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.

Abstract: Systems and methods for efficient consolidation of record blocks in a data base. The system comprises: 1) a deletion record set; an in-memory database representation comprising: tables and records; one or more exclusive locks for the records; and a record block index; 2) a persistent database representation comprising: record blocks; and a transaction log. The method comprises: receiving, by a processor, a deletion record set; acquiring, by the processor, an exclusive lock for one or more records in the deletion record set; consolidating, by the processor, one or more record blocks; updating, by the processor, an in-memory record block index; and adding, by the processor, a transaction log entry for the updated record block index update.

Abstract: Methods and systems to manage permissions in a structured user-environment which provide a User Interface (UI) that provides a simple, intuitive administration to apply permissions at the user and group level to data in the structured user-environment. The UI also provides feedback to the administrator as to the inheritance path of each user and/or group as well as links between permissions, allowing the administrator to determine how a user or group was granted or denied access to a permission or resource.

Abstract: Systems and methods for replicating data in a versioned database, comprising: receiving a maximum replication size; selecting, a lead scenario for placement in a replication set; the lead scenario having a size less than the maximum replication size; marking all scenarios in the versioned database as unprocessed and the lead scenario as processed; initializing the replication set; adding the lead scenario into the replication set; obtaining a list of candidate scenarios to place in the replication set; determining a best candidate scenario from the list of candidate scenarios; adding the best candidate scenario to the replication set the best candidate scenario as processed; and iterating a new list of candidate scenarios to place in the replication set until there are no more scenario candidates to place in the replication set.

Abstract: Systems and methods for replicating data in a versioned database, comprising: receiving a maximum replication size; selecting, a lead scenario for placement in a replication set; the lead scenario having a size less than the maximum replication size; marking all scenarios in the versioned database as unprocessed and the lead scenario as processed; initializing the replication set; adding the lead scenario into the replication set; obtaining a list of candidate scenarios to place in the replication set; determining a best candidate scenario from the list of candidate scenarios; adding the best candidate scenario to the replication set the best candidate scenario as processed; and iterating a new list of candidate scenarios to place in the replication set until there are no more scenario candidates to place in the replication set.

Abstract: Systems and methods for embedding a computational notebook within an enterprise application software. A computational notebook editor embedded is embedded within a software client interface which is in communication with the software client interface. The application server comprises a reverse proxy server that is embedded within the application server. A container management system is in communication with the application server and comprises a multi-user server, a notebook interactive development environment, and a notebook execution tool.

Abstract: A 2-way and 3-way compare-merge tool that allows consultants and customers to reconcile the changes that they have made with a generated copy of the resources. Systems and methods that provide an effective, easy-to-use comparison tool includes: a schema-aware compare; sophisticated handling of collection properties, especially nested collections; effective matching algorithms to recognize that two items are the same business object in spite of outward differences; an ability to accept user hints and input and persist user decisions; a structured report that reflects the structure of the business object; color formatting, tagging, and filtering to find the important changes among the large volume of differences (among the thousands of changes per resource). Furthermore, adding a common ancestor as a third data point in the 3-way compare identifies the source of changes.

Abstract: Systems and methods that for a scalable versioned database that can organize data into scenarios and hives. These define how data visibility is controlled by scenarios, and how data can be transferred between scenarios. In some embodiments, queries and algorithms can be executed in independent processes, which may execute in parallel, and on independent machines. Furthermore, data objects can be placed in shared storage, and metadata objects can be placed in a metadata database which supports ACID transactions. Data objects are not modified in place after they are constructed, while metadata objects can be updated transactionally, using the metadata database. Data can be updated by creating new data objects and connecting them to scenarios with new metadata objects. Finally, obsolete data may be cleaned up by identifying unreachable data, which is safe to delete.

Abstract: A system and method for model auto-selection for a prediction using an ensemble of machine learning models. The method includes: receiving historical data, the historical data including previous outcomes of a plurality of events associated with a plurality of data categories; training candidate machine learning models with the historical data, each candidate machine learning model trained using a respective one of the data categories; and determining an ensemble of machine learning models by determining a median prediction for combinations of candidate machine learning models and determining the combination that has the median prediction that is closest to at least one of the previous outcomes.

Abstract: Systems and methods that provide a mechanism to transition static schema to dynamic schema while maintaining backwards capability. Simple removal of static schema elements, followed by replacement with dynamic schema elements, make a third-party code incompatible since the third-party code references schema entities that no longer exist. Provided is a mechanism to decrease the memory use of non-material static schema entities. Transitioning static schema to dynamic schema allows the database to avoid loading non-material schema entities, thereby decreasing overall memory usage.