Graphically managing data classification workflows in a social networking system with directed graphs
Various embodiments include a classification platform system. A user can define a classification experiment on the classification platform system. For example, the user can define an input data space by selecting at least one of data sources interfaced with the classification platform system and defining a workflow configuration including a directed graph (DG) connecting a plurality of transformation blocks to represent an experiment workflow. The DG can specify how one or more outputs of each of the transformation blocks are fed into one or more other transformation blocks. The DG can be defined graphically. The classification platform system can schedule the experiment workflow to be executed on a distributed computation platform according to the input data space and the workflow configuration.
1. A computer-implemented method, comprising:
interfacing one or more data sources in a communication system to a classification platform system for defining one or more classification experiments;
defining a classification experiment by at least:
defining an input data space by selecting at least one of data sources interfaced with the classification platform system; and
defining, via a definition user interface of the classification platform system, a workflow configuration of the classification experiment by graphically arranging a directed graph (DG) connecting a plurality of transformation blocks to represent an experiment workflow, wherein the DG specifies one or more flows of one or more outputs of each of the transformation blocks being fed into one or more other transformation blocks;
generating, by a compiler system, a data structure in a particular format representing one or more dependencies between the plurality of transformation blocks, wherein the particular format is determined based on a configuration of hardware system components of a distributed computation platform;
formatting the workflow configuration and the input data space into the data structure such that the data structure is interpretable by a plurality of different computation platforms to execute the classification experiment; and
scheduling the distributed computation platform to execute the classification experiment according to the data structure, the input data space, and the workflow configuration.
2. The computer-implemented method of claim 1 , further comprising:
selecting, from the plurality of different computation platforms, the distributed computation platform to execute at least part of the experiment workflow in accordance with the DG.
3. The computer-implemented method of claim 2 , wherein said scheduling includes scheduling a first part of the workflow configuration to execute on a first computation platform and a second part of the workflow configuration to execute on a second computation platform.
4. The computer-implemented method of claim 2 , wherein the distributed computation platform is selected based on a geographical or network location of the distributed computation platform relative to one or more geographical or network locations of input data specified in the input data space.
5. The computer-implemented method of claim 1 , further comprising maintaining a memorization database; wherein said scheduling includes preventing a transformation block from being executed by the distributed computation platform when the transformation block as defined by the workflow configuration matches an entry in the memorization database; and wherein the entry includes pre-computed output result of the transformation block given the same input and configuration.
6. The computer-implemented method of claim 1 , wherein the data sources include a live data source from the communication system, and wherein the live data source produces an open-ended stream of new data entries formatted according to one or more data formats of the defined input data space.
7. The computer-implemented method of claim 1 , wherein the data sources include a static data source from the communication system, and wherein the static data source includes a static data set with a constant data size formatted according to one or more data formats of the defined input data space.
8. The computer-implemented method of claim 1 , wherein the DG is acyclical and thereby prevents execution of the classification experiment to enter an infinite loop.
9. The computer-implemented method of claim 1 , wherein a transformation block in the DG includes logic to dynamically modify the DG during execution of the experiment workflow.
10. The computer-implemented method of claim 9 , wherein the transformation block includes logic to dynamically modify input data of an existing transformation block in the DG.
11. The computer-implemented method of claim 9 , wherein the transformation block includes logic to change or remove an existing transformation block in the DG or to add a new transformation block to the DG.
12. The computer-implemented method of claim 1 , further comprising piping an output result of executing the classification experiment to the communication system to reconfigure at least an application service of the communication system.
13. The computer-implemented method of claim 1 , wherein the input data space is a labeled data space that includes at least a parameter to locate labeled data for training a supervised classifier machine learning model or for evaluating classification precision or recall of a classifier model, wherein said training or said evaluating is represented in a transformation block in the DG.
14. The computer-implemented method of claim 1 , wherein the input data space is a prediction space that includes at least a parameter to locate input data to be classified in the classification experiment.
15. The computer-implemented method of claim 1 , wherein defining the classification experiment further includes defining a domain configuration that includes at least a parameter binding the input data space to the workflow configuration.
16. The computer-implemented method of claim 1 , wherein defining the classification experiment includes inheriting a directed graph for the workflow configuration from a workflow repository.
17. The computer-implemented method of claim 1 , wherein the input data space selects at least a live data source from the data sources to feed into at least one of the transformation blocks; and wherein the distributed computation platform is configured to execute the classification experiment in substantially real-time in response to new data from the live data source.
18. The computer-implemented method of claim 1 , wherein the transformation blocks include a data feature extraction process, data feature filtering process, a data feature transformation process, a classifier deliberation process, a classifier training process, a classifier evaluation process, or any combination thereof.
19. The method of claim 1 , wherein the hardware system components comprise one or more of a processor, a memory, a network adapter, or a storage adapter.
20. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
interface one or more data sources in a communication system to a classification platform system for defining a classification experiment;
define a classification experiment by at least:
defining an input data space by selecting at least one of data sources interfaced with a classification platform system; and
defining, via a definition user interface of the classification platform system, a workflow configuration of the classification experiment by graphically arranging a directed graph (DG) to connect a plurality of transformation blocks to represent an experiment workflow, wherein the DG specifies one or more flows of one or more outputs of each of the transformation blocks being fed into one or more other transformation blocks;
generate, by a compiler system, a data structure in a particular format representing one or more dependencies between the plurality of transformation blocks, wherein the particular format is determined based on a configuration of hardware system components of a distributed computation platform;
format the workflow configuration and the input data space into the data structure such that the data structure is interpretable by a plurality of different computation platforms to execute the classification experiment; and
schedule the distributed computation platform to execute the classification experiment according to the data structure, the input data space and the workflow configuration.
21. A system comprising: one or more processors; and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:
interface one or more data sources in a communication system to a classification platform system for defining a classification experiment;
define a classification experiment by at least:
defining an input data space by selecting at least one of data sources interfaced with the classification platform system; and
defining, via a definition user interface, by graphically arranging a directed graph (DG) to connect a plurality of transformation blocks to represent an experiment workflow, wherein the DG specifies one or more outputs of each of the transformation blocks being fed into one or more other transformation blocks;
generate, by a compiler system, a data structure in a particular format representing one or more dependencies between the plurality of transformation blocks, wherein the particular format is determined based on a configuration of hardware system components of a distributed computation platform;
format the workflow configuration and the input data space into the data structure such that the data structure is interpretable by a plurality of different computation platforms to execute the classification experiment; and
schedule the distributed computation platform to execute the classification experiment according to the data structure, the input data space and the DG.