| Commit message (Collapse) | Author | Age |
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Providing an efficient refinement calculus would be too difficult at this point,
and contains is much more useful as a constraint in predicates that as an
assertions anyway.
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Make sure to merge all clusters reachable from source and target.
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For permissive licenses like MIT and BSD, OR EPL doesn't make sense, because the
copyright original permission notice must be retained at all times.
For code simply copied with minimal modifications that are unrelated to
Refinery, we can just use the plain MIT license or BSD-3-clause license.
For code with complex modifications related to Refinery, we can instead use the
MIT AND EPL-2.0 SPDX expression, which forces downstream users to retain the
original MIT notices as required by the MIT license, but also extends Refinery's
copyleft to our modifications.
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Instead of referencing an external pull request, move the patch into
yarn/.patches and update it to handle pinch-to-zoom.
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Uses patch from https://github.com/d3/d3-zoom/pull/212
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Also makes model seeds cancellable to reduce server load during semantic
analysis.
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Decision trees can only handle relations with 1 level and up, so we use a
special case for nullary relations.
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Reduce server load by introducing a timeout for semantics analysis.
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Since only RETE uses ITC, we may move ITC into the RETE project.
Also removes unused ITC algorithms.
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Make sure the copyright headers are up to date.
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Since we remove EMF dependencies, there is no point to maintaining a base index
separate from the rest of the runtime.
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We don't need Eclipse platform support, table-based scopes, and EMF support.
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Make our modifications more maintainable by editing the source code directly
instead of using reflection.
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https://docs.gradle.org/8.2.1/userguide/upgrading_version_8.html#test_framework_implementation_dependencies
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Avoid creating interpretations not needed for the graph analysis task.
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A new object should not be an instance of any subclasses.
Also, it should be possible cleare a node without any other type.
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Improper equality implementation of representative election literals merged weak
and strong connected components during query canonicalization.
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Also simplifies attributes and flags for now, as we don't translate them to
abstractions.
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Reduce memory use of initial partial models by consolidating value
objects.
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Adds translator for EQUALS and EXISTS symbols based on the multi-object
formalism. Only diagonal equality links are supported (e.g., distinct nodes may
not be EQUALS with each other).
Also introduces initial model seeds to separate partial interpreter construction
and graph initialization better.
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* DuplicateDnfRewriter replaces DNF with their canonical
representatives
* ClauseInputParameterResolver removes input parameters by demand set
transformation
* CompositeRewriter for rewriter stacks
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This is needed for demand set transformation of DNFs with input data parameters,
where the result of the transformation has an out data parameter that has to be
unified with the variable in the parent clause.
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* Use method object pattern to simplify code.
* Optimize existential quantification in transitive queries.
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Support for count and aggregation is still missing.
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Allow computing hash codes up to the renaming of variables.
Also introduces CheckLiteral instead of AssumeLiteral for more straightforward
naming.
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Kristóf Marussy