A Review on Big Data Stream Processing Applications: Contributions, Benefits, and Limitations

Abstract

The amount of data in our world has been rapidly keep growing from time to time. In the era of big data, the efficient processing and analysis of big data using machine learning algorithm is highly required, especially when the data comes in form of streams. There is no doubt that big data has become an important source of information and knowledge in making decision process. Nevertheless, dealing with this kind of data comes with great difficulties; thus, several techniques have been used in analyzing the data in the form of streams. Many techniques have been proposed and studied to handle big data and give decisions based on off-line batch analysis. Today, we need to make a constructive decision based on online streaming data analysis. Many researchers in recent years proposed some different kind of frameworks for processing the big data streaming. In this work, we explore and present in detail some of the recent achievements in big data streaming in term of contributions, benefits, and limitations. As well as some of recent platforms suitable to be used for big data streaming analytics. Moreover, we also highlight several issues that will be faced in big data stream processing. In conclusion, it is hoped that this study will assist the researchers in choosing the best and suitable framework for big data streaming projects.

Adoption of Smart Farming Technology Among Rice Farmers

“…Mohamed et al, [8] agree that the application of IoT can increase productivity. The prospect of implementing advance technology practices is observed more viable in granary areas, where it is under management of large corporation, for instance rice farming started with the introduction of smart farming in 2001 at IADA Barat Laut Selangor, known as PADDY-GIS [13]. PADDY-GIS is a decision support system (DSS) application that assists farmers in managing their paddy fields PADDY-GIS is a type of precision farming by using smart farming technology.…”

“…Precision farming practices using smart farming technology allow farmers to collect and generate high-quality agricultural data such as crop and soil element data so that it can facilitate them on the necessary decision for managing their crop. Several recent and advance technology in smart farming are Remote Sensing, Geographic Information Systems (GIS) and Global Positioning Systems (GPS) [11,13]. Therefore, smart agriculture needs to be comprehensively integrated in the country's rice cultivation activities because the issue of rice consumption that is constantly increasing due to the increase in population is always the main agenda in food security discussions.…”

Streaming Data Processing

“…A streaming analytics application receives the output data and incorporates it into the output stream. Analytics (like predicting the future based on certain past events), transformations (like the alteration of a specified number to the date format after performing certain action), enrichment (i.e., a combination of the data point with other data sources so may the useful & meaningful outcomes can be generated), as well as ingestion (like the data insertion into a database), are some of the actions that stream processing takes on data [1].…”