Automatic Query Driven Data Modelling in Cassandra

Hernandez, Roger; Becerra, Yolanda; Torres, Jordi; Ayguadé, Eduard

doi:10.1016/j.procs.2015.05.441

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…The infrastructure layer delivers availability, scalability, and data integrity for the upper layers, with networking, processing, and storage resources [46]. It manages data with MapReduce in distributed computing [44] and stores data with Apache Hadoop in Cassandra NoSQL DB [47] to improve the error tolerance. This layer also accepts complex computation tasks from upper layers-authentication or interoperability-to alleviate their resources for their primary operations [48].…”

Section: System Designmentioning

confidence: 99%

Medical Prognosis of Infectious Diseases in Nursing Homes by Applying Machine Learning on Clinical Data Collected in Cloud Microservices

Garcés-Jiménez

Calderón-Gómez

Pulido

et al. 2021

IJERPH

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Background: treating infectious diseases in elderly individuals is difficult; patient referral to emergency services often occurs, since the elderly tend to arrive at consultations with advanced, serious symptoms. Aim: it was hypothesized that anticipating an infectious disease diagnosis by a few days could significantly improve a patient’s well-being and reduce the burden on emergency health system services. Methods: vital signs from residents were taken daily and transferred to a database in the cloud. Classifiers were used to recognize patterns in the spatial domain process of the collected data. Doctors reported their diagnoses when any disease presented. A flexible microservice architecture provided access and functionality to the system. Results: combining two different domains, health and technology, is not easy, but the results are encouraging. The classifiers reported good results; the system has been well accepted by medical personnel and is proving to be cost-effective and a good solution to service disadvantaged areas. In this context, this research found the importance of certain clinical variables in the identification of infectious diseases. Conclusions: this work explores how to apply mobile communications, cloud services, and machine learning technology, in order to provide efficient tools for medical staff in nursing homes. The scalable architecture can be extended to big data applications that may extract valuable knowledge patterns for medical research.

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Section: System Designmentioning

confidence: 99%

Medical Prognosis of Infectious Diseases in Nursing Homes by Applying Machine Learning on Clinical Data Collected in Cloud Microservices

Garcés-Jiménez

Calderón-Gómez

Pulido

et al. 2021

IJERPH

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“…Several research works are carried out to identify the suitability of NoSQL database to manage big social data with efficient storage, fast querying, and horizontal scalability [20,33]. Different approaches are proposed to model a huge volume of Twitter data set in Apache Cassandra NoSQL database for an efficient querying [11,26,40].…”

Section: Related Workmentioning

confidence: 99%

Big social data provenance framework for Zero-Information Loss Key-Value Pair (KVP) Database

Rani

Goyal

Gadia

2021

Int J Data Sci Anal

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“…In this work, we used a fusion of computational paradigms (edge and cloud) [31], [32], based on a hybrid cloud architecture, with the aim of supporting the implementation of versatile high-performance applications using the microservices pattern. The main arguments for this approach are as follows: [33], with the aim of achieving greater tolerance to errors in the cloud environment, for example by using MapReduce to manage data in distributed processing [34] and Apache Hadoop to store the data corresponding to each group of microservices within NoSQL database storage structures (Cassandra) [35]. The infrastructure layer also processes complex computation tasks from the upper layers, such as those relating to authentication or interoperability, by alleviating overloaded public or private cloud resources for other primary functions [30], [36], running on Docker containers [37].…”

Section: A Design Of the Architecturementioning

confidence: 99%

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

et al. 2020

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This article describes the design, development and implementation of a set of microservices based on an architecture that enables detection and assisted clinical diagnosis within the field of infectious diseases of elderly patients, via a telemonitoring system. The proposed system is designed to continuously update a medical database fed with vital signs from biosensor kits applied by nurses to elderly people on a daily basis. The database is hosted in the cloud and is managed by a flexible microservices software architecture. The computational paradigms of the edge and the cloud were used in the implementation of a hybrid cloud architecture in order to support versatile high-performance applications under the microservices pattern for the pre-diagnosis of infectious diseases in elderly patients. The results of an analysis of the usability of the equipment, the performance of the architecture and the service concept show that the proposed e-health system is feasible and innovative. The system components are also selected to give a cost-effective implementation for people living in disadvantaged areas. The proposed e-health system is also suitable for distributed computing, big data and NoSQL structures, thus allowing the immediate application of machine learning and AI algorithms to discover knowledge patterns from the overall population.

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Automatic Query Driven Data Modelling in Cassandra

Cited by 7 publications

References 3 publications

Medical Prognosis of Infectious Diseases in Nursing Homes by Applying Machine Learning on Clinical Data Collected in Cloud Microservices

Medical Prognosis of Infectious Diseases in Nursing Homes by Applying Machine Learning on Clinical Data Collected in Cloud Microservices

Big social data provenance framework for Zero-Information Loss Key-Value Pair (KVP) Database

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

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