APPARATUS AND METHOD OF GENERATING DIRECTED GRAPH USING RAW DATA
An apparatus and method of generating directed graph using raw data are disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive raw data from one or more data sources, determine a plurality of execution elements from the raw data, determine a data extrapolation of the plurality of execution elements, wherein determining the data extrapolation further includes determining at least an operation datum for the plurality of execution elements and generate a directed graph as a function of the data extrapolation, wherein the directed graph comprises an ordered series of the plurality of execution elements connected using the at least an operation datum.
1 . An apparatus of generating directed graph using raw data, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive raw data describing an entity;
determine a plurality of execution elements from the raw data;
determine a data extrapolation of the plurality of execution elements; and
generate a directed graph as a function of the data extrapolation.
2 . The apparatus of claim 1 , wherein the memory contains the instructions configuring the at least a processor to analyze the raw data using automatic speech recognition.
3 . The apparatus of claim 1 , wherein the memory contains the instructions configuring the at least a processor to:
determine a weighted value of each of the plurality of execution elements; and
determine the data extrapolation as a function of the weighted value of each of the plurality of execution elements.
4 . The apparatus of claim 1 , wherein the memory contains the instructions configuring the at least a processor to:
generate element training data, wherein the element training data comprises correlations between exemplary raw data and exemplary execution elements;
train an element machine-learning model using the element training data, wherein the element training data is iteratively updated through a feedback loop; and
determine the plurality of execution elements using the trained element machine-learning model.
5 . The apparatus of claim 1 , wherein the memory contains the instructions configuring the at least a processor to:
determine an end user of the plurality of execution elements; and
determine the at least an operation datum as a function of a plurality of characteristics of the end user.
6 . The apparatus of claim 1 , wherein the memory contains the instructions configuring the at least a processor to determine at least an executor of the plurality of execution elements.
7 . The apparatus of claim 1 , wherein the memory contains the instructions configuring the at least a processor to determine an execution token datum of the plurality of execution elements.
8 . The apparatus of claim 1 , wherein the memory contains the instructions configuring the at least a processor to generate a confidence level of the data extrapolation.
9 . The apparatus of claim 1 , wherein the memory contains the instructions configuring the at least a processor to:
generate extrapolation training data, wherein the extrapolation training data comprises correlations between exemplary execution elements and exemplary data extrapolations;
train an extrapolation machine-learning model using the extrapolation training data, wherein the extrapolation training data is iteratively updated through a feedback loop; and
determine the data extrapolation using the trained extrapolation machine-learning model.
10 . The apparatus of claim 1 , wherein the memory contains the instructions configuring the at least a processor to convert the directed graph into a plurality of linguistic terms using a large language model.
11 . A method of generating a directed graph using raw data, the method comprising:
receiving, using at least a processor, raw data describing an entity;
determining, using the at least a processor, a plurality of execution elements from the raw data;
determining, using the at least a processor, a data extrapolation of the plurality of execution elements; and
generating, using the at least a processor, a directed graph as a function of the data extrapolation.
12 . The method of claim 11 , further comprising:
analyzing, using the at least a processor, the raw data using automatic speech recognition.
13 . The method of claim 11 , further comprising:
determining, using the at least a processor, a weighted value of each of the plurality of execution elements; and
determining, using the at least a processor, the data extrapolation as a function of the weighted value of each of the plurality of execution elements.
14 . The method of claim 11 , further comprising:
generating, using the at least a processor, element training data, wherein the element training data comprises correlations between exemplary raw data and exemplary execution elements;
training, using the at least a processor, an element machine-learning model using the element training data, wherein the element training data is iteratively updated through a feedback loop; and
determining, using the at least a processor, the plurality of execution elements using the trained element machine-learning model.
15 . The method of claim 11 , further comprising:
determining, using the at least a processor, an end user of the plurality of execution elements; and
determining, using the at least a processor, the at least an operation datum as a function of a plurality of characteristics of the end user.
16 . The method of claim 11 , further comprising:
determining, using the at least a processor, at least an executor of the plurality of execution elements.
17 . The method of claim 11 , further comprising:
determining, using the at least a processor, an execution token datum of the plurality of execution elements.
18 . The method of claim 11 , further comprising:
generating, using the at least a processor, a confidence level of the data extrapolation.
19 . The method of claim 11 , further comprising:
generating, using the at least a processor, extrapolation training data, wherein the extrapolation training data comprises correlations between exemplary execution elements and exemplary data extrapolations;
training, using the at least a processor, an extrapolation machine-learning model using the extrapolation training data, wherein the extrapolation training data is iteratively updated through a feedback loop; and
determining, using the at least a processor, the data extrapolation using the trained extrapolation machine-learning model.
20 . The method of claim 11 , further comprising:
converting, using the at least a processor, the directed graph into a plurality of linguistic terms using a large language model.