diff --git a/experiments/input/test.csv b/experiments/input/test.csv
new file mode 100644
index 00000000..0bcd92e8
--- /dev/null
+++ b/experiments/input/test.csv
@@ -0,0 +1,18 @@
+,trace_id,event_type,timestamp
+0,0,a,2020-08-15 12:56:42
+1,0,b,2020-08-16 12:56:42
+2,0,a,2020-08-19 12:56:42
+3,0,b,2020-08-20 14:21:02
+4,1,b,2020-08-15 12:11:54
+5,1,c,2020-08-16 12:11:54
+6,2,c,2020-08-15 12:31:04
+7,2,b,2020-08-16 12:31:04
+8,2,a,2020-08-18 12:31:04
+9,0,a,2020-09-03 12:56:42
+10,0,b,2020-09-05 12:56:42
+11,1,a,2020-09-05 12:11:54
+12,2,c,2020-09-07 12:31:04
+13,2,b,2020-09-08 12:31:04
+14,3,c,2020-09-07 12:31:04
+15,3,d,2020-09-08 12:31:04
+16,3,a,2020-09-09 12:31:04
diff --git a/src/main/scala/auth/datalab/siesta/BusinessLogic/IngestData/ReadLogFile.scala b/src/main/scala/auth/datalab/siesta/BusinessLogic/IngestData/ReadLogFile.scala
index e70dba31..7e03e52e 100644
--- a/src/main/scala/auth/datalab/siesta/BusinessLogic/IngestData/ReadLogFile.scala
+++ b/src/main/scala/auth/datalab/siesta/BusinessLogic/IngestData/ReadLogFile.scala
@@ -7,6 +7,7 @@ import org.deckfour.xes.in.XParserRegistry
 import org.deckfour.xes.model.{XLog, XTrace}
 
 import java.io.{File, FileInputStream}
+import java.sql.Timestamp
 import java.text.SimpleDateFormat
 import java.util.Scanner
 import scala.collection.convert.ImplicitConversions.`list asScalaBuffer`
@@ -43,7 +44,9 @@ object ReadLogFile {
       this.readFromXes(fileName)
     } else if (fileName.split('.')(1) == "withTimestamp") {
       this.readWithTimestamps(fileName, ",", "/delab/")
-    } else {
+    } else if (fileName.split('.')(1) == "csv") {
+      this.readFromCSV(fileName)
+    }else {
       throw new Exception("Not recognised file type")
     }
   }
@@ -191,4 +194,37 @@ object ReadLogFile {
     par
   }
 
+  def readFromCSV(fileName: String): RDD[Sequence] = {
+    val spark = SparkSession.builder().getOrCreate()
+    // Read CSV file into DataFrame
+    val df = spark.read.option("header", "true")
+      .csv(fileName)
+      .select("trace_id", "event_type", "timestamp")
+
+    // Convert DataFrame to RDD and group by trace_id
+    val groupedRDD = df.rdd.map(row =>
+      (row.getString(0), new Event(timestamp = row.getString(2), event_type = row.getString(1), trace_id = row.getString(0),
+        position = 0))
+    ).combineByKey(
+      (event: Event) => List(event), // Create a list with the first event
+      (acc: List[Event], event: Event) => event :: acc, // Add event to the list
+      (acc1: List[Event], acc2: List[Event]) => acc1 ++ acc2 // Merge lists from different partitions
+    )
+
+    // Sort by timestamp and assign positions
+    import spark.implicits._
+    val sequencesRDD = groupedRDD.map { case (traceId, events) =>
+      val sortedEvents = events.map(x=>(x,Timestamp.valueOf(x.timestamp).getTime)).toList
+        .sortBy(_._2).zipWithIndex.map {case (event,index)=>
+          new Event(timestamp = event._1.timestamp,event_type = event._1.event_type, trace_id = event._1.trace_id,
+            position = index
+          )
+      }
+      new Sequence(sortedEvents, traceId)
+    }
+
+    sequencesRDD
+  }
+
+
 }