Since the first computers were introduced in the middle of the twentieth century, science and industry alike have learned that processing and analyzing big, raw data sets can be hugely rewarding. However, as datasets grow, so does the computational challenge of processing them.
Image Credit: temp-64GTX/Shutterstock.com
Artificial intelligence (AI) has evolved as one of the primary options for solving this challenge, using AI systems instead of manual data processing to save time and boost industrial innovation.
Data Processing Methods to Handle Big Data
Big data analytics – the science of transforming large, raw datasets into useful knowledge – has disrupted almost every industry and sector in the global economy today. Data analysis is a fundamental technology underpinning almost every technological development in the last half-century, from stock markets to spacecraft.
The value that data owners can glean from these big datasets comes from their size: with a large enough sample, the future behaviors of the sample as a whole become predictable to a surprising degree of accuracy.
Still, manipulation of data sets this size presents a challenge: the larger the dataset is (and the more valuable it is), the harder it is to process. Data processing methods include collecting data in a usable or computer-readable format, storing it, retrieving it, comparing it with other data points, and performing functions or actions on it or with it.
Such data processing methods are achieved with algorithms: mathematical instructions that tell a computer what task to perform on or with what piece of data and when, and what to do with the resulting information.
A computer's processing power can be considered the speed with which it can complete individual tasks: the superfast computers we use today compute almost instantaneously and can perform millions of tasks at once.
Datasets, however, have continued to grow in size and complexity since the first computers were introduced. This is partly due to the realization of how much value they present to their owners; researchers and industry began to actively seek ways to increase their data gathering activities.
In recent years, networks of users have expanded rapidly as companies like Google and Facebook leveraged a useful technological proposition to gain access to meaningful information that is valuable to marketers.
Passive sensing, remote facilities, and automated networks have also added more data streams to the processing workload. The Internet of Things (IoT) and Industrial Internet of Things (IIoT) are extracting data from objects and devices that had previously been mute.
At the same time, our technological capacity for transmitting, storing, and processing large amounts of data has exponentially increased. High-bandwidth broadband, mobile data networks, and cloud computing are factors in the growing scale of the data processing need.
As well as increasing the technological capacity for transmitting, storing, and processing data with better hardware and computer architecture, cutting-edge data science employs artificial intelligence (AI) to process data quickly.
AI is the application of algorithms to make computers behave in a way that seems intelligent. Computers that can perform millions of mathematical functions simultaneously can learn from the data they interact with and even change behaviors to respond to what they have learned. This is called machine learning (ML) and is one example of an AI tool used for data processing.
Advances in AI-Based Electronic Data Processing
The Artificial Intelligence for Data Analytics project (AIDA) was an extended five-year research program from The Alan Turing Institute, a UK research body specializing in AI, that culminated in the summer of 2021.
It filled a research gap for AI solutions for data "wrangling", the laborious tasks of understanding the available data, integrating it from various sources, finding missing, messy, or anomalous data, and extracting metrics for computer modeling.
Researchers said these time-consuming data processing tasks represent as much as 80% of typical data science projects' workloads.
The AIDA project drew on AI and developments in machine learning algorithms to partially automate data processing tasks known as wrangling. Through over 20 papers, and the code and datasets that accompanied them, the researchers demonstrated their successful achievement of all the main objectives. These include building AI assistants that can help with data preparation tasks and integrating them into an open-source platform. The team also provided example case studies of cutting-edge real-world data wrangling.
Many of the AIDA project outcomes led directly to developments in AI that enabled machines to complete the data processing cycle faster.
An early result was the development of a user interface called Data Diff. Data Diff enabled researchers to repeat data analysis tasks on different datasets more easily.
Later, the team developed a family of systems intended to improve AI's so-called "semantic understanding" of data in table format. Semantic AI works by emulating or dealing with language data. The first edition of this family, ColNet was capable of predicting semantic types from a row of data.
In a paper, "Wrangling Messy CSV Files by Detecting Row and Type Patterns" published in the journal Data Mining and Knowledge Discovery, the AIDA team presented a new AI technique to detect formatting parameters automatically in comma separated value (CSV) files. The method automatically standardizes CSV data, rapidly speeding up any kind of data processing.
References and Further Reading
The Alan Turing Institute. (2022) Artificial intelligence for data analytics (AIDA). [Online] Available at: https:l//www.turing.ac.uk/research/research-projects/artificial-intelligence-data-analytics-aida.
EE Paper (2021). Microsoft will use FPGA to speed up the processing of real-time data. [Online] EE-paper.com. Available at: https://ee-paper.com/microsoft-will-use-fpga-to-speed-up-the-processing-of-real-time-data/.
Space Daily (2022). Lion will roam above the planet - KP Labs to release their "king of orbit". [Online] SpaceDaily.com. Available at: https://www.spacedaily.com/reports/Lion_will_roam_above_the_planet___KP_Labs_to_release_their_king_of_orbit__999.html?utm_source=ground.news&utm_medium=referral