Sap Se

Aby Jose

Vikas Gupta

Shruti Hansoge Ramesh

Sagar Madivalar

Sowmya Lakshmi R

A system associated with a distributed cloud service may include an active network node within a network environment between a provider application and a consumer application. The active network node may have an execution environment, an operating system to support the execution environment, and active hardware. The active network node may automatically detect, via a platform and language independent centralized resilience process, a failure event in an active network that routes packets to support the distributed cloud service. The failure event might be associated with, for example, an unreliable network, a low bandwidth, a high latency, a topology change, transport costs, etc. Responsive to the detection of the failure event, the active network node may dynamically implement an application resilience pattern (e.g., a circuit breaker or bounded queue) by the centralized resilience process to facilitate recovery from the detected failure event without participation of the consumer application.

Active network node resilience pattern for cloud service

According to some embodiments, a system associated with predictive analytics may include a data filter dictionary that stores a plurality of electronic data records based on prior input data. A data filtering and noise reduction module may receive input data and access the data filter dictionary. The data filtering and noise reduction module may then utilize data from the data filter dictionary and a latent semantic indexing data filter and noise reduction algorithm to remove information from the input data and create a subset of the input data. A predictive analytic algorithm platform may receive the subset of input data and use a predictive analytic algorithm to output a prediction result. An intelligent loop-back mechanism may then receive the subset of the input data and dynamically update the data filter dictionary based on an impact associated with the output prediction result.

Method to improve prediction accuracy of business data with enhanced data filtering and data reduction mechanism

Various embodiments of systems and methods for trust circle through machine learning are described herein. The method includes identifying a website subscribed with the trust circle for aggregating data based upon a query. The query is received from a user of one or more registered users of the subscribed website. The one or more users' information and their respective authentication token is retrieved from the subscribed website. Based upon the authentication token of the respective user, information of the respective user is retrieved from one or more social media websites. The retrieved one or more users information and their retrieved one or more social media websites information is correlated to generate an output based upon the query. The output comprises reviews by the one or more users as reviewers, relationship of the reviewers with the user raised the query, and ratings clustered based upon various parameters.

System, method, and computer-readable storage medium for determining a trust circle through machine learning

A request with data is received at an execution environment of an application. In response to the request, a first network packet is automatically created. The first network packet includes the data, orchestration code and application code of the application. The first network packet is sent to a first network node. The first network node executes a first section of the application code in a node execution environment. The section to be executed is determined based on the orchestration code. In response to the execution, a microservice associated with the application is invoked. Upon receiving a response from the microservice, a second network packet is dynamically generated. The second network packet includes the received response, the orchestration code, and one or more sections of the application code. The second network packet is sent to a second network node.

Dynamic orchestration of microservices

A computer implemented method of integrating microservices. The computer displays a graphical environment and determines a suggested next microservice according to usage data. At least one of the microservices may access a hardware sensor that generates measurement data for the at least one microservice.

System and method for integrating microservices

Systems and methods for providing an auto-search text box in a content submission system are provided. In example embodiments, a message interface that is used to submit content to a content submission system is provided. The message interface includes a short text field that receives at least one keyword that summarizes the content to be submitted. In response to completion of entry of the at least one keyword in the short text field, a search of a content database for previously submitted content that matches the at least one keyword is automatically performed. Based on the search, a results list is displayed in proximity to the short text field on the message interface. The results list comprises a title and a link for each result in the results lists.

Auto-search textbox in a content submission system

In an example embodiment, a method of performing a health check on a process integration (PI) component is provided. A PI health check scenario is loaded into the PI component, the PI health check scenario including a reference to a list of checks. The PI health check scenario is then executed using the PI component, causing one or more checks in the list of checks to be performed at a predetermined frequency. The system can then automatically determine if one or more of the one or more checks fail.

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Kottayam, India

Aby Jose