diff --git a/CHANGELOG.md b/CHANGELOG.md
index ab7b07daa..01cd994ce 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,13 +1,17 @@
 # Changelog of pm4py
 
 
-## pm4py 2.7.6 (2023.07.XX)
+## pm4py 2.7.6 (2023.08.XX)
 
 ### Added
 * 6760518dea19334a21442200bef647e4c07f3636
   * LLM abstraction of the temporal profile model
 * 13d001c76e3de40786dce75e76e56a13a821173a
   * set of event logs for fairness assessment (hospital, hiring, lending, renting)
+* e3044278b3e7d984c7fdf9e39554cc4551332739
+  * added OCEL filters:
+    * length of a connected components
+    * presence of at least an object of a given object type
 
 ### Changed
 * 84629e2ea342348e30aa04a7d41ad7b39159b400
@@ -16,7 +20,11 @@
   * refactored log_to_interval_tree methods in two methods
       (log to intervals, and intervals to tree)
   * added queue-related examples
-  
+* da3a12f615dba3c46793a2d9977dfca11dad85b0
+  * avoid annotation start/end edges in DFG with performance metrics
+*  37fba9285cfde95309142e4404f9cfbcb2b9296c
+  * visualizations support nanoseconds granularity when needed
+
 ### Deprecated
 
 ### Fixed
@@ -34,14 +42,20 @@
   * fixed path to Graphviz.JS
 * ca79aa9b9e51ba3a95665d5d53c8e5ab5028bf12
   * minor fix TBR generalization parameters
+* 57a30fb452a759bc71f707e67bf0f63118194b7f
+  * method to sample OCEL connected components is fixed
 
 ### Removed
+* bf5574a34a31b93024dd9feb54acc5cc475640bd
+  * change-of-mind on format_dataframe deprecation warning
 
 ### Other
 * 916ea3163119afe7aa0fc9f6c43624147d6c0f9f
   * reference to published paper in OCEL feature extraction
 * 549aa7c6766f1a51425a7a65673173c55d9731e9
   * updated reference to PM4Py website
+* 20ce84db4e195937c77280c950ff12083fc5833b
+  * example for log granularity change
 
 ---
 
diff --git a/docs/source/api.rst b/docs/source/api.rst
index 1ba8b6d71..b1b2edf53 100644
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
@@ -222,7 +222,8 @@ Also, some filtering techniques are offered on top of object-centric event logs:
   * :meth:`pm4py.filtering.filter_ocel_events`; filters a specified collection of event identifiers from the object-centric event log.
   * :meth:`pm4py.filtering.filter_ocel_objects`; filters a specified collection of object identifiers from the object-centric event log.
   * :meth:`pm4py.filtering.filter_ocel_cc_object`; filters a connected component from the object-centric event log to which the object with the provided identifier belongs.
-
+  * :meth:`pm4py.filtering.filter_ocel_cc_length`; filter the connected components from an object-centric event log having a number of objects falling in a provided range.
+  * :meth:`pm4py.filtering.filter_ocel_cc_otype`; filter the connected components from an object-centric event log having at least an object of the specified object type.
 
