Exact String Matching Algorithms: Survey, Issues, and Future Research Directions

Hakak, Saqib; Kamsin, Amirrudin; Shivakumara, Palaiahnakote; Gilkar, Gulshan Amin; Khan, Wazir Zada; Imran, Muhammad

doi:10.1109/access.2019.2914071

Cited by 74 publications

(49 citation statements)

References 83 publications

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“…The set of all queries applied in the evaluation consisted of different keywords taken from the annotation glossary ( ⊆ ). For every query q ∈ , a subset of documents ⊂ with = 100 pictures was randomly preselected and then classified using lexical relatedness measures [34]. Each subset was queried three times: with one, two, or three different words.…”

Section: Evaluation Of Affective Image Retrieval With Lift Charts As mentioning

confidence: 99%

“…Two lexical relatedness measures were used for ranking: naïve string matching and the Levenshtein distance (i.e., edit distance) [34]. Each subset was classified once for each relatedness…”

Section: Evaluation Of Affective Image Retrieval With Lift Charts As mentioning

confidence: 99%

“…For every query q i ∈ Q, a subset of documents Only the terms from D were used as queries, which guaranteed that the retrieved sets were nonempty. The setup for the experiment was reused from a previous research [34] but with the expanded dataset D, which also resulted in a different glossary W, queries Q and assortment of all preselected subsets . D. Compared to [35], the additional images in the dataset provide greater credibility in the results of this study.…”

Section: Evaluation Of Affective Image Retrieval With Lift Charts As mentioning

confidence: 99%

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Lift Charts-Based Binary Classification in Unsupervised Setting for Concept-Based Retrieval of Emotionally Annotated Images from Affective Multimedia Databases

2020

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Evaluation of document classification is straightforward if complete information on the documents’ true categories exists. In this case, the rank of each document can be accurately determined and evaluated. However, in an unsupervised setting, where the exact document category is not available, lift charts become an advantageous method for evaluation of the retrieval quality and categorization of ranked documents. We introduce lift charts as binary classifiers of ranked documents and explain how to apply them to the concept-based retrieval of emotionally annotated images as one of the possible retrieval methods for this application. Furthermore, we describe affective multimedia databases on a representative example of the International Affective Picture System (IAPS) dataset, their applications, advantages, and deficiencies, and explain how lift charts may be used as a helpful method for document retrieval in this domain. Optimization of lift charts for recall and precision is also described. A typical scenario of document retrieval is presented on a set of 800 affective pictures labeled with an unsupervised glossary. In the lift charts-based retrieval using the approximate matching method, the highest attained accuracy, precision, and recall were 51.06%, 47.41%, 95.89%, and 81.83%, 99.70%, 33.56%, when optimized for recall and precision, respectively.

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Section: Evaluation Of Affective Image Retrieval With Lift Charts As mentioning

confidence: 99%

Section: Evaluation Of Affective Image Retrieval With Lift Charts As mentioning

confidence: 99%

Section: Evaluation Of Affective Image Retrieval With Lift Charts As mentioning

confidence: 99%

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Lift Charts-Based Binary Classification in Unsupervised Setting for Concept-Based Retrieval of Emotionally Annotated Images from Affective Multimedia Databases

2020

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“…Hakak et al [7] presented a comprehensive survey on exact string matching algorithms that are commonly used for pattern searching. Unlike exact string matching, Approximate String Matching(ASM) methods allow errors in matching, which makes them useful for applications like spell checker, autocompletion, etc [8], [9].…”

Section: Related Workmentioning

confidence: 99%

Supporting Uncertain Predicates in DBMS Using Approximate String Matching and Probabilistic Databases

Jumde

Keskar

2020

IEEE Access

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Current relational database systems are deterministic in nature and lack the support for approximate matching. The result of approximate matching would be the tuples annotated with the percentage of similarity but the existing relational database system can not process these similarity scores further. In this paper, we propose a system to support approximate matching in the DBMS field. We introduce a '≈' (uncertain predicate operator) for approximate matching and devise a novel formula to calculate the similarity scores. Instead of returning an empty answer set in case of no match, our system gives ranked results thereby providing a glance at existing tuples closely matching with the queried literals. Two variants of the '≈' operator are also introduced for numeric data: '≈+' for higher-the-better and '≈-' for lower-the-better cases. Efficient approximate string matching methods are proposed for matching stringtype data whereas numeric closeness is used for other types of data (date, time, and number). We also provide results of our system taken over several sample queries that illustrate the significance of our system. All experiments are performed using the MySQL database, whereas the IMDb movie database and European Football database are used as sample datasets.

