Sap Se

Prabal Mahanta

Vipul Khullar

Bhavneet Kaur

Sudhir Verma

Aavishkar Bharara

Suchin Chouta

Nishii Bharill

Amritha Pulikool

Swarnima Sneh

Swati Verma

Abdul-Gafoor Mohamed

Technologies are provided for determining a suitability of data payloads for training a machine learning model. A schema can be generated based on sample data payloads that have different data formats. The sample data payloads (and/or additional data payloads) can be converted to a format that conforms to the schema. Feature vectors can then be generated based on the converted data payloads, and used to determine a suitability of the data payloads for training a machine learning model. If the data payloads are sufficiently suitable, the converted data payloads can be used to train the machine learning mode. Otherwise, the schema may be annotated and new converted payloads may be generated based on the annotated schema. The feature vector generation and suitability analysis can then be repeated.

Determining data suitability for training machine learning models

Isolated environments for development of modules of a software system, such as an enterprise resource planning (ERP) system, can be generated using a container image generated from a copy of a central development environment. A graph-based machine learning model can be trained and applied to a graph of the software system to predict dependencies between the modules of the software system. An isolation forest machine learning model can be trained and applied to a selected module to verify its integrity. The container image can be modified based on the predicted dependencies and the integrity verification, among other factors. The modified container image can be executed to generate an isolated environment for the selected module. A version management utility and a transport system can be used during subsequent development in the isolated environment to manage and register repositories and objects associated with the isolated environment.

Isolated environments for enterprise resource planning modules

Some embodiments are associated with application performance optimization in a cloud computing environment. A transaction observer platform may receive transaction information associated with execution of an application in the cloud computing environment A classifier recorder and tagger platform, coupled to the transaction observer platform, may then automatically tag the transaction information. A graph engine relation builder platform, coupled to the transaction observer platform and the classifier recorder and tagger platform, may receive the tagged transaction information and automatically create graph information that represents execution of the application. A recommendation engine platform, coupled to the graph engine relation builder platform, may then receive the graph information and automatically generate and transmit an application performance optimization recommendation.

Graph-based application performance optimization platform for cloud computing environment

Technologies are described for managing database queries based on bill of materials (BOM). For example, a set of database queries can be optimized (e.g., in terms of time and sequence of execution) based on a BOM explosion. In some implementations, database queries are managed by generating a BOM for an application programming interface (API) call. Various data structures can be created from the BOM, which can comprise procedure information, application and action information, and/or hierarchy information. A logical sequence of execution can be determined based, at least in part, on one or more of the data structures, where the logical sequence of execution comprises a plurality of database queries to execute the API call. The logical sequence of execution can be optimized to produce an optimized sequence of execution. The API call can be executed according to the optimized sequence of execution.

Bill of materials traversal to handle logical units of work for in-memory databases

According to some embodiments, systems and methods are provided, comprising receiving a code fragment exhibiting a static code issue; determining, via a trained exemption neural network, whether the received code fragment is exempt or not exempt from resolution; in a case it is not exempt, inputting the code fragment to a trained classification neural network; determining whether the static code issue is a syntactical static code issue or a non-syntactical static code issue; in a case it is a syntactical static code issue, inputting the code fragment to a first trained network to generate a first resolution; and in a case the static code issue is a non-syntactical static code issue, inputting the code fragment to a second trained network to generate a second resolution of the non-syntactical static code issue. Numerous other aspects are provided.

Dynamic recommendations for resolving static code issues

Technologies are provided for assigning developers to source code issues using machine learning. A machine learning model can be generated based on multiple versions of source code objects (such as source code files, classes, modules, packages, etc.), such as those that are managed by a version control system. The versions of the source code objects can reflect changes that are made to the source code objects over time. Associations between developers and source code object versions can be analyzed and used to train the machine learning model. Patterns of similar changes to various source code objects can be detected and can also be used to train the machine learning model. When an issue is detected in a version of a source code object, the model can be used to identify a developer to assign to the issue. Feedback data regarding the developer assignment can be used to re-train the model.

Source code issue assignment using machine learning

According to some embodiments, an automated provisioning system may receive a customer demand associated with an application to be executed in a cloud-based computing environment. The automated provisioning system may include a process allocator to communicate with Virtual Machine ('VM') and container provisioners and determine cluster data. A machine learning based microservice setup platform, coupled to the automated provisioning system, may receive the cluster data and information about the customer demand. The machine learning based microservice setup platform may then execute policy rules based on the cluster data (and information about the customer demand) and generate a recommendation for the customer demand. The automated provisioning system may then assign the customer demand to one of a VM-based infrastructure and a container-based infrastructure in accordance with the recommendation generated by the machine learning based microservice setup platform.

Hypervisor and container placement and cost optimization utilizing machine learning

A business data database may store transaction business information of an enterprise along with existing structures, rules, and classification recommendations. A business rules framework agent platform may execute supervised machine learning and generate industry agnostic relationship scores and classification scores based on data in the business data database. The business rules framework agent platform may then optimize data and table structures, using relation graph-based evaluation, in accordance with taxonomy data and the classification scores. A rules and configuration database may store the optimized data and table structures from the business rules framework agent platform. Further, a business configuration and rules mapper platform may identify business configuration data and business rules classification data based on the optimized data and table structures. In some embodiments, the identified business configuration data and business rules classification data are used to automatically update the existing structures, rules, and classification recommendations in the business data database.

Automated master data classification and curation using machine learning

Systems, methods, and techniques to efficiently optimize values associated with subscription services. The method includes retrieving usage information relating to one or more cloud services subscribed to by a user and provided by a plurality of cloud service providers. The method further includes determining, by the at least one processor and in response to the retrieving, authorization information and value information associated with the user and the one or more cloud services. The method further includes optimizing, by the at least one processor and in response to the determining, a number of cloud services and/or a value associated with the number of cloud services based on the usage information, authorization information, and value information.

Value optimization with intelligent service enablements

Example embodiments for controlling access in a network system based on a distributed ledger are presented. In an example embodiment, a plurality of nodes of a computer network capture information describing requested data transactions in the computer network. At least some of the nodes construct transaction blocks for a distributed ledger, with each of the transaction blocks including information describing one or more of the requested data transactions. The nodes publish the transaction blocks to other nodes. The nodes receiving the transaction blocks add those of the transaction blocks that do not include a disallowable requested data transaction to copies of the distributed ledger. One or more arbitrator nodes approve those of the requested data transactions represented in transaction blocks added to the copies of the distributed ledger by a consensus of the nodes.

Network access control based on distributed ledger

Systems and methods are provided for receiving data and uploading it into an in-memory database using a set of predefined procedures. The data includes a plurality of data records. A plug-in embedded in the in-memory database detects the uploaded data and a structure corresponding to the uploaded data. A user interface (UI) template is created on the detected structure of the uploaded data. From the UI template, a web application is created. By activating the web application, an end-user UI is created which includes a plurality of graphical user interface elements corresponding to the uploaded data. The end-user UI is initiated for rendering in a graphical user interface.

Growing community of inventors

Bengaluru, India

Prabal Mahanta