Abstract: In accordance with an embodiment, described herein are systems and methods for use with a computing environment, for providing a determination of model fitness and stability, for model deployment and automated model generation. A model fitness and stability component can provide one or more features that support model selection, use of a model deployability score and deployability flag, and mitigation of model drift risk, to determine model fitness and stability for a particular application. For example, embodiments may be used with analytic applications, data analytics, or other types of computing environments, to provide, for example, a directly actionable risk prediction, in finance applications or other types of applications.

Abstract: Embodiments classify a product to a product category. Embodiments receive a textual description of the product and create an index of product categories. Embodiments use a plurality of classifiers to classify the textual description to one of the product categories, the classifiers including an index based classifier, an encyclopedia based classifier, a Bayes' rule based classifier, and an embeddings classifier.

Abstract: In accordance with an embodiment, described herein are systems and methods for constructing a period approximation for an irregular time series of data, through maximization of time series characteristics. When assessing a time series of data that has highly variable posting intervals, traditional approaches that rely on mean or mode calculations to estimate the (time series) period can result in period estimates that are misaligned with the actual characteristics of the data. In accordance with an embodiment, the system operates to assess different interval period values, construct a time series for each candidate period, and evaluate their characteristics such as length and population. The system can then determine a time series model based on one or more constructed time series, where the overall characteristics of an input time series are maintained, for use in data analytics, display as time series information within a user interface or dashboard, or other purposes.

Abstract: Embodiments evaluate performance by receiving a plurality of training performance reviews. Embodiments extract from the training performance reviews, using a first machine learning model, a plurality of features comprising a training aspect, a training sentiment, and a corresponding training evidence. Embodiments use the extracted plurality of features to train a second machine learning model. Embodiments receive a first performance review and extract from the first performance review one or more first aspects, one or more corresponding first evidences, and one or more corresponding first sentiments. Embodiments, using the trained second machine learning model, predict first sentiment scores for each of the first aspects.

Abstract: Embodiments predict a target variable for accounts receivable using a machine learning model. For a first customer, embodiments receive a plurality of trained ML models corresponding to the target variable, the plurality of trained ML models trained using the historical data and comprising a first trained model having no grace period for the target variable and two or more grace period trained models, each grace period trained model having different grace periods for the target variable. Embodiments determine a Matthews' Correlation Coefficient (“MCC”) for the first trained model. When the MCC for the first trained model is low, embodiments determine the MCC for each of the grace period trained models, and when one or more MCCs for each of the grace period trained models is higher than the MCC for the first trained model, embodiments select the corresponding grace period trained model having a highest MCC.

Abstract: In accordance with an embodiment, described herein are systems and methods for automated identification of churned client entities (e.g., product purchasers or users, cloud service subscribers, or other types of client entities), generally referred to herein as customers; predictive assessment of customer attrition likelihood; and determination of temporal windows for strategic intervention. The system can be used, for example, to predict if a client (e.g., a customer) will churn; determine a churn timeframe or action window for possible action to address the churn; and/or automatically identify which clients or customers may have already churned. Data or information describing churned clients or customers can be used by the system to automatically determine and/or perform an action directed to particular clients or customers; or can be returned in the form of displayed reports or other data visualizations.

Abstract: In accordance with an embodiment, described herein are systems and methods for use with a computing environment, for providing a determination of model fitness and stability, for model deployment and automated model generation. A model fitness and stability component can provide one or more features that support model selection, use of a model deployability score and deployability flag, and mitigation of model drift risk, to determine model fitness and stability for a particular application. For example, embodiments may be used with analytic applications, data analytics, or other types of computing environments, to provide, for example, a directly actionable risk prediction, in finance applications or other types of applications.

Abstract: In accordance with an embodiment, described herein is a system and method for automated data warehouse creation and extension from user natural language requests. A data augmentation system, operating on one or more computers, can receive a natural language input from a user, including an instruction to augment a set of data, for example to create a fact/dimension, or to extend an existing data entity by bringing additional columns from a source data and publishing the combined data to a target data warehouse instance. The system determines an understanding associated with the user instruction in plain-language terms (for example, “extend sales order transactions with approval status”), and determines and performs a corresponding course of actions to create, extend, or otherwise augment the set of data, without requirement for the user to have a detailed knowledge of the data warehouse, its schemas, or other data dependencies.

Abstract: Embodiments perform the anomaly detection of expense reports in response to receiving an expense report as input data, the expense report including a plurality of expenses. Embodiments create a plurality of groups of expenses, each group corresponding to a different combination of a category of the expense, a location of the expense and a season of the expense. Embodiments generate and train an unsupervised machine learning model corresponding to each group, and assign each of the expenses of the expense report into a corresponding group and input the expenses into the unsupervised machine learning model corresponding to the group. Embodiments then generate an anomaly prediction at each unsupervised machine learning model for each expense of the expense report.

Abstract: Embodiments described herein are generally related to computer data analytics, and computer-based methods of providing business intelligence data, and are particularly related to systems and methods for use with enterprise data for profile matching and generating gap scores and upskilling recommendations. In accordance with an embodiment, the system can operate to match a set of position requirements with candidate attributes or skillsets, ranking them on the basis of match scores. The system can be used, for example, to determine a skill gap between the position requirements and candidate attributes, and recommend which skills might be augmented to better address the position requirements.

