{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T06:09:57Z","timestamp":1790402997247,"version":"4.1.0"},"reference-count":52,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,11]]},"abstract":"<jats:p>\n            We argue that more attention should be devoted to developing self-service string matching (SM) solutions, which lay users can easily use. We show that Falcon, a self-service entity matching (EM) solution, can be applied to SM and is more accurate than current self-service SM solutions. However, Falcon often asks lay users to label many string pairs (e.g., 770-1050 in our experiments). This is expensive, can significantly compound labeling mistakes, and takes a long time. We developed Smurf, a self-service SM solution that reduces the labeling effort by 43-76%, yet achieves comparable F\n            <jats:sub>1<\/jats:sub>\n            accuracy. The key to make Smurf possible is a novel solution to efficiently execute a random forest (that Smurf learns via active learning with the lay user) over two sets of strings. This solution uses RDBMS-style plan optimization to reuse computations across the trees in the forest. As such, Smurf significantly advances self-service SM and raises interesting future directions for self-service EM and scalable random forest execution over structured data.\n          <\/jats:p>","DOI":"10.14778\/3291264.3291272","type":"journal-article","created":{"date-parts":[[2019,2,4]],"date-time":"2019-02-04T13:13:43Z","timestamp":1549286023000},"page":"278-291","source":"Crossref","is-referenced-by-count":10,"title":["Smurf"],"prefix":"10.14778","volume":"12","author":[{"given":"Paul Suganthan G.","family":"C.","sequence":"first","affiliation":[{"name":"University of Wisconsin-Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adel","family":"Ardalan","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"AnHai","family":"Doan","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aditya","family":"Akella","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,11]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Names dataset. https:\/\/catalog.data.gov\/dataset\/. 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