Updated folder structure 2

AI_Experiements/ColabAI/.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (ColabAI)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

AI_Experiements/ColabAI/.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/ColabAI.iml" filepath="$PROJECT_DIR$/.idea/ColabAI.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

AI_Experiements/ColabAI/CleanExperiement.py

+import numpy as np
+import pandas as pd
+from imblearn.over_sampling import SMOTE
+from imblearn.pipeline import Pipeline
+from imblearn.under_sampling import RandomUnderSampler
+from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier, GradientBoostingClassifier
+from sklearn.metrics import cohen_kappa_score, f1_score, recall_score, confusion_matrix
+from sklearn.model_selection import GridSearchCV, StratifiedKFold
+from sklearn.tree import DecisionTreeClassifier
+
+
+def kappa_scorer(estimator, X, y):
+  y_pred = estimator.predict(X)
+  return cohen_kappa_score(y, y_pred)
+
+def BestParams(model,param_grid,X,y):
+    grid_search = GridSearchCV(model, param_grid, scoring=kappa_scorer, cv=10)
+    grid_search.fit(X, y)
+    print("Best Parameters: ", grid_search.best_params_)
+    print("Best Kappa Score: ", grid_search.best_score_)
+    return grid_search.best_params_
+
+def Metrics(model,X,y):
+    cv = StratifiedKFold(n_splits=10, shuffle=False, random_state=None)
+    f1_scores = []
+    sensitivities = []
+    g_means = []
+    kappas = []
+    # Perform cross-validation
+    for train_index, test_index in cv.split(X, y):
+        trains = [True if i in train_index else False for i in range(len(X))]
+        tests = [True if i in test_index else False for i in range(len(X))]
+        train_index = trains
+        test_index = tests
+        X_train, X_test = X[train_index], X[test_index]
+        y_train, y_test = y[train_index], y[test_index]
+        model.fit(X_train, y_train)
+        y_pred = model.predict(X_test)
+        # Calculate the metrics for each class
+        f1_scores.append(f1_score(y_test, y_pred, average=None))
+        sensitivities.append(recall_score(y_test, y_pred, average=None))
+        g_means.append(
+            np.sqrt(recall_score(y_test, y_pred, average=None) * recall_score(y_test, y_pred, average=None)).mean())
+        kappas.append(cohen_kappa_score(y_test, y_pred))
+
+    avg_f1_scores = np.mean(f1_scores, axis=0)
+    avg_sensitivities = np.mean(sensitivities, axis=0)
+    avg_g_mean = np.mean(g_means)
+    avg_kappa = np.mean(kappas)
+    for i, class_label in enumerate(model.classes_):
+        print(f"Metrics for class {class_label}:")
+        print(f"F1-Score: {avg_f1_scores[i]}")
+        print(f"Sensitivity: {avg_sensitivities[i]}")
+        print()
+    print(f"Average G-Mean: {avg_g_mean}")
+    print(f"Average Kappa: {avg_kappa}")
+    cm = confusion_matrix(y_test, y_pred)
+    print(cm)
+
+datasets = ["data_F1.csv","data_F2.csv","data_A1.csv","data_A2.csv"]
+for s in datasets:
+    data = pd.read_csv(s)
+    X = data.drop('class', axis=1)
+    X = X.drop('timestamp', axis=1)
+    y = data['class']
+    over = SMOTE(sampling_strategy="auto")
+    under = RandomUnderSampler(sampling_strategy={"NE":round(0.7*len(X))})
+    # steps = [('u', under),('o', over)]
+    steps = [('o', over)]
+    pipeline = Pipeline(steps=steps)
+    X, y = pipeline.fit_resample(X, y)
+    print(len(X))
+    base_classifier = DecisionTreeClassifier()
+    bagging_classifier = BaggingClassifier(base_classifier)
+    gradient = GradientBoostingClassifier()
+    adaboost = AdaBoostClassifier()
+
+    n_estimators = [10,50,100,150,200]
+    learning_rate = 0.1
+    print("*** Result for file: ", s,"\n")
+    for i in range(len(n_estimators)):
+    #     print("Bagging Results for a number of estimators ",n_estimators[i])
+    # # best = BestParams(bagging_classifier,param_grid_bag,X,y)
+    #     bagging_classifier = BaggingClassifier(base_classifier,n_estimators=n_estimators[i])
+    #     Metrics(bagging_classifier,X,y)
+    #     print("********\n")
+    #     print("AdaBoost Results for a number of estimators",n_estimators[i])
+    #     # best = BestParams(adaboost, param_grid_boost,X,y)
+    #     adaboost = AdaBoostClassifier(n_estimators=n_estimators[i],learning_rate=learning_rate)
+    #     Metrics(adaboost, X, y)
+        print("********\n")
+        print("GradientBoost Results for a number of estimators", n_estimators[i])
+        gradient = GradientBoostingClassifier(n_estimators=n_estimators[i],learning_rate=learning_rate)
+        Metrics(gradient,X,y)
+        print("********\n")
+
+

