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other provinces and regions in Vietnam and other country.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Address\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Headquarters of Can Tho Journal of Medicine and Pharmacy, located Scientific Research and International Cooperation Office: 179 Nguyen Van Cu Street, An Khanh Ward, Ninh Kieu District, Can Tho City, Vietnam.\u003C\u002Fspan>\u003C\u002Fp>","\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Ngày 16\u002F7\u002F2015, Tạp chí Y Dược học Cần Thơ được cấp chỉ số quốc tế: ISSN 2354-1210.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Từ tháng 4\u002F2016, Tạp chí đã được Hội đồng Giáo sư ngành Y đưa vào danh sách các tạp chí khoa học Y học được tính điểm công trình 0-0,5 điểm cho một bài báo đăng.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Năm 2020 Tạp chí Y Dược học Cần Thơ đã được phê duyệt vào danh mục của các Hội đồng Giáo sư ngành Dược học được tính điểm công trình 0-0,5 điểm cho một bài báo đăng.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ ra 12 số\u002Fnăm, 180-200 trang\u002Fsố.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Từ tháng 12\u002F2022 Tạp chí Y Dược học Cần Thơ là thành viên của hệ thống Crossref và từ tháng 01\u002F2023 tạp chí thực hiện bình duyệt online kín 2 chiều nhằm tăng tính minh bạch, tin cậy của các công trình nghiên cứu khoa học và đảm bảo tốt nhất chất lượng khoa học của bài viết.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tôn chỉ, mục đích và phạm vi của tạp chí\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tôn chỉ và mục đích hoạt động của tạp chí: xuất bản nhằm mục đích phổ biến kết quả từ các đề tài nghiên cứu khoa học; giao lưu trao đổi khoa học, chia sẻ kinh nghiệm, học tập, đồng thời cập nhật thông tin khoa học mới trong các lĩnh vực y, sinh, dược học trong và ngoài nước.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Phạm vi của tạp chí: Tạp chí xuất bản được chia thành 3 chuyên mục: (i) Bài báo nghiên cứu khoa học là kết quả công trình nghiên cứu khoa học có giá trị đã được triển khai nghiên cứu, (ii) Bài tổng quan y, sinh, dược học: phục vụ mục tiêu đào tạo liên tục trong lĩnh vực y, sinh, dược học; nhằm hệ thống hóa những kiến thức kinh điển và hiện đại; (iii) Thông tin cập nhật kiến thức mới về y, sinh, dược học trong nước và trên thế giới.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Chính sách truy cập mở\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ áp dụng chính sách truy cập mở đối với các bài báo đã xuất bản đến với độc giả, nhằm mở rộng cơ hội tiếp cận các kết quả nghiên cứu chất lượng cao và tăng cường trao đổi kiến thức. Tạp chí đăng tải trực tuyến (miễn phí) toàn văn các bài báo được công bố trên website của Tạp chí (https:\u002F\u002Ftapchi.ctump.edu.vn).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Đạo đức xuất bản\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ cam kết tuân thủ đạo đức xuất bản phù hợp với các hướng dẫn và tiêu chuẩn của the Committee on Publication Ethics (COPE), tuân thủ các nguyên tắc của COPE’s Core Practices, Best Practices Guidelines for Journal Editors và Guidelines on Good Publication Practices.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Bản thảo bài báo chỉ được chấp nhận khi được tác giả chịu trách nhiệm chính cam kết các nội dung sau: Các nội dung của bản thảo chưa được đăng tải toàn bộ hoặc một phần ở các tạp chí khác; Tất cả các tác giả đều có đóng góp một cách đáng kể vào quá trình nghiên cứu hoặc chuẩn bị bản thảo và cùng chịu trách nhiệm về các nội dung của bản thảo; Tuân thủ các biện pháp đảm bảo đạo đức nghiên cứu (ví dụ thỏa thuận đồng ý tham gia nghiên cứu).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Cam kết bảo mật\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí cam kết thực hiện và tuân thủ các quy định của luật và các văn bản hướng dẫn liên quan đến bảo mật thông tin cá nhân trên không gian mạng. Các thông tin mà người dùng (tác giả, độc giả, biên tập viên, người phản biện) nhập vào các biểu mẫu trên Hệ thống Quản lý xuất bản trực tuyến của tạp chí chỉ được sử dụng vào các mục đích đã được tuyên bố rõ ràng và sẽ không được cung cấp cho bất kỳ bên thứ ba nào khác, hay dùng vào bất kỳ mục đích nào khác.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Phí gửi bài\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Lệ phí gửi đăng bài: 1.000.000đ\u002Fbài báo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Lệ phí gửi đăng nhanh: 1.500.000đ\u002Fbài báo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Đối với tác giả là cán bộ viên chức thuộc Trường Đại học Y Dược Cần Thơ thì được hỗ trợ 50% lệ phí gửi đăng bài.