Abstract: An example operation may include one or more of ingesting data records from a plurality of external data sources via a plurality of API calls, generating a plurality of digital footprints for a plurality of users, respectively, which have access to a common finite space, wherein each digital footprint includes a respective health value of a respective user generated based on ingested data records of the user, identifying a user from among the plurality of users which is about to enter the common finite space, in response to identifying the user, determining a cumulative safety value for the common finite space based on the digital footprint of the identified user and a digital footprint of one or more other users already present within the common finite space, and transmitting an alert to a computing system based on the determined cumulative safety value for the common finite space.

Abstract: An embodiment for recommending routes in an enhanced navigation system is provided. The embodiment may include receiving a destination from a primary user. The embodiment may also include obtaining a tentative list of routes. The embodiment may further include identifying each user and characteristics associated with each user. The embodiment may also include analyzing current conditions of the tentative list of routes and the characteristics. The embodiment may further include identifying one or more optimal routes. The embodiment may also include recommending the one or more optimal routes to the primary user. The embodiment may further include in response to determining the primary user selects an optimal route of the one or more optimal routes, presenting the selected optimal route on a navigation display to the primary user.

Abstract: An embodiment for recommending routes in an enhanced navigation system is provided. The embodiment may include receiving a destination from a primary user. The embodiment may also include obtaining a tentative list of routes. The embodiment may further include identifying each user and characteristics associated with each user. The embodiment may also include analyzing current conditions of the tentative list of routes and the characteristics. The embodiment may further include identifying one or more optimal routes. The embodiment may also include recommending the one or more optimal routes to the primary user. The embodiment may further include in response to determining the primary user selects an optimal route of the one or more optimal routes, presenting the selected optimal route on a navigation display to the primary user.

Abstract: A processor selects a first database and a second database from a plurality of databases. The processor determines one or more terms found in the first and second database, wherein each term of the one or more terms includes metadata of a database of the plurality of databases. The processor identifies one or more common terms between the first database and the second database and determines the one or more common terms found in each of a plurality of groups of databases of the plurality of databases, wherein each group of databases corresponds to a number of databases which constitute the group of databases. The processor determines a similarity score between the first database and the second database of the plurality of databases based on the one or more common terms found in each group of databases of the plurality of databases.

Abstract: Compression of data for database movement, includes: selecting a first group of categorical columns for compression; selecting a next group of categorical columns from remaining columns for compression; repeating the selecting of the next group until a predetermined compression threshold is met; creating first compression files comprising compressed representations of the columns in the first group; creating next compression files comprising compressed representations of the columns in each of the next groups; storing initial row sort order, group identity, and column positions corresponding to each of the next groups; and storing any columns not selected for compression in an uncompressed file in the original row sort order. Decompression of the data includes: rebuilding categorical columns in each group of compression files using group identity and column positions corresponding to the group; and sorting rows comprising the rebuilt categorical columns to the initial row sort order.

Abstract: A processor selects a first database and a second database from a plurality of databases. The processor determines one or more terms found in the first and second database, wherein each term of the one or more terms includes metadata of a database of the plurality of databases. The processor identifies one or more common terms between the first database and the second database and determines the one or more common terms found in each of a plurality of groups of databases of the plurality of databases, wherein each group of databases corresponds to a number of databases which constitute the group of databases. The processor determines a similarity score between the first database and the second database of the plurality of databases based on the one or more common terms found in each group of databases of the plurality of databases.

Abstract: Compression of data for database movement, includes: selecting a first group of categorical columns for compression; selecting a next group of categorical columns from remaining columns for compression; repeating the selecting of the next group until a predetermined compression threshold is met; creating first compression files comprising compressed representations of the columns in the first group; creating next compression files comprising compressed representations of the columns in each of the next groups; storing initial row sort order, group identity, and column positions corresponding to each of the next groups; and storing any columns not selected for compression in an uncompressed file in the original row sort order. Decompression of the data includes: rebuilding categorical columns in each group of compression files using group identity and column positions corresponding to the group; and sorting rows comprising the rebuilt categorical columns to the initial row sort order.

Abstract: Compression of data for database movement, includes: selecting a first group of categorical columns for compression; selecting a next group of categorical columns from remaining columns for compression; repeating the selecting of the next group until a predetermined compression threshold is met; creating first compression files comprising compressed representations of the columns in the first group; creating next compression files comprising compressed representations of the columns in each of the next groups; storing initial row sort order, group identity, and column positions corresponding to each of the next groups; and storing any columns not selected for compression in an uncompressed file in the original row sort order. Decompression of the data includes: rebuilding categorical columns in each group of compression files using group identity and column positions corresponding to the group; and sorting rows comprising the rebuilt categorical columns to the initial row sort order.

Abstract: A processor selects a first database and a second database from a plurality of databases. The processor determines one or more terms found in the first and second database, wherein each term of the one or more terms includes metadata of a database of the plurality of databases. The processor identifies one or more common terms between the first database and the second database and determines the one or more common terms found in each of a plurality of groups of databases of the plurality of databases, wherein each group of databases corresponds to a number of databases which constitute the group of databases. The processor determines a similarity score between the first database and the second database of the plurality of databases based on the one or more common terms found in each group of databases of the plurality of databases.

Abstract: A processor selects a first database and a second database from a plurality of databases. The processor determines one or more terms found in the first and second database, wherein each term of the one or more terms includes metadata of a database of the plurality of databases. The processor identifies one or more common terms between the first database and the second database and determines the one or more common terms found in each of a plurality of groups of databases of the plurality of databases, wherein each group of databases corresponds to a number of databases which constitute the group of databases. The processor determines a similarity score between the first database and the second database of the plurality of databases based on the one or more common terms found in each group of databases of the plurality of databases.

Abstract: Compression of data for database movement, includes: selecting a first group of categorical columns for compression; selecting a next group of categorical columns from remaining columns for compression; repeating the selecting of the next group until a predetermined compression threshold is met; creating first compression files comprising compressed representations of the columns in the first group; creating next compression files comprising compressed representations of the columns in each of the next groups; storing initial row sort order, group identity, and column positions corresponding to each of the next groups; and storing any columns not selected for compression in an uncompressed file in the original row sort order. Decompression of the data includes: rebuilding categorical columns in each group of compression files using group identity and column positions corresponding to the group; and sorting rows comprising the rebuilt categorical columns to the initial row sort order.