AI_Experiements/ColabAI/New Experiement.py

+import numpy as np
+import pandas as pd
+from imblearn.over_sampling import SMOTE
+from imblearn.pipeline import Pipeline
+from imblearn.under_sampling import RandomUnderSampler
+from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier, GradientBoostingClassifier
+from sklearn.metrics import cohen_kappa_score, f1_score, recall_score, confusion_matrix, accuracy_score
+from sklearn.model_selection import GridSearchCV, StratifiedKFold, train_test_split
+from sklearn.tree import DecisionTreeClassifier
+
+data = pd.read_csv('data_A1.csv')
+X = data.drop('class', axis=1)
+X = X.drop('timestamp', axis=1)
+y = data['class']
+
+X_smote, y_smote = X, y
+
+
+smote = SMOTE(sampling_strategy='auto')
+# X_smote, y_smote = smote.fit_resample(X, y)
+
+X_train, X_test, y_train, y_test = train_test_split(X_smote, y_smote, test_size=0.3, random_state=42)
+X_smote, y_smote = smote.fit_resample(X_train, y_train)
+X_train, y_train = X_smote,y_smote
+# X_test, y_test = X, y
+base_classifier = DecisionTreeClassifier()
+bagging_classifier = BaggingClassifier(base_classifier,n_estimators=150)
+bagging_classifier.fit(X_train,y_train)
+y_pred = bagging_classifier.predict(X_test)
+########################
+f1_scores = []
+sensitivities = []
+g_means = []
+kappas = []
+accuracies = []
+f1_scores.append(f1_score(y_test, y_pred, average=None))
+sensitivities.append(recall_score(y_test, y_pred, average=None))
+g_means.append(
+    np.sqrt(recall_score(y_test, y_pred, average=None) * recall_score(y_test, y_pred, average=None)).mean())
+kappas.append(cohen_kappa_score(y_test, y_pred))
+accuracies.append(accuracy_score(y_test, y_pred))
+print("************\n")
+cm = confusion_matrix(y_test, y_pred)
+print(cm)
+avg_f1_scores = np.mean(f1_scores, axis=0)
+avg_sensitivities = np.mean(sensitivities, axis=0)
+avg_g_mean = np.mean(g_means)
+avg_kappa = np.mean(kappas)
+avg_accuracy = np.mean(accuracies)
+for i, class_label in enumerate(bagging_classifier.classes_):
+    print(f"Metrics for class {class_label}:")
+    print(f"F1-Score: {avg_f1_scores[i]}")
+    print(f"Sensitivity: {avg_sensitivities[i]}")
+    print()
+print(f"Average G-Mean: {avg_g_mean}")
+print(f"Average Kappa: {avg_kappa}")
+print(f"Average Accuracy: {avg_accuracy}")
+

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AI_Experiements/tc6ysmh5j8-2/raw data/raw CoreSwitch-I/CoreSwitch-I raw part8.csv

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AI_Experiements/tc6ysmh5j8-2/raw data/raw CoreSwitch-II/CoreSwitch-II raw part8.csv