 Machine Learning (:mod:`pm4py.ml`)
 ------------------------------------------
@@ -547,6 +548,8 @@ Overall List of Methods
    pm4py.filtering.filter_ocel_events
    pm4py.filtering.filter_ocel_objects
    pm4py.filtering.filter_ocel_cc_object
+   pm4py.filtering.filter_ocel_cc_length
+   pm4py.filtering.filter_ocel_cc_otype
    pm4py.ml
    pm4py.ml.split_train_test
    pm4py.ml.get_prefixes_from_log
diff --git a/examples/activities_to_alphabet.py b/examples/activities_to_alphabet.py
new file mode 100644
index 000000000..561ca614d
--- /dev/null
+++ b/examples/activities_to_alphabet.py
@@ -0,0 +1,13 @@
+import pm4py
+from pm4py.objects.log.util import activities_to_alphabet
+from pm4py.util import constants
+
+
+def execute_script():
+    dataframe = pm4py.read_xes("../tests/input_data/running-example.xes")
+    renamed_dataframe = activities_to_alphabet.apply(dataframe, parameters={constants.PARAMETER_CONSTANT_ACTIVITY_KEY: "concept:name"})
+    print(renamed_dataframe)
+
+
+if __name__ == "__main__":
+    execute_script()
diff --git a/examples/ocel_filter_cc.py b/examples/ocel_filter_cc.py
new file mode 100644
index 000000000..3603e4b6a
--- /dev/null
+++ b/examples/ocel_filter_cc.py
@@ -0,0 +1,19 @@
+import pm4py
+import sys
+
+
+def execute_script():
+    ocel = pm4py.read_ocel("../tests/input_data/ocel/example_log.jsonocel")
+    print(ocel)
+    # filters the connected components of the OCEL in which there is at least a delivery,
+    # obtaining a filtered OCEL back.
+    ocel_with_del = pm4py.filter_ocel_cc_otype(ocel, "delivery")
+    print(ocel_with_del)
+    # filters the connected components of the OCEL with at least five different objects,
+    # obtaining a filtered OCEL back.
+    ocel_with_three_objs = pm4py.filter_ocel_cc_length(ocel, 5, sys.maxsize)
+    print(ocel_with_three_objs)
+
+
+if __name__ == "__main__":
+    execute_script()
diff --git a/examples/timestamp_granularity.py b/examples/timestamp_granularity.py
new file mode 100644
index 000000000..0e9c87d61
--- /dev/null
+++ b/examples/timestamp_granularity.py
@@ -0,0 +1,41 @@
+import pandas as pd
+import pm4py
+import time
+
+
+def execute_script():
+    dataframe = pd.read_csv("../tests/input_data/receipt.csv")
+    dataframe = pm4py.format_dataframe(dataframe)
+
+    # prints the original timestamp column of the dataframe
+    print(dataframe["time:timestamp"])
+
+    # Here are some common options that you can use as a granularity:
+    #
+    # 'D': Day
+    # 'H': Hour
+    # 'T' or 'min': Minute
+    # 'S': Second
+    # 'L' or 'ms': Millisecond
+    # 'U': Microsecond
+    # 'N': Nanosecond
+
+    st = time.time_ns()
+    # cast on the minute
+    dataframe["time:timestamp"] = dataframe["time:timestamp"].dt.floor('T')
+    ct = time.time_ns()
+
+    print("required time for the timestamp casting: %.2f seconds" % ((ct-st)/10**9))
+
+    # prints the new timestamp column of the dataframe
+    print(dataframe["time:timestamp"])
+
+    # for completeness, we report some alternatives methods in Pandas to do the same (casting on the minute):
+    #
+    # dataframe["time:timestamp"] = dataframe["time:timestamp"].apply(lambda x: x.replace(second=0, microsecond=0))
+    #
+    # dataframe["time:timestamp"] = dataframe["time:timestamp"].dt.round('min')
+
+
+if __name__ == "__main__":
+    execute_script()
diff --git a/pm4py/__init__.py b/pm4py/__init__.py
index d2084a27b..ce60b90fc 100644
--- a/pm4py/__init__.py
+++ b/pm4py/__init__.py
@@ -30,7 +30,7 @@
     filter_ocel_object_types_allowed_activities, filter_ocel_object_per_type_count, filter_ocel_start_events_per_object_type, \
     filter_ocel_end_events_per_object_type, filter_ocel_events_timestamp, filter_prefixes, filter_suffixes, \
     filter_four_eyes_principle, filter_activity_done_different_resources, filter_ocel_events, filter_ocel_objects, \
-    filter_ocel_object_types, filter_ocel_cc_object
+    filter_ocel_object_types, filter_ocel_cc_object, filter_ocel_cc_length, filter_ocel_cc_otype
 from pm4py.discovery import discover_petri_net_alpha, discover_petri_net_alpha_plus, discover_petri_net_ilp, discover_petri_net_heuristics, \
     discover_petri_net_inductive, discover_process_tree_inductive, discover_heuristics_net, \
     discover_dfg, discover_footprints, discover_eventually_follows_graph, discover_directly_follows_graph, discover_bpmn_inductive, \
diff --git a/pm4py/algo/discovery/batches/variants/pandas.py b/pm4py/algo/discovery/batches/variants/pandas.py
index daf61b0e9..8b2333281 100644
--- a/pm4py/algo/discovery/batches/variants/pandas.py
+++ b/pm4py/algo/discovery/batches/variants/pandas.py
@@ -94,9 +94,14 @@ def apply(log: pd.DataFrame, parameters: Optional[Dict[Union[str, Parameters], A
         attributes_to_consider.add(event_id_key)
 
