Research directions for engineering big data analytics software

IEEE Intelligent Systems

Volumn 30, Issue 1, 2015, Pages 13-19

  Otero, Carlos E ^a   Peter, Adrian ^a  

^a FLORIDA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; INTELLIGENT SYSTEMS;

DATA ANALYTICS; FLORIDA; MULTIHOP;

BIG DATA;

EID: 84922987097     PISSN: 15411672     EISSN: None     Source Type: Journal    
DOI: 10.1109/MIS.2014.76     Document Type: Article

References (23)

1. A. Jacobs, "The Pathologies of Big Data," Comm. ACM , vol. 52, no. 8, 2009, pp. 36-44.
2. S. Madden, "From Databases to Big Data," IEEE Internet Computing, vol. 16, no. 3, 2012, pp. 4-6.
3. J. Cohen et al., "MAD Skills: New Analysis Practices for Big Data," Proc. VLDB Endowment, 2009; http://db.cs.berkeley.edu/jmh/papers/madskills-032009.pdf.
4. H. Chen, R.H. Chiang and V.C. Storey, "Business Intelligence and Analytics: From Big Data to Big Impact," MIS Quaterly, vol. 36, no. 4, 2012, pp. 1165-1188.
5. T. Kraska, "Finding the Needle in the Big Data Systems Haystack," IEEE Internet Computing, vol. 17, no. 1, 2013, pp. 84-86.
6. J. Gama et al., "A Survey on Concept Drift Adaptation," ACM Computing Surveys, vol. 46, no. 4, 2014, article no. 44.
7. K. Murphy, Machine Learning: A Probabilistic Perspective , MIT Press, 2012.
8. A. Bifet et al., "MOA Massive Online Analysis," 2014; http://heanet.dl.sourceforge.net/project/moadatastream/documentation/StreamMining.pdf.
9. M. Masud et al., "Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints," IEEE Trans. Knowledge Data Eng., vol. 23, no. 6, 2001, pp. 859-874.
10. M. Masud et al., "Detecting Recurring and Novel Classes in Concept-Drifting Data Streams," Proc. 2011 IEEE Int'l Conf. Data Mining, 2011, pp. 1176-1181.
11. N. Maiden, "Monitoring Our Requirements," IEEE Software, vol. 30, no. 1, 2013, pp. 16-17.
12. W.N. Robinson, "A Roadmap for Comprehensive Requirements Monitoring," Computer, vol. 43, no. 5, 2010, pp. 64-72.
13. X. Xiaoyuan et al., "Testing and Validating Machine Learning Classifiers by Metamorphic Testing," J. Systems and Software , vol. 84, no. 4, 2011, pp. 544-558.
14. J.D. Tygar, "Adversarial Machine Learning," IEEE Internet Computing, vol. 15, no. 5, 2011, pp. 4-6.
15. Cloud Security Alliance, "Expanded Top Ten Big Data Security and Privacy Challenges: Cloud Security Alliance," Apr. 2013; https://cloudsecurityalliance.org/download/expanded-top-ten-big-data-security-and-privacy-challenges/.
16. J. Yin et al., "Using Social Media to Enhance Emergency Situation Awareness," IEEE Intelligent Systems, vol. 27, no. 6, 2012, pp. 52-59.
17. R. Lu et al., "Toward Efficient and Privacy-Preserving Computing in Big Data Era," IEEE Network, vol. 28, no. 4, 2014, pp. 46-50.
18. S. Soren, "The Need for Open Source Software in Machine Learning," J. Machine Learning, vol. 8, no. 10, 2007, pp. 2443-2466.
19. A. Kumar, F. Niu and C. Re, "Hazy: Making it Easier to Build and Maintain Big-Data Analytics," Comm. ACM, vol. 56, no. 3, 2013, pp. 40-49.
20. D.E. O'Leary, "Artificial Intelligence and Big Data," IEEE Intelligent Systems, vol. 28, no. 2, 2013, pp. 96-99, 2013.
21. C. Longbing, "Domain-Driven Data Mining: Challenges and Prospects," IEEE Trans. Knowledge and Data Eng., vol. 22, no. 6, 2010, pp. 755-769.
22. F. Shull, "Getting an Intuition for Big Data," IEEE Software, vol. 30, no. 4, 2013, pp. 3-6.
23. D. Zhang et al., "Social and Community Intelligence: Technologies and Trends," IEEE Software, vol. 29, no. 4, 2012, pp. 88-92.