#SAP System of Intelligence with #Leonardo                           **PremG**
IOT application enablement Architecture

#SAP System of Intelligence with #Leonardo **PremG**

#SAP System of Intelligence with Leonardo

#SAP #Leonardo which includes a portfolio of offerings ranging from IoT, Blockchain, Machine learning, Big Data, Data Intelligence,Design Thinking & Analytics. #SAP #Leonardo Machine Learning is the new branding for #SAP’s entire portfolio of intelligent applications and services. It ranges from well-established product offerings, such as #SAP HANA Predictive Analytics Library(PAL) services offered through the #SAP #Leonardo Machine learning foundation.

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Provide readily consumable pre-trained models that can be used as a web service by calling simple REST APIs. #SAP now has a growing list of consumable APIs on the business hub.

Error Details

The data set recorded for a particular error instance comprises the following fields:

Time: Date and time when the error occurred.

Category: The errors that may occur during data ingestion are grouped by certain predefined categories. These categories serve the purpose of giving you a first rough estimate at which stage in the data ingestion process the error has occurred. The error category is also offered on the entry screen as a filter criterion to narrow down the number of log entries listed.

Message ID: Technical ID of the error message. The ID consists of the following parts, separated by a dash ("/"):

  • Tenant ID
  • Sensor ID

Internal UUID of the sensor capability related to the failed data ingestion

Tenant: Unique ID of the tenant that has been assigned to your company during the on boarding process.

Sensor ID: Numeric ID of the sensor-equipped device for which the data ingestion failed.

Capability ID: ID of the capability (e.g., read pressure data) to which the sensor belongs whose data couldn't be processed properly

Description: Error message in natural language

Raw Data: JSON data package that contains the identifying metadata of the error instance plus an array of sensor data that couldn't be processed properly


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  • Intelligent Transactions – automate high-frequency repetitive tasks
  • Analytics – make data-driven insights available to everybody
  • Collaboration – tap into the collective intelligence of knowledge workers
  • Digital Assistant – provide the right answer at the right time
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The Data Ingestion Error Log is focused on errors related to sensor data streams that are transmitted from the connected devices to the database. This means that an error can only be detected if a data transfer has taken place. In other words, if no data is transmitted at all (e.g., because of a sensor malfunction), this problem will go unnoticed by the error log. To detect this kind of problem, you would have to extract the error log entries and feed them into a third-party analytics tool.

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#SAP #Cloud Platform Internet of Things:

Life cycle management at scale for devices from on boarding to decommissioning Securely connect to remote devices over a broad variety of IoT protocols (HTTP, MQTT, CoAP, SNMP, File, ModBus, Sigfox, OPC-UA) Collect and process sensor dataat scale already at the edge or in the cloud and store it on #SAP #Cloud Platform Integral part of #SAP #Leonardo #IoT Foundation & Edge to build and run #IoT applications in cloud

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Integration into #SAP #IoT Application Enablement in support of rapid development of IoT applications Support of #SAP Edge Services for advanced processing capabilities on Gateway Edge Continuous delivery of further IoT protocol adapters

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#SAP #Internet of Things (IoT) Application Enablement represents a network of physical objects known as “things,” which collect and exchange data. Use the services to set up a thing model, companies, and service personnel in a company and their relationships. It also includes a collection of REST-based and OData-based services to efficiently store and retrieve data for things, companies, and service personnel.

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In the Internet of Things (IoT), it is typical that in many scenarios, very high amounts of data must be processed. This data is normally generated by different kinds of sensors attached to physical devices of all kinds of size as well as complexity. In these scenarios, system administrators must be aware that it is always possible that something goes wrong or doesn't work as expected, be it software, hardware, or both.

It is typical of IoT scenarios that if an error occurs you will normally find that error occurring many, many times in the log. This is because it's the nature of these scenarios to transmit the same kind of data times and times again. So if an error occurs, you will see that error as often as the system tries to transmit data. This situation can easily lead to an information overflow, which, at the same time, may potentially hide the occurrence of other singular occurrences of other errors in the same log (an effect known as the "needle in the haystack"). In such situations, the tool helps you to concentrate on what is important with the following filtering features:

Defining Time Period Limitations: You can narrow down the amount of errors shown in the log by limiting the period of time that is taken into account by the log. To accomplish this, the tool offers four predefined time frame from 15 minutes to six hours, counted backwards from the current system time. Should these predefined settings not be sufficient in a particular situation, you are also free to define your own custom time frame that the tool shall investigate (but still with a maximum of no more than six hours).

Filtering by Error Category: In the Category selector, the tool lists all error categories that are present in the currently selected time frame of the error log. This makes It easy for you to detect that one "needle in the haystack" that is buried under hundreds or thousands occurrences of the same error that you are currently not interested in.

Individual Filter Criteria: Independent of, or in addition to, the aforementioned filtering features, you can define an individual search string that shall be applied to the current selection of log entries. For example, you might know that a particular device tends to cause more data errors than the rest. In that case, you might enter the thing ID of the device in question into the Search field to verify whether the device works as expected.

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