![]() ![]() This paper presents a systematic review of big data stream analysis. The reason being not only that huge volume of data need to be processed but that data must be speedily processed so that organisations or businesses can react to changing conditions in real-time. The demand for stream processing is increasing. ![]() This necessitates big data stream analysis. In addition, the output must be generated with low-latency and any incoming data must be reflected in the newly generated output within seconds. Most of the data generated in a real-time data stream need real-time data analysis. Big data batch processing is not sufficient when it comes to analysing real-time application scenarios. Due to the nature of big data in terms of volume, velocity, variety, variability, veracity, volatility, and value that are being generated recently, big data computing is a new trend for future computing.īig data computing can be generally categorized into two types based on the processing requirements, which are big data batch computing and big data stream computing. In conclusion, it was recommended that research efforts should be geared towards developing scalable frameworks and algorithms that will accommodate data stream computing mode, effective resource allocation strategy and parallelization issues to cope with the ever-growing size and complexity of data.Īdvances in information technology have facilitated large volume, high-velocity of data, and the ability to store data continuously leading to several computational challenges. Only a few big data streaming tools and technologies can do all of the batch, streaming, and iterative jobs there seems to be no big data tool and technology that offers all the key features required for now and standard benchmark dataset for big data streaming analytics has not been widely adopted. We also found that although, significant research efforts have been directed to real-time analysis of big data stream not much attention has been given to the preprocessing stage of big data streams. The study found that scalability, privacy and load balancing issues as well as empirical analysis of big data streams and technologies are still open for further research efforts. Out of the initial 2295 papers that resulted from the first search string, 47 papers were found to be relevant to our research questions after implementing the inclusion and exclusion criteria. Three major databases, Scopus, ScienceDirect and EBSCO, which indexes journals and conferences that are promoted by entities such as IEEE, ACM, SpringerLink, and Elsevier were explored as data sources. It provides a global view of big data stream tools and technologies and its comparisons. In this paper, a systematic review of big data streams analysis which employed a rigorous and methodical approach to look at the trends of big data stream tools and technologies as well as methods and techniques employed in analysing big data streams. This made it difficult for existing data mining tools, technologies, methods, and techniques to be applied directly on big data streams due to the inherent dynamic characteristics of big data. ![]() Recently, big data streams have become ubiquitous due to the fact that a number of applications generate a huge amount of data at a great velocity.
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