Abstract: Embodiments classify a product to one of a plurality of product classifications. Embodiments receive a description of the product and create a first prompt for a trained large language model (“LLM”), the first prompt including the description of the product and contextual information of the product. In response to the first prompt, embodiments use the trained LLM to generate a hallucinated product classification for the product. Embodiments word embed the hallucinated product classification and the plurality of product classifications and similarity match the embedded hallucinated product classification with one of the embedded plurality of product classifications. The matched one of the embedded plurality of product classifications is determined to be a predicted classification of the product.

Abstract: In accordance with an embodiment, described herein are systems and methods for generating enterprise forecasts based on an analysis of input variables and direct forecasting. In accordance with an embodiment, the system can use linear regression or other mathematical models or modeling techniques to assess a set of variables related to an enterprise forecast, and their values and rate of change of such values, within a particular forecast window. Based on such assessment, the system can generate an enterprise forecast for that time period, or for a subsequent time period.

Abstract: In accordance with an embodiment, described herein are systems and methods for providing a supply chain command center for intelligent procurement assistance, based on an assessment of inventory trends, demand, or other inputs related to the procurement or management of an inventory of items. In accordance with an embodiment, the system can simultaneously optimize for a set of variables related to procurement, by creating time series forecasts of leaf-level independent variables, and performing a simulation within the boundary conditions of historical or expected distributions of each variable, to determine an optimal timing, quantity, location and/or vendor for each order of items that are to be placed in the inventory.

Abstract: In accordance with various embodiments, described herein are systems and methods for use with an analytic applications environment, for ranking of database tables for use in controlling extract, transform, load (ETL) processes. In accordance with an embodiment, the system uses a ranking algorithm or process to rank database tables and/or table columns associated with a set of data. The table/column rankings can then be used to prioritize ETL processing of a customer's data for use with a data warehouse or other data analytics environment. In accordance with an embodiment, the method includes determining a global rank; a business rank; and a tenant or customer-specific rank, for a plurality of tables and columns in a customer's database; and aggregating or otherwise using the determined rankings to control the ETL process for a particular customer (tenant), to load their data into the data warehouse.

Abstract: Embodiments generate a machine learning (“ML”) model. Embodiments receive training data, the training data including time dependent data and a plurality of dates corresponding to the time dependent data. Embodiments date split the training data by two or more of the plurality of dates to generate a plurality of date split training data. For each of the plurality of date split training data, embodiments split the date split training data into a training dataset and a corresponding testing dataset using one or more different ratios to generate a plurality of train/test splits. For each of the train/test splits, embodiments determine a difference of distribution between the training dataset and the corresponding testing dataset. Embodiments then select the train/test split with a smallest difference of distribution and train and test the ML model using the selected train/test split.

Abstract: Embodiments predict a target variable for accounts receivable using a machine learning model. For a first customer, embodiments receive a plurality of trained ML models corresponding to the target variable, the plurality of trained ML models trained using the historical data and comprising a first trained model having no grace period for the target variable and two or more grace period trained models, each grace period trained model having different grace periods for the target variable. Embodiments determine a Matthews' Correlation Coefficient (“MCC”) for the first trained model. When the MCC for the first trained model is low, embodiments determine the MCC for each of the grace period trained models, and when one or more MCCs for each of the grace period trained models is higher than the MCC for the first trained model, embodiments select the corresponding grace period trained model having a highest MCC.

Abstract: Embodiments analyze a customer of an organization. Embodiments select the customer and receive historical data corresponding to a plurality of transactions of the customer with the organization, the historical data including, for each of the transactions, a target variable including a number of days of delayed payment for each transaction. Based on the historical data, embodiments determine a cost of a delayed payment from the customer and determine an average delay of payments of the customer. Embodiments convert the cost of delayed payments to a first Z-score and the average delay of payments to a second Z-score. Embodiments then determine a reliability score of the customer comprising determining a Euclidean distance of the first Z-score and the second Z-score.

Abstract: Embodiments predict a target variable for accounts receivable in response to receiving historical data corresponding to a plurality of transactions corresponding to a plurality of customers, the historical data including, for each of the transactions, the target variable. Embodiments segment each of the customers based on the historical data corresponding to each of the customers, the segmenting including determining a variation of the target variable for each customer and, based on the variation, classifying each customer as having a low variation, a medium variation, or a high variation. For each low variation customer, embodiments create a regular ML model without a grace period that is trained and tested using the historical data. For each medium variation customer, embodiments create the regular ML model and create two or more grace period ML models, each grace period ML model adding a different grace period to the target variable.

Abstract: Embodiments predict a sales order fulfillment of an item. Embodiments receive historical data including past sales orders, and extracts a plurality of machine learning (“ML”) features from the historical data. Embodiments use a portion of the plurality of ML features to train one or more classifiers and generate labeled ML features from the trained classifiers. Embodiments train a ML regression model with the extracted ML features and the labeled ML features. Embodiments then receive a new sales order and generate a prediction on a delivery date for the new sales order using the trained ML regression model.

Abstract: In accordance with an embodiment, described herein are systems and methods for use with an analytic applications environment, for determination of recommendations and alerts in such environments. A data pipeline or process can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. When provided as part of a software-as-a-service (SaaS) or cloud environment, the data sourced from a plurality of organizations can be aggregated, to leverage information gleaned from the collective or shared data. The system can be used to generate semantic alerts, including obtaining permission from; and analyzing the collective data of; the plurality of organizations, to determine operational advantages indicated by the data, and providing alerts associated with those operational advantages.