Abstract: The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes generating a dependency graph based on one or more microservices, computing, for each microservice of the one or more microservices, a complexity score using the dependency graph, identifying a subset of the one or more microservices, wherein each microservice in the subset of the one or more microservices has a complexity score meeting a threshold value, and applying a transactional lock on each microservice in the subset of the one or more microservices.

Abstract: A method for making binary predictions for a subject involves obtaining historical data for multiple subjects, the historical data including, for each subject, a feature set and a binary outcome, generating training data from the historical data, and training a predictive model using the training data to predict the outcomes based on the feature sets. The method further includes obtaining historical data including a feature set for a subject under consideration, and predicting a binary outcome for the subject under consideration, based on the feature set associated with the subject under consideration.

Abstract: The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes generating a dependency graph based on one or more microservices in a transaction, wherein each node of the dependency graph corresponds to each microservice of the one or more microservices in the transaction, respectively, generating a first set of features using the dependency graph, predicting a first set of nodes that are likely to fail using a first machine learning model based on the first set of features, generating a second set of features using the dependency graph, predicting a second set of nodes that are likely to fail using a second machine learning model based on the second set of features, and applying one or more fixes to one or more microservices based on a combination of the first set of nodes and the second set of nodes.

Abstract: Aspects of the present disclosure provide techniques for explainable payroll calculations. Embodiments include receiving a request from a client for a rule-based calculation. Embodiments include receiving one or more user values related to the request. Embodiments include using a calculation graph to determine the rule-based calculation based on the one or more user values. The calculation graph may comprise at least one node that performs an operation using the one or more user values. Embodiments include identifying an explanation template associated with the at least one node. Embodiments include generating an explanation of the rule-based calculation based on the explanation template and the one or more user values. Embodiments include providing the rule-based calculation and the explanation of the rule-based calculation to the client in response to the request.

Abstract: A method of automating a workflow may include obtaining a completeness graph including conditions applied to attributes of an entity, determining that a missing attribute subset of the attributes lacks a corresponding value, determining that a condition corresponding to a missing attribute of the missing attribute subset is satisfied, and obtaining a value for the missing attribute. The value may be a result of performing a task in the workflow. The method may further include modifying a state of the entity by assigning the value to the missing attribute to obtain a modified state of the entity.

Abstract: A method and system provides assistance to users of a data management system. The method and system trains an analysis model with a machine learning process to generate sub-word embeddings corresponding to vectorized representations of portions of a search term entered by a user. The method and system generates augmented query data based on the sub-word embeddings. The method and system provides assistance to the user based on the augmented query data.

Abstract: Aspects of the present disclosure provide techniques for explainable payroll calculations. Embodiments include receiving a request from a client for a rule-based calculation. Embodiments include receiving one or more user values related to the request. Embodiments include using a calculation graph to determine the rule-based calculation based on the one or more user values. The calculation graph may comprise at least one node that performs an operation using the one or more user values. Embodiments include identifying an explanation template associated with the at least one node. Embodiments include generating an explanation of the rule-based calculation based on the explanation template and the one or more user values. Embodiments include providing the rule-based calculation and the explanation of the rule-based calculation to the client in response to the request.

Abstract: A server may receive an inquiry associated with an interaction between a customer and a customer support agent from a device associated with a customer support agent; enter the inquiry as an input to a contextual bandit model; select, using the contextual bandit model, a collection of articles from a plurality of pre-defined collections of articles based on the inquiry; cause, in response to the contextual bandit model selecting the collection of articles, at least one search result to be displayed on a user interface of the device associated with the customer support agent, wherein a search result includes at least a portion of at least one article of the collection of articles; cause text within the at least one search result to be highlighted; receive feedback on the collection of articles from the customer support agent; and update the contextual bandit model based on the feedback.

Abstract: A method to predict a delay involves receiving an open invoice sent by a company to a customer, extracting, using a programmable interface, a set of company data, a set of customer data, and a set of invoice data from a management application (MA) and the open invoice; refining, using a set of algorithms, the set of company data, the set of customer data, and the set of invoice data into a set of invoice attributes, a set of company features, and a set of customer features; predicting a delay in processing the open invoice using a trained model analyzing the set of company features and the set of customer features; updating, at an expiration of a predetermined timespan, the MA to add the delay to a due date of the open invoice by including the set of company features; and updating, at the expiration of the predetermined timespan, the MA to add the delay in processing the open invoice by including the set of customer features.

Abstract: Certain aspects of the present disclosure provide techniques for automatically guiding transaction performance. Embodiments include receiving first input from a first user identifying a term of a transaction between the first user and a second user. Embodiments include receiving second input from the second user confirming the term. Embodiments include deploying a smart contract that corresponds to the term on a hash chain. The smart contract may comprise a program that guides performance of the term, and the hash chain may be resistant to modification. Embodiments include receiving, from a management component of the hash chain, a notification that the smart contract has verified through a trusted authority that the term is satisfied.

Abstract: A method to predict a delay involves receiving an open invoice sent by a company to a customer, extracting, using a programmable interface, a set of company data, a set of customer data, and a set of invoice data from a management application (MA) and the open invoice; refining, using a set of algorithms, the set of company data, the set of customer data, and the set of invoice data into a set of invoice attributes, a set of company features, and a set of customer features; predicting a delay in processing the open invoice using a trained model analyzing the set of company features and the set of customer features; updating, at an expiration of a predetermined timespan, the MA to add the delay to a due date of the open invoice by including the set of company features; and updating, at the expiration of the predetermined timespan, the MA to add the delay in processing the open invoice by including the set of customer features.