skip to main content
research-article
Public Access

Digging into big provenance (with SPADE)

Published:19 November 2021Publication History
Skip Abstract Section

Abstract

A user interface for querying provenance.

References

  1. Ahmad, R., Jung, E., de Senne Garcia, C., Irshad, H., Gehani, A. Discrepancy detection in whole network provenance. In Proceedings of the 12th USENIX Workshop on the Theory and Practice of Provenance; https://www.usenix.org/conference/tapp2020/presentation/ahmad.Google ScholarGoogle Scholar
  2. Fan, J., Gerald, A., Raj, S., Patel, J. The case against specialized graph analytics engines. In Proceedings of the 7th Biennial Conf. on Innovative Data Systems, 2015; http://cidrdb.org/cidr2015/Papers/CIDR15_Paper20.pdf.Google ScholarGoogle Scholar
  3. Gehani, A. SPADE; http://spade.csl.sri.com.Google ScholarGoogle Scholar
  4. Gehani, A., Kim, M., Zhang, J. Steps toward managing lineage metadata in grid clusters. In Proceedings of the 1st Usenix Workshop on Theory and Practice of Provenance, 2009, 1--9 Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Gehani, A., Kim, M. Mendel: Efficiently verifying the lineage of data modified in multiple trust domains, Proceedings of the 19th ACM Intern. Symp. High Performance Distributed Computing 2010; 227--239. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Gehani, A., Tariq, D. SPADE: Support for provenance auditing in distributed environments. In Proceedings of the 13th ACM/IFIP/Usenix Middleware Conf.; 2012 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Gehani, A., Kazmi, H., Irshad, H. Scaling SPADE to "Big Provenance." In Proceedings of the 8th Usenix Workshop on Theory and Practice of Provenance, 2016, 26--33; https://www.usenix.org/conference/tapp16/workshop-program/presentation/gehani.Google ScholarGoogle Scholar
  8. Ghosh, S., Das, A., Porras, P., Yegneswaran, V., Gehani, A. Automated categorization of onion sites for analyzing the dark web ecosystem. In Proceedings of the 23rd ACM Intern. Conf. Knowledge Discovery and Data Mining, 2017, 1793--1802 Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Glavic, B. Big data provenance: challenges and implications for benchmarking. Revised Selected Papers of the 1st Workshop on Specifying Big Data Benchmarks 8163, 2012, 72--80 Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Khoury, J., Upthegrove, T., Caro, A., Benyo, B., Kong, D. An event-based data model for granular information flow tracking. Proceedings of the 12th Usenix Workshop on the Theory and Practice of Provenance, 2020; https://www.usenix.org/biblio-4496.Google ScholarGoogle Scholar
  11. Moreau, L. et al. The Open Provenance Model core specification. Future Generation Computer Systems 27, 6 (2011) Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Patel, J., Deshmukh, H., Zhu, J., Potti, N., Zhang, Z., Spehlmann, M., Memisoglu, H., Saurabh, S. Quickstep: A data platform based on the scaling-up approach. In Proceedings of the VLDB Endowment 11, 6 (2018), 663--676 Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. W3C Working Group. PROV-overview, 2013; https://www.w3.org/TR/prov-overview/.Google ScholarGoogle Scholar

Index Terms

  1. Digging into big provenance (with SPADE)

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image Communications of the ACM
        Communications of the ACM  Volume 64, Issue 12
        December 2021
        101 pages
        ISSN:0001-0782
        EISSN:1557-7317
        DOI:10.1145/3502158
        Issue’s Table of Contents

        Copyright © 2021 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 19 November 2021

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Popular
        • Refereed

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format