Sap Se

Hewlett-Packard Development Company, L.p.

Panish Ramakrishna

Nithya Rajagopalan

Sriram Narasimhan

Ashutosh Patel

Nishant Kumar

Kumaraswamy Gowda

Rajendra Vuppala

Erica Vandenhoek

Arasan Rajendren

Prithvi Krishna Thodla Chandrasekhar

Madan Gopal Devadoss

Anis Kasmani

Debashis Banerjee

Abhishek Chaturvedi

Gurudayal Khosla

Hemanth Rajeswari Anantha

Sunil Gornalle

Surendranath Gopinathan

Prasad Karani

Vengateswaran Chandrasekaran

Vidhya Neelakantan

Ranjith Pavanje Raja Rao

Hari Babu Krishnan

Balaji Raghunathan

Venkatesh Iyengar

Krishna Hindhupur Vijay Sudheendra

Rahul Seshagiri Srivatsa

Prithvi Krishna

Vinay Santurkar

Amit Joshi

Abhilash R

Yogesh Sadashiv Deshpande

Ranveer Kumar Singh

Thirumaleshwar Kunamalla

Jay Sardhara

Arjun Swami

Mayank Kamboj

In an example embodiment, a model generation component may additionally assign various cloud resources to a machine learned model so that the training or retraining of the model can be performed using these resource. The containers may be weighted to handle model generation work of different weight. Having one single configuration for a container responsible for generating all models leads to overuse of hardware resources because machine learning algorithms are very resource intensive, and thus dynamically selecting the weight improves hardware utilization.

Dynamically scalable machine learning model generation and retraining through containerization

A computing architecture is optimized for the reuse of machine learning model. A request is received for a machine learning model recommendation that specifies parameters associated with a desired machine learning model (e.g., schema, etc.). Thereafter, a machine learning-based discovery model, recommends at least one machine learning model (to reuse) based on the parameters specified in the request. Next, data characterizing the recommended at least one machine learning model is provided (e.g., loaded into memory, displayed in a graphical user interface, transmitted to a remote computing system, and/or stored in physical persistence, etc.). In some variations, a graphical user interface can be rendered that allows a client application to select one of the recommended machine learning models and, further optionally, to activate the selected machine learning models. Related apparatus, systems, techniques and articles are also described.

Scalable architecture for machine learning model reuse

A method may include allocating, based on a first load requirement of a first tenant, a first bin having a fixed capacity for handing the first load requirement of the first tenant. In response to the first load requirement of the first tenant exceeding a first threshold of the fixed capacity of the first bin, packing a second bin allocated to handle a second load requirement of a second tenant. The second bin may be packed by transferring, to the second bin, the first load requirement of the first tenant based on the transfer not exceeding the first threshold of the fixed capacity of the second bin. In response to the transfer exceeding the first threshold of the fixed capacity of the second bin, allocating a third bin to handle the first load requirement of the first tenant.

Data curation with capacity scaling

Methods, systems, and computer-readable storage media for generating an expected configuration checksum based on a configuration file associated with an ETL job, the ETL job being executable to provide a target entity for consumption by one or more consuming applications, the target entity including data of one or more source entities; retrieving an ETL timestamp indicating a last time that the ETL job was executed, and determining, based on one of the expected configuration checksum and the ETL timestamp, that a target data schema of the target entity is to be updated, and in response, providing target metadata for the target entity and updating the target data schema based on the target metadata to provide an updated target data schema.

Automatically evolving tenant model schema in data lake in response to source system changes

Some embodiments provide a program that receives a set of data for a first record in a first table. The set of data comprises a set of values for a set of attributes. In a data loading process configured to load a subset of the set of data into a subset of a first set of columns in the first table, the program determines that a first column in a first set of columns does not belong in the subset of the first set of columns. The program generates the first record in the first table. The program generates a value for the first column in the first set of columns that refers to a second record in the second table configured to represent a defined type of record. The program stores the value in the first column in the first set of columns of the first record.

