for big data b669 sublinear...
TRANSCRIPT
![Page 1: for Big Data B669 Sublinear Algorithmshomes.sice.indiana.edu/qzhangcs/B669-17-fall-sublinear/slides/intro.pdfWhat is the meaning of Big Data IN THEORY? We don’t de ne Big Data in](https://reader030.vdocuments.site/reader030/viewer/2022041107/5f0a8d277e708231d42c30ba/html5/thumbnails/1.jpg)
1-1
Qin Zhang
B669 Sublinear Algorithms
for Big Data
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2-1
• : over 2.5 petabytes of sales transactions• : an index of over 19 billion web pages• : over 40 billion of pictures• . . .
Now about the Big Data
Big data is everywhere
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2-2
• : over 2.5 petabytes of sales transactions• : an index of over 19 billion web pages• : over 40 billion of pictures• . . .
Now about the Big Data
Big data is everywhere
Nature ’06 Nature ’08 Economist ’10CACM ’08
Magazine covers
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3-1
• Retailer databases
• Logistics, financial & health data
• Social network
• Pictures by mobile devices
• Internet of Things
• New forms of scientific data
Source and Challenge
Source
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3-2
• Retailer databases
• Logistics, financial & health data
• Social network
• Pictures by mobile devices
• Internet of Things
• New forms of scientific data
Source and Challenge
Source
• Volume
• Velocity
• Variety (Documents, Stock records, Personal profiles,Photographs, Audio & Video, 3D models, Location data, . . . )
Challenge
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3-3
• Retailer databases
• Logistics, financial & health data
• Social network
• Pictures by mobile devices
• Internet of Things
• New forms of scientific data
Source and Challenge
Source
• Volume
• Velocity
• Variety (Documents, Stock records, Personal profiles,Photographs, Audio & Video, 3D models, Location data, . . . )
Challenge
} The focus of algorithm design
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4-1
What is the meaning of Big Data IN THEORY?
We don’t define Big Data in terms of TB, PB, EB, . . .
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4-2
What is the meaning of Big Data IN THEORY?
We don’t define Big Data in terms of TB, PB, EB, . . .
The data is stored there, but no time to read them all.What can we do?
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4-3
What is the meaning of Big Data IN THEORY?
We don’t define Big Data in terms of TB, PB, EB, . . .
The data is stored there, but no time to read them all.What can we do?
Read some of them. Sublinear in time
![Page 10: for Big Data B669 Sublinear Algorithmshomes.sice.indiana.edu/qzhangcs/B669-17-fall-sublinear/slides/intro.pdfWhat is the meaning of Big Data IN THEORY? We don’t de ne Big Data in](https://reader030.vdocuments.site/reader030/viewer/2022041107/5f0a8d277e708231d42c30ba/html5/thumbnails/10.jpg)
4-4
What is the meaning of Big Data IN THEORY?
We don’t define Big Data in terms of TB, PB, EB, . . .
The data is stored there, but no time to read them all.What can we do?
The data is too big to fit in main memory.What can we do?
Read some of them. Sublinear in time
![Page 11: for Big Data B669 Sublinear Algorithmshomes.sice.indiana.edu/qzhangcs/B669-17-fall-sublinear/slides/intro.pdfWhat is the meaning of Big Data IN THEORY? We don’t de ne Big Data in](https://reader030.vdocuments.site/reader030/viewer/2022041107/5f0a8d277e708231d42c30ba/html5/thumbnails/11.jpg)
4-5
What is the meaning of Big Data IN THEORY?
We don’t define Big Data in terms of TB, PB, EB, . . .
The data is stored there, but no time to read them all.What can we do?
The data is too big to fit in main memory.What can we do?
Read some of them. Sublinear in time
Store on the disk (page/block access) Sublinear in I/O
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4-6
What is the meaning of Big Data IN THEORY?
We don’t define Big Data in terms of TB, PB, EB, . . .
The data is stored there, but no time to read them all.What can we do?
The data is too big to fit in main memory.What can we do?
Read some of them. Sublinear in time
Store on the disk (page/block access) Sublinear in I/O
Throw some of them away. Sublinear in space
![Page 13: for Big Data B669 Sublinear Algorithmshomes.sice.indiana.edu/qzhangcs/B669-17-fall-sublinear/slides/intro.pdfWhat is the meaning of Big Data IN THEORY? We don’t de ne Big Data in](https://reader030.vdocuments.site/reader030/viewer/2022041107/5f0a8d277e708231d42c30ba/html5/thumbnails/13.jpg)
4-7
What is the meaning of Big Data IN THEORY?
We don’t define Big Data in terms of TB, PB, EB, . . .