     log = log[list(attributes_to_consider)]
-    log[timestamp_key] = log[timestamp_key].values.astype(np.int64) // 10**9
+    # the timestamp columns are expressed in nanoseconds values
+    # here, we want them to have the second granularity, so we divide by 10**9
+    # for example 1001000000 nanoseconds (value stored in the column)
+    # is equivalent to 1,001 seconds.
+    log[timestamp_key] = log[timestamp_key].values.astype(np.int64) / 10**9
     if start_timestamp_key != timestamp_key:
-        log[start_timestamp_key] = log[start_timestamp_key].values.astype(np.int64) // 10**9
+        # see the aforementioned explanation.
+        log[start_timestamp_key] = log[start_timestamp_key].values.astype(np.int64) / 10**9
 
     actres_grouping0 = log.groupby([activity_key, resource_key]).agg(list).to_dict()
     start_timestamps = actres_grouping0[start_timestamp_key]
diff --git a/pm4py/analysis.py b/pm4py/analysis.py
index e8b945acc..b8398afef 100644
--- a/pm4py/analysis.py
+++ b/pm4py/analysis.py
@@ -17,7 +17,7 @@
 __doc__ = """
 """
 
-from typing import List, Optional, Tuple, Dict, Union, Generator, Set
+from typing import List, Optional, Tuple, Dict, Union, Generator, Set, Any
 
 from pm4py.objects.log.obj import Trace, EventLog, EventStream
 from pm4py.objects.conversion.log import converter as log_converter
@@ -153,7 +153,7 @@ def solve_extended_marking_equation(trace: Trace, sync_net: PetriNet, sync_im: M
 
 
 def check_soundness(petri_net: PetriNet, initial_marking: Marking,
-                    final_marking: Marking) -> bool:
+                    final_marking: Marking, print_diagnostics: bool = False) -> Tuple[bool, Dict[str, Any]]:
     """
     Check if a given Petri net is a sound WF-net.
     A Petri net is a WF-net iff:
@@ -165,11 +165,15 @@ def check_soundness(petri_net: PetriNet, initial_marking: Marking,
         - it contains no deadlocks
         - we are able to always reach the final marking
     For a formal definition of sound WF-net, consider: http://www.padsweb.rwth-aachen.de/wvdaalst/publications/p628.pdf
+    In the returned object, the first element is a boolean indicating if the Petri net is a sound workflow net.
+    The second element is a set of diagnostics collected while running WOFLAN
+    (expressed as a dictionary associating the keys [name of the diagnostics] with the corresponding diagnostics).
 
     :param petri_net: petri net
     :param initial_marking: initial marking
     :param final_marking: final marking
-    :rtype: ``bool``
+    :param print_diagnostics: boolean value that sets up additional prints during the execution of WOFLAN
+    :rtype: ``Tuple[bool, Dict[str, Any]]``
 
     .. code-block:: python3
 
@@ -179,7 +183,8 @@ def check_soundness(petri_net: PetriNet, initial_marking: Marking,
         is_sound = pm4py.check_soundness(net, im, fm)
     """
     from pm4py.algo.analysis.woflan import algorithm as woflan
-    return woflan.apply(petri_net, initial_marking, final_marking, parameters={"return_asap_when_not_sound": True, "return_diagnostics": True})
+    return woflan.apply(petri_net, initial_marking, final_marking,
+                        parameters={"return_asap_when_not_sound": True, "return_diagnostics": True, "print_diagnostics": print_diagnostics})
 