Determining references to predefined types of records

Some embodiments provide a program that receives a set of data for a first record in a first table. The set of data includes a set of values for a set of attributes. In a data loading process configured to load a subset of the set of data into a subset of a first set of columns in the first table, the program determines that a first column in a first set of columns does not belong in the subset of the first set of columns. The program generates the first record in the first table. The program generates a value for the first column in the first set of columns that refers to a second record in the second table configured to represent a defined type of record. The program stores the value in the first column in the first set of columns of the first record.

Independently loading related data into data storage

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program may receive a request to extract data for a first entity type. The program may, in response to the request, determine a subset of a plurality of data extraction configurations associated with the first entity type. The program may, for each data extraction configuration in the subset of the plurality of data extraction configurations, process the data extraction configuration based on a set of transaction data to generate a set of data, the set of data comprising a first set of data included in a set of instances of the first entity type and a second set of data included in a set of instances of a second entity type.

Extracting data based on data extraction configurations

Various examples are directed to systems and methods for generating a contract form using a contract library data structure. A computing device may receive, from a user, a contract form request comprising a set of contract classifiers. The computing device may access a contract library data structure and executes a filter routine to traverse the contract library data structure to identify at least one contract term associated with one or more filter tags corresponding to the set of contract classifiers. The filter routine may return an indication of the first contract term associated with the first node. The computing device may generate a contract form comprising the first contract term.

Contract library data structures and uses thereof

The present disclosure involves systems, software, and computer implemented methods for creating line item information from tabular data. One example method includes receiving event data values at a system. Column headers of columns in the event data values are identified. At least one column header is not included in standard line item terms used by the system. Column values of the columns in the event data values are identified. The identified column headers and the identified column values are processed using one or more models to map each column to a standard line item term used by the system. The processing includes using context determination and content recognition to identify standard line item terms. An event is created in the system, including the creation of line items from the identified column value. Each line item includes standard line item terms mapped to the columns.

Creating line item information from free-form tabular data

Data is received that specifies a term generated by user input in a graphical user interface. Thereafter, the term is looked up in a dictionary in which there are multiple classes for terms. The term can be classified based on a first class having a top ranked effective count for the term within the dictionary when a ratio of the first class relative to a second class having a second ranked effective count for the term in the dictionary is above a pre-defined threshold. In addition, the term is classified using a machine learning model when the ratio of the first class relative to the second class is below the pre-defined threshold. Data can be provided which characterizes the classifying. Related apparatus, systems, techniques and articles are also described.

Multi-stage adaptable continuous learning / feedback system for machine learning models

User-generated input is received that includes a sequence of words associated with initiation of a computer-implemented event. Thereafter, such input is parsed using at least one natural language processing (NLP) model. This parsed input is then used by a machine learning model to determine a suggested template having a plurality of fields for initiating the event. The template can then be presented in a graphical user interface. Related apparatus, systems, techniques and articles are also described.

Sourcing object creation from natural language inputs

A system includes reception of a definition of a first attribute to associate with a logical object of the first application, reception of a definition of a second attribute to associate with a second logical object of the first application, determination, based on the definition of the first attribute and the definition of the second attribute, that the first attribute and the second attribute are associated with a same semantic, based on the determination, generation of e a first identifier associated with the first attribute and the second attribute and storing the first identifier associated with the first attribute and the second attribute in a first data schema associated with the first application, and storage of the first identifier associated with the first attribute and the second attribute in an analytics schema associated with an analytics application.

Custom term unification for analytical usage

Various examples are directed to systems and methods for identifying database records in a database table. A database management system receives a search request comprising a first set of strings associated with a first column of the database table and a second set of strings associated with a second column of the database table. The database management system selects a set of first column keywords using the first set of strings and executes a first tier query at the database table. Responsive to determining that no database record returned by the first tier query has a relevance score greater than a threshold value, the database management system executes a second tier query at the database table.

Database record searching with multi-tier queries

A method of processing raw data as it is received from a data provider via an input channel is disclosed. Values are derived from the raw data as it is received from the data provider via the input channel. The derived values correspond to custom fields of a summary table. The summary table is configured to store a summary of the raw data. The custom fields correspond to data capable of improving an analysis of an entity by an analysis tool. The derived values are inserted into the custom fields of the summary table. Access to the summary table is provided as enriched data for use by the analysis tool to improve the analysis of the entity.

Growing community of inventors

Bengaluru, India

Panish Ramakrishna