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Journals / Mühendislik Bilimleri ve Tasarım Dergisi / 2026 / Cilt: 7 Sayı: 3

COMPARISON OF THE DATA MATCHING PERFORMANCES OF STRING SIMILARITY ALGORITHMS IN BIG DATA

BÜYÜK VERİDE METİN BENZERLİK ALGORİTMALARININ VERİ EŞLEME PERFORMANSLARININ KARŞILAŞTIRILMASI

Bekir Aksoy(ısparta uygulamalı bilimler üniversitesi)0000-0001-8052-9411
Sinan Uğuz(ısparta uygulamalı bilimler üniversitesi)0000-0003-4397-6196
Okan Oral(AKDENIZ UNIVERSITY)0000-0003-4256-0930
Pages
608–618
Publication Date
2019-09-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The Dice coefficient algorithm yielded better results in terms of correct matching, and the Levenshtein algorithm yielded better results in terms of process time.

📌 3 Key Takeaways

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Objective & Hypothesis:The great mobility in the world tourism in recent years has also enabled this sector to be included among the study areas of big data.
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Key Findings & Data:Therefore, 2599 hotels of a tourism agency with a wide hotel network located in London were selected as the sample, and the Map-Reduce process was performed by using the Soundex algorithm to match these hotels with approximately three million hotel data coming from seventy different providers.
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Conclusion & Impact:The Dice coefficient algorithm yielded better results in terms of correct matching, and the Levenshtein algorithm yielded better results in terms of process time.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Algoritmalar · Metin analizi · Doğal dil işleme

Abstract

The great mobility in the world tourism in recent years has also enabled this sector to be included among the study areas of big data. In this study, a solution proposal was put forward by using the big data and string similarity algorithms (SSA) for the problems arising from the entry of the hotel data coming from different providers into databases with different names and addresses. Therefore, 2599 hotels of a tourism agency with a wide hotel network located in London were selected as the sample, and the Map-Reduce process was performed by using the Soundex algorithm to match these hotels with approximately three million hotel data coming from seventy different providers. Matching with Map-Reduce ensured a significant reduction in process count and process time. Furthermore, the Dice coefficient, Levenshtein and Longest common subsequence (LCS) algorithms were compared in terms of the data that they correctly matched, and process time. In this stage, the words decreasing the score of the algorithms in the database were detected and removed before the algorithms were implemented. The Dice coefficient algorithm yielded better results in terms of correct matching, and the Levenshtein algorithm yielded better results in terms of process time.

Özet

Son yıllarda dünya turizmindeki büyük hareketlilik, bu sektörün büyük verinin çalışma alanları arasına girmesini sağlamıştır. Bu çalışmada farklı sağlayıcılardan gelen otel bilgilerinin, veritabanlarına farklı isim ve adreslerle girilmesi sonucu oluşan problemler için, büyük veri ve string similarity algoritmaları (SSA) kullanarak bir çözüm önerisi ortaya konulmuştur. Bunun için geniş bir otel ağına sahip bir turizm acentasının Londra’da bulunan 2599 oteli örneklem olarak seçilmiş ve bu oteller ile yetmiş farklı sağlayıcıdan gelen yaklaşık üç milyon otel bilgisinin eşleştirilmesi için, soundex algoritmasından faydalanılarak Map-Reduce işlemi gerçekleştirilmiştir. Map-Reduce ile eşleme işlem sayısı ve işlem süresinde önemli ölçüde azalma sağlanmıştır. Çalışmanın diğer aşamasında ise Dice coefficient, Levenshtein ve Longest common subsequence (LCS) algoritmaları, doğru eşleyebildikleri veri ve işlem süresi açısından kıyaslanmıştır. Bu aşamada algoritmalar uygulanmadan önce veri tabanında algoritmaların skorunu düşüren kelimeler tespit edilerek çıkartılmıştır. Doğru eşleme bakımından Dice coefficient algoritması, işlem süresi açısından ise Levenshtein algoritması daha iyi sonuçlar üretmiştir.

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Cite This Article

Aksoy, B., Uğuz, S., Oral, O. (2019). COMPARISON OF THE DATA MATCHING PERFORMANCES OF STRING SIMILARITY ALGORITHMS IN BIG DATA. Mühendislik Bilimleri ve Tasarım Dergisi, Cilt: 7 Sayı: 3, 608–618. https://doi.org/10.21923/jesd.467036

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