TECHNICAL FIELD

The technical field relates in general to the problem of constraint optimization problem solving.

BACKGROUND

A non-trivial multi-objective optimization problem may involve inputting data into a network, for example information presented as 50 columns of data with (probably) inconsistent field names. Classifying the information and bundling the information under appropriate fields/categories involves machine learning. A user does not want to be bothered with the tedium of verbal classification of all of the individual pieces of data into the network.

Thereafter, conventionally, when trying to pick an optimal subgraph of the network, a user manually selects subgraphs on an ad hoc basis to try to achieve a preferred optimization objective.

Traditionally, single objective function optimization, e.g., linear optimization (based on a single column of data) is the standard approach for subgraph choice and analysis. In particular, linear optimization is the conventional solution to the problem of finding the “best” selection of elements to include in a subgraph. However, single objective function optimization, especially linear optimization, suffers severe defects in solving a problem of choosing an optimal subgraph.

For a non-trivial multi-objective optimization problem, no conventional solution exists that simultaneously optimizes each objective. The objective functions are conflicting, and in any event the number of possible solutions as a practical matter approaches infinity.

Combinatorial optimization may be conventionally employed to finding the optimal solution from a finite set of feasible solutions; these problems are familiar to mathematicians as the well-known minimum spanning tree problem and the travelling salesman problem. In computer science today, as a practical matter, these are some of the principal unsolved problems as a consequence of computational complexity and other issues.

SUMMARY

Accordingly, one or more embodiments provide an apparatus, method, and/or computer-readable medium. In an embodiment, there is provided a subgraph storage memory that stores a plurality of subgraphs each populated with data classified into features based on data title, wherein each of the subgraphs has a plurality of elements, wherein each of the elements has a plurality of features with different values; and a processor in communication with the subgraph storage memory. The processor is configured to provide, from the subgraph storage memory, the plurality of subgraphs. For each subfeature of a plurality of subfeatures, the different values assigned to a combination of an element of the elements and one or more features are used by the processor to create a method to evaluate the plurality of subgraphs on the one or more features, as the subfeature which provides what is a knowledge system which provides scoring information about bits of data (scores) in a subgraph. For each metafeature, plural subfeatures among the plurality of subfeatures are selected to be composited into the metafeature; and for each subgraph, the plural subfeatures of each metafeature are composited to provide metafeature scores for each subgraph. The processor is configured to present selected subgraphs from amongst a universe of subgraphs with metafeature scores as context for comparing the selected subgraphs, wherein each of the selected subgraphs has different selections of elements.

In another embodiment, a subfeature takes in a plurality of subgraphs and provides, as a result, subfeature scores for each of the subgraphs, wherein each of the subfeature scores is responsive to a plurality of values of at least one feature in the elements in one of the subgraphs.

In yet another embodiment, a metafeature takes in the plurality of subgraphs which have the subfeatures scores, and provides as a result, the scored metafeatures for each of the subgraphs, wherein each of the scored metafeatures is responsive to a composite of a plurality of the subfeature scores and/or an other scored metafeature of one of the subgraphs.

In still another embodiment, prior to providing the plurality of subgraphs, the universe of subgraphs is prepared, including the plurality of subgraphs which have values which are transformed, wherein each of the subgraphs in the universe of subgraphs is prepared from the elements which have the plurality of features with the different values; the selected subgraphs which are presented as context for comparison are provided from the universe of subgraphs.

An embodiment performs an analysis of an impact of one of the elements on a given subgraph, wherein the impact which is analyzed includes both the impact of elements in the given subgraph and the impact of elements not in the given subgraph.

In a further embodiment, the scored metafeatures are scored against the universe of subgraphs.

Another embodiment, responsive to the metafeatures which result from the compositing of subfeatures, uses the metafeatures which result from the compositing of subfeatures and create pre-determined metafeatures for each subgraph.

In another embodiment, the processor determines the subgraphs in the universe of subgraphs which are most optimized among the universe of subgraphs for a best scored metafeature of at least one of the metafeatures, understanding that “best” according to one utility measurement may not be achievable, for example, might decrease confidence.

Another embodiment can be a method of performing any or all of the above.

Still another embodiment can be a computer readable storage medium comprising instructions for the described method and/or apparatus.

