Session

Computer Science and Communication Engineering

Description

In today's digital landscape, the vast flow of data has become an integral part of every sector, from healthcare to finance. This continuous generation of voluminous data, often referred to as "data streaming," plays a critical role in processing real-time information, where low-latency and highthroughput solutions are essential. We explored the key frameworks and technologies used for event and data streaming, focusing on managing medical signals such as temperature and oxygen levels. The study evaluates the effectiveness of platforms like Apache Kafka, Apache Storm, Apache Flank, Apache Spark, and Amazon Kinesis in processing large-scale data streams. An experimental REST API was developed using C# and .NET, integrated with Kafka to handle real-time medical signals. The results demonstrated Kafka’s superior ability to manage continuous data streams, making it a prime solution for real-time medical data streaming, while also identifying areas for future improvements in event processing and data management strategies. In today's digital landscape, the vast flow of data has become an integral part of every sector, from healthcare to finance. This continuous generation of voluminous data, often referred to as "data streaming," plays a critical role in processing real-time information, where low-latency and high-throughput solutions are essential. We explored the key frameworks and technologies used for event and data streaming, focusing on managing medical signals such as temperature and oxygen levels. The study evaluates the effectiveness of platforms like Apache Kafka, Apache Storm, Apache Flank, Apache Spark, and Amazon Kinesis in processing large-scale data streams. An experimental REST API was developed using C# and .NET, integrated with Kafka to handle real-time medical signals. The results demonstrated Kafka’s superior ability to manage continuous data streams, making it a prime solution for real-time medical data streaming, while also identifying areas for future improvements in event processing and data management strategies.

Keywords:

Data Streaming, Event streaming, medical signals, Apache Kafka, Apache Storm, Apache Spark, Apache Flank, Amazon Kinesis

Proceedings Editor

Edmond Hajrizi

Location

UBT Kampus, Lipjan

Start Date

25-10-2024 9:00 AM

End Date

27-10-2024 6:00 PM

DOI

10.33107/ubt-ic.2024.405

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Oct 25th, 9:00 AM Oct 27th, 6:00 PM

Handling mass-data in modern healthcare: a review of data streaming technologies

UBT Kampus, Lipjan

In today's digital landscape, the vast flow of data has become an integral part of every sector, from healthcare to finance. This continuous generation of voluminous data, often referred to as "data streaming," plays a critical role in processing real-time information, where low-latency and highthroughput solutions are essential. We explored the key frameworks and technologies used for event and data streaming, focusing on managing medical signals such as temperature and oxygen levels. The study evaluates the effectiveness of platforms like Apache Kafka, Apache Storm, Apache Flank, Apache Spark, and Amazon Kinesis in processing large-scale data streams. An experimental REST API was developed using C# and .NET, integrated with Kafka to handle real-time medical signals. The results demonstrated Kafka’s superior ability to manage continuous data streams, making it a prime solution for real-time medical data streaming, while also identifying areas for future improvements in event processing and data management strategies. In today's digital landscape, the vast flow of data has become an integral part of every sector, from healthcare to finance. This continuous generation of voluminous data, often referred to as "data streaming," plays a critical role in processing real-time information, where low-latency and high-throughput solutions are essential. We explored the key frameworks and technologies used for event and data streaming, focusing on managing medical signals such as temperature and oxygen levels. The study evaluates the effectiveness of platforms like Apache Kafka, Apache Storm, Apache Flank, Apache Spark, and Amazon Kinesis in processing large-scale data streams. An experimental REST API was developed using C# and .NET, integrated with Kafka to handle real-time medical signals. The results demonstrated Kafka’s superior ability to manage continuous data streams, making it a prime solution for real-time medical data streaming, while also identifying areas for future improvements in event processing and data management strategies.