data mining – ako Ďalej s dÁtami · 2018. 10. 15. · 0 500 1000 1500 2000 90000 90050 90100...
TRANSCRIPT
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0
500
1000
1500
2000
90000 90050 90100 90150 90200 90250Osoba
Celk.nákla
dy
2.5
5.0
7.5
10.0Po...dokladov
Jindřich Růžička, [email protected]
DATA MINING – AKO ĎALEJ S DÁTAMI
1. S&T SK Kompetencie
2. Data mining1.Dátová analýza2.Business analýza3.SAP Data Analysis
3. Príklady
Agenda
2
S&T zákazníci 1993(0) - 2018:
• 1 – 15 TB ERP dát• 200 GB SAP systém
Pozn.:Tabuľka 1000 x 20 000 znakov zaberá 80MB (80 000 736B)1TB = 3 125 000 000 tabuliek
Dáta v SAPe
3
• SnT SK kompetencie:• SnT SAP zákazníci• Produktové know how• Procesní know how• BI + dátová analýza know how
• Group SnT kompetencia:
q Angažovanosť v IoT
S&T SK kompetencie
4
• IoTOčakáva sa, že počet objektov pripojených k internetu vecí do roku 2020 dosiahne 50 miliárd, čím vznikne obrovské množstvo cenných dát.
Údaje zhromaždené od zariadení IoT budú použité k pochopeniu a kontrole komplexných prostredí kolem nás, čo umožní lepšie rozhodovanie, väčšiu automatizáciu, vyššiu efektivitu, produktivitu a presnosť.
• Data miningData mining a iné metódy umelej inteligencie budú hrať rozhodujúcu úlohu pri získaní užitočných informácii z IoT pre ďalšie rozhodovanie.
S&T SK kompetencie
5
Data mining, (Text mining, Process mining)
Dátová analýza
Modelovanie
Predikcia
Data Mining
6
• Data mining - Odhalenie vzťahov medzi dátami•ERP + lokálne systémy + globálne dáta (registre) + sociálne média) + …
•Štruktúrované vs neštruktúrované dáta
• Hypotézy
• Dashboardy
Data Mining
7
• Deskriptívna: Čo sa stalo?1. Agregácia dát, štatistka:
manažment reporting (post-mortem analýza)
• Prediktívna: Čo sa môže stať?1. Modelovanie:
pravdepodobný budúci vývoj (procesu, udalosti, …)
• Preskriptívna: Čo by sme mali robiť?1. Simulácia:
čo a kedy sa môže stať a prečo sa to stane a čo ďalej
Biznis analýza
8
1. Integrované so SAP HANA
2. SAP používa kombináciu vlastných algoritmov a open-source (R, Python) knižníc pre modelovanie prediktívnej analýzy
SAP Predictive Analysis
9
1. Príklad Intro
2. Príklad SAP ERP
3. Príklad SAP ERP
4. Machine Learning
Príklady
10
11
12
10 hodov
13
A,B,C,D,E,F vektory 1 000 000 údajov (hodov)
14
A,B,C,D,E,F vektory 1 000 000 údajov (hodov)
A * BA+B+C+D+E+F
SAP Solution Manager
15
16Prehľad
17
SAP Travel Management
18
19
20
21
y = k * x + q
Model
22
N K q100 0,3378 6,57341000 0,3250 6,674010000 0,3205 6,7335
X (#dokladov) Y (čas spracovania)
% #10 000
5 8,3360 100%10 9,9385 119,22% 16 02620 13,1435 157,67% 48 075
Process Mining
23
Process Mining
24
Process Mining
25
AI: Machine Learning
26
AI: Machine Learning
27
Na základe algoritmudokáže program samostatne zaradiť objekt
AI: Machine Learning
28
Po dostatočnom počte analyzovaných objektovsú známe hranice oblastí výskytuobjektov