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SLIDE 20

20

AUT OMAT E D PRE DI CT I VE MODE L I NG WI T H T HI NGWORX ANAL YT I CS

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SLIDE 21

21

RE AL

  • T

I ME I NT E L L I GE NT ANOMAL Y DE T E CT I ON

  • F

inds a no ma lie s in re a l-time

  • Auto ma tic a lly o b se rve s a nd

le a rns the no rma l sta te pa tte rn

  • No ne e d fo r se tting rule s o r

a pplying pre -c a lc ula tio ns

  • Mo nito rs fo r a no ma lie s a nd

de live rs re a l-time

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22

  • F

unc tio na lity to a llo w de plo yme nt a nd e xe c utio n o f c o mputa tio na l mo de ls fro m e xte rna l a pplic a tio ns

  • L

e ve ra g e pro duc t-b a se d a na lysis mo de ls de ve lo pe d using PT C a nd third-pa rty to o ls

  • Pro vide s a fra me wo rk fo r the

e xe c utio n o f c o mputa tio ns in e xte rna l a pplic a tio ns b a se d o n e ve nts a nd da ta

MANAGE E XT E RNAL APPL I CAT I ONS

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SLIDE 23

23 23

T HI NGWORX ANAL YT I CS 8.1

ARCHI T E CT URE SL I DE – SI NGL E SE RVE R, NAT I VE OS

ThingWorx Analytics Server (single server)

ThingWorx Analytics Extensions

Analytic s Se r ve r T hing

  • Mic ro se rvic e T

hing s

  • RE

ST ful API suppo rt (via pla tfo rm)

  • Na tive sc ript suppo rt (in pla tfo rm)

Analytic s Builde r

UX fo r T ra ining & De sc riptive

Analytic s Manage r

De plo y/ e xe c ute mo d e ls

Ana lytic s T

  • ols / SDKs

T hingPr e dic tor

Pre dic tive Sc o ring

Native Anomaly Ale r ts

Na tive Ano ma ly De te c tio n

T hingWatc he r Svc s

T ra ining , Mo d e l Mg t

E dge Age nt Route r Signals Mic r

  • se r

vic e

Mutua l Info Ca lc ula tio ns

Data Mic r

  • se r

vic e

Da ta ma na g e me nt, filte rs

T r aining Mic r

  • se r

vic e

Mo d e l c re a tio n

Pr e dic tive Sc or ing Mic r

  • se r

vic e

Ba tc h & Re a l-T ime

Mode l Validation Mic r

  • se r

vic e

Va lid a te mo d e l a c c ura c y

Pr

  • file s Mic r
  • se r

vic e

Cha ra c te rize to p & b o tto m pe rfo rme rs

Cluste r ing Mic r

  • se r

vic e

Cluste r c a lc ula tio ns

Pr e sc r iptive Sc or ing Mic r

  • se r

vic e

Re a l-T ime

Re sults Mic r

  • se r

vic e

Mo d e ls, c o mputa tio ns

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SLIDE 24

24

RE AL

  • T

I ME I NT E L L I GE NT ANOMAL Y DE T E CT I ON - E DGE OR CL OUD

  • Monitor

s and le ar ns fr

  • m your

T hings in r e al time

Use s ma c hine le a rning te c hno lo g y to le a rn, in re a l-time, what “normal”

state is for every data stream or Thing that is monitored or “watched”.

  • Rule s F

r e e

Do e s no t de pe nd o n pre -de fine d rule s o r c o nfig ura tio n to unde rsta nd no rma l a nd a b no rma l sta te fo r a T hing . L e a rns fro m o b se rving the T hing itse lf, with a rtific ia l inte llig e nc e .

  • E

asy to Inte gr ate into Solutions

Simple to inte g ra te the o utput o f re a l time a no ma ly de te c tio n into a so lutio n fo r de ve lo pe rs to he lp use rs o r syste ms to ta ke a c tio n.

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25

  • T

hing pro pe rty-b a se d a no ma ly de te c tio n a nd c o nfig ura tio n

  • Train “normal” behavior

fo r e a c h ma c hine o r sha re a c e ntra lly tra ine d a no ma ly mo de l

  • Ale rts a re g e ne ra te d

b a se d o n c o nfig ura tio n

– Re po rting – Mo nito ring – Ac kno wle dg ing – Sub sc rib ing

ANOMAL Y DE T E CT I ON AND AL E RT S

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SLIDE 26

26

PRE DI CT I ON MODE L S AND OPT I MI ZAT I ON T OOL S

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27

AUT OMAT E D PRE DI CT I VE I NSI GHT S

  • Auto ma tic a lly b uild a nd va lida te pre dic tive mo de ls witho ut

a ssista nc e fro m a sta tistic ia n, using yo ur T hing da ta a s a le a rning so urc e

  • Subscribe your “things” to one or more predicted outcomes

(time to fa ilure , future e ffic ie nc y, e tc .)