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Đối với sinh viên thực hiện đề tài nghiên cứu khoa học cấp trường được hỗ trợ 100% lệ phí đăng bài ( Tác giả gửi đính kèm “ Quyết định về việc giao tổ chức thực hiện đề tài nghiên cứu khoa học cấp Trường của sinh viên”).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Hình thức nộp lệ phí:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Tiền mặt:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Nộp trực tiếp tại Phòng Tài chính - Kế toán, Trường Đại học Y Dược Cần Thơ, số 179 Nguyễn Văn Cừ, P. An Khánh, Q. Ninh Kiều, thành phố Cần Thơ.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Chuyển khoản:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tên Tài khoản: Trường ĐHYD Cần Thơ, Số TK: 0111000115668, tại ngân hàng Vietcombank chi nhánh Cần Thơ.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Thời gian: Áp dụng từ ngày 01\u002F02\u002F2023.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">* Phí gửi bài không được hoàn trả khi bài viết bị từ chối hoặc tác giả xin rút bài viết.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Quy trình phản biện bài báo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ thực hiện quy trình phản biện kín hai chiều nghiêm ngặt. Danh tính của những người phản biện không được tiết lộ cho các tác giả và ngược lại. Quy trình thẩm định bài báo đăng gồm các bước sau:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tiếp nhận bản thảo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tác giả liên hệ gửi bản thảo đến Tạp chí qua hệ thống trực tuyến tại website: https:\u002F\u002Ftapchi.ctump.edu.vn. Hướng dẫn về cách đăng ký, gửi bài và chuẩn bị bản thảo được cung cấp trên website của Tạp chí.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Sàng lọc sơ bộ\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Sau khi Tòa soạn nhận được bài báo của tác giả, Ban Thư ký sẽ tiến hành kiểm tra sơ bộ bài báo (các yêu cầu về nội dung và hình thức). Những bài báo không đúng quy cách hoặc có nội dung không phù hợp hoặc vi phạm bản quyền sẽ bị từ chối (Ban Thư ký thông báo phản hồi đến tác giả trong vòng 1 tuần). Những bài báo đủ điều kiện, được Ban Thư ký tòa soạn chuyển đến Ban Biên tập có cùng chuyên môn với nội dung bài báo để đề xuất người phản biện. Thời gian kể từ khi Ban Biên tập nhận bài báo đến khi đề xuất người phản biện bài báo chậm nhất là 5 ngày.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Vòng phản biện\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Ban Thư ký gửi bài và yêu cầu phản biện đến 02 phản biện độc lập.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Các phản biện gởi nhận xét cho Ban Thư ký. Thời gian từ khi gửi bài cho phản biện đến khi nhận ý kiến của phản biện tối đa là 20 ngày.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Xử ký kết quả phản biện\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Nếu ý kiến đồng ý cho đăng và không cần chỉnh sửa, Ban Thư ký tiếp tục đăng bài theo qui trình.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Nếu ý kiến đồng ý đăng và cần chỉnh sửa, Ban Thư ký sẽ thông tin đến tác giả chỉnh sửa theo yêu cầu của người phản biện. Thời gian chỉnh sửa và gửi lại kéo dài không quá 2 tuần, từ khi tác giả bài báo nhận được thông tin (Quá trình này có thể lặp lại tối đa 2 lần\u002F1 bài báo). Khi có sự thống nhất, đồng ý của người phản biện; bài báo được tiếp tục đăng theo qui trình.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">3. Những bài báo có chất lượng không đạt yêu cầu, cả 2 phản biện không đồng ý cho đăng sẽ bị Tòa soạn từ chối đăng.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Xuất bản\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Ban Thư ký tổng hợp các bản thảo đã được tác giả hoàn thiện sau thẩm định trình Ban Biên tập xem xét, Tổng Biên tập phê duyệt, quyết định bài đăng theo các tiêu chí: sự phù hợp nội dung với tôn chỉ và mục đích, thể loại bài viết (ưu tiên các bài có bài có nghiên cứu chuyên sâu, hàm lượng khoa học cao), đóng góp mới bài báo, bài báo được ưu tiên đăng trong số gần nhất của Tạp chí theo thứ tự: tính thời sự, chất lượng bài báo và thời gian gửi bài.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Ban Biên tập và Ban Thư ký biên tập bản thảo, chế bản, đọc rà soát lỗi. Thời gian hoàn thành từ 10-15 ngày.