Abstract: Systems and methods for partitioning forecast items into segments, or groups, based on an attribute. Once partitioned, segments are aggregated based on a predetermined memory size limit using an aggregation method. Aggregation methods include methods for providing segments that have a similar number of records. Segmentation is further enhanced by including variance reduction gain as a metric for selecting the order of attributes.

Abstract: Systems and methods for deleting data in a versioned database, comprising: generating a version visibility data structure (VVDS) from a version graph and scenario structure; determining each combination of feasible scenarios that when deleted, delete memory; evaluating an amount of memory reclaimed for each combination of feasible scenarios; and deleting one or more combination of scenarios to free up a specific amount of memory.

Abstract: Systems and methods for obtaining product information via a conversational user interface. The communication channel receives communication from a user, the intent and entities of which are deduced by the NLP. These are communicated by the fulfillment API to the knowledge engine which retrieves information that fulfills the intent. The information is communicated to the fulfillment API, which converts the intent into a response, which in turn is forwarded by the NLP to the communication channel, and back to the user.

Abstract: Systems and methods for partitioning forecast items into segments, or groups, based on an attribute. Once partitioned, segments are aggregated based on a predetermined memory size limit using an aggregation method. Aggregation methods include methods for providing segments that have a similar number of records. Segmentation is further enhanced by including variance reduction gain as a metric for selecting the order of attributes.

Abstract: Systems and methods for features engineering, in which internal and external signals are received and fused. The fusing is based on meta-data of each of the one or more internal signals and each of the one or more external signals. A set of features is generated based on one or more valid combinations that match a transformation input, the transformation forming part of library of transformations. Finally, a set of one or more features is selected from the plurality of features, based on a predictive strength of each feature. The set of selected features can be used to train and select a machine learning model.

Abstract: A system, method and non-transitory computer-readable storage medium for computing a full dependency graph before obtaining a result of an analytic; and constructing a scheduling graph to optimally distribute work between the available threads, based on the full dependency graph.

Abstract: Systems and methods disclose herein procedures for accelerated tree learning. In one class, the acceleration is based on self-adapting learning rates, while in another class, the acceleration is based on a plurality of learning rates, wherein each learning rate varies over the training; each learning rate increases linearly as a respective pseudo residual maintains a direction across sequential training iterations; and each learning rate decreases exponentially as the respective pseudo residual changes direction across sequential training iterations. The latter can be incorporated with other methodologies, such as momentum-augmented gradient boosting and Nesterov Accelerated Gradient Boosting. These systems and methods for accelerated tree learning exhibit a marked reduction in training time and resources required for gradient boosted trees.

Abstract: Systems and methods for editing a file-backed table in a database having a concurrency control mechanism, that include reading the content of the file-backed table, and modifying the content of the file-backed table.

Abstract: A business analytics conversational tool comprising: a device comprising a communication channel, a natural language processor (NLP), a fulfillment application program interface (F-API), a database application program interface (D-API), and a business management database; wherein: the NLP receives a user-input from a user through the communication channel; the NLP deduces an intent of the user-input; the NLP communicates the intent to the F-API; the F-API communicates a request for data associated with the intent to the database via the D-API; the D-API communicates the data associated with the intent to the F-API; the F-API converts the data associated with the intent to conversational form and sends the conversational form for voice output through the communication channel.

Abstract: Systems, methods, and computer-readable media for solving large, sparse convex optimization problems on highly parallel hardware. A computing apparatus receives an initial point and iteratively updates an optimization point via a first subsystem that constructs and updates a Karush-Kuhn-Tucker (KKT) system and applies small steps in an improving direction. In parallel, a second subsystem incrementally solves the updated KKT system, while a third subsystem optimizes parameters such as preconditioners and matrix permutations. Outputs are exchanged among subsystems during each iteration to enable continuous refinement. A small-step criterion, enforced via line search or trust-region procedures, ensures convergence. Execution leverages GPUs or TPUs to partition and process KKT systems concurrently, dynamically adjusting step size based on convergence indicators.

