Intro

Real-time analytics means instant access to insights and data patterns derived from information as it is collected.

Data analysts can collect valuable information from an array of connected sources as data is continuously made available—everything from web-services activity to online shopping information. Once collected, data can be quickly examined using real-time analytics to derive business intelligence allowing organizations to operate more smoothly and maximize their business profits.

Technologies For Real-Time Analysis

Clearly, there is significant potential in implementing real-time analysis. The question isn’t whether to implement or not—it’s “What technologies should we use in order to reap the benefit?”

There are many technologies available to support implementing real-time analysis in your business, but the following are a few basic concepts that are absolutely critical to understand.

 

The question isn’t 'Do you implement real-time analytics or not?'—it’s 'What technologies should we use?' Click To Tweet

Traditional Database Analysis

Traditional analysis relies on using a database-based program to examine data that has been stored directly into a traditional database. That is, a user would select and implement the correct “analytical business logic as database” program or use separate logic, called Persistent Stored Module (PSM). PSM is an ISO standard extension of SQL created as a means for storing “repeatable procedures”, since SQL was originally a “data-only” language.

Traditional database analysis can be referred to as a “passive model” of analytics because it is depended on users to execute queries and act reactively when data is received. Traditionally, Oracle, IBM’s DB2 and Microsoft’s MS-SQL are the big providers in this category.

Hardware-Based Analysis

Data latency, or “Hardware-based Analysis” is used to quicken response time by minimizing the interval between “stimulation” and “action”. Hardware-based analysis enables users to transfer business logic or algorithms for real-time analysis to hardware chips, removing human response time from the equation. This helps to get the fastest response speed possible for your business.

To achieve this, field-programmable gate array (FPGA) would be integrated into hardware to configure specific user logic. An FPGA is an integrated circuit designed to be configured by an end-user, rather than by the manufacturing—thus making it “field-programmable”. Xilinx and Altera are both great examples of FPGA providers.

Data Warehouse Appliance

A “Data Warehouse Appliance” is a form of a specialized server that combines database software and dedicated hardware.

This type of server typically features high-performance architecture designed to optimized the hardware network and storage manager structure software in order to increase the performance of the database software.

Oracle’s ExaData and IBM’s Greenplum are server-based and high-speed databases. These specific examples also allow serves to improve the data processing performance by adding additional servers.

In-Memory Analysis

Analysis of data stored directly in memory is called “In-Memory Analysis”. From a database perspective, these products are designed to perform tens or hundreds of times faster than traditional disk-based databases, such as Oracle’s Times Ten or SAP’s HANA.  

In business categories where real-time analysis is important, Complex Event Processing (CEP) is often a better option. Event processing is a method of monitoring and examining real-time, event-based data streams. Complex event processing is event processing that combines information from multiple event-based data streams in order to analyze more complex input. CEP is beneficial when businesses need to quickly identify and react to potentially critical events, such as sudden, limited opportunities or possible security threats.

CEP can be referred to as an “active model” of analytics because user data is stored in memory and allows queries to be actively registered and analyzed automatically, based on data changes.

Real-Time Analysis In IIoT

Real-time data analysis is a core competency in industrial IoT. The ability to quickly analyze extreme volumes of data in real time helps to mitigate business risks while also allowing organizations to scale through analytical case recycling.

Following are just a few examples of real-time data analysis applications in IIoT.

 

 

Prevent Danger By Detecting Abnormal Data

Using abnormal data detection for danger or injury prevention is the most common use-case for the real-time analysis of sensor data such as temperature, pressure, and vibration.

For example, in plants dealing with petroleum or other hazardous chemicals, systems can be designed to monitor the physical stress and temperature of high-pressure equipment such as input and output valves.

Real-time analysis provides the ability to instantly detect and react to hundreds of data-points from strategically placed sensors along valves and other physical equipment before a dangerous situation arises.

Actively monitoring data changes for abnormal limit breaks is an effective, proactive way to improve safety.

Predict Equipment Failure By Monitoring Condition Data

Large and complex pieces of equipment used in factories often have characteristics unique to their specific set-up, making it potentially difficult to predict machine issues. Moreover, many businesses use proprietary methods and process that further increase the complexity of predicting disastrous equipment failure

In recent years, more modern equipment has been produced with many sensors pre-installed to allow for condition monitoring of the equipment at all times in order to prevent critical situations that can be harmful to operational throughput.

Through such monitoring, it is possible not only to check the degree of deterioration of any given piece of equipment but also to accurately determine the replacement timing for parts. Newer configurations also allow for pattern analysis of specific sensor data to automatically predict future failures.

Forecast Trends Through Big Data Analysis

Insights from data stored and generated from sensors can be used as a predictor of future threats. The forecasting potential of big data analysis is immense.

Although seemingly simple, finding trends or patterns from data and predicting the future implications of that pattern in real-time can help leaders make organizational decisions in advance of lost opportunities or otherwise unforeseen threats. This ability to “see the future” is an incredible business advantage.

Harmony between Machbase and Real-time analysis

Massive data storage, rapid data extraction, and its analysis are the main keys for real-time analytics in IIoT.

Machbase not only store tens of thousands of sensor data per second, but also provides the basis for analyzing hundreds of millions or billions of data through standard SQL. In particular, by providing a scale-out through the cluster structure, users can expand the amount of data to manage and improve the data processing performance. It also provides innovative data processing technology that can simultaneously utilize memory and disk, according to the time importance of recent real-time data analysis.

In recent years, large-scale data analysis for small-scale equipment, that is, real-time analysis in the terminal area has become extremely important. In order to meet various market demands, Machbase supports the data processing operation at high speed based on ARM chips.

By installing Machbase as the basis for data storage and processing, and by arranging various types of solutions in a high-level application area for the harmony with one another, the high cost and time required for IIoT real-time analysis can be greatly saved.

 


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