Big Data Science. Prof. Lise Getoor University of Maryland, College Park. October 17, 2013


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1 Big Data Science Prof Lise Getoor University of Maryland, College Park October 17,
2 BIG Data is not flat lonnitaylor
3 Data is multimodal, multirelational, spatiotemporal, multimedia Entities and relationships are important!
4 NEED: Data Science for Graphs New V: Vinculate
5 Diverse applications areas: social networks & social media, computational biology, computer vision, computational linguistics, computational sustainability, computational social science, personalized medicine, cybersecurity, etc Research Overview Combining statistical & logical methods for machine learning and reasoning under uncertainty ML Visual Analytic Tools MIT Press 2007 Probabilistic databases, data integration and alignment DB DDupe VIZ CGroup
6 Statistical Relational Learning (SRL) AI/DB representations + statistics for multirelational data l Entities can be of different types l Entities can participate in a variety of relationships l examples: Markov logic networks, relational dependency networks, Bayesian logic programs, probabilistic relational models, many others Key ideas l Relational feature construction l Collective reasoning l Lifted representation, inference and learning For more details, see NIPS 2012 Tutorial, nips2012pdf
7 Common Graph Data Analysis Patterns Joint inference over large networks for: l Collective Classification inferring labels of nodes in graph l Link Prediction inferring the existence of edges in graph l Entity Resolution clustering nodes that refer to the same underlying entity
8 Common Graph Data Analysis Patterns Joint inference over large networks for: l Collective Classification inferring labels of nodes in graph l Link Prediction inferring the existence of edges in graph l Entity Resolution clustering nodes that refer to the same underlying entity
9 Common Graph Data Analysis Patterns Joint inference over large networks for: l Collective Classification inferring labels of nodes in graph l Link Prediction inferring the existence of edges in graph l Entity Resolution clustering nodes that refer to the same underlying entity
10 Common Graph Data Analysis Patterns Joint inference over large networks for: l Collective Classification inferring labels of nodes in graph l Link Prediction inferring the existence of edges in graph l Entity Resolution clustering nodes that refer to the same underlying entity
11 What s Needed Next? Methods which can perform and interleave these tasks Desiderata: Flexible, scalable, declarative support for collective classification, link prediction, entity resolution and other information fusion and information alignment problems
12 Probabilistic Soft Logic (PSL) Stephen Bach Matthias Broecheler Alex Memory Lily Mihalkova Stanley Kok Bert Huang Angelika Kimmig
13 Probabilistic Soft Logic (PSL) Declarative language based on logics to express collective probabilistic inference problems  Predicate = relationship or property  Atom = (continuous) random variable  Rule = capture dependency or constraint  Set = define aggregates PSL Program = Rules + Input DB
14 Probabilistic Soft Logic (PSL) Declarative language based on logics to express collective probabilistic inference problems  Predicate = relationship or property  Atom = (continuous) random variable  Rule = capture dependency or constraint  Set = define aggregates PSL Program = Rules + Input DB
15 Node Labeling?
16 Voter Opinion Modeling? Status update $ $ Tweet
17 Voter Opinion Modeling spouse colleague friend friend spouse friend spouse friend colleague
18 Voter Opinion Modeling vote(a,p) friend(b,a) à vote(b,p) : 03 spouse colleague friend friend spouse friend spouse friend colleague vote(a,p) spouse(b,a) à vote(b,p) : 08
19 Link Prediction Entities  People, s Attributes  Words in s Relationships  communication, work relationship Goal: Identify work relationships  Supervisor, subordinate, colleague #
20 Link Prediction People, s, words, communication, relations Use rules to express evidence  If content suggests type X, it is of type X  If A sends deadline s to B, then A is the supervisor of B  If A is the supervisor of B, and A is the supervisor of C, then B and C are colleagues #
21 Link Prediction People, s, words, communication, relations Use rules to express evidence  If content suggests type X, it is of type X  If A sends deadline s to B, then A is the supervisor of B  If A is the supervisor of B, and A is the supervisor of C, then B and C are colleagues # complete by due
22 Link Prediction People, s, words, communication, relations Use rules to express evidence  If content suggests type X, it is of type X  If A sends deadline s to B, then A is the supervisor of B  If A is the supervisor of B, and A is the supervisor of C, then B and C are colleagues #
23 Link Prediction People, s, words, communication, relations Use rules to express evidence  If content suggests type X, it is of type X  If A sends deadline s to B, then A is the supervisor of B  If A is the supervisor of B, and A is the supervisor of C, then B and C are colleagues #
24 Entity Resolution Entities  People References Attributes  Name Relationships  Friendship Goal: Identify references that denote the same person John Smith J Smith name name C E A B friend friend D F G = = H
25 Entity Resolution References, names, friendships Use rules to express evidence  If two people have similar names, they are probably the same  If two people have similar friends, they are probably the same  If A=B and B=C, then A and C must also denote the same person John Smith J Smith name name C E A B friend friend D F G = H =
26 Entity Resolution References, names, friendships Use rules to express evidence  If two people have similar names, they are probably the same  If two people have similar friends, they are probably the same  If A=B and B=C, then A and C must also denote the same person Aname {str_sim} Bname => A B : 08 C John Smith A E name friend J Smith B name D F G = = H friend
27 Entity Resolution References, names, friendships Use rules to express evidence  If two people have similar names, they are probably the same  If two people have similar friends, they are probably the same  If A=B and B=C, then A and C must also denote the same person John Smith J Smith name name C E A B friend friend D F G = H {Afriends} {} {Bfriends} => A B : 06 =
28 Entity Resolution References, names, friendships Use rules to express evidence  If two people have similar names, they are probably the same  If two people have similar friends, they are probably the same  If A=B and B=C, then A and C must also denote the same person John Smith J Smith name name C E A B friend friend D F G = H = A B ^ B C => A C :
29 Mathematical Foundation
30 Probabilistic Model Probability density over interpretation I Ground rules distance to satisfaction d r (I) = max{i r,body I r,head, 0} # P (I) = 1 Z exp " X r2r w r (d r (I)) p r Normalization constant Ground rules Rule weight Distance exponent (in {1, 2})
31 Hingeloss MRFs
32 Hingeloss Markov Random Fields 2 P (Y X) = 1 Z exp 4 3 mx w j max{`j(y, X), 0} p j 5 j=1 Continuous variables in [0,1] Potentials are hingeloss functions Subject to arbitrary linear constraints Logconcave!