 
 def cluster_log(log: Union[EventLog, EventStream, pd.DataFrame], sklearn_clusterer=None, activity_key: str = "concept:name", timestamp_key: str = "time:timestamp", case_id_key: str = "case:concept:name") -> Generator[EventLog, None, None]:
diff --git a/pm4py/filtering.py b/pm4py/filtering.py
index 1498b7077..65e6e4684 100644
--- a/pm4py/filtering.py
+++ b/pm4py/filtering.py
@@ -1114,3 +1114,81 @@ def filter_ocel_cc_object(ocel: OCEL, object_id: str) -> OCEL:
     for cc in ocel_splits:
         if object_id in cc.objects[ocel.object_id_column].unique():
             return cc
+
+
+def filter_ocel_cc_length(ocel: OCEL, min_cc_length: int, max_cc_length: int) -> OCEL:
+    """
+    Keeps only the objects in an OCEL belonging to a connected component with a length
+    falling in a specified range
+
+    Paper:
+    Adams, Jan Niklas, et al. "Defining cases and variants for object-centric event data." 2022 4th International Conference on Process Mining (ICPM). IEEE, 2022.
+
+    :param ocel: object-centric event log
+    :param min_cc_length: minimum allowed length for the connected component
+    :param max_cc_length: maximum allowed length for the connected component
+    :rtype: ``OCEL``
+
+    .. code-block:: python3
+
+        import pm4py
+
+        ocel = pm4py.read_ocel('log.jsonocel')
+        filtered_ocel = pm4py.filter_ocel_cc_length(ocel, 2, 10)
+    """
+    from pm4py.algo.transformation.ocel.graphs import object_interaction_graph
+    import networkx as nx
+
+    g0 = object_interaction_graph.apply(ocel)
+    g = nx.Graph()
+
+    for edge in g0:
+        g.add_edge(edge[0], edge[1])
+
+    conn_comp = list(nx.connected_components(g))
+    conn_comp = [x for x in conn_comp if min_cc_length <= len(x) <= max_cc_length]
+    objs = [y for x in conn_comp for y in x]
+
+    return filter_ocel_objects(ocel, objs)
+
+
+def filter_ocel_cc_otype(ocel: OCEL, otype: str, positive: bool = True) -> OCEL:
+    """
+    Filters the objects belonging to the connected components having at least an object
+    of the provided object type.
+
+    Paper:
+    Adams, Jan Niklas, et al. "Defining cases and variants for object-centric event data." 2022 4th International Conference on Process Mining (ICPM). IEEE, 2022.
+
+    :param ocel: object-centric event log
+    :param otype: object type
+    :param positive: boolean that keeps or discards the objects of these components
+    :rtype: ``OCEL``
+
+    .. code-block:: python3
+
+        import pm4py
+
+        ocel = pm4py.read_ocel('log.jsonocel')
+        filtered_ocel = pm4py.filter_ocel_cc_otype(ocel, 'order')
+    """
+    if positive:
+        objs = set(ocel.objects[ocel.objects[ocel.object_type_column] == otype][ocel.object_id_column])
+    else:
+        objs = set(ocel.objects[~(ocel.objects[ocel.object_type_column] == otype)][ocel.object_id_column])
+
+    from pm4py.algo.transformation.ocel.graphs import object_interaction_graph
+    import networkx as nx
+
+    g0 = object_interaction_graph.apply(ocel)
+    g = nx.Graph()
+
+    for edge in g0:
+        g.add_edge(edge[0], edge[1])
+
+    conn_comp = list(nx.connected_components(g))
+    conn_comp = [x for x in conn_comp if len(set(x).intersection(objs)) > 0]
+
+    objs = [y for x in conn_comp for y in x]
+
+    return filter_ocel_objects(ocel, objs)
diff --git a/pm4py/objects/log/util/activities_to_alphabet.py b/pm4py/objects/log/util/activities_to_alphabet.py
new file mode 100644
index 000000000..4129428a2
--- /dev/null
+++ b/pm4py/objects/log/util/activities_to_alphabet.py
@@ -0,0 +1,78 @@
+'''
+    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
+
+    PM4Py is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    PM4Py is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
+'''
+
+from enum import Enum
+from pm4py.util import exec_utils, xes_constants
+import pandas as pd
+from typing import Optional, Dict, Any, Union, Tuple
+
+
+class Parameters(Enum):
+    ACTIVITY_KEY = "activity_key"
+    RETURN_MAPPING = "return_mapping"
+
+
+def apply(dataframe: pd.DataFrame, parameters: Optional[Dict[Any, Any]] = None) -> Union[
+    pd.DataFrame, Tuple[pd.DataFrame, Dict[str, str]]]:
+    """
+    Remap the activities in a dataframe using an augmented alphabet to minimize the size of the encoding
+
+    Running example:
+
+        import pm4py
+        from pm4py.objects.log.util import activities_to_alphabet
+        from pm4py.util import constants
+
+        dataframe = pm4py.read_xes("tests/input_data/running-example.xes")
+        renamed_dataframe = activities_to_alphabet.apply(dataframe, parameters={constants.PARAMETER_CONSTANT_ACTIVITY_KEY: "concept:name"})
+        print(renamed_dataframe)
+
+    Parameters
+    --------------
+    dataframe
+        Pandas dataframe
+    parameters
+        Parameters of the method, including:
+        - Parameters.ACTIVITY_KEY => attribute to be used as activity
+        - Parameters.RETURN_MAPPING => (boolean) enables the returning the mapping dictionary (so the original activities can be re-constructed)
+
+    Returns
+    --------------
+    ren_dataframe
+        Pandas dataframe in which the activities have been remapped to the (augmented) alphabet
+    inv_mapping
+        (if required) Dictionary associating to every letter of the (augmented) alphabet the original activity
+    """
+    if parameters is None:
+        parameters = {}
+
+    activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY)
+    return_mapping = exec_utils.get_param_value(Parameters.RETURN_MAPPING, parameters, False)
