Download Scalable Data Mining

Scalable Data Mining
Gianmarco De Francisci Morales
Abstract
Research activity on scalable data mining and big data.
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Research Statement
My research focuses on scalable data mining. In particular, my interest is
in mining the Web by using data-intensive scalable computing systems. As
such, my research develops along two main axes: (i) tapping into the Web as
a source of data and (ii) dealing with its immense scale. These two aspects
combined create what people call “big data.” I usually apply my research
to help people dealing with information overload.
In the first area, I am interested by the insights that come from exploring
news and social networks. I enjoy finding creative ways to extract new
information from the data. In the second area, I am thrilled by the challenges
created by the sheer size of the data. I like developing new scalable data
mining algorithms that take advantage of parallel and streaming solutions.
Inspired by Ernest Hemingway, my research in 6 words:
Big data: because more is different.
References
[1] R. Baraglia, G. De Francisci Morales, and C. Lucchese. Document
Similarity Self-Join with MapReduce. In ICDM ’10: 10th IEEE International Conference on Data Mining, pages 731–736, 2010.
[2] R. Baraglia, C. Lucchese, and G. De Francisci Morales. Large-scale
Data Analysis on the Cloud. In XXIV Convegno Annuale del CMGItalia, [best paper award] 2010.
[3] A. Bifet and G. De Francisci Morales. Big Data Stream Learning with
SAMOA. In ICDM ’14: 14th IEEE International Conference on Data
Mining, pages 1199–1202, [demo] 2014.
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[4] A. Bifet, G. De Francisci Morales, J. Read, G. Holmes, and
B. Pfahringer. Efficient Online Evaluation of Big Data Stream Classifiers. In KDD ’15: 21th ACM International Conference on Knowledge
Discovery and Data Mining, pages 59–68, 2015.
[5] R. Blanco, G. De Francisci Morales, and F. Silvestri. Towards Leveraging Closed Captions for News Retrieval. In WWW ’13: 22nd International World Wide Web Conference, pages 135–136, [poster] 2013.
[6] R. Blanco, G. De Francisci Morales, and F. Silvestri. IntoNews: Online
News Retrieval using Closed Captions. IP&M: Information Processing
& Management, 51(1):148–162, 2015.
[7] F. Bonchi, G. De Francisci Morales, A. Gionis, and A. Ukkonen. Activity Preserving Graph Simplification. DMKD: Data Mining and Knowledge Discovery, 27(3):321–343, 2013.
[8] F. Bonchi, G. De Francisci Morales, and M. Riondato. Centrality measures on big graphs: Exact, approximated, and distributed algorithms.
In WWW ’16: 25th International World Wide Web Conference, pages
1017–1020, [tutorial] 2016.
[9] I. Bordino, G. De Francisci Morales, I. Weber, and F. Bonchi. From
Machu Picchu to “rafting the urubamba river”: Anticipating information needs via the Entity-Query Graph. In WSDM ’13: 6th ACM International Conference on Web Search and Data Mining, pages 275–284,
2013.
[10] C. Castillo, G. De Francisci Morales, M. Mendoza, and N. Khan. Says
Who? Automatic Text-Based Content Analysis of Television News.
In MNLP ’13: 1st Workshop on Mining unstructured big data using
Natural Language Processing @CIKM, pages 53–60, 2013.
[11] C. Castillo, G. De Francisci Morales, and A. Shekhawat. Online Matching of Web Content to Closed Captions in IntoNow. In SIGIR ’13: 36th
ACM International Conference on Research and Development in Information Retrieval, pages 1115–1116, [demo] 2013.
[12] V. Catania, G. De Francisci Morales, A. G. D. Nuovo, M. Palesi, and
D. Patti. High Performance Computing for Embedded System Design:
A Case Study. In DSD ’08: 11th Conference on Digital System Design
Architectures, Methods and Tools, pages 656–659, 2008.
[13] V. Catania, A. G. D. Nuovo, M. Palesi, D. Patti, and G. De Francisci Morales. An Effective Methodology to Multi-objective Design
of Application Domain-specific Embedded Architectures. In DSD ’09:
12th Conference on Digital System Design, Architectures, Methods and
Tools, pages 643–650, 2009.
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[14] M. Das, G. De Francisci Morales, A. Gionis, and I. Weber. Learning to
Question: Leveraging User Preferences for Shopping Advice. In KDD
’13: 19th ACM Conference on Knowledge Discovery and Data Mining,
pages 203–211, 2013.
[15] G. De Francisci Morales. Cloud Computing for Large Scale Data Analysis. Technical report, IMT Institute for Advanced Studies, February
2010.
[16] G. De Francisci Morales. SAMOA: A Platform for Mining Big Data
Streams. In RAMSS’13: 2nd International Workshop on Real-Time
Analysis and Mining of Social Streams @WWW, pages 777–778, [extended abstract] 2013.
[17] G. De Francisci Morales and A. Bifet. SAMOA: Scalable Advanced
Massive Online Analysis. JMLR: Journal of Machine Learning Research, 16(Jan):149–153, 2015.
[18] G. De Francisci Morales, A. Bifet, L. Khan, J. Gama, and W. Fan.
