1. Big Data Research Progress Chao Jan 22, 2013

2. Big Data Lab Big Data@CSAIL, MIT – http://bigdata.csail.mit.edu/ http://bigdata.csail.mit.edu/ – 23 nodes – GROWING BIG LINKED DATA FROM SEED: BUILDING A DEMO – VISION MACHINE: LEARNING ONLINE FROM 25 MILLION IMAGES – NATURAL LANGUAGE INTERFACE FOR BIG DATA – SCIDB – MACHINE LEARNING – SOCIAL: CONDENSR – SOCIAL: TWITINFO – SOCIAL: INFLUENCE MODELING – …

3. Big Data Lab NASA tournament lab – http://www.nasa.gov/directorates/heo/ntl/ http://www.nasa.gov/directorates/heo/ntl/ Big data challenge – http://open.nasa.gov/blog/2012/10/03/nasa- tournament-labs-big-data-challenge/ http://open.nasa.gov/blog/2012/10/03/nasa- tournament-labs-big-data-challenge/ – Apply the process of open innovation to conceptualizing new and novel approaches to using “big data” information sets from various U.S. government agencies, e.g., health, energy and earth science.

4. Big Data People Jimmy Lin (University of Maryland) – http://www.umiacs.umd.edu/~jimmylin/ http://www.umiacs.umd.edu/~jimmylin/ Ron Bekkerman (LinkedIn) – http://people.cs.umass.edu/~ronb/ Misha Bilenko (MSR) – http://research.microsoft.com/en-us/um/people/mbilenko/ John Langford (Yahoo! Research) – http://hunch.net/~jl/

5. Tutorial Scaling Up Machine Learning-Parallel and Distributed Approaches KDD’2011 Ron Bekkerman (LinkedIn), Misha Bilenko (MSR) and John Langford (Yahoo! Research) http://hunch.net/~large_scale_survey/

6. Tutorial State-of-the-art platforms and algorithm choices Hardware options (from FPGAs and GPUs to multi-core systems and commodity clusters) Programming frameworks (including CUDA, MPI, MapReduce, and DryadLINQ) Learning settings (e.g., semi-supervised and online learning) Example-driven, covering a number of popular algorithms (e.g., boosted trees, spectral clustering, belief propagation) and diverse applications (e.g., speech recognition and object recognition in vision).

7. Parallelization: platform choices PlatformCommunication SchemeData size Peer-to-PeerTCP/IPPetabytes Virtual ClustersMapReduce / MPITerabytes HPC ClustersMPI / MapReduceTerabytes MulticoreMultithreadingGigabytes GPUCUDAGigabytes FPGAHDLGigabytes

8. The Book Cambridge Uni Press Due in November 2011 21 chapters Covering – Platforms – Algorithms – Learning setups – Applications

9. 10 2 Chapter contributors 3 4 5 6 7 8 9 11 12 13 14 15 16 17 18 19 20 21

10. New age of big data The world has gone mobile – 5 billion cellphones produce daily data Social networks have gone online – Twitter produces 200M tweets a day Crowdsourcing is the reality – Labeling of 100,000+ data instances is doable Within a week

11. Big Data Data DATA.GOV – http://www.data.gov/developers/community/dev elopers http://www.data.gov/developers/community/dev elopers – Data portal provided by US government

12. Big Data in Q&A It is estimated that 2.5 quintillion bytes of new data are created daily with an estimated 80% of this produced as "unstructured" data IBM Watson deep Q&A – http://www.research.ibm.com/articles/watson.shtml http://www.research.ibm.com/articles/watson.shtml – Evidence-based decision support – Jeopardy! – Provide a single correct answer with confidence – Analyze over 200 million pages in three seconds

13. Big Data in Q&A IBM Watson deep Q&A – Health care 2011, pilot program with WellPoint, whose affiliated health plans cover one in nine Americans 2012, partnership with Memorial Sloan-Kettering Cancer Center, where work is under way to teach Watson about oncology diagnosis and treatment options

14. Big Data Blog http://whatsthebigdata.com/ – News and events about Big Data http://www.greenplum.com/industry- buzz/big-data/research-papers http://www.greenplum.com/industry- buzz/big-data/research-papers – News and research papers about Big Data

15. Big Data Publication Fast Data in the Era of Big Data: Twitter’s Real-Time Related Query Suggestion Architecture http://arxiv.org/pdf/1210.7350v1.pdf Architecture behind Twitter's real-time related query suggestion and spelling correction service – First implementation: typical Hadoop-based analytics stack, did not meet the latency requirement – Second implementation: system deployed in production, custom in-memory processing engine

16. Big Data Publication Fast Candidate Generation for Two-Phase Document Ranking: Postings List Intersection with Bloom Filters http://www.umiacs.umd.edu/~jimmylin/publications/Asadi_Lin_CIK M2012.pdf http://www.umiacs.umd.edu/~jimmylin/publications/Asadi_Lin_CIK M2012.pdf Most modern web search engines employ a two-phase ranking strategy: a candidate list of documents is generated using a “cheap” but low-quality scoring function, which is then reranked by an “expensive" but high-quality method Candidate generation for conjunctive query processing in this context A fast, approximate postings list intersection algorithms based on Bloom Filters

17. Big Data Publication Why Not Grab a Free Lunch? Mining Large Corpora for Parallel Sentences to Improve Translation Modeling – http://www.umiacs.umd.edu/~jimmylin/publications/Ture_Lin_ NAACL-HLT2012.pdf http://www.umiacs.umd.edu/~jimmylin/publications/Ture_Lin_ NAACL-HLT2012.pdf Large-Scale Machine Learning at Twitter – http://www.umiacs.umd.edu/~jimmylin/publications/Lin_Kolcz_ SIGMOD2012.pdf http://www.umiacs.umd.edu/~jimmylin/publications/Lin_Kolcz_ SIGMOD2012.pdf Smoothing Techniques for Adaptive Online Language Models: Topic Tracking in Tweet Streams – http://www.umiacs.umd.edu/~jimmylin/publications/Lin_etal_K DD2011.pdf

18. Big Data Book Data-Intensive Text Processing with MapReduce http://lintool.github.com/MapReduceAlgorith ms/MapReduce-book-final.pdf