Abstract
Proliferation of structural, semi-structural and no-structural data, has challenged the scalability, flexibility and processability of the traditional relational database management systems (RDBMS). The next generation systems demand horizontal scaling by distributing data over autonomously addable nodes to a running system. For schema flexibility, they also want to process and store different data formats along the sequence factor in the data. NoSQL approaches are solutions to these, hence big data solutions are vital nowadays. But in monitoring scenarios sensors transmit the data continuously over certain intervals of time and temporal factor is the main property of the data. Therefore the key research aspect is to investigate schema flexibility and temporal data integration aspects together. We need to know that: what data modelling should we adopt for a data driven real-time scenario; that we could store the data effectively and evolve the schema accordingly during data integration in NoSQL environments without losing big data advantages. In this paper we explain a middleware based schema model to support the temporal oriented storage of real-time data of ANT+ sensors as hierarchical documents. We explain how to adopt a schema for the data integration by using an algorithm based approach for flexible evolution of the model for a document oriented database, i.e, MongoDB. The proposed model is logical, compact for storage and evolves seamlessly upon new data integration.
Highlights
The emergence of Web 2.0 systems, the Internet of Things (IoT) and millions of users have played a vital role to build a global society, which generates volumes of data
A monitoring Windows service transmits the ANT+ sensor data in near real-time over the WebSockets protocol, where as the NodeJS [96] context data server accepts, processes and stores the Java Script Object Notations (JSON) messages in MongoDB database according to the defined schema models
The algorithm will use the object of the Mongoose schema model to perform the inserts and the updates to the document database, and such manipulations must be performed in a manner that the new data integration would result into a storage formation as defined and desired in the model
Summary
The emergence of Web 2.0 systems, the Internet of Things (IoT) and millions of users have played a vital role to build a global society, which generates volumes of data. Data stream and data stream management systems (DSMS) Data streams, as continuous and ordered flow of incoming data records, are common in wired or wireless sensor network based monitoring applications [31] Such widely used data intensive applications don’t directly target their data models for persistence storage, because the continuously arriving multiple, rapid, time-varying, and unbounded streams lose the support for storage as an entirety [31], and a portion of arrived stream is required to keep in the memory for initial processing. Limitations of RDBMS This section explains what traditional relational approaches lack, why they are not best fit for managing time-variant, dynamically large and flowing data This absence has opened the door for a disruptive technology to enter into the market and to gain widespread adoption in the form of NoSQL databases, as it offers better, efficient, cheaper, flexible and scalable solutions [43].
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