The data is stored there, but no time to read them all.What can we do?
The data is too big to fit in main memory.What can we do?
The data is too big to be stored in a single machine.What can we do if we do not want to throw them away?
Read some of them. Sublinear in time
Store on the disk (page/block access) Sublinear in I/O
Throw some of them away. Sublinear in space
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4-8
What is the meaning of Big Data IN THEORY?
We don’t define Big Data in terms of TB, PB, EB, . . .
The data is stored there, but no time to read them all.What can we do?
The data is too big to fit in main memory.What can we do?
The data is too big to be stored in a single machine.What can we do if we do not want to throw them away?
Read some of them. Sublinear in time
Store on the disk (page/block access) Sublinear in I/O
Throw some of them away. Sublinear in space
Store in multiple machines, which collaborate via communication
Sublinear in communication
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5-1
What do we mean by “sublinear”?
Time/space/communication
spent is o(input size)
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6-1
Conceretly, theory folks talk about the following ...
Sublinear time algorithms
Sublinear time approximation algorithms
Property testing (not in this course)
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6-2
Conceretly, theory folks talk about the following ...
Sublinear time algorithms
Sublinear time approximation algorithms
Property testing (not in this course)
Sublinear space algorithms
Data stream algorithms
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6-3
Conceretly, theory folks talk about the following ...
Sublinear time algorithms
Sublinear time approximation algorithms
Property testing (not in this course)
Sublinear space algorithms
Data stream algorithms
Sublinear communication algorithms
Multiparty communication protocols/algorithms(particular models: MapReduce, BSP, . . . )
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6-4
Conceretly, theory folks talk about the following ...
Sublinear time algorithms
Sublinear time approximation algorithms
Property testing (not in this course)
Sublinear space algorithms
Data stream algorithms
Sublinear communication algorithms
Multiparty communication protocols/algorithms(particular models: MapReduce, BSP, . . . )
Sublinear I/O algorithms (not in this course)
External memory data structures/algorithms
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7-1
Sublinear in time
Given a social network graph, we want to compute its average degree.(i.e., the average # of friends people have in the network)
Can we do it without quering the degrees of all nodes?(i.e., asking everyone)
Example:
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8-1
Why hard? You can’t see everything in sublinear time!
Computing exact average degree is impossible withoutquerying at least n − 1 nodes (n: # total nodes).
So our goal is to get a (1 + ε)-approximation w.h.p.(ε is a very small constant, e.g., 0.01)
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8-2
Why hard? You can’t see everything in sublinear time!
Computing exact average degree is impossible withoutquerying at least n − 1 nodes (n: # total nodes).
So our goal is to get a (1 + ε)-approximation w.h.p.(ε is a very small constant, e.g., 0.01)
Can we simply use sampling?
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8-3
Why hard? You can’t see everything in sublinear time!
Computing exact average degree is impossible withoutquerying at least n − 1 nodes (n: # total nodes).
So our goal is to get a (1 + ε)-approximation w.h.p.(ε is a very small constant, e.g., 0.01)
Can we simply use sampling?
No, it doesn’t work. Consider the star, with degree sequence(n − 1, 1, . . . , 1).
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8-4
Why hard? You can’t see everything in sublinear time!
Computing exact average degree is impossible withoutquerying at least n − 1 nodes (n: # total nodes).
So our goal is to get a (1 + ε)-approximation w.h.p.(ε is a very small constant, e.g., 0.01)
Can we simply use sampling?
So can we do anything non-trivial?
(think about it, and we will discuss later in the course)
No, it doesn’t work. Consider the star, with degree sequence(n − 1, 1, . . . , 1).
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9-1
Sublinear in space
The data stream model (Alon, Matias and Szegedy 1996)
RAM
CPU
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9-2
Sublinear in space
The data stream model (Alon, Matias and Szegedy 1996)
Applications
Internet Router.
RAM
CPU
Router
Packets limited space
Stock data, ad auction, flight logs on tapes, etc.
The router wants to maintain some statistics on data.E.g., want to detect anomalies for security.
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10-1
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
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10-2
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
52
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10-3
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
45
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10-4
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
18
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10-5
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
23
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10-6
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
17
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10-7
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
41
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10-8
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
33
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10-9
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
29
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10-10
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
49
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10-11
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
12
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10-12
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
35
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10-13
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
Q: What’s the median?
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10-14
Why hard? You do see everything but then “forget”!
Game 1: A sequence of numbers
A:
Q: What’s the median?
