SAP HANA Cloud Platform predictive services · 2019-11-12 · © 2015 SAP SE or an SAP affiliate...
Transcript of SAP HANA Cloud Platform predictive services · 2019-11-12 · © 2015 SAP SE or an SAP affiliate...
SAP HANA Cloud Platform
predictive servicesOctober 15, 2015 Public
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 2Public
Agenda
1. Exploratory Analytics – Why, what, who
2. An example of the process to use a predictive service
3. List of SAP HANA Cloud Platform predictive services and architecture
4. Developing with predictive services – Key Influencers workflow
5. The other services
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 3Public
What is SAP HANA Cloud Platform predictive services?
A collection of ready-to-be-integrated web
services that delivers predictive analytics
insights
Highly specific web services that respond
to specific business needs
1 service returns 1 type of insight. All the data
mining process is packaged inside the service and is
hidden from the user.
Services are available on HCP to allow any
existing or new applications on HCP to
embed predictive analytics features.
Simple to use
• SAP HANA Cloud Platform predictive services are
based on APL(*)
• 1 call 1 business need
• Usage of APL is embedded into services
Simple to exploit
• Results are understandable
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Advantages to use SAP HANA Cloud Platform predictive
services?
Lower TCO
REST Web Services easy to understand and to use
Reduced development cycle. Data analysis possible with a single service call. You don’t
have to code it yourself from scratch.
Operational Support - SAP HANA Cloud Platform predictive services
• Provide Synchronous or Asynchronous(*) model execution.
• Take advantage of the infrastructure and services of the underlying HANA Cloud platform.
Flexible Data Access
• Ability to upload new datasets,
• Execute predictive models on data created or uploaded by other applications and
services, or on dataset streams.
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Who will use SAP HANA Cloud Platform predictive
services?
Business Consultant
Person who expresses the business needs
Doesn’t need to have a lot of data mining skills
In order to choose the most appropriate services, needs to understand : what drives each
service and what is the value generated by each service
1. What is the objective of each service and what kind of business question each service
answers
2. What kind of data is necessary
3. How to interpret and use the results
4. What is the value/profit created for his customers?
Developer
• Doesn’t need to have data mining skills
• Needs to have programming experience with Web services
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Which kind of customers can use SAP HANA Cloud
Platform predictive services?
SAP Internal
• Uses SAP applications and needs improvements and/or new features (examples: finance
to detect potential fraud, CRM to detect churn, HR to understand and analyze results of
survey, …)
• SAP runs SAP
SAP Partners
Build custom HCP applications for their customers
Customers
• Use SAP HANA Cloud Platform predictive services to generate answers and solutions to
their specific problems.
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Agenda
1. Exploratory Analytics – Why, what, who
2. An example of the process to use a predictive service
3. List of SAP HANA Cloud Platform predictive services and
architecture
4. Developing with predictive services – Key Influencers workflow
5. The other services
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 8Public
An example of the process to use a SAP HANA Cloud
Platform predictive service
Lucy is a business analyst. She would like
to boost the revenues of X Travel Agency.
.
.
Lucy asks Dave to build an Exploratory
Analytics app. She would like an app that
will provide her various kinds of insights
that she could use to increase revenues
on travel bookings.
Dave develops the Exploratory Analytics app
using the HCP platform. Among other kinds of
services, he integrates the Key Influencers
Service to identify variables which
influence high value travel bookings.
Dave shows the first version of the app to Lucy.
She comes up with various improvements to
make and Dave integrates these changes
before redeploying the app. They continue
iterating on it until Lucy is satisfied.
After a week, the Exploratory Analytics App is
operational. The insights provided by the app are a
great help for Lucy to come up with plans to
increase revenues.
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 9Public
Agenda
1. Exploratory Analytics – Why, what, who
2. An example of the process to use a predictive service
3. List of SAP HANA Cloud Platform predictive services and
architecture
4. Developing with predictive services – Key Influencers workflow
5. The other services
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 10Public
Description of Predictive Services
Service Description
Dataset Provide a series of functions to manage datasets
• Register/unregister a dataset to the SAP HANA database schema for the
application
• Retrieve dataset and variable information
Forecast Predict next values of a time series from a reference date.
Key Influencers Return the variables which have an influence on a specified target.
