Detection of matching datasets using encode values

1. A method, comprising:

performing, by a data monitoring system, encoding operations on a first dataset to generate a set of encode values including first and second encode values generated using different types of encoding, wherein the first dataset includes first data organized into a first plurality of fields, the first data includes data records having data values for multiple fields within the first plurality of fields, and the first encode value corresponds to a particular field of the first plurality of fields;

determining, by the data monitoring system, whether the first dataset matches a previously analyzed dataset, wherein the previously analyzed dataset includes second data organized into a second plurality of fields, the second data includes data records having data values for multiple fields within the second plurality of fields, and the determining includes:

comparing the set of encode values to a previous set of encode values, wherein the previous set of encode values includes third and fourth encode values generated using different types of encoding and the third encode value corresponds to a second field, of the second plurality of fields, of the previously analyzed dataset;

based on the comparing, generating an output value that is indicative of a similarity between the set of encode values and the previous set of encode values; and

based on the output value, determining whether the first dataset matches the previously analyzed dataset.

2. The method of claim 1 , wherein, for the particular field, the performing the encoding operations includes:

selecting a particular one of a plurality of encoder modules based on a data type associated with the particular field; and

encoding data included in the particular field of the first dataset, using the particular encoder module, to generate the first encode value.

3. The method of claim 2 , wherein the second field of the previously analyzed dataset has a same data type as the data type associated with the particular field of the first dataset; and

wherein the determining whether the first dataset matches the previously analyzed dataset further includes:

retrieving the third encode value corresponding to the second field of the previously analyzed dataset, wherein the third encode value was generated by encoding data included in the second field of the previously analyzed dataset using the particular encoder module.

4. The method of claim 2 , wherein

the particular field of the first dataset includes string-type data;

a type of encoding used to generate the first encode value includes semantic encoding; and

the encoding data included in the particular field of the first dataset includes:

generating one or more vector word-embedding representations of the string-type data included in the particular field of the first dataset.

5. The method of claim 2 , wherein

the particular field of the first dataset includes string-type data;

a type of encoding used to generate the first encode value includes value-format encoding; and

the encoding data included in the particular field of the first dataset includes:

generating a first regular expression based on the string-type data included in the particular field of the first dataset.

6. The method of claim 5 , wherein the third encode value is a second regular expression generated based on string-type data included in the second field of the previously analyzed dataset; and

wherein the comparing the set of encode values to the previous set of encode values includes comparing the first and second regular expressions.

7. The method of claim 2 , wherein

the particular field of the first dataset includes numerical data;

a type of encoding used to generate the first encode value includes numerical distribution encoding; and

the encoding data included in the particular field of the first dataset includes:

calculating a first latent probability distribution corresponding to the numerical data in the particular field of the first dataset.

8. The method of claim 7 , wherein the third encode value is a second latent probability distribution corresponding to numerical data included in the second field of the previously analyzed dataset; and

wherein the comparing the set of encode values to the previous set of encode values includes comparing the first and second latent probability distributions.

9. The method of claim 1 , further comprising:

prior to determining whether the first dataset matches the previously analyzed dataset, comparing, by the data monitoring system, properties of a first schema associated with the first dataset to properties of a second schema associated with the previously analyzed dataset, wherein properties of the first schema include characteristics of the data records or the first plurality of fields; and

in response to a determination that the first schema does not match the second schema, determining that the first dataset does not match the previously analyzed dataset.

10. The method of claim 1 , wherein the output value is specified using Kullback-Leibler divergence.

11. A non-transitory, computer-readable medium having instructions stored thereon that are executable by a data monitoring system to perform operations comprising:

performing encoding operations on a first dataset to generate a set of encode values including first and second encode values generated using different types of encoding, wherein the first dataset includes first data organized into a first plurality of fields, the first data includes data records having data values for multiple fields within the first plurality of fields, and the first encode value corresponds to a particular field of the first plurality of fields;

determining whether the first dataset matches a previously analyzed dataset, wherein the previously analyzed dataset includes second data organized into a second plurality of fields, the second data includes data records having data values for multiple fields within the second plurality of fields, and the determining includes:

comparing the set of encode values to a previous set of encode values, wherein the previous set of encode values includes third and fourth encode values generated using different types of encoding and the third encode value corresponds to a second field, of the second plurality of fields, of the previously analyzed dataset;

based on the comparing, generating an output value that is indicative of a similarity between the set of encode values and the previous set of encode values; and

based on the output value, determining whether the first dataset matches the previously analyzed dataset.

