A New Method for Time-Series Big Data Effective Storage
Mahmoudreza Tahmassebpour · IEEE Access · 2017
Today, one of the main challenges of big data research is the processing of big time-series data. Moreover, time data analysis is of considerable importance, because previous trends are useful for predicting the future. Due to the considerable delay when the volume of the data increases, the presence of redundancy, and the innate lack of time-series structures, the traditional relational data model does not seem to be adequately able to analyze time data. Moreover, many traditional data structures do not support time operators, which results in an inefficient access to time data. Therefore, relational database management systems have difficulty in dealing with big data-it may require massively parallel software that runs on many servers. This has led us to implement Chronos Software, an in-memory background-based time database for key-value pairs; this software was implemented using C++ language. An independent design has been suggested through appropriately using temporal algorithms, parallelism algorithms, and methods of data storage in RAM. Our results indicate that the employment of RAM for storing the data and of the Timeline Index algorithm for getting access to the time background of the keys in Chronos translate into an increase of about 40%-90% in the efficiency as compared with other databases, such as MySQL and MongoDB.