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The Biggest Data Mining Challenges Facing IoT

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The Biggest Data Mining Challenges Facing IoT

With data being a main focal point of IoT, platforms and solutions must find ways to deal with more and more data from disparate sources with varying levels of integrity.

· IoT Zone
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Kevin Ashton is credited with coining the term “Internet of Things” just before the turn of the century. However, the term has only recently started to become a household word. Advances in big data have transformed the IoT into a disruptive technology, but some limitations still exist.

According to Feng Chen of the Department of Computer Science at Louisiana State University and other big data research pioneers, data mining is one of the biggest challenges facing the IoT. Fortunately, new data mining tools are making it easier, so the proliferation of the IoT will be the next stage of the big data revolution.

How Do Data Mining Processes Work?

IoT data mining processes are broken up into multiple stages, as follows:

  • Data is integrated according to different data sources.
  • Data is cleaned, so it can be easily extracted and processed.
  • Some parts of data are extracted and prepared for future processing.
  • Sophisticated algorithms are used to identify patterns.
  • Data is restructured and presented to the users in a coherent way.

While all data mining tools follow the same template, their functionality differs. Unfortunately, several problems exist.

Data Mining Challenges with the IoT

According to recent trending reports, the following challenges can complicate data mining efforts.

Increasingly Large Volumes of Data

As new applications become increasingly complex, developers are facing greater pressure to process much larger data sets. Some applications require data scientists to extract and analyze multiple petabytes of data. This is a

“With the rapid development of IoT, big data, and cloud computing, the most fundamental challenge is to explore the large volumes of data and extract useful information or knowledge for future actions,” writes Fang.

According to EMC, IT support companies are under increased pressure to help their customers find solutions to manage large volumes of data. “It is doubling in size every two years, and by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes.”

Data Sets Aren’t Homogenous

Before the IoT took off, most applications received data from a single source. Since data was already structured in the same format, data scientists rarely encountered compatibility issues.

The IoT has introduced a new layer of complexity for data analysis. Data is curated from many different sources in multiple formats, such as web documents, CSV sheets, and SQL tables. Before big data analytics tools can process it, they must clean the data and convert it into a single structure.

Integrity of Different Sources

Since data is curated from various sources, it may be difficult to make apples-to-apples comparisons between data sets. Each system may use its own methodology to develop data, which will always introduce some level of uncertainty. Unfortunately, a viable solution to this challenge has yet to present itself, but the complications should be minimal for most real-world applications.

Need for Real-Time Analysis

Some applications require data to be extracted and processed in real-time. This can be a challenge when analyzing data sets that are terabytes or petabytes in size.

Solutions to These Challenges

While a number of big data mining problems have surfaced with the IoT, new solutions have been developed to address them. Hadoop and other big data extraction tools have helped make it easier than ever to extract large data sets. Hadoop is an invaluable big data tool for companies like Photolemur that have to process millions of pixels in a matter of minutes.

Forrester principal analyst Mike Gualtieri told ZDNet that Hadoop has been a profoundly useful tool for large organizations. Almost half of large companies are conducting Hadoop proofs of concept to determine if the technology is a viable solution to their big data needs.

"Hadoop is not big data. It's a big-data technology. You can break down the silos but Hadoop is also a framework for processing the data. Hadoop is the first data operating system — that's what makes it so powerful, and 81 percent of large enterprises are interested in it. But maybe they're not all believers yet."

More companies are expected to take advantage of Hadoop as the IoT becomes more widely adopted.

Data Mining Challenges Will Be Addressed as the IoT Ages

The IoT is changing the world in remarkable ways. While some roadblocks remain with big data mining, these problems are gradually being addressed with newer and more versatile data mining algorithms.

Accelerate the delivery of high-quality software in the connected IoT era through an integrated analysis, testing, security, and analytics platform. Parasoft's comprehensive portfolio of testing toolsautomates time-consuming testing tasks and provides management with intelligent analytics and reporting so they can focus on what matters.

Topics:
data mining ,iot data ,big data processing

Opinions expressed by DZone contributors are their own.

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