Introduction to Research

Data Management and Databasehttp://www.cs.fsu.edu/~lifeifei

lifeifei@cs.fsu.eduFeifei Li

Outline

• Background• My Research Focus and Experience• Some Problems I have worked on• Current Interest and Activity• My Experience as a PhD student• Q&A

Outline

• Background• My Research Focus and Experience• Some Problems I have worked on• Current Interest and Activity• My Experience as a PhD student• Q&A

A Short History Class

• Undergraduate study in Tsinghua University (1997) (China) + Nanyang Technological University (Singapore) (1998-2002)– B. Applied Science

• PhD study in Boston University (2002-2007)– Computer Science, Research Area: Database

• Now…

Outline

• Background• My Research Focus and Experience• Some Problems I have worked on• Current Interest and Activity• My Experience as a PhD student• Q&A

Research Focus

• Data Management in General and (roughly in the order of the sequence I worked on) :

– Efficient indexing, querying and managing large scale databases, or high dimensional databases

– Spatio-temporal databases and applications– Sensor and Stream databases– Privacy preservation issues for data management– Query security for various types of data models– Uncertain databases and data cleaning

Experience

• SDE intern at M$ SQL server group, summer 2005 (Redmond, WA)

• Research intern at IBM T. J. Watson Research Center (Hawthorne, NY), database research group, summer 2006

• Visiting research student at AT&T Labs Research (Florham Park, NJ), database research group, winter 2006 and spring 2007

• Research intern at MSR, database research group, summer 2007

Outline

• Background• My Research Focus and Experience• Some Problems I have worked on• Current Interest and Activity• My Experience as a PhD student• Q&A

Outline

• Background• My Research Focus and Experience• Some Problems I have worked on

– Retrieving structured data from Web– Spatio-temporal databases– Sensor databases

• Current Interest and Activity• My Experience as a PhD student• Q&A

The First Step• My FYP (Final Year Project), around 2000-2001

– Analyze and build structures of different websites• How to automate this process??• View a website as a tree structure and?• Given a group of similar websites, summarizing a suitable

schema…– Retrieve information from certain part(s) of a website

as specified by the user• With the structure information obtained at the first step• Why bother? Information integration, BBC in favors of Bush

and CNN ‘hates’ him, then what’s the response to event A?• Another issue: semi-structured data (HTML) to structured

data (XML)

So What Happened

Possible Research Problems• Automatic Schema Identification

– Given a collection of data sources, find a common schema that maximally describes the dataset.

• Information retrieval & search from Web – IR techniques (IR is a separate field by itself, unstructured

data) + database techniques (structured data), how to combine the two?

– Google • Information Integration

– Given data source A and data source B, both refers to the same schema, but with (slightly) different instances, how to link/combine the two?

Then Boston

• Quite a pleasant transition in the summer: Singapore (90+ year round) to Boston (80 in the summer)

• Winter:

– Anyway…

Outline

• Background• My Research Focus and Experience• Some Problems I have worked on

– Retrieving structured data from Web– Spatio-temporal databases– Sensor databases

• Current Interest and Activity• My Experience as a PhD student• Q&A

What to Do Now?• Spatio-temporal databases and applications

– Why? My advisor was in this area and…• Examples:

– Indexing higher dimensional data:• 1d- B+ tree, 2d, 3d, 4d, …? kd-tree, R-tree• Space partitioning vs. Data partitioning

– Queries• eg: continuous nearest neighbor query– continuously find

the closest gas station when I am driving from Boston to NY.– Moving object

• On Euclidian space• On a road network

Indexing High Dimensional Data: R-tree

• eg., w/ fanout 4: group nearby rectangles to parent MBRs; each group -> disk page

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Example

• F=4

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P1

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Example

• F=4

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R-trees:Search

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R-trees:Search

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Query in Spatio-Temporal Databases

• Trip Planning Queries (TPQ):– Given a starting location, a destination and

arbitrary points of interest try to find the best possible trip.

• Example:– Minimize the total traveling time from Boston to

Providence, while visiting a post office, a hardware store and a gas station.

Visual Example

Home Work

Gas station

• We can minimize the total distance, time, etc.• We can have different categories of points of interest

(gas stations, hotels, etc.).

The Nearest Neighbor Algorithm

S D

A1

A2

B3 B1

B2

C2

C1

Yields a 2m+1 - 1 approximation where m is the total number of categories.

The Minimum Distance Algorithm

S D

A1

A2

B3 B1

B2

C2

C1

Yields an m-approximation where m is the total number of categories.

Search over R-Tree and Road network

• R-Tree:– Euclidian space, how to utilize R-tree to speed up

the search?

• Road network:M

AS

Dp

Outline

• Background• My Research Focus and Experience• Some Problems I have worked on

– Retrieving structured data from Web– Spatio-temporal databases– Sensor databases

• Current Interest and Activity• My Experience as a PhD student• Q&A

Sensor Network Model

• Large set of sensors distributed in a sensor field.• Communication via a wireless ad-hoc network.• Node and links are failure-prone. • Sensors are resource-constrained

– Limited memory, battery-powered, messaging is costly.

Sensor Databases

• Useful abstraction:– Treat sensor field as a distributed database

• But: data is gathered, not stored nor saved.

