A centralized system may collect and aggregate assessments from multiple websites. An aggregate score may be calculated for the resource that cumulatively considers assessments from a plurality of different websites from which assessments are received from users. Text descriptions associated with each of the assessments may be provided to a machine-learning system that uses a trained model to assign identifiers to the assessments as they are received. These identifiers may include common words or text that are descriptive of different facets of user experiences related to receiving and using the resource. After selecting one or more identifiers, assessments associated with that identifier may be included or excluded from the display. Additionally, the overall aggregate score for the resource may be recalculated by removing components of that score that are based on assessments with identifiers that have been selected for exclusion.

Disclosed herein are systems and methods to efficiently execute predictions models to identify future values associated with various nodes. A server retrieves a set of nodes and generates a primary prediction model using data aggregated based on all nodes. The server then executes various clustering algorithms in order to segment the nodes into different clusters. The server then generates a secondary (corrective) prediction model to calculate a correction needed to improve the results achieved by executing the primary prediction model for each cluster. When a node with unknown/limited data and attributes is identified, the server identifies a cluster most similar the new node and further identifies a corresponding secondary prediction model. The server then executes the primary prediction model in conjunction with the identified secondary prediction model to populate a graphical user interface with an accurate predicted future attribute for the new node.

A search and data analysis collaboration system is described. The search and data analysis collaboration system enables users to search for and process stored data, and further includes a home page component that can help guide users embarking on data analyses; a discovery component that enables users to discover what data is available for search and analysis; a search component that enables users to efficiently search accessible data and to iterate on search queries and corresponding results; a workbooks component that enables users to create aggregated collections of data analysis artifacts; and an actions component that enables users to configure various actions to be performed in response to analyses.

At least some embodiments are directed to a prediction system of software objects. The prediction system predicts a first aspect of a user profile utilizing a categorization machine learning model and a user activity profile, the user profile and the user activity profile are associated with a user. The user activity profile comprises a plurality of values associated with demographics and historical activity data of the user. The prediction system predicts a software object associated with the user profile utilizing an optimization machine learning model, the first aspect of the user profile, and a second aspect of the user profile. The software object is optimized with respect to at least one competitive interest between the user associated with the user profile and an entity associated with the software object. The prediction system outputs the software object to a client computing device of the user.

Methods, apparatuses, or computer program products provide for triggering component workflows within a multi-component system. An update to one or more component metadata records of a component metadata vector associated with a first component identifier may be received. The component metadata vector may include a plurality of records. Each record of the plurality of records may include a unique component metadata record identifier and a component metadata value. The component metadata vector associated with the first component identifier may be traversed after updating the one or more component metadata records. Based at least in part on detecting a component metadata condition associated with a component workflow trigger associated with the first component identifier, a first component workflow action of a first component workflow action series comprising a plurality of component workflow actions may be executed. Furthermore, a component workflow trigger notification may be transmitted to a first computing device.

A method, a system, and a computer program product for execution master data substitution. One or more first data objects in a master data storage are determined for replacement. One or more second data objects are identified for replacing the first data objects for storage in the master data storage. Replacement of the first data objects with the second data objects is performed in accordance with one or more data object requirements. Replacement of the first data objects by the second data objects is executed in accordance with the one or more data object requirements. A resulting replacement data set is generated and stored.

A robotic system for arranging packages at a destination according to a stacking sequence. The robotic system uses a storage area for temporarily storing packages that arrive out-of-sequence until they are next-in-sequence for placement at the destination. The robotic system processes an incoming package, determines if it is next-in-sequence for placement at the destination, and if it is, places the package at the destination. On the other hand, if it is not next-in-sequence for placement at the destination, it stores the package in the storage area. A package in the storage area is transferred to the destination when it is next-in-sequence for placement at the destination. By using the temporary storage for storing out of sequence packages, the robotic system eliminates the need for receiving the packages in a stacking sequence, which also eliminates the need for sequencing machines.

Techniques and technologies for providing a fully managed datastore for clients to securely store, discover, retrieve, remove, and share curated data, or features, to develop machine learning (ML) models in an efficient manner. The feature store service may provide clients with the ability to create and store feature groups that include features and associated metadata providing clients with a quick understanding of features so that they may determine which features are suitable for training ML models and/or use with ML models. The feature store service may provide first a data store configured to store the most recent values associated with a feature group, such that client can access the features and utilize ML models to make real-time predictions with low latency and high throughput, and a second datastore configured to store historical values associated with a feature group, such that a client can utilize the features to train ML models.

Embodiments of the invention can include a system and method for managing a plurality of data feeds to be loaded into a database. The method includes obtaining a script which specifies a query. The method includes executing the query against a source to extract a result set and consulting the script to determine a format for the data feed. The method also includes converting the result set in accordance with the format to create the data feed and transporting the data feed to be loaded into the database.

Embodiments of the invention can include a system and method for managing a plurality of data feeds to be loaded into a database. The method includes obtaining a script which specifies a query. The method includes executing the query against a source to extract a result set and consulting the script to determine a format for the data feed. The method also includes converting the result set in accordance with the format to create the data feed and transporting the data feed to be loaded into the database.