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Revolutionizing Time Series Data Compression

A new method enhances time series data storage and access.

Andrea Guerra, Giorgio Vinciguerra, Antonio Boffa, Paolo Ferragina

― 6 min read


Time Series Data Time Series Data Compression Breakthrough and access speed. New method boosts storage efficiency
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In today’s fast-paced world, big data is everywhere. Among this vast amount of data, time series data stands out. Time series data consists of a sequence of data points collected or recorded at specific time intervals. This type of data is crucial in many areas, from finance (think stock prices) to healthcare (monitoring patient vitals) and even environmental tracking (recording temperature changes). Let's face it, without efficient ways to store and analyze this data, we would be swimming in an ocean of numbers with no life vest.

The Challenge with Time Series Data

Time series data is like an eager puppy that never stops growing. As more data is generated, storing and retrieving it can become a daunting task. Often, organizations have to make tough choices, sacrificing valuable historical data just to fit in new data. That's like throwing out your old jeans to make room for a new pair, only to realize later that the old ones actually fit better!

Enter data Compression, the magical solution that allows us to store more data without needing an endless supply of hard drives. Compressing data reduces the amount of space it takes, making it easier to manage.

General-Purpose Compressors vs. Special-Purpose Compressors

There are two main types of data compressors: general-purpose and special-purpose. General-purpose compressors can handle a variety of data types but aren't always the best fit for time series data. They focus on the compression ratio but can lag behind when it comes to speed and efficient access to data.

On the other hand, special-purpose compressors are designed specifically for certain types of data, like time series. Think of them as the tailor-made suits of the compression world. They may be faster and more efficient, but they often sacrifice some of the compression quality.

The Great Compromise

While traditional techniques can help with compression, they face limitations when it comes to Random Access. Random access means being able to retrieve specific pieces of data quickly without needing to go through everything. This is crucial since analyzing time series often requires accessing data within specific time intervals. Imagine trying to find a specific episode in a long series on streaming services without a search feature; incredibly frustrating!

Moreover, existing methods often ignore certain regular patterns found in time series data, which can be modeled using linear and nonlinear functions. For those not into math, that basically means some patterns can be described by simple equations, making them easier to work with.

A New Approach to Compression

To tackle these challenges, researchers have developed a new compression scheme that takes into account the unique features of time series data. This approach allows data to be approximated using a sequence of nonlinear functions. Think of it as using a mix of different colors to paint a picture, where each color represents a different function, and together they create a beautiful image (or in this case, a well-compressed time series).

The new method not only compresses data more effectively but also provides an efficient way to access specific pieces of data without breaking a sweat.

How It Works

This new compression scheme involves a few key steps. First, it breaks the time series data into smaller fragments, each associated with different nonlinear functions. This is like chopping up a long loaf of bread into smaller slices for easier handling. Each slice holds its own function, making the overall picture clearer and more manageable.

Next, the Approximation Error—the difference between the original data and the approximated data—is kept within certain bounds. This allows for lossless data recovery or a lossy representation with guaranteed maximum errors. In non-technical terms, you can either keep all the original details perfectly or allow for some minor imperfections—kind of like a pizza made with just a little less cheese.

Experimental Testing

To see how well this new method performs, extensive testing was conducted using a selection of real-world time series datasets. These experiments compared the new approach with existing lossy and lossless compressors. Results showed that this new method improved compression ratios significantly, while also accelerating decompression speed and random access. That's like finding a better way to make your favorite dish in half the time while still getting all the flavors right!

The Perfect Balance

One of the most engaging aspects of this new approach is its ability to strike a balance between compression, decompression, and random access speed. In the tech world, this triad is often at odds. Most solutions excel at one or two of these factors while falling short on the others. However, with this new method, users can enjoy swift access to their data, faster Decompressions, and better compression ratios — all without compromising other areas.

Real-World Applications

What does all this mean in practical terms? Imagine organizations managing large amounts of time-sensitive data, like financial institutions tracking stock market trends or hospitals monitoring patient health in real-time. With this new compression method, they can store vast amounts of historical data without worrying about where to find more storage space.

These advancements make analyzing historical trends more accessible, leading to better decision-making and improved outcomes in various fields.

Future Directions

As with any new technology, there’s always room for improvement. Future research could delve into enhancing the compression further by looking into the similarities between functions. By sharing features among different functions, compressors might squeeze out even more space.

Additionally, researchers may want to explore how the information from these nonlinear functions could be utilized for efficient data aggregation and query answering. After all, in a data-driven world, being able to quickly and accurately retrieve insights is priceless.

Conclusion

New methods in compressing time series data provide a significant leap forward in data management practices. With effective compression ratios, fast decompression, and efficient random access capabilities, this approach not only meets current demands but also prepares us for the inevitable data deluge that lies ahead.

So, as our world becomes increasingly digital, it's comforting to know that while data may grow like a wild weed, there's a new gardener in town doing wonders with compression techniques. The future looks bright and less cluttered—like a freshly organized closet after a good spring cleaning!

Original Source

Title: Learned Compression of Nonlinear Time Series With Random Access

Abstract: Time series play a crucial role in many fields, including finance, healthcare, industry, and environmental monitoring. The storage and retrieval of time series can be challenging due to their unstoppable growth. In fact, these applications often sacrifice precious historical data to make room for new data. General-purpose compressors can mitigate this problem with their good compression ratios, but they lack efficient random access on compressed data, thus preventing real-time analyses. Ad-hoc streaming solutions, instead, typically optimise only for compression and decompression speed, while giving up compression effectiveness and random access functionality. Furthermore, all these methods lack awareness of certain special regularities of time series, whose trends over time can often be described by some linear and nonlinear functions. To address these issues, we introduce NeaTS, a randomly-accessible compression scheme that approximates the time series with a sequence of nonlinear functions of different kinds and shapes, carefully selected and placed by a partitioning algorithm to minimise the space. The approximation residuals are bounded, which allows storing them in little space and thus recovering the original data losslessly, or simply discarding them to obtain a lossy time series representation with maximum error guarantees. Our experiments show that NeaTS improves the compression ratio of the state-of-the-art lossy compressors that use linear or nonlinear functions (or both) by up to 14%. Compared to lossless compressors, NeaTS emerges as the only approach to date providing, simultaneously, compression ratios close to or better than the best existing compressors, a much faster decompression speed, and orders of magnitude more efficient random access, thus enabling the storage and real-time analysis of massive and ever-growing amounts of (historical) time series data.

Authors: Andrea Guerra, Giorgio Vinciguerra, Antonio Boffa, Paolo Ferragina

Last Update: Dec 20, 2024

Language: English

Source URL: https://arxiv.org/abs/2412.16266

Source PDF: https://arxiv.org/pdf/2412.16266

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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