An evaluation of the performance of a NoSQL document database in a simulation of a large scale Electronic Health Record (EHR) system

Electronic Healthcare Record (EHR) systems can provide significant benefits by improving the effectiveness of healthcare systems. Research and industry projects focusing on storing healthcare information in NoSQL databases has been triggered by practical experience demonstrating that a relational database approach to managing healthcare records has become a bottleneck. Previous studies show that NoSQL databases based on consistency, availability and partition tolerance (CAP) theorem have significant advantages over relational databases such as easy and automatic scaling, better performance and high availability. However, there is limited empirical research that has evaluated the suitability of NoSQL databases for managing EHRs. This research addressed this identified research problem and gap in the literature by investigating the following general research: How can a simulation of a large EHR system be developed so that the performance of NoSQL document databases comparative to relational databases can be evaluated? Using a Design Science approach informed by a pragmatic worldview, a number of IT artefacts were developed to enable an evaluation of performance of a NoSQL document oriented database comparative to a relational database in a simulation of a large scale EHR system. These were healthcare data models (NoSQL document database, relational database) for the Australian Healthcare context, a random healthcare data generator and a prototype EHR system. The performance of a NoSQL document database (Couchbase) was evaluated comparative to a relational database (MySQL) in terms database operations (insert, update, delete of EHRs), scalability, EHR sharing and data analysis (complex querying) capabilities in a simulation of a large scale EHR system, constructed in the cloud environment of Amazon Web Services (AWS). Test scenarios consisted of a number of different configurations ranging from 1, 2, 4, 8 and 16 nodes for 1Million, 10 Million, 100 Million and 500 Million records to simulate database operations in a large scale and distributed EHR system environment. The Couchbase NoSQL document database was found to perform significantly better than the MySQL relational database in most of the test cases in terms of database operations -insert, update, delete of EHRs, scalability and EHR sharing. However, the MySQL relational database was found to perform significantly better than the Couchbase NoSQL document database for the complex query test that demonstrates basic analysis capabilities. Furthermore, the Couchbase NoSQL document database used significantly more disk space than the MySQL relational database to store the same number of EHRs. This research made a number of important contributions to knowledge, theory and practice. The main theoretical contribution to design theory was the design and evaluation of a prototype EHR system for simulating database management operations in a large scale EHR system environment. The prototype EHR system was underpinned by the development of two data models with data structures designed for a NoSQL document database and a relational database and a random healthcare data generator which were based on Australian Healthcare data characteristics and statistics. The design of a data model for EHRs for a NoSQL document database using an aggregated document modelling approach provided an important contribution to data modelling theory for NoSQL document databases using de-normalisation and document aggregation. The design of a random healthcare data generator was another important contribution to design theory and was based on a data distribution algorithm (multinomial distribution and probability theory) informed by National Health Data Dictionary and published Australian Healthcare statistics. The prototype EHR system allowed this study to demonstrate through a simulated performance evaluation that a NoSQL document database has significant and proven performance advantages over relational databases in most of the database management test cases. Hence this study demonstrated the utility and efficacy of a NoSQL document database in the simulation of a large scale EHR system. This research has made a number of important contributions to practice. Foremost is that the IT artefacts (namely, a data model for storing EHRs in a NoSQL document database, a random healthcare data generator and a prototype EHR system) developed and evaluated in this research can be readily adopted by practitioners. Another important practical contribution of this research is that it is based on the open source availability of NoSQL database and relational database alternatives. Hence, this research can provide a sound basis for lower-income countries as well higher-income countries to establish their own cost-effective national EHR systems without the restrictions, limitations, complexity or complications of similar proprietary relational database systems

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

Towards a Novel Cooperative Logistics Information System Framework

Supply Chains and Logistics have a growing importance in global economy. Supply Chain Information Systems over the world are heterogeneous and each one can both produce and receive massive amounts of structured and unstructured data in real-time, which are usually generated by information systems, connected objects or manually by humans. This heterogeneity is due to Logistics Information Systems components and processes that are developed by different modelling methods and running on many platforms; hence, decision making process is difficult in such multi-actor environment. In this paper we identify some current challenges and integration issues between separately designed Logistics Information Systems (LIS), and we propose a Distributed Cooperative Logistics Platform (DCLP) framework based on NoSQL, which facilitates real-time cooperation between stakeholders and improves decision making process in a multi-actor environment. We included also a case study of Hospital Supply Chain (HSC), and a brief discussion on perspectives and future scope of work

Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks

Sensors are present in various forms all around the world such as mobile phones, surveillance cameras, smart televisions, intelligent refrigerators and blood pressure monitors. Usually, most of the sensors are a part of some other system with similar sensors that compose a network. One of such networks is composed of millions of sensors connect to the Internet which is called Internet of things (IoT). With the advances in wireless communication technologies, multimedia sensors and their networks are expected to be major components in IoT. Many studies have already been done on wireless multimedia sensor networks in diverse domains like fire detection, city surveillance, early warning systems, etc. All those applications position sensor nodes and collect their data for a long time period with real-time data flow, which is considered as big data. Big data may be structured or unstructured and needs to be stored for further processing and analyzing. Analyzing multimedia big data is a challenging task requiring a high-level modeling to efficiently extract valuable information/knowledge from data. In this study, we propose a big database model based on graph database model for handling data generated by wireless multimedia sensor networks. We introduce a simulator to generate synthetic data and store and query big data using graph model as a big database. For this purpose, we evaluate the well-known graph-based NoSQL databases, Neo4j and OrientDB, and a relational database, MySQL.We have run a number of query experiments on our implemented simulator to show that which database system(s) for surveillance in wireless multimedia sensor networks is efficient and scalable