Abstract: A computer-implemented method may include: performing word tokenization on identifiers in a first identifier set to obtain a first set of tokens; performing word tokenization on identifiers in a second identifier set to obtain a second set of tokens; obtaining a weighting value for each of a plurality of tokens in the first set of tokens and the second set of tokens based on a frequency of occurrence; for each identifier of a plurality of identifiers in the first identifier set and of a plurality of identifiers in the second identifier set, obtaining embeddings for the identifier based on the embeddings of tokens obtained by performing word tokenization on that identifier and based on the weighting value for such tokens; obtaining similarity metrics. The similarity metrics may be used for rapid query handling to generate user interface screens in real time. Obtaining weighting values may use machine learning.

Abstract: In an aspect, a computer-implemented method may include: performing word tokenization on identifiers in a first identifier set to obtain a first set of tokens; performing word tokenization on identifiers in a second identifier set to obtain a second set of tokens; obtaining a weighting value for each of a plurality of tokens in the first set of tokens and the second set of tokens based on a frequency of occurrence; for each identifier of a plurality of identifiers in the first identifier set and of a plurality of identifiers in the second identifier set, obtaining embeddings for the identifier based on the embeddings of tokens obtained by performing word tokenization on that identifier and based on the weighting value for such tokens; obtaining similarity metrics. The similarity metrics may be used for rapid query handling to generate user interface screens in real time.

Abstract: In an aspect, a computer-implemented method may include: performing word tokenization on identifiers in a first identifier set to obtain a first set of tokens; performing word tokenization on identifiers in a second identifier set to obtain a second set of tokens; obtaining a weighting value for each of a plurality of tokens in the first set of tokens and the second set of tokens based on a frequency of occurrence; for each identifier of a plurality of identifiers in the first identifier set and of a plurality of identifiers in the second identifier set, obtaining embeddings for the identifier based on the embeddings of tokens obtained by performing word tokenization on that identifier and based on the weighting value for such tokens; obtaining similarity metrics. The similarity metrics may be used for rapid query handling to generate user interface screens in real time.

Abstract: SCAN operations for databases where scan time is dependent on a payload size consume too much memory space and computing time as payload sizes increase. A database table is configured to include an additional index mapping column that stores bitmaps related to the corresponding row of the table. Each bit in the bitmap corresponds to a column and indicates whether that column stores a value. Inclusion of an index column in a table decouples the time it takes to perform the SCAN operation on a column from the payload size of data stored in the column. The bitmaps stored in the index column are relatively small and uniform in size, so the SCAN operation on such a database requires only for the bitmap values of the applicable rows to be obtained from the index column and inspected.

Abstract: In an aspect, a computer-implemented method may include: performing word tokenization on identifiers in a first identifier set to obtain a first set of tokens; performing word tokenization on identifiers in a second identifier set to obtain a second set of tokens; obtaining a weighting value for each of a plurality of tokens in the first set of tokens and the second set of tokens based on a frequency of occurrence; for each identifier of a plurality of identifiers in the first identifier set and of a plurality of identifiers in the second identifier set, obtaining embeddings for the identifier based on the embeddings of tokens obtained by performing word tokenization on that identifier and based on the weighting value for such tokens; obtaining similarity metrics. The similarity metrics may be used for rapid query handling to generate user interface screens in real time.

Abstract: SCAN operations for databases where scan time is dependent on a payload size consume too much memory space and computing time as payload sizes increase. A database table is configured to include an additional index mapping column that stores bitmaps related to the corresponding row of the table. Each bit in the bitmap corresponds to a column and indicates whether that column stores a value. Inclusion of an index column in a table decouples the time it takes to perform the SCAN operation on a column from the payload size of data stored in the column. The bitmaps stored in the index column are relatively small and uniform in size, so the SCAN operation on such a database requires only for the bitmap values of the applicable rows to be obtained from the index column and inspected.

Abstract: SCAN operations for databases where scan time is dependent on a payload size consume too much memory space and computing time as payload sizes increase. A database table is configured to include an additional index mapping column that stores bitmaps related to the corresponding row of the table. Each bit in the bitmap corresponds to a column and indicates whether that column stores a value. Inclusion of an index column in a table decouples the time it takes to perform the SCAN operation on a column from the payload size of data stored in the column. The bitmaps stored in the index column are relatively small and uniform in size, so the SCAN operation on such a database requires only for the bitmap values of the applicable rows to be obtained from the index column and inspected.

Abstract: SCAN operations for databases where scan time is dependent on a payload size consume too much memory space and computing time as payload sizes increase. A database table is configured to include an additional index mapping column that stores bitmaps related to the corresponding row of the table. Each bit in the bitmap corresponds to a column and indicates whether that column stores a value. Inclusion of an index column in a table decouples the time it takes to perform the SCAN operation on a column from the payload size of data stored in the column. The bitmaps stored in the index column are relatively small and uniform in size, so the SCAN operation on such a database requires only for the bitmap values of the applicable rows to be obtained from the index column and inspected.