33
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11-1
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
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11-2
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
Alice and Bob become friends
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11-3
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
Carol and Eva become friends
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11-4
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
Eva and Bob become friends
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11-5
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
Dave and Paul become friends
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11-6
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
Alice and Paul become friends
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11-7
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
Eva and Bob unfriends
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11-8
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
Alice and Dave become friends
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11-9
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
Bob and Paul become friends
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11-10
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
Dave and Paul unfriends
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11-11
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
Dave and Carol become friends
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11-12
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
Q: Are Eva and Bob connected by friends?
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11-13
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
Q: Are Eva and Bob connected by friends?
A: YES. Eva ⇔ Carol ⇔ Dave ⇔ Alice ⇔ Bob
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11-14
Why hard? Cannot store everything.
Game 1: A sequence of numbers
Game 2: Relationships between
Alice, Bob, Carol, Dave, Eva and Paul
Q: What’s the median?
A: 33
Q: Are Eva and Bob connected by friends?
A: YES. Eva ⇔ Carol ⇔ Dave ⇔ Alice ⇔ Bob
Have to allow approx/randomization given a small memory.
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12-1
Sublinear in communication
x1 = 010011 x2 = 111011
x3 = 111111
xk = 100011
They want to jointly compute f (x1, x2, . . . , xk) (e.g., f is # distinct ele)
Goal: minimize total bits of communication
The model
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12-2
Sublinear in communication
x1 = 010011 x2 = 111011
x3 = 111111
xk = 100011
They want to jointly compute f (x1, x2, . . . , xk) (e.g., f is # distinct ele)
Goal: minimize total bits of communication
The model
Applicaitons
etc.
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13-1
Why hard? You do not have a global view of the data.
Let’s think about the graph connectivity problem:k machine each holds a set of edges of a graph.
Goal: compute whether the graph is connected.
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13-2
Why hard? You do not have a global view of the data.
Let’s think about the graph connectivity problem:k machine each holds a set of edges of a graph.
Goal: compute whether the graph is connected.
A trivial solution: each
machine sends a local
spanning tree to the first
machine. Cost O(kn log n)
bits.
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13-3
Why hard? You do not have a global view of the data.
Let’s think about the graph connectivity problem:k machine each holds a set of edges of a graph.
Goal: compute whether the graph is connected.
A trivial solution: each
machine sends a local
spanning tree to the first
machine. Cost O(kn log n)
bits.
Can we do better, e.g., o(kn) bits of communication?
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13-4
Why hard? You do not have a global view of the data.
Let’s think about the graph connectivity problem:k machine each holds a set of edges of a graph.
Goal: compute whether the graph is connected.
A trivial solution: each
machine sends a local
spanning tree to the first
machine. Cost O(kn log n)
bits.
What if the graph is node partitioned among the k machines?
That is, each node is stored in 1 machine with all adjancent edges.
Can we do better, e.g., o(kn) bits of communication?
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14-1
Problems
Statisticalproblems
• Frequency moments Fp
F0: #distinct elements
F2: size of self-join
• Heavy hitters
• Quantile
• Entropy
• . . .
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14-2
Problems
Statisticalproblems
• Frequency moments Fp
F0: #distinct elements
F2: size of self-join
• Heavy hitters
• Quantile
• Entropy
• . . .
Graph problems
• Connectivity
• Bipartiteness
• Counting triangles
• Matching
• Minimum spanning tree
• . . .
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14-3
Problems
Statisticalproblems
• Frequency moments Fp
F0: #distinct elements
F2: size of self-join
• Heavy hitters
• Quantile
• Entropy
• . . .
Numericallinear algebra
• Lp regression
• Low-rank
approximation
• . . .
Graph problems
• Connectivity
• Bipartiteness
• Counting triangles
• Matching
• Minimum spanning tree
• . . .
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14-4
Problems
Statisticalproblems
• Frequency moments Fp
F0: #distinct elements
F2: size of self-join
• Heavy hitters
• Quantile
• Entropy
• . . .
Numericallinear algebra
• Lp regression
• Low-rank
approximation
• . . .
Graph problems
• Connectivity
• Bipartiteness
• Counting triangles
• Matching
• Minimum spanning tree
• . . .
DB queries
Strings
Geometry problems
• Conjuntive
queries
• Edit distance
• Longest increasingsequence
• Clustering
• Earth-Mover Distance
. . .
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15-1
Example: random sampling
in data stream
RAM
CPU
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16-1
A toy example: Reservoir Sampling
Tasks: Find a uniform sample from a stream of unknownlength, can we do it in O(1) space?
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16-2
A toy example: Reservoir Sampling
Tasks: Find a uniform sample from a stream of unknownlength, can we do it in O(1) space?
Algorithm: Store 1-st item. When the i-th (i > 1) item arrives
With probability 1/i , replace the current sample;With probability 1− 1/i , throw it away.
