Towards Real-time Streaming Analytics based on Cloud Computing

Abstract

Nowadays, streaming data overflows from a diversity of sources and technologies, making traditional data analytics technologies unsuitable to handle the latency of data processing relative to the growing demand for high processing speed and algorithmically scalability. Real-time streaming data analytics is needed to allow applications to analyze streaming data effectively and efficiently. The open source software Apache Storm, which is a distributed computation system for processing streaming data in real-time, has been widely used for building applications to analyze streaming data in real-time because it is fast, scalable, fault tolerant and reliable. This paper proposes a cloud-based architecture based on Apache Storm for supporting an entire streaming data analytics workflow, which consists of data ingestion, data processing, data visualization and data storing. The application on the proposed system is available at http://206.167.181.16:8000/map4.html.

A Study Review of Common Big Data Architecture for Small-medium Enterprise

“…For instance, geolocation data from mobile phone apps could help the enterprise in targeting its users with some campaign and advertisement. There is ta research that geotagged tweets could be collected to analyze Twitter's trending topics to be mapped to the map chart an visualize the relevant geo-tagged tweet in realtime [15]. It's not limited to the tagged content such as mentioned before, there is also research that enables the mobile apps to collect the geolocation of travelers and monitored them whenever they arrived in the city of their destination and spending time on the place visited by the tourists [16].…”