A computer-implemented position-centric personnel modeling apparatus, system, and method may be provided. A nodal hierarchy may be created to correlate with the personnel structure of a business enterprise, wherein each node may be a cyberspace representation of an individual within the personnel structure of the business enterprise. Attributes correlating to individual personnel in the business enterprise may be mapped to a respective node. Objectives may subsequently be tasked within the model and the nodal hierarchy may be rearranged based on the results. Results from the model may optionally be reflected by a business enterprise.

Methods, systems, and devices supporting querying disparate data sources are described. Querying disparate data sources may include receiving an input for data stored at a first data source from a plurality of data sources, selecting a first data connector from a plurality of data connectors, wherein the first data connector corresponds to the first data source, and identifying a first query language corresponding to the first data source from a plurality of query languages. Querying the disparate data sources may further include generating a converted query based at least in part on the first query language and retrieving the data from the first data source using the first data connector based at least in part on the converted query.

A search request for content can be initiated by a user, and the content that is relevant to the search request can be identified and presented in a manner that indicates the content's trustworthiness or relevancy. The identified content can be ranked based on the number of times the content has been referenced as well as by source that referenced the content. The relevant identified content can then be displayed in an ordered list that is ordered based on the number of times the content has been referenced. In some cases, the order may be modified by the authority of the source.

A columnar storage method and a query method and system for time series data. The storage method includes: dividing a column of time series data into a plurality of pages, wherein each page stores a part of data points of the column of time series data and the sum of the data points stored in all the pages is all the data points in the column of time series data (S1); and setting two parts, i.e., a page header and a page body, for each page, storing summary index information of all the data points in the page in the page header of the page and storing data value information of all the data points in the page in the page body of the page (S2).

A method and system identify characteristics of transaction description strings. The method and system extracts time data and location data from transaction description strings. The method and system generate estimated time data and location data for transaction strings that lack time data and location data by analyzing the time data and location data extracted from other transaction description strings. The method and system generate a user profile based on the estimated time data and estimated location data.

In an illustrative embodiment, an automated system engineers customized feature vectors from geospatial information system (GIS) metadata. The system may include computing systems and devices for extracting metadata for GIS features located within a predetermined distance of a property from a GIS map file and storing the extracted GIS features within a feature vector. The system can augment each of the extracted GIS features with amplifying data features extracted from external data sources. The system can calculate a distance between the property and each extracted GIS feature, which establishes a relationship between the property and each GIS feature and associated amplifying data features. Amounts of correlation between each of the extracted GIS features and associated amplifying data features within the feature vector and a market assessment of the property location can be identified using a data model trained with a data set customized to characteristics of the property.

In accordance with an embodiment, described herein are systems and methods for supporting slowly changing dimensions and metadata versioning in a multidimensional database, comprising. A system can comprise a computer that includes one or more microprocessors, and a multidimensional database server executing on the computer, wherein the multidimensional database server supports at least one hierarchical structure of data dimensions. A data dimension can slowly change over time. When such changes occur, metadata associated with the data dimension can be updated. Advantageously, a current snapshot of the data structure can allow searching of previous changes to the slowly changing dimension based upon the metadata.

Embodiments of the present disclosure describe systems, methods, and computer program products for executing and tuning a machine learning operation within a database. An example method can include receiving a data query referencing an input data set of a database, executing a plurality of machine learning operations to generate, in view of the input data set, a plurality of output data sets each having a respective accuracy value, wherein each of the plurality of machine learning operations is executed by a processing device according to one of a plurality of unique sets of hyperparameters, selecting a first output data set of the plurality of output data sets in view of the accuracy values, and returning the first output data set in response to the data query.

According to an embodiment, an electronic device comprises at least one processor, and a memory that stores instructions configured to cause the at least one processor to obtain first data associated with original data based on random number using a first program, obtain first similarity information between the original data and the first data, obtain second data associated with the original data based on the random number using a second program, obtain second similarity information between the original data and the second data, in response to receiving a request, and provide the first program or the second program based on information included in a request that corresponds to a range that includes at least one of the first similarity information or the second similarity information.

Computer-implemented systems and methods for efficiently searching large data volumes for one or more items with a definable degree of similarity. The systems and methods may include functionality directed to selecting at least one token from the one or more tokens in a target item, the token including an identifiable character string defining, fully or partially, at least one of a name, an address, an entity or other identifier associated with the target item; extracting a character from the identifiable character string after the character string is standardized to a known common version of the character string; responsive to a character distribution lookup, determining that the extracted character corresponds to a first shard from among a plurality of discrete shards; and grouping the item into the first shard, the character distribution lookup being adjustable overtime to provide for a balanced distribution of items across the plurality of discrete shards.