Expediting assessments of database performance for streams of respiratory parameters (original) (raw)
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Proceedings of the 11th International Conference on e-Health 2019, 2019
In recent years, there has been a significant increase in the number and variety of application scenarios studied under the e-health. Each application generates an immense data that is growing constantly. In this context, it becomes an important challenge to store and analyze the data efficiently and economically via conventional database management tools. The traditional relational database systems may sometimes not answer the requirements of the increased type, volume, velocity and dynamic structure of the new datasets. Effective healthcare data management and its transformation into information/knowledge are therefore challenging issues. So, organizations especially hospitals and medical centers that deal with immense data, either have to purchase new systems or re-tool what they already have. The new data models so-called NOSQL, its management tool Hadoop Distributed File Systems is replacing RDBMs especially in real-time healthcare data analytics processes. It becomes a real challenge to perform complex reporting in these applications as the size of the data grows exponentially. Along with that, there is customers demand complex analysis and reporting on those data. Compared to the traditional DBs, Hadoop Framework is designed to process a large volume of data. In this study, we examine the query performance of a traditional DBs and Big Data platforms on healthcare data. In this paper, we try to explore whether it is really necessary to invest on big data environment to run queries on the high volume data or this can also be done with the current relational database management systems and their supporting hardware infrastructure. We present our experience and a comprehensive performance evaluation of data management systems in the context of application performance.
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International Journal of Database Management Systems (IJDMS ), 2018
The amount of data stored in IoT databases increases as the IoT applications extend throughout smart city appliances, industry and agriculture. Contemporary database systems must process huge amounts of sensory and actuator data in real-time or interactively. Facing this first wave of IoT revolution, database vendors struggle day-by-day in order to gain more market share, develop new capabilities and attempt to overcome the disadvantages of previous releases, while providing features for the IoT. There are two popular database types: The Relational Database Management Systems and NoSQL databases, with NoSQL gaining ground on IoT data storage. In the context of this paper these two types are examined. Focusing on open source databases, the authors experiment on IoT data sets and pose an answer to the question which one performs better than the other. It is a comparative study on the performance of the commonly market used open source databases, presenting results for the NoSQL MongoD...
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PLOS ONE, 2016
Wearable devices that measure physiological signals to help develop self-health management habits have become increasingly popular in recent years. These records are conducive for follow-up health and medical care. In this study, based on the characteristics of the observed physiological signal records-1) a large number of users, 2) a large amount of data, 3) low information variability, 4) data privacy authorization, and 5) data access by designated users-we wish to resolve physiological signal record-relevant issues utilizing the advantages of the Database as a Service (DaaS) model. Storing a large amount of data using file patterns can reduce database load, allowing users to access data efficiently; the privacy control settings allow users to store data securely. The results of the experiment show that the proposed system has better database access performance than a traditional relational database, with a small difference in database volume, thus proving that the proposed system can improve data storage performance.
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Proceedings of the 7th International Conference on Data Science, Technology and Applications, 2018
Sensors gained a significant role in the Internet of Things (IoT) applications in various industry sectors. The information retrieved from the sensors are generally stored in the database for post-processing and analysis. This sensor database could grow rapidly when the data is frequently collected by several sensors altogether. It is thus often required to scale databases as the volume of data increases dramatically. Cloud computing and new database technologies has become key technologies to solve these problems. Traditionally relational SQL databases are widely used and have proved reliable over time. However, the scalability of SQL databases at large scale has always been an issue. With the ever-growing data volumes, various new database technologies have appeared which proposes performance and scalability gains under severe conditions. They have often named as NoSQL databases as opposed to SQL databases. One of the challenges that have arisen is knowing how and when to migrate existing relational databases to NoSQL databases for performance and scalability. In the current paper, we present a work in progress with the DataBio project for the SensLog application case study with some initial success. We will report on the ideas and the migration approach of SensLog platform and the performance benchmarking.
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Proceedings …, 2003
Data retrieved from body sensors such as ECG machines and new-generation multi-sensor systems such as respiratory monitors are varied and abundant. Managing and integrating this streaming data with existing types of medical information such as patient records, laboratory results, procedures, medical documents, and medical images in a logical and intuitive way is a challenge. Not only is the management of such data difficult but so is the visualization and presentation of the data to physicians, specialists, and patients. Using a new generation of lifeshirts embedded with real time monitors for respiratory and heart functions as a testbed, we propose and have initiated development of a data management system for dealing with such streaming body sensor data, under the framework of an extensible architecture that will support easy visualization of such data.
Procedia Computer Science, 2015
The Internet of Things (IoT) generates massive streams of data which call for ever more efficient real time processing. Designing and implementing a big data service for the real time processing of such data requires an extensive knowledge of both input load and data distribution in order to provide a service which can cope with the workload. In this context, we study in this paper the challenges inherent to the real time processing of massive data flows from the IoT. We provide a detailed analysis of traces gathered from a well-known healthcare sport-oriented application in order to illustrate our conclusions from a big data perspective.
2022
Time-series data has an increasingly growing usage in Industrial Internet of Things (IIoT) and large-scale scientific experiments. Managing time-series data needs a storage engine that can keep up with their constantly growing volumes while providing an acceptable query latency. While traditional ACID databases favor consistency over performance, many time-series databases with novel storage engines have been developed to provide better ingestion performance and lower query latency. To understand how the unique design of a time-series database affects its performance, we design SciTS, a highly extensible and parameterizable benchmark for time-series data. The benchmark studies the data ingestion capabilities of time-series databases especially as they grow larger in size. It also studies the latencies of 5 practical queries from the scientific experiments use case. We use SciTS to evaluate the performance of 4 databases of 4 distinct storage engines: ClickHouse, InfluxDB, TimescaleDB, and PostgreSQL.
Evaluating connectivity for a heart failure database
Computers in Cardiology
In this study it has been evaluated whether connecting a heart failure database with other information sources is technically andfinancially feasible. This database is used to collect research data for the outpatient clinic for heart failure patients and has been implemented in Microsoft Access. Four connections are required for a maximum link-up of the database. However, only one of these connections is technically feasible. Therefore it was concluded that connecting a medium scale research application like this heart failure database to other information systems is not (yet) a realistic option.