12. The non-transitory, computer-readable medium of claim 11 , wherein, for the particular field, the performing the encoding operations includes:

selecting a particular one of a plurality of encoder modules based on a data type associated with the particular field; and

encoding data included in the particular field of the first dataset, using the particular encoder module, to generate the first encode value.

13. The non-transitory, computer-readable medium of claim 12 , wherein the second field has a same data type as the data type associated with the particular field of the first dataset; and

wherein the determining whether the first dataset matches the previously analyzed dataset further includes:

retrieving the third encode value corresponding to the second field of the previously analyzed dataset, wherein the third encode value was generated by encoding data included in the second field of the previously analyzed dataset using the particular encoder module.

14. The non-transitory, computer-readable medium of claim 12 , wherein

the particular field of the first dataset includes string-type data;

a type of encoding used to generate the first encode value includes semantic encoding or value-format encoding; and

the encoding data included in the particular field of the first dataset includes:

generating one or more vector word-embedding representations of the string-type data included in the particular field of the first dataset; or

generating a first regular expression based on the string-type data included in the particular field of the first dataset.

15. The non-transitory, computer-readable medium of claim 12 , wherein

the particular field of the first dataset includes numerical data;

a type of encoding used to generate the first encode value includes numerical distribution encoding; and

the encoding data included in the particular field of the first dataset includes:

applying a trained autoencoder machine learning model to the numerical data in the particular field to calculate a first latent probability distribution corresponding to the particular field of the first dataset.

16. The non-transitory, computer-readable medium of claim 11 , wherein the operations further comprise:

prior to the determining whether the first dataset matches the previously analyzed dataset, generating encode values for one or more of the second plurality of fields included in the previously analyzed dataset.

17. A method, comprising:

receiving, by a data monitoring system, a first dataset that includes first data organized into a first plurality of fields, wherein the first data includes data records having data values for multiple fields within the first plurality of fields;

performing, by the data monitoring system, encoding operations on the first dataset to generate a set of encode values corresponding to the first dataset, wherein the set of encode values includes first and second encode values generated using different types of encoding and, for a particular field of the first plurality of fields, the performing the encoding operations includes:

selecting a particular one of a plurality of encoder modules based on a data type associated with the particular field; and

encoding data included in the particular field of the first dataset, using the particular encoder module, to generate a first encode value;

determining, by the data monitoring system, whether the first dataset matches a previously analyzed dataset, wherein the previously analyzed dataset includes second data organized into a second plurality of fields, the second data includes data records having data values for multiple fields within the second plurality of fields, and the determining includes:

comparing the set of encode values to a previous set of encode values wherein the previous set of encode values incudes third and fourth encodes values generated using different types of encoding and the third encode value corresponds to a second field, of the second plurality of fields, of the previously analyzed dataset; and

based on the comparing, generating an output value that is indicative of a similarity between the set of encode values and the previous set of encode values.

18. The method of claim 17 , further comprising:

prior to determining whether the first dataset matches the previously analyzed dataset, comparing, by the data monitoring system, properties of a first schema associated with the first dataset to properties of a second schema associated with the previously analyzed dataset, wherein properties of the first schema include characteristics of the data records or the first plurality of fields; and

in response to a determination that the first schema does not match the second schema, determining that the first dataset does not match the previously analyzed dataset.

19. The method of claim 17 , wherein a type of encoding implemented by the encoding operations is numerical distribution encoding; wherein the encoding data included in the particular field of the first dataset includes calculating a first latent probability distribution corresponding to numerical data in the particular field; wherein the previous set of encode values includes a second latent probability distribution corresponding to numerical data included in a second field of the previously analyzed dataset; and wherein the comparing includes comparing the first and second latent probability distributions.

20. The method of claim 17 , further comprising:

based on the output value, determining, by the data monitoring system, whether the first dataset matches the previously analyzed dataset.