+
+    activities_count = list(dataframe[activity_key].value_counts().to_dict())
+    remap_dict = {}
+    for index, act in enumerate(activities_count):
+        result = ''
+        while index >= 0:
+            result = chr((index % 26) + ord('A')) + result
+            index = index // 26 - 1
+        remap_dict[act] = result
+    dataframe[activity_key] = dataframe[activity_key].map(remap_dict)
+    if return_mapping:
+        inverse_dct = {y: x for x, y in remap_dict.items()}
+        return dataframe, inverse_dct
+    return dataframe
diff --git a/pm4py/objects/ocel/util/ocel_to_dict_types_rel.py b/pm4py/objects/ocel/util/ocel_to_dict_types_rel.py
new file mode 100644
index 000000000..1907bc550
--- /dev/null
+++ b/pm4py/objects/ocel/util/ocel_to_dict_types_rel.py
@@ -0,0 +1,110 @@
+'''
+    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
+
+    PM4Py is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    PM4Py is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
+'''
+
+from pm4py.objects.ocel.obj import OCEL
+from typing import Dict, Union, Tuple
+import pandas as pd
+
+
+def apply(ocel: OCEL) -> Dict[str, Dict[Union[str, Tuple[str, str]], pd.DataFrame]]:
+    """
+    Gets from an object-centric event log (OCEL)
+    a dictionary associating to every event/object/e2o/o2o/change type
+    a dataframe containing the associated information.
+    This effectively splits the information of different event/object types
+    in dense dataframes.
+
+    Running example:
+
+        import pm4py
+        from pm4py.objects.ocel.util import ocel_to_dict_types_rel
+
+
+        ocel = pm4py.read_ocel("tests/input_data/ocel/example_log.jsonocel")
+        dct_types_rel = ocel_to_dict_types_rel.apply(ocel)
+
+        # prints the dense dataframes for every event type of the log
+        for evt, table in dct_types_rel["ev_types"].items():
+            print("\n")
+            print(evt)
+            print(table)
+
+    Parameters
+    -------------
+    ocel
+        Object-centric event log
+
+    Returns
+    -----------
+    dct_types_rel
+        Dictionary associating to every type the corresponding dense table.
+
+        Keys at the first level:
+        - ev_types: pointing to the different event types of the object-centric event log
+        - obj_types: pointing to the different object types of the object-centric event log
+        - e2o: pointing to the different event-object relationships of the object-centric event log
+        - o2o: pointing to the different object-object relationships of the object-centric event log
+        - object_changes: pointing to temporal changes in the attributes of the different object types of an OCEL
+
+        Keys at the second level:
+        - for "ev_types", "obj_types" and "object_changes": the name of the event/object type related to the dense table
+        - for "e2o": a tuple in which the first element is an event type, and the second element is an object type
+        - for "o2o": a tuple in which the two elements are interconnected object types
+
+        Value: a Pandas dataframe (dense table).
+    """
+    ev_types_list = list(ocel.events[ocel.event_activity].unique())
+    obj_types_list = list(ocel.objects[ocel.object_type_column].unique())
+    e2o_list = list(ocel.relations.groupby([ocel.event_activity, ocel.object_type_column]).size().to_dict())
+
+    obj_type_map = ocel.objects[[ocel.object_id_column, ocel.object_type_column]].to_dict("records")
+    obj_type_map = {x[ocel.object_id_column]: x[ocel.object_type_column] for x in obj_type_map}
+    overall_o2o = ocel.o2o.copy()
+    overall_o2o[ocel.object_type_column] = overall_o2o[ocel.object_id_column].map(obj_type_map)
+    overall_o2o[ocel.object_type_column+"_2"] = overall_o2o[ocel.object_id_column+"_2"].map(obj_type_map)
+
+    o2o_list = list(overall_o2o.groupby([ocel.object_type_column, ocel.object_type_column+"_2"]).size().to_dict())
+    changes_list = list(ocel.object_changes[ocel.object_type_column].unique())
+
+    dct_types_rel = {"ev_types": {}, "obj_types": {}, "e2o": {}, "o2o": {}, "changes": {}}
+
+    for evt in ev_types_list:
+        events = ocel.events[ocel.events[ocel.event_activity] == evt]
+        events.dropna(axis="columns", how="all")
+        dct_types_rel["ev_types"][evt] = events
+
+    for objt in obj_types_list:
+        objects = ocel.objects[ocel.objects[ocel.object_type_column] == objt]
+        objects.dropna(axis="columns", how="all")
+        dct_types_rel["obj_types"][objt] = objects
+
+    for e2ot in e2o_list:
+        e2o = ocel.relations[(ocel.relations[ocel.event_activity] == e2ot[0]) & (ocel.relations[ocel.object_type_column] == e2ot[1])]
+        e2o.dropna(axis="columns", how="all")
+        dct_types_rel["e2o"][e2ot] = e2o
+
+    for o2ot in o2o_list:
+        o2o = overall_o2o[(overall_o2o[ocel.object_type_column] == o2ot[0]) & (overall_o2o[ocel.object_type_column+"_2"] == o2ot[1])]
+        o2o.dropna(axis="columns", how="all")
+        dct_types_rel["o2o"][o2ot] = o2o
+
+    for objt in changes_list:
+        objects = ocel.object_changes[ocel.object_changes[ocel.object_type_column] == objt]
+        objects.dropna(axis="columns", how="all")
+        dct_types_rel["changes"][objt] = objects
+
+    return dct_types_rel
diff --git a/pm4py/ocel.py b/pm4py/ocel.py
index f3b8826d6..a17d712d1 100644
--- a/pm4py/ocel.py
+++ b/pm4py/ocel.py
@@ -25,6 +25,7 @@
 from pm4py.objects.ocel.obj import OCEL
 from pm4py.util import constants
 import sys
+import random
 