  • Re a l-time or batch predictions (“scoring”)
  • Use s pre dic tio n mo de ls g e ne ra te d b y T

hing Wo rx Ana lytic s Se rve r

  • r e q uiva le nt PMML
  • c o mplia nt pre dic tio n mo de l g e ne ra tio n to o l
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28

OPT I MI ZE F UT URE OUT COME S

  • Pe rfo rm a d-ho c o utc o me simula tio ns b e fo re yo u ta ke a n a c tio n
  • I

de ntify c a sua l a nd ke y c o ntrib uting fa c to rs a sso c ia te d with pre dic te d

  • utc o me s
  • I

de ntify o ptima l se tting s to ma ximize o r minimize the risk o f a n o utc o me

  • Use s pre dic tio n mo de ls g e ne ra te d b y T

hing Wo rx Ana lytic s Se rve r o r e q uiva le nt PMML

  • c o mplia nt pre dic tio n mo de l g e ne ra tio n to o l
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29

PRE DI CT I VE I NT E L L I GE NCE E NGI NE

Ma c hine le a rning is use d to a uto ma tic a lly b uild a nd va lida te pre dic tive mo de ls witho ut a ssista nc e fro m a sta tistic ia n, using da ta fro m yo ur yo ur T hing s da ta a s a le a rning so urc e .

  • A pa te nt-pe nding te c hno lo g y e xplo re s va rio us so phistic a te d pre dic tive mo de ling

a lg o rithms to de te rmine the b e st a lg o rithm to use fo r e a c h da ta se t a nd pre dic tive to pic

  • Auto ma te d ma c hine le a rning re a dy da ta pre pa ra tio n witho ut E

T L (whe n use d with T hing Wo rx Co mpo se r)

  • Dra ma tic a lly re duc e s o r e limina te s the ne e d fo r a n e xpe rt te a m in mo de ling a lg o rithms
  • r te c hno lo g ie s
  • Auto ma tic a lly ide ntifie s a nd re ve a ls whic h da ta sig na ls a re mo st impo rta nt in pre dic ting
  • utc o me s
  • T

he se pre dic tive mo de ls a re insta ntly usa b le to pe rfo rm pre dic tio ns a nd o ptimiza tio n de te rmine re c o mme nda tio ns

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SLIDE 30

30

SI MUL AT I ON DRI VE N MACHI NE L E ARNI NG

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SLIDE 31

31

I NDUST RI AL I NNOVAT I ON PL AT F ORM

Mo de l Dig itize via sc a n, pho to , o r vide o

SOURCE CONT E XT UAL IZE SYNT HE SIZE ORCHE ST RAT E E NGAGE

Mic ro so ft Azure AWS

OR CHE ST RAT E

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SLIDE 32

32

  • Wo rkflo w Builde r

e na b le s b usine ss use rs to q uic kly b uild a uto ma te d a nd re pe a ta b le wo rkflo ws

  • Wo rkflo w Ma na g e r

pro vide s mo nito ring a nd a na lyzing wo rkflo ws

  • I

nte g ra tio n c o nne c to rs a llo w b a c k-e nd syste m c o nne c tivity (SAP E RP, Windc hill)

I NT E GRAT E BUSI NE SS SYST E MS AND PROCE SS

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SLIDE 33

33

I NDUST RI AL I NNOVAT I ON PL AT F ORM

Mo de l Dig itize via sc a n, pho to , o r vide o

SOURCE CONT E XT UAL IZE SYNT HE SIZE ORCHE ST RAT E E NGAGE

Mic ro so ft Azure AWS

E NGAGE

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SLIDE 34

34

  • Ma shups a re the fa ste st wa y to b uild I
  • T

de skto p a nd we b a pplic a tio ns

  • Ma shups a llo w yo u to se e a nd inte ra c t with yo ur T

hing s

  • T

he Ma shup Builde r is the WYSI WYG de ve lo pe r to o l fo r c re a ting c o nte nt

