diff --git a/src_py/apiServer/NerlCsvDataSetDB.py b/src_py/apiServer/NerlCsvDataSetDB.py
new file mode 100644
index 00000000..34b2684f
--- /dev/null
+++ b/src_py/apiServer/NerlCsvDataSetDB.py
@@ -0,0 +1,52 @@
+
+
+class CsvDataSet():
+    def __init__(self, csv_path, csv_temp_path, batch_size, num_of_features, num_of_labels):
+        self.csv_path = csv_path
+        self.csv_temp_path = csv_temp_path
+        self.batch_size = batch_size
+        self.num_of_features = num_of_features
+        self.num_of_labels = num_of_labels
+
+    def get_csv_path(self):
+        return self.csv_path
+    
+    def get_csv_temp_path(self):
+        return self.csv_temp_path
+
+    def get_batch_size(self):
+        return self.batch_size
+    
+    def get_num_of_features(self):
+        return self.num_of_features
+
+    def get_num_of_labels(self):
+        return self.num_of_labels
+
+    def generate_source_pieceDS(self, source_name : str, batch_size, phase : str, starting_offset = 0, num_of_batches = 0):
+        return SourcePieceDS(source_name, batch_size, phase, starting_offset, num_of_batches)
+        
+    
+
+class SourcePieceDS():
+    def __init__(self, source_name : str, batch_size, phase : str, starting_offset = 0, num_of_batches = 0):
+        self.source_name = source_name
+        self.batch_size = batch_size
+        self.phase = phase
+        self.starting_offset = starting_offset
+        self.num_of_batches = num_of_batches
+        #self.workers
+        self.pointer_to_CsvDataSet = None # which csvDataSet
+
+    def get_source_name(self):
+        return self.source_name
+    
+    def get_batch_size(self):
+        return self.batch_size
+
+    def get_phase(self):
+        return self.phase
+
+    def get_starting_offset(self):
+        return self.starting_offset 
+    
diff --git a/src_py/apiServer/experiment_flow.py b/src_py/apiServer/experiment_flow.py
new file mode 100644
index 00000000..6ba8f0b8
--- /dev/null
+++ b/src_py/apiServer/experiment_flow.py
@@ -0,0 +1,64 @@
+
+import pandas as pd
+import globalVars as globe
+from definitions import *
+from workerResult import *
+from collections import OrderedDict
+from NerlComDB import *
+from NerlDB import *
+from NerlDatasetDB import *
+# Todo check imports and remove unused ones
+
+PARAM_CSV_DB_PATH = "csv_db_path"
+PARAM_BATCH_SIZE = "batch_size"
+
+
+class Experiment():
+
+    DATA_SOURCE_TYPE_CSV = 0
+    DATA_SOURCE_TYPE_CAMERA = 1
+    def __init__(self ,experiment_name = "Untitled", temp_data_path = NERLNET_TEMP_DATA_DIR, data_source_type = DATA_SOURCE_TYPE_CSV):
+        self.exp_name = experiment_name
+        #self.gentrate_stats = 
+        #self.csv_dataset = CsvDataset()
+        self.exp_phase_list = []
+        self.exp_flow_json = None
+
+    def get_exp_name(self):
+        return self.exp_name
+
+    def get_exp_phase_list(self):
+        return self.exp_phase_list
+
+    def new_exp_phase(self, phase : str):
+        exp_phase = ExperimentPhase(phase)
+        self.exp_phase_list.append(exp_phase)
+
+    #  def parse_experiment_flow_json(self, json_path : str):
+    #     # read json file from nerlPlanner output
+    #     with open(json_path) as json_file:
+    #         self.exp_flow_json = json.load(json_file)
+    #     # parse experiment flow json to dataset_db
+    #     csv_dataset_path = self.exp_flow_json[PARAM_CSV_DB_PATH]
+    #     csv_temp_path = self.temp_data_path
+    #     batch_size = self.exp_flow_json[PARAM_BATCH_SIZE]
+    #     self.dataset_db = DataSetCsvDB(csv_dataset_path, csv_temp_path, batch_size)
+    #     # add training and prediction phases source_dbs to dataset_db
+    #     phases_list = [TRAINING_DBS, PREDICTION_DBS]
+    #     for phase in phases_list:
+    #         for source_db_dict in self.exp_flow_json[phase]:
+    #             source_db_name = source_db_dict[PARAM_SOURCE_DB_NAME]
+    #             target_workers_names_list = source_db_dict[PARAM_TARGET_WORKERS_NAMES_LIST]
+    #             starting_offset = source_db_dict[PARAM_STARTING_OFFSET]
+    #             numof_batches = source_db_dict[PARAM_NUMOF_BATCHES]
+    #             self.dataset_db.add_source_db(source_db_name, target_workers_names_list, starting_offset, starting_offset + numof_batches * batch_size, DataSetCsvDB.PHASE_TRAINING)
+
+class ExperimentPhase():
+    def __init__(self, phase : str):
+        self.phase = phase # training/prediction
+        self.nerl_comm_db = NerlComDB(globe.components)  # Todo check if this is the right place to put it
+        self.nerl_model_db = NerlDB()  # Todo will change to NerlModelDB
+        self.source_pieces_list = []  # list of SourcePieceDS
+     
+     def get_phase(self):
+        return self.phase
diff --git a/src_py/apiServer/receiver.py b/src_py/apiServer/receiver.py
index a7b3732c..36df4c21 100644
--- a/src_py/apiServer/receiver.py
+++ b/src_py/apiServer/receiver.py
@@ -94,7 +94,7 @@ def post(self):
         # receiver.logger.info("Training result received")
         # Result preprocessing:
         # Receiving from Erlang: "worker#loss" 
-        # TODO example "s1$w1#source_name|batch_id|loss_value|duration"
+        # TODO example "w1#source_name|batch_id|loss_value|duration"
         # TODO GUY - Add all attributes of nerl_db (batch_id etc.)
         source_csv_dict = {}
         resData = request.get_data().decode('utf-8')