Extensive analysis of techniques in data streams - Balasubramaniam, Ramesh; Nandhini, K. (International Journal of Engineering and Technology, 2019)
Applications are generating huge capacities (volume) of data at high speeds (velocity) from various sources such as images, text, audio, and video (variety). Big data streams are generated by many applications in todayâ€™s world like IoT devices, online purchases, internet traffic, social media, stock exchanges and more. The data source decides whether the processing should be by batch or stream. It is impossible and unnecessary to record all incoming data, hence the need for data reduction techniques in data streaming. These techniques (sampling, sketching, hashing, dimension reduction, and more) enable us to narrow down the big data to relevant data. This data sampled, filtered, hashed, or processed through other techniques is used as input for data analysts to derive meaningful information. A well-designed Data Stream Management System will strike a balance between the right data processing and the cost of processing. This paper highlights the different techniques used in streaming data, related work in that area and the uses in todayâ€™s world. Â Â