Lightweight Fingerprints for Fast Approximate Keyword Matching Using Bitwise Operations (1711.08475v1)

Abstract: We aim to speed up approximate keyword matching by storing a lightweight, fixed-size block of data for each string, called a fingerprint. These work in a similar way to hash values; however, they can be also used for matching with errors. They store information regarding symbol occurrences using individual bits, and they can be compared against each other with a constant number of bitwise operations. In this way, certain strings can be deduced to be at least within the distance $k$ from each other (using Hamming or Levenshtein distance) without performing an explicit verification. We show experimentally that for a preprocessed collection of strings, fingerprints can provide substantial speedups for $k = 1$, namely over $2.5$ times for the Hamming distance and over $10$ times for the Levenshtein distance. Tests were conducted on synthetic and real-world English and URL data.

• The paper introduces lightweight fingerprints designed for fast approximate keyword matching through novel bitwise operations.
• The method significantly speeds up matching processes, achieving over 10x improvement in Levenshtein and Hamming distance computations.
• The approach is highly effective on large string datasets, offering practical benefits for text retrieval and genome sequencing applications.

Lightweight Fingerprints for Fast Approximate Keyword Matching Using Bitwise Operations

The paper entitled "Lightweight Fingerprints for Fast Approximate Keyword Matching Using Bitwise Operations" by Aleksander Cislak and Szymon Grabowski examines an innovative approach to speed up approximate keyword matching. This approach involves storing lightweight, fixed-size fingerprints for each string, which are analogous to hash values but extended for handling errors. These fingerprints aim to efficiently determine the proximity of strings within a certain error distance by leveraging bitwise operations, primarily under the Hamming and Levenshtein distance metrics.

Technical Contributions

The fundamental contribution of this paper is the introduction and analysis of string fingerprints that enable rapid approximate string comparisons in preprocessed string collections. The motivation lies in transcending the limitations of conventional hash values, which are inefficient for approximate matching purposes. These fingerprints can reject non-match candidates up to a specified error threshold without explicit verification, facilitating a much faster search process over large datasets.

Fingerprint Construction and Types

Several types of fingerprints are described, each with unique properties:

• Occurrence Fingerprints: Bit-masks store information on the presence of certain characters.
• Occurrence Halved: Similar to occurrence fingerprints, but separated into two halves of the string.
• Count Fingerprints: Encapsulate the frequency of occurrences of characters using a fixed number of bits.
• Position Fingerprints: Encode positions of the first occurrence of characters using a limited number of bits.

These designs are tailored to efficiently handle specific types of queries under varied circumstances, such as differing alphabet sizes and typical string lengths.

Empirical Results

Experimentation conducted on both synthetic and real-world datasets, including English text and URL data, demonstrated that these fingerprint types significantly speed up approximate matching processes. The results indicate speedup factors over 10 times for Levenshtein distance computations. Notably, the Occurrence and Count fingerprints exhibit robust performance across different datasets when evaluated with Hamming and Levenshtein distances, demonstrating substantial effectiveness on larger string sets typically encountered in real-world URL datasets.

Potential Applications and Limitations

Fingerprints, as introduced, are not standalone data structures but augment string representations to enhance computational efficiency in applications requiring multiple approximate string comparisons, such as text retrieval systems or genome sequencing tasks. The method notably excels with larger strings like URLs, where space overhead is negligible. However, their effectiveness is curtailed on data with a uniformly random symbol distribution, or DNA sequences, due to small alphabet sizes and uniform symbol representation.

Future Directions

The authors propose future research into the expansion of fingerprint utility, including encoding q-gram distributions to better approximate string proximities and improving performance on datasets with small alphabets. Further potential lies in hybrid approaches incorporating multiple fingerprint types per string to balance speed and resource utilization optimally.

In conclusion, the presented research extends the frontier of approximate string matching by integrating lightweight fingerprints with bitwise operations, offering a compelling benchmark for future investigations in efficient string processing.