• Have a better understanding of the profile of the target population
• Identify the drivers of success and learn how to improve performance
Outliers Identify the odd profiles of a dataset whose target indicator is significantly different
from what is expected
Scoring Equation Build a model to explain a target and exports it as a scoring equation which will be
applied in order to get a value of the target variable for new cases.
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Architecture
HCP Services (admin, monitoring,
authorization, authentication,
…)
HCP
HCP account of customer
JDBC
Exploratory
Analytics
Applications
Key
Influencers
Scoring
EquationForecast
Dataset
ServicesOutliers
SAP HANA Cloud Platform predictive services
HANA Instance
Data Meta-Data
APL
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Agenda
1. Exploratory Analytics – Why, what, who
2. An example of the process to use a predictive service
3. List of SAP HANA Cloud Platform predictive services and
architecture
4. Developing with predictive services – Key Influencers workflow
5. The other services
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 13Public
Develop with predictive services
Services are deployed on each HCP customer’ account.
To start the development of a HCP application, refer to the HCP developer
documentation.
Services are implemented using REST (Representational State Transfer) architecture
style:
• Web services are easy to leverage by most tools.
• Learning curve to use REST services is reduced. Developers prefer it and this saves time, which
saves money.
• REST is designed for use over Internet, use small message.
Implement the service calls either in synchronous or asynchronous mode
Mode Decription
Synchronous 1. Call the service with dataset ID and other parameters
POST /api/analytics/keyinfluencer/sync
Asynchronous 1. Create the job and retrieve its ID.
POST /api/analytics/keyinfluencer
2. Retrieve the job status using its ID.
GET /api/analytics/keyinfluencer/<jobID>/status
3. Retrieve the results in JSON format.
GET /api/analytics/keyinfluencer/<jobID>
4. Delete the job
DELETE /api/analytics/keyinfluencer/<jobID>
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Key Influencers Predictive Service
Description
Analyze a dataset to identify the variables that have an influence on a specified target.
Services features
Identify the key influencers of a specified target
Get detailed information on how a variable influences the target
Show indicators on the quality of the results
Possibility of adjustment on the training process (variables’ descriptions, variables’ roles)
Applications Example
Have a better understanding of the profile of the population he targets
Identify the drivers of success and how to improve performances
Have leads on potential causes of a targeted event
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Key Influencers workflow
Register a dataset
Input: a CSV file uploaded into HANA table or
a HANA table
Output: a dataset ID
POST /api/analytics/dataset/sync"hanaURL": "C4PA_SAMPLES/CENSUS_ORDERED"
Results"id": 156,
"name": "CENSUS_ORDERED",
"rows_number": 48842,
"columns_number": 16,
"variables": [
{
"rank": 1,
"name": "age",
"storage": "integer",
"value": "continuous"
},
…
{
"rank": 15,
"name": "class",
"storage": "integer",
"value": "nominal"
}
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Key Influencers workflow
Register a dataset
Input: a CSV file uploaded into HANA table or
a HANA table
Output: a dataset ID
Create key influencer job
Inputs: Dataset ID and name of target variable
Output: A job ID and a status
POST /api/analytics/keyinfluencer"datasetID": 156,
"targetColumn": "class"
Results"ID": 1353,
"status": "PROCESSING",
"type": "key_influencer",
"input": numberOfInfluencers :null,
targetKey :null,
autoSelection :null,
datasetID :156,
targetColumn :class,
skippedVariables :null,
weightVariable :null,
variableDescription :null
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Key Influencers workflow
Register a dataset
Input: a CSV file uploaded into HANA table or
a HANA table
Output: a dataset ID
Create key influencer job
Inputs: Dataset ID and name of target variable
Output: A job ID and a status
Check status of the job
Input: Job ID
Output: Status of the job: Processing, Failed,
Successful
GET /api/analytics/keyinfluencer/1353/status
Results"ID": 1353,
"status": “PROCESSING",
"type": "key_influencer"
After a while"ID": 1353,
"status": "SUCCESSFUL",
"type": "key_influencer"
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Key Influencers workflow
Register a dataset
Input: a CSV file uploaded into HANA table or
a HANA table
Output: a dataset ID
Create key influencer job
Inputs: Dataset ID and name of target variable
Output: A job ID and a status
Check status of the job
Input: Job ID
Output: Status of the job: Processing, Failed,