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\ No newline at end of file

Docker_Files/data.sql

+CREATE DATABASE IF NOT EXISTS USER_APP;
+
+USE USER_APP;
+
+CREATE TABLE users(
+      id int(7) NOT NULL,
+      username VARCHAR(255) NOT NULL,
+      fname VARCHAR(255) NOT NULL,
+      lname VARCHAR(255) NOT NULL,
+      gender VARCHAR(255) NOT NULL,
+      password VARCHAR(255) NOT NULL,
+      country VARCHAR(255) NOT NULL,
+      email VARCHAR(255) NOT NULL,
+      workingDate DATE DEFAULT NULL,
+      role  VARCHAR(255) NOT NULL,
+      PRIMARY KEY(userID)
+);
+ALTER TABLE users ADD CONSTRAINT unique_username UNIQUE (username);
+ALTER TABLE users ADD CONSTRAINT unique_username UNIQUE (email);
+
+
+-- password: reem
+INSERT INTO users (id,username,fname,lname, gender, country, email, workingDate, password, role)
+VALUES (1,'reem.hasan','John', 'Doe', 'female', 'USA', 'johndoe@example.com', '2023-07-24',
+	'$2a$10$rC.uZ0ivWwiFJ7PHp8A8XOEFz7Al82SjHsvX27W7PcADX2GHdyu6q', 'admin');
+
+-- password: reem 
+INSERT INTO users (id,username,fname,lname, gender, country, email, workingDate, password, role)
+VALUES (2,'bashar.hussain','John', 'Doe', 'male', 'USA', 'bashar@example.com', '2023-07-24',
+	'$2a$10$4xbRR/mP2xrbSjN1tK8t/OBZLeTzLplQtG3VvB8mm4T8yA.Fti/AS', 'user');

Docker_Files/docker-compose-kafka.yml

+version: '3'
+services:
+  zookeeper:
+    image: confluentinc/cp-zookeeper:7.0.1
+    container_name: zookeeper
+    environment:
+      ZOOKEEPER_CLIENT_PORT: 2181
+      ZOOKEEPER_TICK_TIME: 2000
+
+  broker:
+    image: confluentinc/cp-kafka:7.0.1 
+    container_name: broker
+    ports:
+      - "9092:9092"
+    depends_on:
+      - zookeeper
+    environment:
+      KAFKA_BROKER_ID: 1
+      KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181'
+      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_INTERNAL:PLAINTEXT
+      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://172.29.3.220:9092,PLAINTEXT_INTERNAL://broker:29092
+      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
+      KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1
+      KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1

Docker_Files/docker-compose-sql.yml

+version: '3'
+
+services:
+  db:
+    image: mysql
+    restart: always
+    environment:
+      MYSQL_ROOT_PASSWORD: 'password'
+    ports:
+      - "3306:3306"
+    volumes:
+      - ./data.sql:/docker-entrypoint-initdb.d/data.sql
\ No newline at end of file

Model_System/.idea/workspace.xml

-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
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-  </component>
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-    "RunOnceActivity.ShowReadmeOnStart": "true"
-  }
-}]]></component>
-  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
-</project>
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Model_System/event-correlation-service/.idea/compiler.xml

-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="CompilerConfiguration">
-    <annotationProcessing>
-      <profile default="true" name="Default" enabled="true" />
-      <profile name="Maven default annotation processors profile" enabled="true">
-        <sourceOutputDir name="target/generated-sources/annotations" />
-        <sourceTestOutputDir name="target/generated-test-sources/test-annotations" />
-        <outputRelativeToContentRoot value="true" />
-        <module name="event-correlation-service" />
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-    </annotationProcessing>
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-    <option name="ADDITIONAL_OPTIONS_OVERRIDE">
-      <module name="event-correlation-service" options="-parameters" />
-    </option>
-  </component>
-</project>
\ No newline at end of file

Model_System/event-correlation-service/HELP.md

-# Read Me First
-The following was discovered as part of building this project:
-
-* The original package name 'com.example.event-correlation-service' is invalid and this project uses 'com.example.eventcorrelationservice' instead.
-
-# Getting Started
-
-### Reference Documentation
-For further reference, please consider the following sections:
-
-* [Official Apache Maven documentation](https://maven.apache.org/guides/index.html)
-* [Spring Boot Maven Plugin Reference Guide](https://docs.spring.io/spring-boot/docs/3.1.2/maven-plugin/reference/html/)
-* [Create an OCI image](https://docs.spring.io/spring-boot/docs/3.1.2/maven-plugin/reference/html/#build-image)
-

Model_System/event-correlation-service/target/classes/application.properties

-server.port=7373
-spring.application.name=FEEDBACK-SERVICE
-eureka.client.serviceUrl.defaultZone=http://192.168.27.227:8761/eureka
-#eureka.instance.hostname=192.168.24.47
\ No newline at end of file

