@article{article_467036, title={COMPARISON OF THE DATA MATCHING PERFORMANCES OF STRING SIMILARITY ALGORITHMS IN BIG DATA}, journal={Mühendislik Bilimleri ve Tasarım Dergisi}, volume={7}, pages={608–618}, year={2019}, DOI={10.21923/jesd.467036}, author={Aksoy, Bekir and Uğuz, Sinan and Oral, Okan}, keywords={Algoritmalar,Metin analizi,Doğal dil işleme,Veri analizi,Veri tabanları}, abstract={<p class="MsoNormal" style="margin-bottom:.0001pt;text-align:justify;line-height:normal;"> <span style="font-size:10pt;font-family:’Calibri Light’, sans-serif;">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. </span> </p> <p> </p>}, number={3}, publisher={Süleyman Demirel Üniversitesi}