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ORCID2nd BIGDSE@ICSE 2016: Austin, Texas, USA
- Proceedings of the 2nd International Workshop on BIG Data Software Engineering, BIGDSE@ICSE 2016, Austin, Texas, USA, May 16, 2016. ACM 2016, ISBN 978-1-4503-4152-3
Real-time data analytics
- Patrick Tendick, Audris Mockus
:
Decisions as a service for application centric real time analytics. 1-7 - Georgios Gousios, Dominik Safaric, Joost Visser:
Streaming software analytics. 8-11
Data management
- Yang Zhang, Fangzhou Xu, Erwin Frise, Siqi Wu, Bin Yu, Wei Xu:
DataLab: a version data management and analytics system. 12-18 - Nathan Wilder, Jared M. Smith, Audris Mockus:
Exploring a framework for identity and attribute linking across heterogeneous data systems. 19-25 - Marco Scavuzzo, Damian A. Tamburri, Elisabetta Di Nitto:
Providing big data applications with fault-tolerant data migration across heterogeneous NoSQL databases. 26-32 - Andreas Ringlstetter, Stefanie Scherzinger, Tegawendé F. Bissyandé:
Data model evolution using object-NoSQL mappers: folklore or state-of-the-art? 33-36
Engineering data-intensive systems
- Michele Guerriero, Saeed Tajfar, Damian A. Tamburri, Elisabetta Di Nitto:
Towards a model-driven design tool for big data architectures. 37-43 - Hong-Mei Chen, Rick Kazman, Juan Garbajosa, Eloy González:
Toward big data value engineering for innovation. 44-50
Applications and industry
- John Klein, Ross Buglak, David Blockow, Troy Wuttke, Brenton Cooper:
A reference architecture for big data systems in the national security domain. 51-57 - Saeed Zareian, Marios Fokaefs, Hamzeh Khazaei, Marin Litoiu, Xi Zhang:
A big data framework for cloud monitoring. 58-64 - Rahul Krishna, Zhe Yu, Amritanshu Agrawal, Manuel Dominguez, David Wolf:
The "BigSE" project: lessons learned from validating industrial text mining. 65-71 - Hong-Mei Chen, Rick Kazman, Ira Monarch, Ping Wang:
Predicting and fixing vulnerabilities before they occur: a big data approach. 72-75
Engineering data-intensive systems
- Ibtehal Noorwali, Darlan Arruda, Nazim H. Madhavji:
Understanding quality requirements in the context of big data systems. 76-79
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