Systems and methods for dynamic computer aided innovation via multidimensional complementary difference recommendation and exploration are disclosed including categorizing a first and second data element in a database with a first attribute and second attribute, respectively, of a first dimension, a dimension being an aspect of a situation, problem, or thing. The first and second data elements are categorized with a first attribute and a second attribute of a second dimension, the second dimension being different from the first dimension. Analyzing the first and second attribute of the first dimension and the first and second attribute of the second dimension to determine a ratio of similarity and dissimilarity; calculating a composite score of the ratio of the first dimension and the ratio of the second dimension; and generating and storing a link between the first and second data element when the composite score is within numerical limits.

Techniques disclosed herein relate generally to evaluating and selecting candidate datasets for use by software applications, such as selecting candidate datasets for training machine-learning models used in software applications. Various machine-learning and other data science techniques are used to identify unique entities in a candidate dataset that are likely to be part of target entities for a software application. A merit attribute is then determined for the candidate dataset based on the number of unique entities that are likely to be part of the target entities, and weights associated with these unique entities. The merit attribute is used to identify the most efficient or most cost-effective candidate dataset for the software application.

The present embodiments relate to automated memory creation and retrieval from moment content items. In some implementations, the automated memory creation and retrieval system can obtain moment content items from user-designated sources with a single user perspective or multiple user perspectives. The moment content items can be assigned tags and arranged in chronological order. The arranged moment content items can be clustered into memory content items based on clustering conditions. Once memory content items are created, they can be arranged into a memory hierarchy made up of connected nodes. In some implementations, the memory content items are also linked together based on similarity in various dimensions in a memory graph. The automated memory creation and retrieval system can receive search criteria for memories from a user interface and provide the user with memories from matched nodes in the memory hierarchy or linked memories in the memory graph.

Methods are provided of optimizing a tree-structured distributed-index with tree-nodes including data-elements and parent-child relations between tree-nodes. The distributed-index is stored in distributed system including computer-nodes each storing tree-nodes and a tree-map structurally describing the distributed-index. The methods include: inspecting the tree-map in first computer-node to determine whether the distributed-index is imbalanced due to a first tree-node in first computer-node and, in said case: notifying to other computer-nodes that first tree-node is replicable, to provoke that any request from other computer-nodes of inserting a data-element in first-tree-node includes inserting the data-element in corresponding child-node of first-tree-node; and verifying whether the other computer-nodes have been notified and, in said case, replicating data-elements stored in first tree-node into children-nodes thereof. Methods of inserting into and/or querying such distributed indexes or similar are also provided, along with computer programs and (computing) systems that are suitable for performing said optimizing, inserting and querying methods.

System and methods are described for automated recovery from errors occurring during replication of a database. The method includes getting text from one or more log files generated during database replication processing in a cloud computing environment, transforming the text into a structured language form represented by vectors, and identifying patterns in the vectors. The method further includes classifying one or more errors based on the identified patterns using supervised learning as either a recoverable error or an unrecoverable error, analyzing the one or more errors to determine one or more recovery jobs associated with database replication processing in the cloud computing environment for each of the recoverable errors, and invoking the one or more recovery jobs.

Systems and methods are described for providing a user interface through which a user can program operation of a data processing pipeline by specifying a graph of nodes that transform data and interconnections that designate routing of data between individual nodes within the graph. In response to a user request, a preview mode can be activated that causes the data processing pipeline to retrieve data from at least one source specified by the graph, transform the data according to the nodes of the graph, sample the transformed data, and display the sampling of the transformed data to at least one node without writing the transformed data to at least one destination specified by the graph.

Systems and methods are described for processing ingested data, detecting anomalies in the ingested data, and providing explanations of a possible cause of the detected anomalies as the data is being ingested. For example, a token or field in the ingested data may have an anomalous value. Tokens or fields from another portion of the ingested data can be extracted and analyzed to determine whether there is any correlation between the values of the extracted tokens or fields and the anomalous token or field having an anomalous value. If a correlation is detected, this information can be surfaced to a user.

A computer implemented method for optimizing a delivery or settlement process for a plurality of portfolios of a plurality of participants. Data records indicative of obligations between the plurality of participants are identified. A weighted directed graph data structure is generated that comprises vertex data records representing the plurality of participants and edge data records representing the obligations between the participants. All paths of edge data records where a vertex data record is reachable from itself in the weighted directed graph data structure are identified. The data records indicative of obligations between the plurality of participants are altered based on the identified paths.

Techniques are disclosed relating to the execution of queries in an online manner. For example, in some embodiments, a server system may include a distributed computing system that, in turn, includes a distributed storage system operable to store transaction data associated with a plurality of users, and a distributed computing engine operable to perform distributed processing jobs based on the transaction data. In various embodiments, the server system preemptively creates a compute session on the distributed computing engine, where the compute session provides access to various functionalities of the distributed computing engine. The distributed computing engine may then use these preemptively created compute sessions to execute queries (e.g., for end users of the server system) against the transaction data and return the results dataset to the requesting users in an online manner.

Aspects of this disclosure relate to managing hypercubes. A plurality of variables may be received from a user. Features of these variables are identified. A new hypercube data structure is generated. The hypercube is generated by assigning, using the features, a first set of variables of the plurality of variables as one or more row variables of the hypercube, assigning a second set of variables of the plurality of variables as one or more column variables of the hypercube, and assigning a variable of the plurality of variables as a nested variable of the hypercube.