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16-3
A toy example: Reservoir Sampling
Tasks: Find a uniform sample from a stream of unknownlength, can we do it in O(1) space?
Algorithm: Store 1-st item. When the i-th (i > 1) item arrives
With probability 1/i , replace the current sample;With probability 1− 1/i , throw it away.
Space: O(1)
Correctness: each item is included in the final sample w.p.1i × (1− 1
i+1 )× . . .× (1− 1n ) = 1
n (n: total # items)
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17-1
Maintain a sample for Sliding Windows
Tasks: Find a uniform sample from the last w items.
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17-2
Maintain a sample for Sliding Windows
Tasks: Find a uniform sample from the last w items.
Algorithm:
– For each xi , we pick a random value vi ∈ (0, 1).– In a window < xj−w+1, . . . , xj >, return value xi withsmallest vi .– To do this, maintain the set of all xi in sliding windowwhose vi value is minimal among subsequent values.
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17-3
Maintain a sample for Sliding Windows
Tasks: Find a uniform sample from the last w items.
Algorithm:
– For each xi , we pick a random value vi ∈ (0, 1).– In a window < xj−w+1, . . . , xj >, return value xi withsmallest vi .– To do this, maintain the set of all xi in sliding windowwhose vi value is minimal among subsequent values.
Space (expected): 1/w + 1/(w − 1) + . . .+ 1/1 = logw .
Correctness: Obvious.
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18-1
Tentative course plan
Part 0 : Introductions– New models for Big Data, interesting problems– Basic probabilistic tools
Part 1 : Sublinear in space– Distinct elements, heavy hitters, `0-sampling
Part 2 : Sublinear in communication– Connectivity, min-cut and sparsification
Part 3 : Sublinear in time– Average degree, minimum spanning tree
Part 4 : Random topics– E.g., distributed monitoring
Part 5 : Student presentations
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19-1
Resources
There is no textbook for the class.
Reference for part of the course: lecture notes by AmitChakrabarti
Background on Randomized Algorithms:
• Probability and Computing
by Mitzenmacher and Upfal
(Advanced undergraduate textbook)
• Randomized Algorithms
by Motwani and Raghavan
(Graduate textbook)
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20-1
Surveys
Surveys
• Sketch Techniques for Apporximate Query Processing
by Cormode
• Data Streams: Algorithms and Applications
by Muthukrishnan
Check course website for more resources
http://homes.soic.indiana.edu/qzhangcs/
B669-17-fall-sublinear/index.html
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21-1
Instructors
• Instructor: Qin ZhangEmail: [email protected] hours: Wed. 4-5pm at LH430A
• Associate Instructor: Ruiyu ZhuEmail: [email protected] hours: Mon. 4-5pm at Lindley Abyss
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22-1
Grading
Assignments 40% : – Two homework assignments.– Solutions should be typeset in LaTeXand submitted via Canvas.– Will be a HW0 for practicing Latex(0 pt; −5 if not submitted)
Project 60% : Consists of the following components:(details see course website)1. Write a proposal.2. Write a report.3. Make a presentation.4. Grade others’ presentations.
Final grade with be curved
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23-1
Prerequisites
A research-oriented course. Will be quite mathematical.
One is expected to know:basics on algorithm design and analysis + basic probability.
e.g., have taken B403 “Introduction to Algorithm Designand Analysis” or equivalent courses.
I will NOT start with things like big-O notations, thedefinitions of expectation and variance, and hashing.
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24-1
Frequently asked questions
Is this a course good for my job hunting in industry?
Yes and No.
Yes, if you get to know some advanced (and easily implementable)algorithms for handling big data, that will certainly help. (e.g.,Google interview questions)
But, this is a research-oriented course, and is NOT designed forteaching commercially available techniques.
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24-2
Frequently asked questions
Is this a course good for my job hunting in industry?
Yes and No.
Yes, if you get to know some advanced (and easily implementable)algorithms for handling big data, that will certainly help. (e.g.,Google interview questions)
But, this is a research-oriented course, and is NOT designed forteaching commercially available techniques.
I haven’t taken B403 “Introduction to Algorithm Designand Analysis” or equivalent courses. Can I take thecourse? Or, will this course fit me?
Generally speaking, this is an advanced algorithm course. It mightbe difficult if you do not have enough background (math +programming). So think carefully before taking this course!
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25-1
Summary for today
We have introduced three types of sublinear algorithms:in time, space, and communication
We have talked about why algorithmic design in thesemodels/paradigms are difficult.
We have discussed some simple problems
We have talked about the course plan and assessment.
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26-1
Thank you!