IoT Big Data Stream Mining. In KDD ’16: 22nd ACM International
Conference on Knowledge Discovery and Data Mining, [tutorial] 2016.
[19] G. De Francisci Morales and A. Gionis. Streaming Similarity Self-Join.
PVLDB: Proceedings of the VLDB Endowment, 9(10):792–803, 2016.
[20] G. De Francisci Morales, A. Gionis, and C. Lucchese. From Chatter
to Headlines: Harnessing the Real-Time Web for Personalized News
Recommendation. In WSDM ’12: 5th ACM International Conference
on Web Search and Data Mining, pages 153–162, 2012.
[21] G. De Francisci Morales, A. Gionis, and M. Sozio. Social Content
Matching in MapReduce. PVLDB: Proceedings of the VLDB Endowment, 4(7):460–469, 2011.
[22] G. De Francisci Morales, C. Lucchese, and R. Baraglia. Scaling Out
All Pairs Similarity Search with MapReduce. In LSDS-IR ’10: 8th
Workshop on Large-Scale Distributed Systems for Information Retrieval
@SIGIR, pages 25–30, 2010.
[23] G. De Francisci Morales and A. Shekhawat. The Future of Second
Screen Experience. In Workshop on Exploring and Enhancing the User
Experience for Television @CHI, 2013.
[24] K. Garimella, G. De Francisci Morales, A. Gionis, and M. Mathioudakis. Exploring Controversy in Twitter. In CSCW ’16: 19th ACM
Conference on Computer-Supported Cooperative Work and Social Computing, pages 33–36, [demo] 2016.
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[25] K. Garimella, G. De Francisci Morales, A. Gionis, and M. Mathioudakis. Quantifying Controversy in Social Media. In WSDM ’16:
9th ACM International Conference on Web Search and Data Mining,
pages 33–42, 2016.
[26] K. Garimella, G. De Francisci Morales, A. Gionis, and M. Sozio. Scalable Facility Location for Massive Graphs on Pregel-like Systems. In
CIKM ’15: 24th ACM International Conference on Information and
Knowledge Management, pages 273–282, 2015.
[27] S. Gonzalez-Bailon, G. De Francisci Morales, M. Mendoza, N. Khan,
and C. Castillo. Cable News Coverage and Online News Stories: A
Large-Scale Comparison of Media Bias. SSRN: Social Science Research
Network, 2389525(Feb), 2014.
[28] C. M. Iacono-Manno, M. Fargetta, R. Barbera, A. Falzone, G. Andronico, S. Monforte, A. Muoio, R. Bruno, P. D. Primo, S. Orlando,
E. Leggio, A. Lombardo, G. Passaro, G. De Francisci Morales, and
S. Blandino. The Sicilian Grid Infrastructure for High Performance
Computing. International Journal of Distributed Systems and Technologies, 1(1):40–54, 2010.
[29] N. Kourtellis, G. De Francisci Morales, and F. Bonchi. Scalable Online
Betweenness Centrality in Evolving Graphs. TKDE: IEEE Transaction
on Knowledge and Data Engineering, 27(9):2494–2506, 2015.
[30] N. Kourtellis, G. De Francisci Morales, and F. Bonchi. Scalable Online Betweenness Centrality in Evolving Graphs. In ICDE ’16: 32nd
IEEE International Conference on Data Engineering, pages 1580–1581,
[extended abstract] 2016.
[31] D. Marron, A. Bifet, and G. De Francisci Morales. Random Forests
of Very Fast Decision Trees on GPU for Mining Evolving Big Data
Streams. In ECAI ’14: 21st European Conference on Artificial Intellingence, pages 615–620, 2014.
[32] M. A. U. Nasir, G. De Francisci Morales, D. Garcı́a-Soriano, N. Kourtellis, and M. Serafini. Partial Key Grouping: Load-Balanced Partitioning
of Distributed Streams. arXiv:1510.07623, 2015.
[33] M. A. U. Nasir, G. De Francisci Morales, D. Garcı́a-Soriano, N. Kourtellis, and M. Serafini. The Power of Both Choices: Practical Load Balancing for Distributed Stream Processing Engines. In ICDE ’15: 31st
IEEE International Conference on Data Engineering, pages 137–148,
2015.
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[34] M. A. U. Nasir, G. De Francisci Morales, N. Kourtellis, and M. Serafini.
When Two Choices Are not Enough: Balancing at Scale in Distributed
Stream Processing. In ICDE ’16: 32nd IEEE International Conference
on Data Engineering, pages 589–600, 2016.
[35] D. M. Shankaralingappa, G. De Francisci Morales, and A. Gionis. Extracting Skill Endorsements from Personal Communication Data. In
CIKM ’16: 25th ACM International Conference on Information and
Knowledge Management, 2016.
[36] B. Thomee and G. De Francisci Morales. Automatic Discovery of Global
and Local Equivalence Relationships in Labeled Geo-Spatial Data. In
HT ’14: 25th ACM Conference on Hypertext and Social Media, pages
158–168, 2014.
[37] A. T. Vu, G. De Francisci Morales, J. Gama, and A. Bifet. Distributed
Adaptive Model Rules for Mining Big Data Streams. In BigData ’14:
2nd IEEE International Conference on Big Data, pages 345–353, 2014.
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