Model_System/event-correlation-service/target/classes/utils/policy.json

-{
-  "phases": {
-    "hot": {
-      "min_age": "0ms",
-      "actions": {
-        "set_priority": {
-          "priority": 100
-        },
-        "rollover": {
-          "max_primary_shard_size": "50gb",
-          "max_age": "60m"
-        }
-      }
-    },
-    "warm": {
-      "min_age": "60m",
-      "actions": {
-        "set_priority": {
-          "priority": 50
-        }
-      }
-    },
-    "cold": {
-      "min_age": "60m",
-      "actions": {
-        "set_priority": {
-          "priority": 0
-        }
-      }
-    }
-  }
-}
\ No newline at end of file

Model_System/event-correlation-service/target/classes/utils/settings.json

-{
-    "settings": {
-      "index.lifecycle.name": "knowledge-base-policy",
-      "index.number_of_shards": 1,
-      "index.number_of_replicas": 0
-    }
-}
\ No newline at end of file

Model_System/model-service/.idea/workspace.xml

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-    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
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-  </component>
-  <component name="RecentsManager">
-    <key name="CopyFile.RECENT_KEYS">
-      <recent name="C:\Users\HP\PycharmProjects\model-service" />
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-  <component name="RunManager" selected="Python.main">
-    <configuration name="eureka" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
-      <module name="model-service" />
-      <option name="INTERPRETER_OPTIONS" value="" />
-      <option name="PARENT_ENVS" value="true" />
-      <envs>
-        <env name="PYTHONUNBUFFERED" value="1" />
-      </envs>
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-      <option name="REDIRECT_INPUT" value="false" />
-      <option name="INPUT_FILE" value="" />
-      <method v="2" />
-    </configuration>
-    <configuration name="main" type="PythonConfigurationType" factoryName="Python" nameIsGenerated="true">
-      <module name="model-service" />
-      <option name="INTERPRETER_OPTIONS" value="" />
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-      <envs>
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-      <option name="EMULATE_TERMINAL" value="false" />
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-      <option name="INPUT_FILE" value="" />
-      <method v="2" />
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Model_System/stream-service/.idea/compiler.xml

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Model_System/stream-service/.idea/encodings.xml

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Model_System/stream-service/.idea/jarRepositories.xml

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\ No newline at end of file

Model_System/stream-service/HELP.md

-# Getting Started
-
-### Reference Documentation
-For further reference, please consider the following sections:
-
-* [Official Apache Maven documentation](https://maven.apache.org/guides/index.html)
-* [Spring Boot Maven Plugin Reference Guide](https://docs.spring.io/spring-boot/docs/3.1.1/maven-plugin/reference/html/)
-* [Create an OCI image](https://docs.spring.io/spring-boot/docs/3.1.1/maven-plugin/reference/html/#build-image)
-

Model_System/stream-service/target/classes/application.properties

-server.port = 7777
-spring.kafka.bootstrap-servers=http://172.29.3.220:9092
-spring.kafka.template.default-topic=Stream-Instance
-spring.kafka.consumer.group-id= Stream-Instance-Id
-spring.kafka.consumer.key-deserializer=org.apache.kafka.common.serialization.StringDeserializer
-spring.kafka.consumer.value-deserializer=org.apache.kafka.common.serialization.StringDeserializer
-
-spring.cloud.circuitbreaker.resilience4j.enabled=true
-management.health.circuitbreakers.enabled=true
-management.health.elasticsearch.enabled=false
-management.endpoints.web.exposure.include=*
-management.endpoint.health.show-details=always
-
-#Resilinece4j Properties
-resilience4j.circuitbreaker.instances.model-service.registerHealthIndicator=true
-resilience4j.circuitbreaker.instances.model-service.event-consumer-buffer-size=10
-resilience4j.circuitbreaker.instances.model-service.slidingWindowType=COUNT_BASED
-resilience4j.circuitbreaker.instances.model-service.slidingWindowSize=5
-resilience4j.circuitbreaker.instances.model-service.failureRateThreshold=30
-resilience4j.circuitbreaker.instances.model-service.waitDurationInOpenState=5s
-resilience4j.circuitbreaker.instances.model-service.permittedNumberOfCallsInHalfOpenState=3
-resilience4j.circuitbreaker.instances.model-service.automaticTransitionFromOpenToHalfOpenEnabled=true
-
-
-##Resilience4J Timeout Properties
-#resilience4j.timelimiter.instances.model-service.timeout-duration=3s
-#
-##Resilience4J Retry Properties
-#resilience4j.retry.instances.model-service.max-attempts=3
-#resilience4j.retry.instances.model-service.wait-duration=5s
-
-spring.application.name=MODEL-STREAM-SERVICE
-eureka.client.serviceUrl.defaultZone=http://192.168.27.227:8761/eureka
-eureka.instance.hostname=192.168.24.47
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Model_System/stream-service/target/classes/utils/index_template.json

