An efficient and scalable plagiarism checking system using Bloom filters

Geravand, Shahabeddin; Ahmadi, Mahmood

doi:10.1016/j.compeleceng.2014.06.003

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…Hence, the Bloom filter has the ability to solve this problem. e matrix Bloom filter (MBF) [39] is used for similar document detection. In this, each row is a standard Bloom filter.…”

Section: Databasementioning

confidence: 99%

“…Interestingly, Bloom filters are highly adaptable. Bloom filters are adapted in diverse field, namely, recommendation engine to prevent duplicate recommendation [18], metadata server to enhance lookup performance [19][20][21][22], network packet filtering to filter unnecessary data flow [23], IP address lookup [24], network security to prevent malicious user [25][26][27], Big Data security analytics [28,29], biometric [30], BigTable [3], P2P [31][32][33], error correction [34,35], wireless sensor network [36], duplicate filtering [37,38], plagiarism checking [39], biomedical engineering [35,[40][41][42], Web search [43], network router [44,45], searchable encryption scheme [46], IoT environment [47,48], spatial Bloom filter [49], in-packet multicasting [50], and databases [51].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hunting the Pertinency of Bloom Filter in Computer Networking and Beyond: A Survey

Patgiri

Nayak

Borgohain

2019

Journal of Computer Networks and Communications

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Bloom filter is a probabilistic data structure to filter a membership of a set. Bloom filter returns “true” or “false” with an error tolerance depending on the presence of the element in the set. Bloom filter is used to boost up the performance of a system using small space overhead. It is extensively used since its inception. The Bloom filter has met wide area of applications. Bloom filter is used in entire computing field irrespective of application and research domain. Bloom filter poses (i) high adaptability, (ii) low memory space overhead as compared to hashing algorithms, (iii) high scalability, and (iv) high performance. In this article, we uncover the application area of Bloom filter in computer networking and its related domain.

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“…Hence, the Bloom filter has the ability to solve this problem. e matrix Bloom filter (MBF) [39] is used for similar document detection. In this, each row is a standard Bloom filter.…”

Section: Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hunting the Pertinency of Bloom Filter in Computer Networking and Beyond: A Survey

Patgiri

Nayak

Borgohain

2019

Journal of Computer Networks and Communications

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“…In [14], utilization of a Matrix Bloom filter in a plagiarism checking system is proposed. In this system, for each document in the library, a matrix Bloom filter is constructed then the achieved Bloom filter for queried document is compared to the set of Bloom filters.…”

Section: Related Workmentioning

confidence: 99%

A Bloom Filter with the Integrated Hash Table Using an Additional Hashing Function

Ahmadi

Pourian

2015

NPA

Self Cite

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A Bloom filter is a simple space-efficient randomized data structure for representing a set in order to support membership queries. In recent years, Bloom filters have increased in popularity in database and networking applications. A Bloom filter has two steps that called programming and membership query. In this paper, we introduce a new approach to integrate a hash table (HT) with Bloom filter to decrease the HT access time. This means that when a Bloom filter for an incoming item is programmed, the incoming item simultaneously is stored in a HT. In addition in the membership query step, if the query is successful, simultaneously the address of item in the HT is generated. Furthermore, we analyze the average bucket size, maximum search length and number of collisions for the proposed approach and compare to the fast hash table (FHT) approach. We implemented our approach in a software packet classifier based on tuple space search with the H3 class of universal hashing functions. Our results show that our approach is able to reduce the average bucket size, maximum search length and number of collisions when compared to a FHT.

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“…Recently, it made a significant issue in both academic and non-academic worlds. In plagiarism, the plagiarists attempt to change the contribution, the idea or the words of others as their own work (Geravand & Ahmadi, 2014;Osman, Salim, Binwahlan, Alteeb, & Abuobieda, 2012). The plagiarism can be performed by exact copy, cutting sentences, combining sentences, paraphrasing, replacing the original words with the similar words or synonym words (El-Alfy, Abdel-Aal, Al-Khatib, & Alvi, 2015;Sánchez-Vega, Villatoro-Tello, Montes-yGomez, Villaseñor-Pineda, & Rosso, 2013).…”

Section: Introductionmentioning

confidence: 97%