diff --git a/cbrkit/case_sim/__init__.py b/cbrkit/case_sim/__init__.py
index d9cecfb..deeecd5 100644
--- a/cbrkit/case_sim/__init__.py
+++ b/cbrkit/case_sim/__init__.py
@@ -1,5 +1,5 @@
 from cbrkit.case_sim.generic import equality
-from cbrkit.case_sim.helpers import aggregate, apply
+from cbrkit.case_sim.helpers import aggregate, batchify
 from cbrkit.case_sim.tabular import factory as tabular
 
-__all__ = ["apply", "aggregate", "tabular", "equality"]
+__all__ = ["batchify", "aggregate", "tabular", "equality"]
diff --git a/cbrkit/case_sim/generic.py b/cbrkit/case_sim/generic.py
index f54c20b..115ee63 100644
--- a/cbrkit/case_sim/generic.py
+++ b/cbrkit/case_sim/generic.py
@@ -1,11 +1,11 @@
 from typing import Any
 
-from cbrkit.case_sim.helpers import apply
+from cbrkit.case_sim.helpers import batchify
 from cbrkit.typing import CaseSimBatchFunc, SimilarityValue
 
 
 def equality() -> CaseSimBatchFunc[Any, Any]:
-    @apply
+    @batchify
     def wrapped_func(case: Any, query: Any) -> SimilarityValue:
         return case == query
 
diff --git a/cbrkit/case_sim/helpers.py b/cbrkit/case_sim/helpers.py
index 089e99f..70edd8c 100644
--- a/cbrkit/case_sim/helpers.py
+++ b/cbrkit/case_sim/helpers.py
@@ -15,7 +15,7 @@
 )
 
 
-def apply(
+def batchify(
     func: CaseSimFunc[CaseType],
 ) -> CaseSimBatchFunc[Any, CaseType]:
     def wrapped_func(
diff --git a/cbrkit/data_sim/__init__.py b/cbrkit/data_sim/__init__.py
index 7d43c67..f2fa981 100644
--- a/cbrkit/data_sim/__init__.py
+++ b/cbrkit/data_sim/__init__.py
@@ -1,9 +1,9 @@
 from . import collections, generic, numeric, strings
 from .generic import equality, table
-from .helpers import apply, dist2sim
+from .helpers import batchify, dist2sim
 
 __all__ = [
-    "apply",
+    "batchify",
     "dist2sim",
     "table",
     "equality",
diff --git a/cbrkit/data_sim/collections.py b/cbrkit/data_sim/collections.py
index 0b2288c..ca3ec8d 100644
--- a/cbrkit/data_sim/collections.py
+++ b/cbrkit/data_sim/collections.py
@@ -1,14 +1,14 @@
 from collections.abc import Collection, Set
 from typing import Any
 
-from cbrkit.data_sim.helpers import apply, dist2sim
+from cbrkit.data_sim.helpers import batchify, dist2sim
 from cbrkit.typing import DataSimBatchFunc
 
 
 def jaccard() -> DataSimBatchFunc[Collection[Any]]:
     from nltk.metrics import jaccard_distance
 
-    @apply
+    @batchify
     def wrapped_func(x: Collection[Any], y: Collection[Any]) -> float:
         if not isinstance(x, Set):
             x = set(x)
diff --git a/cbrkit/data_sim/generic.py b/cbrkit/data_sim/generic.py
index 56036bf..3b493e4 100644
--- a/cbrkit/data_sim/generic.py
+++ b/cbrkit/data_sim/generic.py
@@ -1,7 +1,7 @@
 from collections import defaultdict
 from typing import Any
 
-from cbrkit.data_sim.helpers import apply
+from cbrkit.data_sim.helpers import batchify
 from cbrkit.typing import (
     DataSimBatchFunc,
     DataType,
@@ -32,7 +32,7 @@ def wrapped_func(*args: tuple[DataType, DataType]) -> SimilaritySequence:
 
 
 def equality() -> DataSimBatchFunc[Any]:
-    @apply
+    @batchify
     def wrapped_func(x: Any, y: Any) -> SimilarityValue:
         return x == y
 
diff --git a/cbrkit/data_sim/helpers.py b/cbrkit/data_sim/helpers.py
index 0604deb..f0a3319 100644
--- a/cbrkit/data_sim/helpers.py
+++ b/cbrkit/data_sim/helpers.py
@@ -6,7 +6,7 @@
 )
 