MASHUPS

Pr

  • pe r

tie s Pane l Widge t Ar e a L ayout Ar e a

Conne c tions Ar e a

Se r vic e s Ar e a Se r vic e Pr

  • pe r

tie s

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SLIDE 35

35

SAMPL E MASHUP CONT E NT

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SLIDE 36

36

T hing Human Data

Colle c t Data Se nd Data Monitor & Contr

  • l T

hing

IoT: Ab ility to dig ita lly talk & liste n to

physic a l thing s to mo nito r a nd c o ntro l

AR : Ab ility to se e & e xpe r ie nc e the

dig ita l a ttrib ute s o f physic a l thing s

AR COMPL E ME NT S I OT

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SLIDE 37

37

VUF ORI A ST UDI O SUI T E

Cre a te

E xpe r ie nc e s

Co nsume

E xpe r ie nc e s

Ma na g e a nd De live r

E xpe r ie nc e s

T hing Mar

k

I de ntify a nd tra c k T hing s

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SLIDE 38

38

I NT E GRAL PART OF T HE T HI NGWORX I OT PL AT F ORM

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SLIDE 39

39 39

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SLIDE 40

40

Pr

  • ble m state me nt
  • Co mpa nie s se e Ho lo L

e ns a s a wa y to put AR in pro duc tio n – b ut it is ha rd to c re a te the e xpe rie nc e s fo r Ho lo L e ns!

Ne w Vufor ia Studio

  • Out-o f-the b o x, fa st & e a sy c re a tio n
  • f Ho lo L

e ns e xpe rie nc e s witho ut c o ding

Be ne fit

  • F
  • r ma ny c o mpa nie s – it is the AR

c o nte nt a utho ring b re a kthro ug h ne e de d to de plo y Ho lo L e ns c o mme rc ia lly a t sc a le

  • A via b le we a ra b le o ptio n

MI CROSOF T HOL OL E NS SUPPORT

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SLIDE 41

41

PL AT F ORM ARCHI T E CT URE

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SLIDE 42

42

T HI NGWORX PL AT F ORM

  • T

he T hing Wo rx Pla tfo rm is ma de up o f the applic ation itse lf, a nd a pe r

siste nc e laye r

  • T

he pla tfo rm is a Ja va a pplic a tio n running in a n Apa c he T

  • mc a t c o nta ine r

– A c o nne c to r a b stra c ts the spe c ific

pe rsiste nc e pro vide r(s)

  • Pe rsiste nc e o ptio ns va ry

– T

he sta nda rd pla tfo rm use s o nly Po stg re SQL

– E

nte rprise E ditio n use s b o th Po stg re SQL a nd Da ta Sta x E nte rprise (Ca ssa ndra & So lr)

– SAP Ha na – E

xte nsib le

T hingWor x Platfor m

Apa c he T

  • mc a t

T hing Wo rx Applic a tio n

Pe rsiste nc e Pro vide r(s)

E nte rprise E ditio n

  • nly

Sta nda rd a nd E nte rprise E ditio n

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SLIDE 43

43

DE PL OYME NT OPT I ONS

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SLIDE 44

44

DE PL OYME NT OPT I ONS – CL OUD HOST E D

Custome r Infr astruc ture

Corporate Wire d/ Wire le ss Ne twork

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SLIDE 45

45

DE PL OYME NT OPT I ONS – ON PRE MI SE HOST E D

Custome r Infr astr uc tur e

Cor por ate Wir e d/ Wir e le ss Ne twor k

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SLIDE 46

46

DE PL OYME NT OPT I ONS - HYBRI D

F ac ility Wire d/ Wire le ss Ne twork

Custome r F ac ility n

F a c ility Wire d/ Wire le ss Ne twork

Custome r F ac ility 1

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SLIDE 47

47

SE CURI T Y

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SLIDE 48

48

  • De vic e initia te d, T

L S e nc rypte d c o mmunic a tio n to o ne a nd o nly ONE se rve r!

  • I

nfra struc ture to distrib ute se c urity pa tc he s thro ug h So ftwa re Co nte nt Ma na g e me nt

  • I

de ntity Ac c e ss Ma na g e me nt fo r the E nte rprise thro ug h SSO

T HI NGWORX I S SE CURE BY DE SI GN

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SLIDE 49

49

  • T

hing Wo rx ro le -b a se d a c c e ss c o ntro ls a llo w fo r g ra nula r c o ntro l o f yo ur T hing s, the ir da ta , a nd the a c tio ns a va ila b le in yo ur a pplic a tio n.

ACCE SS CONT ROL S

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SLIDE 50

50

DE PL OYME NT & SCAL E

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SLIDE 51

51 51

Da ta Ac q uisitio n Rule s Busine ss L

  • g ic

Ano ma ly De te c tio n I ndustria l Co nne c tivity Rule s Busine ss L

  • g ic

Adva nc e d Ana lytic s Applic a tio ns

Public or Private Cloud

De vic e Clo ud Co nne c tivity Rule s Busine ss L

  • g ic

Adva nc e d Ana lytic s Applic a tio ns

  • E

na b le s de plo yme nt o ptio ns to put the rig ht c a pa b ilitie s in the rig ht pla c e