 
 def ocel_get_object_types(ocel: OCEL) -> List[str]:
@@ -355,13 +356,22 @@ def sample_ocel_objects(ocel: OCEL, num_objects: int) -> OCEL:
     return sampling.sample_ocel_objects(ocel, parameters={"num_entities": num_objects})
 
 
-def sample_ocel_connected_components(ocel: OCEL, connected_components: int = 1) -> OCEL:
+def sample_ocel_connected_components(ocel: OCEL, connected_components: int = 1,
+                                     max_num_events_per_cc: int = sys.maxsize,
+                                     max_num_objects_per_cc: int = sys.maxsize,
+                                     max_num_e2o_relations_per_cc: int = sys.maxsize) -> OCEL:
     """
     Given an object-centric event log, returns a sampled event log with a subset of the executions.
     The number of considered connected components need to be specified by the user.
 
+    Paper:
+    Adams, Jan Niklas, et al. "Defining cases and variants for object-centric event data." 2022 4th International Conference on Process Mining (ICPM). IEEE, 2022.
+
     :param ocel: Object-centric event log
     :param connected_components: Number of connected components to pick from the OCEL
+    :param max_num_events_per_cc: maximum number of events allowed per connected component (default: sys.maxsize)
+    :param max_num_objects_per_cc: maximum number of events allowed per connected component (default: sys.maxsize)
+    :param max_num_e2o_relations_per_cc: maximum number of event-to-object relationships allowed per connected component (default: sys.maxsize)
     :rtype: ``OCEL``
 