 
-def apply(func: DataSimFunc[DataType]) -> DataSimBatchFunc[DataType]:
+def batchify(func: DataSimFunc[DataType]) -> DataSimBatchFunc[DataType]:
     def wrapped_func(*args: tuple[DataType, DataType]) -> SimilaritySequence:
         return [func(data1, data2) for (data1, data2) in args]
 
diff --git a/cbrkit/data_sim/numeric.py b/cbrkit/data_sim/numeric.py
index aae79ca..1c284d6 100644
--- a/cbrkit/data_sim/numeric.py
+++ b/cbrkit/data_sim/numeric.py
@@ -1,6 +1,6 @@
 import math
 
-from cbrkit.data_sim.helpers import apply
+from cbrkit.data_sim.helpers import batchify
 from cbrkit.typing import DataSimBatchFunc, SimilarityValue
 
 Number = float | int
@@ -18,7 +18,7 @@ def linear(max: float, min: float = 0.0) -> DataSimBatchFunc[Number]:
     ![linear](../../assets/numeric/linear.png)
     """
 
-    @apply
+    @batchify
     def wrapped_func(x: Number, y: Number) -> SimilarityValue:
         return (max - abs(x - y)) / (max - min)
 
@@ -34,7 +34,7 @@ def threshold(threshold: float) -> DataSimBatchFunc[Number]:
     ![threshold](../../assets/numeric/threshold.png)
     """
 
-    @apply
+    @batchify
     def wrapped_func(x: Number, y: Number) -> SimilarityValue:
         return 1.0 if abs(x - y) <= threshold else 0.0
 
@@ -50,7 +50,7 @@ def exponential(alpha: float = 1.0) -> DataSimBatchFunc[Number]:
     ![exponential](../../assets/numeric/exponential.png)
     """
 
-    @apply
+    @batchify
     def wrapped_func(x: Number, y: Number) -> SimilarityValue:
         return math.exp(-alpha * abs(x - y))
 
@@ -67,7 +67,7 @@ def sigmoid(alpha: float = 1.0, theta: float = 1.0) -> DataSimBatchFunc[Number]:
     ![sigmoid](../../assets/numeric/sigmoid.png)
     """
 
-    @apply
+    @batchify
     def wrapped_func(x: Number, y: Number) -> SimilarityValue:
         return 1.0 / (1.0 + math.exp((abs(x - y) - theta) / alpha))
 
diff --git a/cbrkit/data_sim/strings.py b/cbrkit/data_sim/strings.py
index a01428a..f61dd4f 100644
--- a/cbrkit/data_sim/strings.py
+++ b/cbrkit/data_sim/strings.py
@@ -1,7 +1,7 @@
 import itertools
 
 from cbrkit.data_sim._taxonomy import Taxonomy, TaxonomyMeasure
-from cbrkit.data_sim.helpers import apply
+from cbrkit.data_sim.helpers import batchify
 from cbrkit.typing import (
     DataSimBatchFunc,
     FilePath,
@@ -31,7 +31,7 @@ def taxonomy(
 ) -> DataSimBatchFunc[str]:
     taxonomy = Taxonomy(path)
 
-    @apply
+    @batchify
     def wrapped_func(x: str, y: str) -> SimilarityValue:
         return taxonomy.similarity(x, y, measure)
 
@@ -41,7 +41,7 @@ def wrapped_func(x: str, y: str) -> SimilarityValue:
 def levenshtein(score_cutoff: float | None = None) -> DataSimBatchFunc[str]:
     import Levenshtein
 
-    @apply
+    @batchify
     def wrapped_func(x: str, y: str) -> SimilarityValue:
         return Levenshtein.ratio(x, y, score_cutoff=score_cutoff)
 
@@ -51,7 +51,7 @@ def wrapped_func(x: str, y: str) -> SimilarityValue:
 def jaro(score_cutoff: float | None = None) -> DataSimBatchFunc[str]:
     import Levenshtein
 
-    @apply
+    @batchify
     def wrapped_func(x: str, y: str) -> SimilarityValue:
         return Levenshtein.jaro(x, y, score_cutoff=score_cutoff)
 
@@ -63,7 +63,7 @@ def jaro_winkler(
 ) -> DataSimBatchFunc[str]:
     import Levenshtein
 
-    @apply
+    @batchify
     def wrapped_func(x: str, y: str) -> SimilarityValue:
         return Levenshtein.jaro_winkler(
             x, y, score_cutoff=score_cutoff, prefix_weight=prefix_weight