@inproceedings{bae5bbc363dc4bc9ab0533727f6bda7d,
title = "Delve: A Data Set Retrieval and Document Analysis System",
abstract = "Academic search engines (e.g., Google scholar or Microsoft academic) provide a medium for retrieving various information on scholarly documents. However, most of these popular scholarly search engines overlook the area of data set retrieval, which should provide information on relevant data sets used for academic research. Due to the increasing volume of publications, it has become a challenging task to locate suitable data sets on a particular research area for benchmarking or evaluations. We propose Delve, a web-based system for data set retrieval and document analysis. This system is different from other scholarly search engines as it provides a medium for both data set retrieval and real time visual exploration and analysis of data sets and documents.",
author = "Uchenna Akujuobi and Xiangliang Zhang",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 ; Conference date: 18-09-2017 Through 22-09-2017",
year = "2017",
doi = "10.1007/978-3-319-71273-4_39",
language = "English (US)",
isbn = "9783319712727",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "400--403",
editor = "Michelangelo Ceci and Saso Dzeroski and Donato Malerba and Yasemin Altun and Kamalika Das and Jesse Read and Marinka Zitnik and Jerzy Stefanowski and Taneli Mielik{\"a}inen",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings",
address = "Germany",
}