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.

In some embodiments, a program generates a query for a set of data from a dataset. The dataset includes a set of measures and a plurality of dimensions for categorizing the set of measures. The set of data includes a set of locations and measure values for a measure in the set of measures categorized according to a dimension in the plurality of dimensions. The program further sends the query to a computing system configured to manage the dataset. The program also receives the set of data from the computing system. The program further renders a visualization comprising a set of visual elements. Each visual element is configured to present a set of measure values for the measure associated with a location in the set of locations. The set of measure values are categorized according to the dimension. The program also presents the visualization on a display of the device.

Improved techniques for processing large-scale data and various large-scale data applications (e.g., large-scale Data Mining (DM), large-scale data analysis (LSDA)) in computing systems (e.g., Data Information Systems, Database Systems) are disclosed. Redundancy-reduced data (RRDS) can be provided as data that can be used more efficiently by various applications, especially, large-scale data applications. In doing so, at least one assumption about the distribution of a multi-dimensional data set (MDDS) and its corresponding set of responses (Y) can be made in order to reduce the multi-dimensional data set (MDDS). For example, a normal distribution (e.g., bell-shape, symmetric) can be assumed and Mutual information of the combination of a multi-dimensional set (X) and its corresponding responses (Y) can be optimized, for example, by using linear transformations, iterative numerical procedures, one or more constraints associated with the at least one assumption, and using one or more Lagrange multipliers to provide a constraint optimization function.

A system and method including determining, for a specified target measure column of a first dataset including a plurality of records, the metadata of the first dataset, including a probability distribution for the specified target column and dimension scores for the dimensions for the first dataset conditioned on the specified target measure column, where the first dataset comprises a plurality of columns including the at least one target measure column and a plurality of non-numeric, dimension columns for the records of the first dataset; determining, for a subset of data of the first dataset based on one or more specified variables, dimension scores for the dimensions of the subset of data approximately derived from the determined metadata of the first dataset; and providing recommendations of top contributors based on the approximated dimension scores of dimensions of the subset of data.

A system and method for implementing a dynamic query mechanism to facilitate on-demand data visualization is disclosed. The method includes receiving user interaction in association with rendering a visualization of a data cube, determining a set of visibility constraints based on the user interaction, periodically checking in with a data container to determine whether there is data to load based on the set of visibility constraints, issuing a query to fetch values of the data from a database responsive to determining that there is data to load, dynamically loading the fetched values of the data into the data container, and rendering the visualization of the data cube using the data container.

A KV database configuration method, a query method, a device, and a storage medium, wherein the method comprises: taking unique index data of a first data table as a key value, taking a plurality of common index data as value value, and storing the first data table into a data storage area (S11); taking first index information generated according to the first data in the first data table as the key value, taking the unique index data of the first data as the value value, and generating a plurality of index data pairs and storing the index data pairs into the index storage area (S12); wherein, the first index information comprises the name of the first index, and the index data of the first index in the first data. The method realizes automatically storing all relational data in the data table into the kv database, greatly simplifies the workload of manually constructing the kv, and meanwhile, a common kv database can also achieve the effect of a relational database.

Systems and methods for natural language processing are described. One or more embodiments of the present disclosure receive a query related to information in a table, compute an operation selector by combining the query with an operation embedding representing a plurality of table operations, compute a column selector by combining the query with a weighted operation embedding, compute a row selector based on the operation selector and the column selector, compute a probability value for a cell in the table based on the row selector and the column selector, where the probability value represents a probability that the cell provides an answer to the query, and transmit contents of the cell based on the probability value.

An associative graph search system includes a KNN graph determiner to determine in advance W neighbors of each item in a dataset and to store each item and its neighbors in a KNN graph, a reduced dimension vector finder implemented on an associative processing unit (APU) to find a first number of first nearest neighbors of a query vector, the APU operating in a constant complexity irrespective of the size of the number, a result expander to find for each first nearest neighbor, W second nearest neighbors using the KNN graph thereby creating a group of neighbors, and a KNN full dimension vector re-ranker to find a final number of full dimension nearest neighbors of the full dimension query vector from the group of neighbors.

Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for receiving first and second data sets, both the first and second data sets including structured data in a plurality of columns, for each of the first data set and the second data set, inputting each column into an encoder specific to a column type of a respective column, the encoder providing encoded data for the first data set, and the second data set, respectively, providing a first multi-dimensional vector based on encoded data of the first data set, providing a second multi-dimensional vector based on encoded data of the second data set, and outputting the first multi-dimensional vector and the second multi-dimensional vector to a loss-function, the loss-function processing the first multi-dimensional vector and the second multi-dimensional vector to provide an output, the output representing matched data points between the first and second data sets.

With respect to a search method, for execution by a computer, for searching for multidimensional data that satisfies a search condition in a table representing a multidimensional data set including a plurality of search target parameters, the search method includes creating multidimensional indexes including a plurality of transposition blocks for identifying record numbers based on values of parameters of the plurality of search target parameters by using the multidimensional data set included in the table, and identifying a target record number of the multidimensional data that satisfies the search condition in the multidimensional data set included in the table by using the multidimensional indexes.