DATA PREPROCESSING AND REFINEMENT TOOL

1 . A method of labeling building data, comprising:

receiving, by a labeling system, a plurality of strings relating to data associated with a building, wherein the plurality of strings are received from one or more building devices;

segregating, by the labeling system, the plurality of strings based on at least one semantic element included in the plurality of strings;

clustering, by the labeling system, the plurality of strings based on at least one related element shared by at least a portion of the plurality of strings;

labeling, by the labeling system, at least some of the plurality of strings based on the segregated and clustered plurality of strings; and

generating, by the labeling system, a digital representation of the building using the labeled strings.

2 . The method of claim 1 , wherein labeling the at least some of the plurality of strings includes generating at least one of a class, a location, or an identifier associated with each of the at least some of the plurality of strings.

3 . The method of claim 1 , further comprising executing one or more predefined operators, wherein the one or more predefined operators include a series of actions.

4 . The method of claim 1 , wherein labeling the at least some of the plurality of strings includes executing a machine learning algorithm to suggest labels associated with the at least some of the plurality of strings.

5 . The method of claim 1 , further comprising interacting with an external system using at least one of an application programming interface (API) or a software development kit (SDK).

6 . The method of claim 5 , wherein the external system is a parsing system configured to parse at least one of the plurality of strings to extract a semantic element.

7 . The method of claim 5 , wherein the external system is a lookup system configured to generate supplemental data associated with at least one of the plurality of strings.

8 . The method of claim 1 , wherein labeling the at least some of the plurality of strings includes identifying information associated with one or more building assets from the plurality of strings.

9 . One or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to:

receive a plurality of strings relating to data associated with a building, wherein the plurality of strings are received from one or more building devices;

segregate the plurality of strings based on at least one semantic element included in the plurality of strings;

cluster the plurality of strings based on at least one related element shared by at least a portion of the plurality of strings;

label at least some of the plurality of strings based on the segregated and clustered plurality of strings; and

generate a digital representation of the building using the labeled strings.

10 . The one or more non-transitory computer-readable storage media of claim 9 , wherein labeling the at least some of the plurality of strings includes generating at least one of a class, a location, or an identifier associated with each of the at least some of the plurality of strings.

11 . The one or more non-transitory computer-readable storage media of claim 9 , wherein the one or more processors are further configured to execute one or more predefined operators, wherein the one or more predefined operators include a series of actions.

12 . The one or more non-transitory computer-readable storage media of claim 9 , wherein labeling the at least some of the plurality of strings includes executing a machine learning algorithm to suggest labels associated with the at least some of the plurality of strings.

13 . The one or more non-transitory computer-readable storage media of claim 9 , wherein the one or more processors are further configured to interact with an external system using at least one of an application programming interface (API) or a software development kit (SDK).

14 . The one or more non-transitory computer-readable storage media of claim 13 , wherein the external system is a parsing system configured to parse at least one of the plurality of strings to extract a semantic element.

15 . The one or more non-transitory computer-readable storage media of claim 14 , wherein the external system is a lookup system configured to generate supplemental data associated with at least one of the plurality of strings.

16 . The one or more non-transitory computer-readable storage media of claim 9 , wherein labeling the at least some of the plurality of strings includes identifying information associated with one or more building assets from the plurality of strings.

17 . A data preprocessing and refinement tool configured to:

receive a plurality of strings relating to data associated with a building, wherein the plurality of strings are received from one or more building devices;

segregate the plurality of strings based on at least one semantic element included in the plurality of strings;

cluster the plurality of strings based on at least one related element shared by at least a portion of the plurality of strings;

label at least some of the plurality of strings based on the segregated and clustered plurality of strings; and

generate a digital representation of the building using the labeled strings.

18 . The data preprocessing and refinement tool of claim 17 , wherein labeling the at least some of the plurality of strings includes generating at least one of a class, a location, or an identifier associated with each of the at least some of the plurality of strings.

19 . The data preprocessing and refinement tool of claim 17 , further configured to execute one or more predefined operators, wherein the one or more predefined operators include a series of actions.

20 . The data preprocessing and refinement tool of claim 17 , wherein labeling the at least some of the plurality of strings includes executing a machine learning algorithm to suggest labels associated with the at least some of the plurality of strings.