Stream analytics or real-time analytics analyzes and processes the data continuously. It has become popular worldwide because of the velocity of the big data collected from different sources. Due to its velocity and dynamic characteristic, it made it difficult for conventional data mining tools, technologies, and methods to analyze. This analytics is applicable while the data sources send the data in very small in size, and continuously while the data is generated. The continuous data can be collected from various Internet of Things (IoT), applications, websites, sensors, social media, mobile devices, and so on. For example, an ATM provides real-time data to its operator 24 hours a day. A person executes the transaction from an ATM, and with the help of the internet; the data is verified, stored, and executed. That means the stream is generated by a special event that is a result of the action.
Using stream analytics helps organizations can get the business information from the contentious data as conventional and historical data gave them. However, there will be no need to upload the data for analysis, but the system automatically collects the data from different sources itself. It has its benefit based on the industry. This method of data analytics brings many benefits to the industry such as healthcare, finance, retail or customer service, manufacturing, supply chain, and so forth. Below is the simple process of stream analytics.
Stream data mining
Stream data mining refers to information structure extraction as models and patterns from a continuous data stream (Tidke et al., 2018). This means stream data mining is collecting real-time data from various sources, a process in the data stream mining tools, and finally getting the insight from the data. In the stream analysis, there would be no starting and ending of the data as it continuously flows among the sources to mining tools. The below picture would help us with the process of the streaming mining process.
Picture 1: Stream data analysis process
Sources (Tidke et al., 2018).
Picture 1 explains how the data are collected from different sources such as IoT, social media, and search engines, and then passes to the data ingestion layer. The data ingestions process is significantly important for the stream data mining process as the decision is generally based on the current state of evolution of the data. So, the relevant data that is associated with the current situation are ingested as a stream. In the next stage, the generated data are cleaned and integrated. In this stage, data are summarized and made clean for analytics purposes, where it eliminates the data which was generated for example from human error or default in the sensors, and so forth. While the data is arriving contentiously and in large amounts, a fraction of the contentious data is stored in the limited memory of databases and will be processed with a real-time processing system (Warren & Marz, 2015). Furthermore, to get insight from the data, two methods are implemented. One is visualization while another method is to use the algorithm or models. Visualization is depended on the purpose of what type of insight we are looking for. Visualization can be done through pie-chart, plots, tables, graphs, and so on. It is an important component of the big data analysis process because Plaisant et al. (2015) explain that it highlights some of the business problems while visualizing a large amount of data.
In terms of using the algorithm, there is numerous method available for stream analysis. The frequently used ones are classification, regression, clustering, and pattern mining. Classification of the data is mined through the training data that which organization already has. With the training data, a method of data analysis is built and predicts the future based on the previously taught data model. We can use a decision tree and logistic regression for this method of data analysis and mining. As far as regression is concerned, it supervised learning technique used to predict real values of the label, not the discrete values as classification. However, the similarity is that regression also uses train the data and adjust in the regressor model while using the known data with the label.
Furthermore, clustering is the sorting of the data based on the similarities in characteristics. The data does not have a known group before clustering, however, with clustering it is stored under a different group. For example, sensors or CCTV cameras provide the information from all the directions, but while system stored the data as per its direction such as front door, rear door, yard, and so on. At last, frequent pattern mining helps to describe the data and find the association feature in the data that enables it to be categorized and clustered. A frequent pattern mining collects the data occurring together frequently, and the data which has a strong relationship between two items.
Stream data mining tools
There are various tools available to analyze real-time data. The tools enable to process, store, and convert the data into analytics-ready data to get the insight. While the big data concept has brought more and more attention, so do the data mining tools. One of the excessive usable tools for stream data analysis is Massive Online Analysis (MOA). It is an open-source framework that is written in Java and can be extended with new mining algorithms and stream evaluation measures (Kumar & Singh, 2017). MOA provides support for the implementation of algorithms and executes the investigation of dynamic data streams (Bifet et al., 2010).
Another platform to mine the data is Apache Sparks which is open-source software that can co-operate with Python, R, Java, SQL, and Scala APIs, which helps miners to work in their preferred environment. It was introduced in 2012 and is a unified data analytic platform. The system analyzes a large amount of data using clustered computing.
Last but not least, RapidMiner tools are cloud-based software that helps us to build the end-to-end streaming data analytics platform. It provides an integrated environment for machine learning, text mining & predictive analysis (Kumar & Singh, 2017). The system allows using a third-party application for working with statistical methods. It pre-processes, clusters, predicts, and transforms the model. The tool can be used for structured or unstructured data such as audio, video, images, and so on. Due to its interactive feature in charts and graphs, it is a highly useful tool for real-time data analysis. It enables the major steps of machine learning such as data preparation, result visualization, validation, and optimization.
Reference
Bifet, A., Holmes, G., Pfahringer, B., Kranen, P., Kremer, H., Jansen, T., & Seidl, T. (2010, September). Moa: Massive online analysis, a framework for stream classification and clustering. In Proceedings of the first workshop on applications of pattern analysis (pp. 44-50). PMLR
Kumar, A., & Singh, A. (2017). Stream mining a review: Tool and techniques. In 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) (2), 27-32. IEEE.
Plaisant, C., Monroe, M., Meyer, T., & Shneiderman, B. (2014). Interactive visualization. Big Data and Health Analytics, 243-262.
Tidke, B., Mehta, R. G., & Dhanani, J. (2018). Real-Time Bigdata Analytics: A Stream Data Mining Approach. Recent Findings in Intelligent Computing Techniques, 345-351.
Warren, J., & Marz, N. (2015). Big Data: Principles and best practices of scalable Realtime data systems. Simon and Schuster.
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