intractable data

USAGE SUMMARY

"intractable data" is a correct and usable term in written English.
It is typically used to describe data sets that are difficult to work with or analyze. For example, "We found the analysis of this intractable data set to be particularly challenging."

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

1 human-written examples

The available materials are always massively incomplete and you're always having to think of ways of deriving some kind of reasonably likely answer from intractable data.

The New Yorker

Human-verified similar examples from authoritative sources

Similar Expressions

59 human-written examples

The time complexity of SPM is more than exponential and easily becomes intractable as data get larger.

BMC Genomics

This human-dependent approach becomes cumbersome, then intractable as the data sets reach the thousands range.

Journal of Cheminformatics

The FPGA architecture of pMCMC is 12.1x and 10.1x faster than state-of-the-art, parallel CPU and GPU implementations of pMCMC and up to 53x more energy efficient; the FPGA architecture of ppMCMC increases these speedups to 34.9x and 41.8x respectively and is 173x more power efficient, bringing previously intractable SSM-based data analyses within reach.

International Journal of Approximate Reasoning

This also highlights that standard ontologies and terms are not systematically followed making data intractable for automated data analysis.

F1000Research

However, the problems turned out as intractable given the data and analytical procedures at hand [ 15].

BMC Evolutionary Biology

This property makes resampling techniques like bootstrapping intractable for realistic data sets due to the increasingly large number of bootstrap samples not having a maximum-likelihood estimator (MLE).

Genetics

However, this is computationally intractable for larger data-sets.

BMC Medical Informatics and Decision Making

However, the presence of highly correlated columns makes the data analysis intractable.

Journal of Complexity

This immediacy removes the most intractable problems with correct data representation.

Journal of Cheminformatics

We estimate that exclusion of the two most intractable genera in our data set (Solidago, Symphyotrichum) would result in an increase in resolution of approximately 10% for all of the single gene regions and combinations.

Plosone

Expert writing Tips

Best practice

When dealing with "intractable data", start by clearly defining the problem you're trying to solve. This helps focus your analysis and prevents you from getting lost in the complexity.

Common error

Don't apply the same analytical methods to all datasets. Recognize that "intractable data" requires specialized techniques and tools for effective analysis.

Linguistic Context

The phrase "intractable data" functions as a noun phrase where the adjective "intractable" modifies the noun "data". It is used to describe data that is difficult or impossible to analyze using standard methods, as evidenced by Ludwig's examples.

Expression frequency: Common

Frequent in

Science

75%

News & Media

15%

Formal & Business

10%

Less common in

Wiki

0%

Encyclopedias

0%

Reference

0%

Ludwig's WRAP-UP

The phrase "intractable data" refers to data that is difficult or impossible to analyze with standard methods. As Ludwig AI confirms, it's grammatically correct and commonly used, particularly in scientific and academic contexts. When facing "intractable data", defining the problem is crucial before applying specialized techniques. Alternative phrases include "complex data" and "challenging data". Remembering to avoid applying the same analytical methods to all datasets is also essential. By recognizing these insights, professionals can more effectively approach and interpret data that initially seems overwhelming.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

complex data

Emphasizes the intricate nature of the data, focusing on the difficulty in understanding its structure and relationships.

unmanageable data

Highlights the difficulty in controlling or organizing the data effectively.

challenging data

Focuses on the stimulating and demanding aspects of working with the data.

problematic data

Indicates that the data presents obstacles or difficulties that need to be addressed.

difficult data

A general term for data that is not easy to handle or interpret.

complicated data

Similar to complex data, but emphasizes the entanglement and interconnectedness of the data elements.

unyielding data

Suggests that the data is resistant to analysis or manipulation, implying a degree of inflexibility.

stubborn data

Informal way of saying that data analysis doesn't bring insights easily, because data is hard to process or contains errors.

cumbersome data

Stresses the unwieldy nature of the data, implying that it is heavy and difficult to carry or handle.

onerous data

Emphasizes the burdensome or oppressive nature of dealing with the data.

FAQs

How can I simplify the analysis of "intractable data"?

Consider breaking down the data into smaller subsets or using dimensionality reduction techniques to make it more manageable. Feature selection can also help by focusing on the most relevant variables.

What does it mean for data to be "intractable"?

It means the data is difficult to analyze or process, often due to its size, complexity, or the presence of errors. It may require specialized tools or techniques to extract meaningful insights.

When is "complex data" considered "intractable"?

Data becomes "intractable" when its complexity is so high that standard analytical methods fail, and extracting meaningful information becomes exceptionally challenging, requiring advanced or novel approaches.

What are some strategies for dealing with "challenging data" sets that seem "intractable"?

Try data cleaning and preprocessing, employing machine learning algorithms to find patterns, or visualizing the data in different ways to identify trends and anomalies. Collaboration with experts in the field can also provide valuable insights.