33 Inference as Convex Optimization Maximum Aposteriori Probability (MAP) Objective: arg max P (Y X) Y mx = arg min Y This is convex! Can solve using offtheshelf convex optimization packages or custom solver j=1 w j max{`j(y, X), 0} p j
34 Consensus Optimization Idea: Decompose problem and solve subproblems independently (in parallel), then merge results  Subproblems are ground rules  Auxiliary variables enforce consensus across subproblems Framework: Alternating direction method of multipliers (ADMM) [Boyd, 2011] Inference with ADMM is fast, scalable, and straightforward to implement [Bach et al, NIPS 2012, UAI 2013]
35 [Bach et al, UAI 2013; London et al, 2013] Speed Average running time Cora Citeseer Epinions Ac/vity Discrete MRF 1109 s 1843 s 2124 s 3442 s HL MRF 04 s 07 s 12 s 06 s Variables 10k 10k 1k 8k Poten/als 14k 19k 18k 75k Inference in HLMRFs is orders of magnitude faster than in discrete MRFs which use MCMC approximate inference In practice, scales linearly with the number of potentials
36 Distributed Processing
37 Distributed MAP Inference ADMM consensus optimization problem can be implemented naturally in distributed setting For k+1 iteration, it consists three steps in which sub problems can run independently (1 st and 2 nd step): 1 Update Lagrangian multiplier Miao, Liu, Huang, Getoor, IEEE Big Data Update each sub problem OR 3 Update the global variables
38 Distributed MAP: MapReduce Job Bootstrap Mapper sub problem load global variable X as side data read global variable X read/write subproblem Pros: Straightforward Design Cons: Job bootstrapping cost between iterations Difficult to schedule subset of nodes to run local variable copy z1 z2 zq zp z1 zq z2 z1 Reducer update global component z1 z1 z1 z2 z2 zq zq zp HDFS or HBase write new global variable
39 Distributed MAP: GraphLab sub problem node global variable component Advantages: No need to touch disk, no job bootstrapping cost Easy to express local convergence conditions to schedule only subset of nodes gather get z gather get local z,y apply update y update x scatter notify z apply update z scatter unless converge notify X update i update i+1
40 Experimental Results Using PSL for knowledge graph cleaning task  16M+ vertices, 22M+ edges, for small running instances  Takes 100 minutes to finish in Java single machine implementation using 40G+ memory  Distributed GraphLab implementation takes less than 15 minutes using 4 smaller machines  Possible to use commodity machines on large models!
41 Miao, Liu, Huang, Getoor, BigData 13 Experimental Results Voter model using commodity machines Name Subproblem Consensus Edge Fit in One Machine? Run time (sec) m = 8 SN 1M 33M 11M 6M Yes 2230 SN 2M 66M 21M 12M No 3997 SN 3M 10M 31M 18M No 4395 SN 4M 13M 42M 24M No 5376 Machine: Intel Core2 Quad CPU 266GHz machines with 4GB RAM running Ubuntu 1204 Linux Running time (sec) Hyper Greedy Number of Machines (SN 2M ) Running time (sec) Hyper Greedy Number of machines Strong scaling with fixed dataset Weak scaling with increasing size
42 Applications
43 Application Domains Computer Vision  Lowlevel image reconstruction  Activity recognition in Video Computational Biology & Health Informatics  Drugtarget prediction  Event discovery in EMR data Computational Social Science  Voter Opinion Analysis & discovery of Latent Groups in Social Media  Inferring bias in political discourse  Psychological modeling on online social networks Information Integration & Extraction  Entity resolution  Ontology alignment & schema mapping  Knowledge graph identification Upcoming: climate graphs, discourse analysis, MOOCs, more!
44 Closing Comments Great opportunities to do good work and do useful things in the current era of big data, information overload and network science big data science Statistical relational learning provides some of the tools, much work still needed, developing theoretical bounds for relational learning, scalability, etc Compelling applications abound! Looking for students, programmers, & postdocs
45 pslumiacsumdedu
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