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“…Time management is one of the greatest challenges in processing and transmission of large and complex files. The processing of a large and complex file on a CPU with any integrated security algorithm enhances the latency and transmission delay [38], [39]. As a consequence, data loss occurs during transmission of a large file over the internet.…”

Section: E Time Optimizationmentioning

confidence: 99%

Unified GPU Technique to Boost Confidentiality, Integrity and Trim Data Loss in Big Data Transmission

Bhattacharjee¹,

Rahim

Watada

et al. 2020

IEEE Access

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Data integrity, confidentiality and data loss are the issues that arise during transmission because of the use of an inadequate security scheme. These issues become particularly critical for big data transmission due to its own individual overhead causes. Moreover, multiple executions of distinct security algorithms for maintaining confidentiality and integrity reduce throughput and add a large number of additional bits as security overhead that hampers the robustness against data loss. Conversely, an efficient compression technique minimizes data confidentiality, as it eliminates redundant data during compression. Contemporary studies shows the lack of security policies for solving the mentioned issues in a combinatorial manner. The current study proposes an innovative integrated technique to collectively addresses the above security issues. It increases confidentiality and offers a backup for accidental data loss by combining the simplified data encryption standard (SDES) and an advanced pattern generation technique that uses a unique pattern generation table. A novel dual round of error control technique has been introduced to maximize data integrity by considering an arbitrary number of transmission errors. A new compression technique is adopted to enhance the robustness against data loss along with high compression efficiency and resistance against transmission errors. Confidentiality and integrity are further enhanced by integrating advanced audio steganography that uses a distinctive sample selection for hiding bits. Additionally, the implementation of the proposed innovative integrated technique in the graphics processing unit (GPU) environment increases the execution speed and reduces time complexity with extended parallel processing power. Furthermore, the application of a GPU enhances the execution speed at least 28-fold compared to the CPU performance. Experiments are performed using the standard Calgary Corpuses, text files (sized up to 1 TB), and audio files to validate the objectives. The proposed method offers a higher signal-to-noise ratio (SNR), entropy, and avalanche effect (AE) and lower amplitude difference (AD), and uncorrectable error rate (UER) as well as a lower percentage of information loss (IL), which substantiates its potential to offer higher data confidentiality and integrity. The capacity to reduce the computational complexity is further measured with compression ratio (CR) and throughput. The results further depicts the method's superiority in offering confidentiality and integrity over contemporary approaches. INDEX TERMS SDES, pattern string generation and the incorporation technique, dual round XOR operations to control transmission error, data compression, LSB-based audio steganography, GPU-oriented integrated technique, signal-to-noise ratio (SNR), entropy, avalanche effect (AE), amplitude difference (AD), uncorrectable error rate (UER), percentage of information loss (IL), compression ratio (CR) and throughput.

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Exact String Matching Algorithms: Survey, Issues, and Future Research Directions

Cited by 74 publications

References 83 publications

Lift Charts-Based Binary Classification in Unsupervised Setting for Concept-Based Retrieval of Emotionally Annotated Images from Affective Multimedia Databases

Lift Charts-Based Binary Classification in Unsupervised Setting for Concept-Based Retrieval of Emotionally Annotated Images from Affective Multimedia Databases

Supporting Uncertain Predicates in DBMS Using Approximate String Matching and Probabilistic Databases

Unified GPU Technique to Boost Confidentiality, Integrity and Trim Data Loss in Big Data Transmission

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