Abstract: Systems and methods in which a historical data set is pre-processed once per trained machine-learning model; a value of an unknown sample is forecast while tracking a leaf path of the unknown sample; the leaf path of the unknown sample is limited to a subset of trees in each trained-machine model; a set of related historical samples is determined based on the leaf path of the unknown sample, and a set of quantiles is determined from the leaf path of the unknown sample. Inventory is loaded according to the set of quantiles.

Abstract: There is provided a method and system for generating an output analytic for a promotion. The method includes training and instantiating a machine learning model comprising at least a Random Forest model, with a selection training set, the selection training set comprising the historical data and the one or more input parameters; selecting, by the processor, using the machine learning model a configuration and a layout for the one or more products on the promotional materials; outputting, by the processor, the promotional materials based on the selection of the configuration and layout.

Abstract: Systems and methods for computer memory management by a memory coordinator and a plurality of memory consumers. An urgency and memory quota of each memory consumer is initialized by the memory coordinator, which then adjusts the memory quota of each memory consumer such that the sum of the memory quota of each memory consumer does not exceed a finite amount of computer memory. Each memory consumer adjusts its memory usage in response to the quota input and urgency input from the memory coordinator.

Abstract: Methods and systems for resolving these transaction conflicts in a way that does not involve aborting or rolling back conflicting transactions.

Abstract: Systems and methods are disclosed for assessing the forecastability of time series data using machine learning. A large set of time series is preprocessed and partitioned into training and testing sets. Multiple forecasting models are trained on the training data, and the best-performing model is used to compute a sample forecastability metric. Features are extracted from the time series and paired with the forecastability metric to train a predictive model. This trained model is then used to estimate the forecastability of new time series data based on extracted features, bypassing the need for computationally intensive forecasting. The approach enables efficient and reliable forecastability assessment across large volumes of time series data.

Abstract: Systems and methods for partitioning forecast items into segments, or groups, based on an attribute. Once partitioned, segments are aggregated based on a predetermined memory size limit using an aggregation method. Aggregation methods include methods for providing segments that have a similar number of records. Segmentation is further enhanced by including variance reduction gain as a metric for selecting the order of attributes.

Abstract: Systems and methods for constraint-based optimization, comprising: an AI demand forecasting engine, an optimization engine, a user-defined objective, and a user-defined set of constraints. Using historical sales data, the AI demand forecasting engine generates a plurality of entities, each entity defined by a placement of an item in a promotion platform; and forecasts the objective associated with each entity. The optimization engine generates a plurality of plans, each plan consisting of a unique subset of entities. Plans that violate at least one constraint are eliminated by the optimization engine, leaving a set of candidate solutions. An optimum plan is selected from the set of candidate solutions based on maximization of the objective.

Abstract: Systems and methods for obtaining product information via a conversational user interface. The communication channel receives communication from a user, the intent and entities of which are deduced by the NLP. These are communicated by the fulfillment API to the knowledge engine which retrieves information that fulfills the intent. The information is communicated to the fulfillment API, which converts the intent into a response, which in turn is forwarded by the NLP to the communication channel, and back to the user.

Abstract: Machine learning segmentation methods and systems that perform segmentation quickly, efficiently, cheaply, and optionally provides an interactive feature that allows a user to alter the segmentation until a desired result is obtained. The automated machine learning segmentation tool receives all potentially important attributes and provides segmentation of items. It also receives information about important features of the data and finds how best to differentiate between groups using cluster-based machine learning algorithms. In addition, visualization of the segmentation explains to a user how the segmentation was obtained.

Abstract: Systems and methods for deleting data in a versioned database, comprising: generating a version visibility data structure (VVDS) from a version graph and scenario structure; determining each combination of feasible scenarios that when deleted, delete memory; evaluating an amount of memory reclaimed for each combination of feasible scenarios; and deleting one or more combination of scenarios to free up a specific amount of memory.