-{
-  "index_patterns": ["model-data-stream*"],
-  "data_stream": { },
-  "composed_of": [ "model-setting", "model-mapping"],
-  "priority": 670,
-  "_meta": {
-    "description": "Template for my time series data",
-    "my-custom-meta-field": "More arbitrary metadata"
-  }
-}
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Model_System/stream-service/target/classes/utils/mappings.json

-{
-  "mappings": {
-    "properties": {
-      "Device_uptime": {
-        "type": "wildcard"
-      },
-      "EX_Bits_received": {
-        "type": "wildcard"
-      },
-      "EX_Bits_sent": {
-        "type": "wildcard"
-      },
-      "EX_Inbound_packets_discarded": {
-        "type": "wildcard"
-      },
-      "EX_Inbound_packets_with_errors": {
-        "type": "wildcard"
-      },
-      "EX_Interface_type": {
-        "type": "wildcard"
-      },
-      "EX_Operational_status": {
-        "type": "wildcard"
-      },
-      "EX_Outbound_packets_discarded": {
-        "type": "wildcard"
-      },
-      "EX_Outbound_packets_with_errors": {
-        "type": "wildcard"
-      },
-      "EX_Speed": {
-        "type": "wildcard"
-      },
-      "ICMP_loss": {
-        "type": "wildcard"
-      },
-      "ICMP_ping": {
-        "type": "wildcard"
-      },
-      "ICMP_response_time": {
-        "type": "wildcard"
-      },
-      "IN_Bits_received": {
-        "type": "wildcard"
-      },
-      "IN_Bits_sent": {
-        "type": "wildcard"
-      },
-      "IN_Inbound_packets_discarded": {
-        "type": "wildcard"
-      },
-      "IN_Inbound_packets_with_errors": {
-        "type": "wildcard"
-      },
-      "IN_Interface_type": {
-        "type": "wildcard"
-      },
-      "IN_Operational_status": {
-        "type": "wildcard"
-      },
-      "IN_Outbound_packets_discarded": {
-        "type": "wildcard"
-      },
-      "IN_Outbound_packets_with_errors": {
-        "type": "wildcard"
-      },
-      "IN_Speed": {
-        "type": "wildcard"
-      },
-      "P_Bits_received": {
-        "type": "wildcard"
-      },
-      "P_Bits_sent": {
-        "type": "wildcard"
-      },
-      "P_Inbound_packets_discarded": {
-        "type": "wildcard"
-      },
-      "P_Inbound_packets_with_errors": {
-        "type": "wildcard"
-      },
-      "P_Interface_type": {
-        "type": "wildcard"
-      },
-      "P_Operational_status": {
-        "type": "wildcard"
-      },
-      "P_Outbound_packets_discarded": {
-        "type": "wildcard"
-      },
-      "P_Outbound_packets_with_errors": {
-        "type": "wildcard"
-      },
-      "P_Speed": {
-        "type": "wildcard"
-      },
-      "SNMP_availability": {
-        "type": "wildcard"
-      },
-      "class": {
-        "type": "wildcard"
-      },
-      "range": {
-        "type": "wildcard"
-      },
-      "timestamp": {
-        "type": "date",
-        "format": "date_optional_time||epoch_second"
-      },
-      "@timestamp": {
-        "type": "date",
-        "format": "date_optional_time||epoch_second"
-      }
-    }
-  }
-}
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Model_System/stream-service/target/classes/utils/policy.json

-{
-  "phases": {
-    "hot": {
-      "min_age": "0ms",
-      "actions": {
-        "set_priority": {
-          "priority": 100
-        },
-        "rollover": {
-          "max_primary_shard_size": "50gb",
-          "max_age": "60m"
-        }
-      }
-    },
-    "warm": {
-      "min_age": "60m",
-      "actions": {
-        "set_priority": {
-          "priority": 50
-        }
-      }
-    },
-    "cold": {
-      "min_age": "60m",
-      "actions": {
-        "set_priority": {
-          "priority": 0
-        }
-      }
-    }
-  }
-}
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Model_System/stream-service/target/classes/utils/settings.json

-{
-    "settings": {
-      "index.lifecycle.name": "model-policy",
-      "index.number_of_shards": 1,
-      "index.number_of_replicas": 0
-    }
-}
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