  • Re d uc e s so lutio n a nd

de plo yme nt a rc hite c ture c o mple xity

  • Ove rc o me s inte rne t la te nc y

a nd b a ndwidth limita tio ns

Single , unifie d, manage me nt syste m to

  • r

c he strate da ta, busine ss logic , and analytic wor kflows E mbe dde d / T e the re d Ga te way T he E dge / L

  • c al Site

“IOT-NATIVE” APPROACH ENABLES FLEXIBLE DEPLOYMENT

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SLIDE 52

52

T HI NGWORX DE PL OYME NT OPT I ONS VE RSI ON 7.X

EMS Mashup / Mobile External Apps Custom Agents Edge SDKs EMS Mashup / Mobile External Apps Custom Agents Edge SDKs

ThingWorx Platform w/ Embedded DB

EMS Mashup / Mobile External Apps Custom Agents Edge SDKs

Small

  • On Mac hine
  • Small On-

Pr e mise

Me dium

  • L

ar ge Plant

  • Small to Midsize

E nte r pr ise

L ar ge

  • High Volume
  • High Ve loc ity

Connection Server 1 Connection Server 2 Connection Server n

Postgres Model

ThingWorx Platform

DSE RING

Cassandra Cassandra Cassandra SOLR SOLR

...

Connection Server 1 Connection Server 2 Connection Server n

Postgres

ThingWorx Platform

Model + Runtime Data

...

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SLIDE 53

53

E d g e / OT E nviro nme nts

Co ntro lle rs Se nso rs Histo ria ns

CAD & PL M AE C & BIM Dig ita l Co nte xt

T hingWor x Compose r

Mo d e l De finitio n Busine ss L

  • g ic

Da ta b a se infra struc ture (On pre mise o r Clo ud )

T hingWor x Mashup Builde r

Mo b ile De skto p/ L a pto p AR/ We a ra b le s VR/ We a ra b le s

IoT Clouds

  • E

MS - E d g e Mic ro Se rve r

  • T

hing Wo rx E d g e SDK s Ga te wa y

Pe r siste nc e Pr

  • vide r

s

H2 (E mb e dd e d ) Po stg re SQL SAP HANA MS SQL DSE JDBC Co nne c to r RE ST APIs Runtime d a ta Ana lytic s De finitio n T hing Wo rx Ana lytic s T hing Wo rx Stud io T hing Wo rx Ma rke tpla c e

Se c ur e Data F low

T hing Wo rx Sto ra g e

Runtime

Busine ss/ I T Syste ms T hing Wo rx c o nne c tio n Se rve r T hing Wo rx Ind ustria l Co nne c tivity Inte g ra tio n Co nne c to rs

HI GH L E VE L ARCHI T E CT URE

slide-54
SLIDE 54

54

DAT A ST ORAGE

slide-55
SLIDE 55

55

  • Ma na g e me nt / Syste m Da ta

– Use rs – App ke ys – Co nfig ura tio n I

nfo rma tio n

  • De vic e Da ta

– Ofte n stre a ming in na ture – Usua lly time se rie s da ta – So me time s struc ture d da ta

  • F

ile s

– L

  • g file s

– So ftwa re Upda te s – Co nfig ura tio n F

ile s

T YPE S OF DAT A

Highly optimize d

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SLIDE 56

56

  • Ma na g e me nt / Syste m Da ta

– Use rs – App ke ys – Co nfig ura tio n I

nfo rma tio n

  • De vic e Da ta

– Ofte n stre a ming in na ture – Usua lly time se rie s da ta – So me time s struc ture d da ta

  • F

ile s

– L

  • g file s

– So ftwa re Upda te s – Co nfig ura tio n F

ile s

WHE RE I S DAT A ST ORE D

Be lo w ~500GB Of T ime Se rie s Da ta Ab o ve ~500GB Of T ime Se rie s Da ta

slide-57
SLIDE 57

57

T HI NGWORX PL AT F ORM

  • T

he T hing Wo rx Pla tfo rm is ma de up o f the applic ation itse lf, a nd a pe r

siste nc e laye r

  • T

he pla tfo rm is a Ja va a pplic a tio n running in a n Apa c he T

  • mc a t c o nta ine r

– A c o nne c to r a b stra c ts the spe c ific

pe rsiste nc e pro vide r(s)

  • Pe rsiste nc e o ptio ns va ry

– T

he sta nda rd pla tfo rm use s o nly Po stg re SQL

– E

nte rprise E ditio n use s b o th Po stg re SQL a nd Da ta Sta x E nte rprise (Ca ssa ndra & So lr)

– SAP Ha na – E

xte nsible

Apa c he T

  • mc a t

T hing Wo rx Applic a tio n

Pe rsiste nc e Pro vide r(s)

E nte rprise E ditio n

  • nly

Sta nda rd a nd E nte rprise E ditio n

E xte nsible !

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SLIDE 58