REACT: Context-sensitive recommendations for data analysis

Tova Milo, Amit Somech

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations


Data analysis may be a dificult task, especially for nonexpert users, as it requires deep understanding of the investigated domain and the particular context. In this demo we present REACT, a system that hooks to the analysis UI and provides the users with personalized recommendations of analysis actions. By matching the current user session to previous sessions of analysts working with the same or other data sets, REACT is able to identify the potentially best next analysis actions in the given user context. Unlike previous work that mainly focused on individual components of the analysis work, REACT provides a holistic approach that captures a wider range of analysis action types by utilizing novel notions of similarity in terms of the individual actions, the analyzed data and the entire analysis workow. We demonstrate the functionality of REACT, as well as its effectiveness through a digital forensics scenario where users are challenged to detect cyber attacks in real life data achieved from honeypot servers.

Original languageEnglish
Title of host publicationSIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450335317
StatePublished - 26 Jun 2016
Externally publishedYes
Event2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016 - San Francisco, United States
Duration: 26 Jun 20161 Jul 2016

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078


Conference2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016
Country/TerritoryUnited States
CitySan Francisco

Bibliographical note

Funding Information:
We thank Daniel Deutch and Amir Gilad for their insightful comments. This work has been partially funded by the European Research Council under the FP7, ERC grant MoDaS, agreement 291071 and by a grant from the Blavatnik Interdisciplinary Cyber Research Center.


Dive into the research topics of 'REACT: Context-sensitive recommendations for data analysis'. Together they form a unique fingerprint.

Cite this