@article{
 title = {Debugging Big Data Analytics in Spark with BigDebug},
 type = {article},
 year = {2017},
 identifiers = {[object Object]},
 keywords = {automatic fault localization,big data analytics,data-intensive scalable computing,debugging,disc,interactive tools},
 pages = {1627-1630},
 websites = {http://doi.acm.org/10.1145/3035918.3058737},
 id = {aec13644-b5b6-38e5-9e50-6dd0852d73ba},
 created = {2018-01-04T17:56:48.126Z},
 file_attached = {true},
 profile_id = {0ecaa748-3fac-3117-bdf6-9c4c3c7996d4},
 group_id = {d5687afc-7996-37cd-8cc9-b955ea15e0aa},
 last_modified = {2018-01-10T17:12:33.778Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Gulzar2017a},
 private_publication = {false},
 abstract = {© 2017 Copyright held by the owner/author(s).To process massive quantities of data, developers leverage Data-Intensive Scalable Computing (DISC) systems such as Apache Spark. In terms of debugging, DISC systems support only postmortem log analysis and do not provide any debugging functionality. This demonstration paper showcases BIGDEBUG: a tool enhancing Apache Spark with a set of interactive debugging features that can help users in debug their Big Data Applications.},
 bibtype = {article},
 author = {Gulzar, Muhammad Ali and Interlandi, Matteo and Condie, Tyson and Kim, Miryung},
 journal = {Proceedings of the 2017 ACM International Conference on Management of Data}
}

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