Successful
Retrieve the key influencers
Input: Job ID
Output: Model performance indicators and key
influencers
GET /api/analytics/keyinfluencer/1353
ResultsmodelPerformance:
"predictivePower": 0.8091,
"predictionConfidence": 0.9952,
"qualityRating": 5,
"confidenceIndicator": 1,
Influencers:
"contribution": 0.24391355161624884,
"variable": "marital-status",
"groups": [
"group": "{Divorced}",
"significance": -0.10312004005736641,
"normalProfit": -0.13866835617242768,
"frequency": 0.13545532008465958,
"targetMean": 0.10085227272727272,
"groupDefinition": {
"categories": ["Divorced"],
"higherBound": null,
"higherBoundIncluded": null,
"lowerBound": null,
"lowerBoundIncluded": null,
"kxmissingIncluded": 0
ConfidenceIndicator is computed from the predictionConfidence:
If predictionConfidence >= 0,95 Then 1
Else 0
PredictivePower < 0,2 < 0,3 < 0,5 < 0,6 < 0,8 >= 0,8
qualityRating 0 1 2 3 4 5
KI
KR
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Key Influencers workflow
Register a dataset
Input: a CSV file uploaded into HANA table or
a HANA table
Output: a dataset ID
Create key influencer job
Inputs: Dataset ID and name of target variable
Output: A job ID and a status
Check status of the job
Input: Job ID
Output: Status of job: Processing, Failed, Successful
Retrieve the key influencers
Input: Job ID
Output: Model performance indicators and key
influencers
Delete the jobs and unregistrer dataset
Input: Job ID or dataset ID
Output: The HTTP status of the query is 200 other it is
400 if it fails.
For the Key Influencer job
DELETE /api/analytics/keyinfluencer/1353
Result:
HTTP status: 200
For the dataset
DELETE /api/analytics/dataset/156
Result:
HTTP status: 200
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 20Public
Agenda
1. Exploratory Analytics – Why, what, who
2. An example of the process to use a predictive service
3. List of SAP HANA Cloud Platform predictive services and architecture
4. Developing with predictive services – Key Influencers workflow
5. The other services
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 21Public
Dataset Service
Provide a list of features to manage datasets to be used by the SAP HANA Cloud
Platform predictive services to get insights.
Register a dataset in the application database schema
Retrieve registered dataset information
Get dataset Information
Get variables information
Unregister a dataset
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Forecast Cloud Service
Description
Analyze a time series and predicts its future next values.
The prediction is based on the trend, periodicities and fluctuations detected in the time
series along with context information represented by extra-predictive variables when
available.
The granularity of the predictions is the same as the granularity used in the dataset.
Service features
Analyze a time series and generate forecasts based on identified patterns
Get confidence intervals computed for each forecast
Possibility to define the analysis and forecasts periods
Provide indicators on the reliability of the results
Applications examples
Estimate how much visitors to expect in the next days
Estimate how many products to order depending of expected consumption
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Outliers Cloud Service
Description:
Detect the outliers contained in a dataset, that is, the odd profiles whose target indicator is
significantly different from what would be expected.
Services features:
Identification of outliers contained in a dataset
Reasons explaining why an outlier is odd
Outliers are sorted from most interesting to less interesting
Shows performance indicators on the quality of the model which identified the outliers
Possibility of adjustments of the modeling process (variable descriptions, variables roles)
Application Example
• Identification of odd profiles, potential frauds, point of interests
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Scoring Equation Service
Description
Build a predictive model from a dataset and export its scoring equation for later use outside of
the cloud services
Services features
Build a predictive model and export its scoring equation
Get indicators on the performance of the predictive model
Possibility of adjustment on the training process (variables’ descriptions, variables’ roles)
Available export format : SQL HANA, CSV
Application Examples :
Integrate the predictive model inside an application
Use the predictive model as many times as desired
Apply the predictive model on new data as soon as they come
© 2015 SAP SE or an SAP affiliate company. All rights reserved.
Thank you
Contact information:
Idea Place : https://ideas.sap.com/PredictiveAnalytics
SCN Predictive Analytics
Starter Kit page for SAP HANA Cloud Platform predictive services
HCP Web site
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