     .. code-block:: python3
@@ -376,10 +386,14 @@ def sample_ocel_connected_components(ocel: OCEL, connected_components: int = 1)
     events = None
     objects = None
     relations = None
-    ocel_splits = sorted(list(ocel_splits), key=lambda x: (len(x.events), len(x.relations)))
-    i = 0
-    while i < min(connected_components, len(ocel_splits)):
-        cc = ocel_splits[i]
+    ocel_splits = [x for x in ocel_splits if
+                   len(x.events) <= max_num_events_per_cc and len(x.objects) <= max_num_objects_per_cc and len(
+                       x.relations) <= max_num_e2o_relations_per_cc]
+
+    if len(ocel_splits) > 0:
+        ocel_splits = random.sample(ocel_splits, min(connected_components, len(ocel_splits)))
+
+    for cc in ocel_splits:
         if events is None:
             events = cc.events
             objects = cc.objects
@@ -388,7 +402,6 @@ def sample_ocel_connected_components(ocel: OCEL, connected_components: int = 1)
             events = pd.concat([events, cc.events])
             objects = pd.concat([objects, cc.objects])
             relations = pd.concat([relations, cc.relations])
-        i = i + 1
 
     return OCEL(events, objects, relations)
 
diff --git a/pm4py/util/vis_utils.py b/pm4py/util/vis_utils.py
index 42ffc4e4c..8eab41abd 100644
--- a/pm4py/util/vis_utils.py
+++ b/pm4py/util/vis_utils.py
@@ -18,46 +18,55 @@
 import os
 import subprocess
 import sys
+from typing import Optional, Dict
+
 
 MAX_EDGE_PENWIDTH_GRAPHVIZ = 2.6
 MIN_EDGE_PENWIDTH_GRAPHVIZ = 1.0
 
 
-def human_readable_stat(timedelta_seconds, stat_locale: dict = {}):
+def human_readable_stat(timedelta, stat_locale: Optional[Dict[str, str]] = None) -> str:
     """
-    Transform a timedelta expressed in seconds into a human readable string
+    Transform a timedelta into a human readable string
 
     Parameters
     ----------
-    timedelta_seconds
-        Timedelta expressed in seconds
-    stat_locale
-        Dict mapping stat strings
+    timedelta
+        Timedelta
 
     Returns
     ----------
     string
         Human readable string
     """
-    timedelta_seconds = int(float(timedelta_seconds))
-    years = timedelta_seconds // 31104000
-    months = timedelta_seconds // 2592000
-    days = timedelta_seconds // 86400
-    hours = timedelta_seconds // 3600 % 24
-    minutes = timedelta_seconds // 60 % 60
-    seconds = timedelta_seconds % 60
-    
+    if stat_locale is None:
+        stat_locale = {}
+
+    c = int(float(timedelta))
+    years = c // 31104000
+    months = c // 2592000
+    days = c // 86400
+    hours = c // 3600 % 24
+    minutes = c // 60 % 60
+    seconds = c % 60
     if years > 0:
         return str(years) + stat_locale.get("year", "Y")
-    if months > 0:
+    elif months > 0:
         return str(months) + stat_locale.get("month", "MO")
-    if days > 0:
+    elif days > 0:
         return str(days) + stat_locale.get("day", "D")
-    if hours > 0:
+    elif hours > 0:
         return str(hours) + stat_locale.get("hour", "h")
-    if minutes > 0:
+    elif minutes > 0:
         return str(minutes) + stat_locale.get("minute", "m")
-    return str(seconds) + stat_locale.get("second", "s")
+    elif seconds > 0:
+        return str(seconds) + stat_locale.get("second", "s")
+    else:
+        c = int(float(timedelta*1000))
+        if c > 0:
+            return str(c) + stat_locale.get("millisecond", "ms")
+        else:
+            return str(int(float(timedelta * 10**9))) + stat_locale.get("nanosecond", "ns")
 
 
 def get_arc_penwidth(arc_measure, min_arc_measure, max_arc_measure):
diff --git a/pm4py/utils.py b/pm4py/utils.py
index 3cf834ba7..3861cf665 100644
--- a/pm4py/utils.py
+++ b/pm4py/utils.py
@@ -35,7 +35,6 @@
 CASE_INDEX_COLUMN = "@@case_index"
 
