大数据研究徐宗本

Outline
Big Data: Opportunities and Challenges Some More Scientific Problems in Big
Data Analysis and Processing
Some Advances on Big Data Research
Big Data: Opportunities and Challenges
Big data research: A real inter/multidisciplinary activities.
Management Science Information Science Math and statistics
Sub-sampling problem: A big data set has to be processed by some types
of ‗divide-and-conquer‘ schemes, like Hadoop system.
Map (random sub-sampling)
D1
Reduce (aggregation)
X1 X2 X3 … … Xn D
Intermediate solution f1
Dk
Intermediate solution f2
Dm
Intermediate solution fm
The Big Data Bootstrap. Kleiner et.al. 2012 ICML
…. ….
Final estimation f*
Scientific Researches
• • • • High-energy physics Astronomy Life science Geosciences and remote sensing
• • •
Social Governance
The fourth paradigm of research A systematic approach uniquely applicable to modern management (Jims Gray) Big data view of assessing public policies • • •
Data Analysis and Processing
Some Advances on Big Data Research
Problem 1: High Dimensionality
High dimensionality problem：The number of features (p) is far
Problem 2: Sub-sampling
Core open questions

How to sub-sampling/aggregate so that the
final f* models properly D Is distributed processing feasible? How about traditional sub-sampling technologies work? Sub-sampling axiom (Similarity; Transitivity, …)
Big Data: Opportunities and Challenges
Big Data
A term for a collection of data that are very large and complex so that it is difficult to process and analyze using on-hand database management tools, traditional data processing methods and analysis methodologies .
)
(Wikipedia )
ZB(1021), EB(1018), PB(1015), TB(1012), GB(109), MB(106)
Big Data: Opportunities and Challenges
Why difficulty? Big data challenges the existing information technologies, management paradigm, statistical and computational sciences.
Volume
PB—ZB in scale Distributed storage and processing necessary

Velocity
Growing tremendously Data flow

Variety
Multisource, correlated, heterogeneous Unstructured, unreliable, inconsistent.
Fundamental Challenge 1
Fundamental Challenge 2
Fundamental Challenge 3
Fundamental Challenge 4
Big Data: Opportunities and Challenges
Big data research: A real inter/multidisciplinary activities.
Total
Value
dataset embodies great value Individual or small subset contains less information
Big Data: Opportunities and Challenges
What opportunities：Big data embody great values that might not be explored in small sized data.
Representation (Uniform scheme; Complexity); Modeling (Parent space identification; sampling); Mining Acquisition; Quality; Standard; Sharing; Privacy protection; Data-driven Highly domain-specific; Any data-driven (Social media based; Safety; Trade data based; Record (Survey, Architecture; System/Software/Algorithm; Scalability/Complexity; Real time processing (Clustering; Classification; Regression; Prediction; fields Variable Selection) ; Analytics (Relevance Analysis; Latent management Observation) based; Empirical data based; Experimental data based) computation) variable analytics; Statistical inference) ; Computation (Subsampling; Complexity; Distributed
larger than the sample size (n), and n varies with p (n=n(p))
Classical：n>>p; High-D：p>>n; Big data：p>>n(p).
Linear model: y = b1x1 + b2 x2 +, , b p x p Data：D = {( x1, y1 ),( x2, y2 ), ,( xn, yn )} Matrix form:
Management Science Information Science Math and statistics
Engineerings
Data acquisition& data management
Data storagea understanding
Applications
Hot Issues：Sparse modeling (compressed sensing; low rank
decomposition of matrix; sparse learning） Core open questions

How to add priors so that a high-D problem
Exploring Big Data Analysis: Fundamental Scientific Problems
Zongben Xu
(Xi’an Jiaotong University)
Email: zbxu@ Homepage:
Y = Xn´ p b p´1
-1
ˆ = ( X ' X ) X 'Y Solution b Asymptotical normality
ˆ - b ) ~ N (0, 1 ( X ' X )-1s 2 ) ® N (0, s 2 I ) n (b p´ p n
d
Problem 1: High Dimensionality
Management Science Information Science Math and Statistics
Engineerings
Data acquisition& data management