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">3. Ban Thư ký có trách nhiệm thông báo cho tác giả bài báo (bằng e-mail) về tình hình phê duyệt bài báo, thời gian, số kỳ, tập xuất bản bài báo theo qui định.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">4. 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In: IEEE international conference on data engineering (ICDE’19), pp 1382–1393\nYue X, Wang Z, Huang J, Parthasarathy S, Moosavinasab S, Huang Y, Lin SM, Zhang W, Zhang P, Sun H (2019) Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics 36(4):1241–1251\nZhang D, Yin J, Zhu X, Zhang C (2020) Network representation learning: a survey. IEEE Trans Big Data 6(1):3–28\nZhang F, Yuan NJ, Lian D, Xie X, Ma WY (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’16), pp 353–362",{"EN":781},"Graphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end-to-end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end-to-end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non-autoregressive methods and autoregressive methods. Non-autoregressive methods predict a solution for a CO problem in one shot. A non-autoregressive method predicts a matrix that denotes the probability of each node\u002Fedge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node\u002Fedge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning-based CO methods. The survey ends with several remarks on future research directions.",{"EN":783},"Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art",{"VOID":785},"10.1007\u002Fs41019-021-00155-3",{"VOID":787},"[\"6552971659531576999\"]","PUBLICATION","2024-05-06T16:18:31.561+00:00","Auto 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         \u003Cjats:p>This paper explores the challenges raised by big data in privacy-preserving data management. First, we examine the conflicts raised by big data with respect to preexisting concepts of private data management, such as consent, purpose limitation, transparency and individual rights of access, rectification and erasure. Anonymization appears as the best tool to mitigate such conflicts, and it is best implemented by adhering to a privacy model with precise privacy guarantees. For this reason, we evaluate how well the two main privacy models used in anonymization (\u003Cjats:italic>k\u003C\u002Fjats:italic>-anonymity and \u003Cjats:inline-formula>\n              \u003Cjats:alternatives>\n                \u003Cjats:tex-math>$$\\varepsilon $$\u003C\u002Fjats:tex-math>\n                \u003Cmml:math xmlns:mml=\"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML\">\n                  \u003Cmml:mi>ε\u003C\u002Fmml:mi>\n                \u003C\u002Fmml:math>\n              \u003C\u002Fjats:alternatives>\n            \u003C\u002Fjats:inline-formula>-differential privacy) meet the requirements of big data, namely composability, low computational cost and linkability.\u003C\u002Fjats:p>",{"EN":898},"Big Data Privacy: Challenges to Privacy Principles and Models",{"VOID":900},"10.1007\u002Fs41019-015-0001-x",[102],"https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs41019-015-0001-x",[904],{"id":905,"sortIndex":36,"researcher":26,"roles":906,"affiliations":907,"properties":916},"8a9b5e95-c325-4686-a06a-64da1b4c20de",[],[908],{"id":26,"sortIndex":36,"affiliation":909,"properties":26},{"id":910,"createTime":911,"updateTime":911,"relativeEntities":912,"slug":26,"properties":913,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"f00a2820-7414-4915-8460-c0305dbb02b1","2023-12-01T05:36:48.918+00:00",[],{"title":914},{"VI":915},"Department of Computer Engineering and Mathematics, UNESCO Chair in Data Privacy, Universitat Rovira i Virgili, Tarragona, Catalonia",{"openalex":917,"title":919},{"VOID":918},"A5017093964",{"EN":920},"Jordi Soria-Comas",{"url":26,"publisher":922,"properties":26},{"id":659,"createTime":660,"updateTime":661,"relativeEntities":923,"slug":663,"properties":924,"entityType":24,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36,"subjectFields":928,"manageAffiliations":929,"indexDatabases":930,"url":749,"thumbnailPath":26,"statistic":945,"gsStatistic":26,"type":175,"analyzePriority":26},[],{"issn":925,"eissn":926,"title":927},{"VOID":666},{"VOID":668},{"EN":670},[],[],[931,938],{"id":730,"indexDatabase":932,"url":743,"indexYears":744,"academicFieldIds":937,"indexDatabaseRanking":748},{"id":732,"createTime":733,"updateTime":734,"relativeEntities":933,"label":934,"description":935,"key":740,"publicationTags":936,"standard":26},[],{"EN":737,"VI":737},{"EN":737,"VI":739},[742],[746,747],{"id":711,"indexDatabase":939,"url":726,"indexYears":26,"academicFieldIds":944,"indexDatabaseRanking":26},{"id":713,"createTime":714,"updateTime":715,"relativeEntities":940,"label":941,"description":942,"key":722,"publicationTags":943,"standard":26},[],{"EN":718,"VI":718},{"VI":720,"EN":721},[724,725],[728],{"impactFactor":36,"impactFactorByYear":946,"i10Index":103,"i10IndexLast5Year":162,"totalPublication":615,"totalPublicationByYear":947,"totalCitation":757,"totalCitationByYear":948,"totalCitationPerPublication":759,"totalCitationPerPublicationByYear":949,"hindexLast5Year":116,"hindex":116},{"2017":270,"2018":752,"2019":753,"2020":408,"2021":753,"2022":754,"2023":755},{"2015":114,"2016":51,"2017":239,"2018":242,"2019":124,"2020":242,"2021":516,"2022":173,"2023":516,"2024":50},{"2016":630,"2017":349,"2018":260,"2019":401,"2020":298,"2021":284,"2022":239,"2024":115},{"2016":761,"2017":762,"2018":763,"2019":764,"2020":478,"2021":765,"2022":766,"2024":404},{"total":401,"publishYear":26,"statisticByYear":951},{"2015":115,"2016":162,"2017":52,"2018":336,"2019":116,"2020":52,"2021":52,"2022":50,"2023":52,"2024":115},"2016-03-01",2016,[955,959,962,965,968,972,975,979,983,986,990,994,998,1001,1005,1009,1013,1017,1021,1025,1029,1033,1037,1040,1044,1047,1051,1054,1058,1062,1066,1070],{"id":26,"text":956,"url":26,"identifiers":957},"Aggarwal G, Feder T, Kenthapadi K, Motwani R, Panigrahy R, Thomas D, Zhu A (2005) Anonymizing tables. 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ACM, p 1423–1434",{"doi":1073},"10.1145\u002F2588555.2588573",{"id":1075,"createTime":1076,"updateTime":1077,"relativeEntities":1078,"slug":1079,"properties":1080,"entityType":788,"verifyStatus":25,"verifyTime":1077,"verifyNote":790,"syncStatus":28,"languages":1091,"translateLanguages":26,"viewCount":36,"primaryUrl":1092,"fullTextUrl":26,"authors":1093,"publicationType":841,"publisherRelationship":1171,"citationCount":43,"citationInfo":1206,"publishDate":952,"publishYear":953,"citationAnalyzeStatus":28,"lastCitationAnalyze":26,"indexDatabases":26,"openAccess":26,"references":1208,"isForceReanalyzing":883},"87632539-5fd3-4ab4-b406-794606a7ea99","2024-04-18T21:28:50.962+00:00","2025-02-24T22:57:58.840+00:00",[],"On-the-Meaningfulness-of-Big-Data-Quality-Invited-Paper-",{"mag":1081,"keywords":1083,"openalex":1084,"abstract":1086,"title":1087,"doi":1089},{"VOID":1082},"2316007180",{},{"VOID":1085},"W2316007180",{},{"EN":1088},"On the Meaningfulness of “Big Data Quality” (Invited 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Accessed Aug 2015",{},{"id":26,"text":1385,"url":26,"identifiers":1386},"W3C. http:\u002F\u002Fwww.w3.org\u002FWAI\u002F . Accessed Aug 2015",{},{"id":26,"text":1388,"url":26,"identifiers":1389},"Wang RY, Strong DM (1996) Beyond accuracy: what data quality means to data consumers. J Manag Inf Syst 12(4):5–34",{"doi":1390},"10.1080\u002F07421222.1996.11518099",{"id":26,"text":1392,"url":26,"identifiers":1393},"Wayne SR (1983) Quality control circle and company wide quality control. Qual Prog 16(10):14–17",{},{"id":26,"text":1395,"url":26,"identifiers":1396},"Wu FJ, Kao YF, Tseng YC (2011) From wireless sensor networks towards cyber physical systems. Pervasive Mobile Comput 7(4):397–413",{"doi":1397},"10.1016\u002Fj.pmcj.2011.03.003",{"id":26,"text":1399,"url":26,"identifiers":1400},"Wu W, Yu C, Doan A, Meng W (2004) An interactive clustering-based approach to integrating source query interfaces on the deep web. 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MD, Niemeijer M, Russell SR (2010) Automated detection of diabetic retinopathy: barriers to translation into clinical practice. Expert Rev Med Devices 7(2):287–296",{"doi":1586},"10.1586\u002Ferd.09.76",{"id":26,"text":1588,"url":26,"identifiers":1589},"Acharya UR, Lim CM, Ng EYK, Chee C, Tamura T (2009) Computer-based detection of diabetes retinopathy stages using digital fundus images. Proc Inst Mech Eng Part H J Eng Med 223(5):545–553",{"doi":1590},"10.1243\u002F09544119JEIM486",{"id":26,"text":1592,"url":26,"identifiers":1593},"Association BD et al (1997) Retinal photography screening for diabetic eye disease. BDA, London",{},{"id":26,"text":1595,"url":26,"identifiers":1596},"Bargshady G, Zhou X, Deo R, Soar J, Whittaker F, Wang H (2020) Enhanced deep learning algorithm development to detect pain intensity from facial expression images. 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