 
-@deprecation.deprecated(deprecated_in="2.3.0", removed_in="3.0.0", details="the format_dataframe function does not need application anymore.")
 def format_dataframe(df: pd.DataFrame, case_id: str = constants.CASE_CONCEPT_NAME,
                      activity_key: str = xes_constants.DEFAULT_NAME_KEY,
                      timestamp_key: str = xes_constants.DEFAULT_TIMESTAMP_KEY,
diff --git a/pm4py/visualization/dfg/util/dfg_gviz.py b/pm4py/visualization/dfg/util/dfg_gviz.py
index 4eb1430d0..efc143d4a 100644
--- a/pm4py/visualization/dfg/util/dfg_gviz.py
+++ b/pm4py/visualization/dfg/util/dfg_gviz.py
@@ -239,14 +239,14 @@ def graphviz_visualization(activities_count, dfg, image_format="png", measure="f
     if start_activities_to_include:
         viz.node("@@startnode", "<&#9679;>", shape='circle', fontsize="34")
         for act in start_activities_to_include:
-            label = str(start_activities[act]) if isinstance(start_activities, dict) else ""
+            label = str(start_activities[act]) if isinstance(start_activities, dict) and measure == "frequency" else ""
             viz.edge("@@startnode", activities_map[act], label=label, fontsize=font_size)
 
     if end_activities_to_include:
         # <&#9632;>
         viz.node("@@endnode", "<&#9632;>", shape='doublecircle', fontsize="32")
         for act in end_activities_to_include:
-            label = str(end_activities[act]) if isinstance(end_activities, dict) else ""
+            label = str(end_activities[act]) if isinstance(end_activities, dict) and measure == "frequency" else ""
             viz.edge(activities_map[act], "@@endnode", label=label, fontsize=font_size)
 
     viz.attr(overlap='false')
diff --git a/requirements_stable.txt b/requirements_stable.txt
index 0503d1153..1ef1b3014 100644
--- a/requirements_stable.txt
+++ b/requirements_stable.txt
@@ -7,14 +7,14 @@ graphviz==0.20.1
 intervaltree==3.1.0
 kiwisolver==1.4.4
 lxml==4.9.3
-matplotlib==3.7.2
+matplotlib==3.7.1
 networkx==3.1
 numpy==1.25.1
 packaging==23.1
 pandas==2.0.3
 pillow==10.0.0
 pydotplus==2.0.2
-pyparsing==3.1.0
+pyparsing==3.1.1
 python-dateutil==2.8.2
 pytz==2023.3
 scipy==1.11.1
diff --git a/safety_checks/20230728 b/safety_checks/20230728
new file mode 100644
index 000000000..df406eb00
--- /dev/null
+++ b/safety_checks/20230728
@@ -0,0 +1,47 @@
++==============================================================================+
+
+                               /$$$$$$            /$$
+                              /$$__  $$          | $$
+           /$$$$$$$  /$$$$$$ | $$  \__//$$$$$$  /$$$$$$   /$$   /$$
+          /$$_____/ |____  $$| $$$$   /$$__  $$|_  $$_/  | $$  | $$
+         |  $$$$$$   /$$$$$$$| $$_/  | $$$$$$$$  | $$    | $$  | $$
+          \____  $$ /$$__  $$| $$    | $$_____/  | $$ /$$| $$  | $$
+          /$$$$$$$/|  $$$$$$$| $$    |  $$$$$$$  |  $$$$/|  $$$$$$$
+         |_______/  \_______/|__/     \_______/   \___/   \____  $$
+                                                          /$$  | $$
+                                                         |  $$$$$$/
+  by pyup.io                                              \______/
+
++==============================================================================+
+
+ REPORT 
+
+  Safety is using PyUp's free open-source vulnerability database. This
+data is 30 days old and limited. 
+  For real-time enhanced vulnerability data, fix recommendations, severity
+reporting, cybersecurity support, team and project policy management and more
+sign up at https://pyup.io or email sales@pyup.io
+
+  Safety v2.3.5 is scanning for Vulnerabilities...
+  Scanning dependencies in your files:
+
+  -> requirements_stable.txt
+
+  Using non-commercial database
+  Found and scanned 25 packages
+  Timestamp 2023-07-28 13:17:25
+  0 vulnerabilities found
+  0 vulnerabilities ignored
++==============================================================================+
+
+ No known security vulnerabilities found. 
+
++==============================================================================+
+
+  Safety is using PyUp's free open-source vulnerability database. This
+data is 30 days old and limited. 
+  For real-time enhanced vulnerability data, fix recommendations, severity
+reporting, cybersecurity support, team and project policy management and more
+sign up at https://pyup.io or email sales@pyup.io
+
++==============================================================================+