– Express query in SQL-like language• COUNT, SUM, AVG, MIN, GROUP-BY

– Query processor distributes query and aggregates responses

– Exemplified by systems like TAG (Berkeley/MIT) and Cougar (Cornell)

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A Motivating Example

• Each sensor has a single sensed value.

• Sink initiates one-shot queries such as: What is the…– maximum value?– mean value?

• Continuous queries are a natural extension.

F124

AVG Aggregation (no losses)

• Build spanning tree• Aggregate in-network

– Each node sends one summary packet

– Summary has SUM and COUNT of sub-tree

• Reliability problem when there are losses (common for sensor network)

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4F12

2,1

6,1

9,215,3

6,1 10,1

26,4

12,1

10,29,1

AVG=70/10=7

AVG Aggregation (naive)

• What if redundant copies of data are sent?

• AVG is duplicate-sensitive– Duplicating data changes

aggregate– Increases weight of

duplicated data

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6,1 22,2

26,4

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10,29,1

AVG=82/11≠7

12,1

AVG Aggregation (TAG++)

• Can compensate for increased weight– Send halved SUM and

COUNT instead

• Does not change expectation!

• Only reduces variance

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20,3.5

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10,29,1

AVG=70/10=7

6,0.5

AVG Aggregation (LIST)

• Can handle duplicates exactly with a list of <id, value> pairs

• Transmitting this list is expensive!

• Lower bound: linear space is necessary if we demand exact results.

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A,2;G,7;H,6H,6 F,1;I,

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AVG=70/10=7

F,1

COUNT Sketches

• Problem: Estimate the number of distinct item IDs in a data set with only one pass.

• Constraints: – Small space relative to stream size.– Small per item processing overhead.– Union operator on sketch results.

• Exact COUNT is impossible without linear space.• First approximate COUNT sketch in [FM’85].

– O(log N) space, O(1) processing time per item.

Counting Paintballs

• Imagine the following scenario:– A bag of n paintballs is

emptied at the top of a long stair-case.

– At each step, each paintball either bursts and marks the step, or bounces to the next step. 50/50 chance either way.

Looking only at the pattern of marked steps, what was n?

Counting Paintballs (cont)

• What does the distribution of paintball bursts look like?– The number of bursts at

each step follows a binomial distribution.

– The expected number of bursts drops geometrically.

– Few bursts after log2 n steps

1st

2nd

S th

B(n,1/2)

B(n,1/2 S)

B(n,1/4)

B(n,1/2 S)

Counting Paintballs (cont)

• Many different estimator ideas [FM'85,AMS'96,GGR'03,DF'03,...]

• Example: Let pos denote the position of the highest unmarked stair,

E(pos) ≈ log2(0.775351 n)

2(pos) ≈ 1.12127

• Standard variance reduction methods apply• Either O(log n) or O(log log n) space

Application to Sensornets Each sensor computes k independent sketches of itself using

its unique sensor ID.– Coming next: sensor computes sketches of its value.

Use a robust routing algorithm to route sketches up to the sink.

Aggregate the k sketches via in-network XOR.– Union via XOR is duplicate-insensitive.

The sink then estimates the count.

Similar to gossip and epidemic protocols. How about SUM and other aggregates??

COUNT vs Link Loss (grid)

Outline

• Background• My Research Focus and Experience• Some Problems I have worked on• Current Interest and Activity

– Privacy Preservation– Query Security

• My Experience as a PhD student• Q&A

Privacy Preservation

Feifei Piyush PhD A Dave Univ. President Master

$300 $400 $200 $1000 $5000 $100

It is not legal to query about individual person’s salary. However, we areInteresting (and often time legal) at retrieving the avg. what do you do?

Perturb the data… How?

$200 -$100 $600 -$500 -$500 $300

Add random noise…in a particular way

Feifei Piyush PhD A Dave Univ. President Master

$500 $300 $800 $500 $4500 $400

Sum=$7,000

Sum=$0

Sum=$7,000

Basic Intuition:Add Identical Independent Distributed Random (IID)

Noise with Zero Mean

How about Multiple Attributes (multi-dimensional data)?

• Is IID noise really preserving the privacy??

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Principal Component Analysis: PCA

i.i.d Noise

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Principal Component Analysis: PCA

Correlated Noise

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PCA Based Data Reconstruction

A

A~

Removed Noise

Principal Direction

Remaining Noise

Privacy

A*

σ2

Added Noise: Utility

Projection Error

A*: Perturbed Data

A: Original Data

A~: Reconstructed Data

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PCA Based Data Reconstruction

A

A~ Principal Direction

Remaining Noise

Privacy

A*σ2

Added Noise: Utility

Projection Error

A*: Perturbed Data

A: Original Data

A~: Reconstructed DataCorrelated Noise!

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Data Perturbation: main idea

• Observations– The amount of the random noise controls

privacy/utility tradeoff– i.i.d (identical independently distributed) noise

does not preserve the privacy! Not well enough

Lesson learned

– Noise should be correlated with original data

How about Streaming Data?

• Streaming data: Data continuously arrives , no global data is available, hence cannot get the global trends.

i.i.d Noise Correlated Noise

OnlineCorrelated

Noise

Outline

• Background• My Research Focus and Experience• Some Problems I have worked on• Current Interest and Activity

– Privacy Preservation– Query Security

• My Experience as a PhD student• Q&A

Example of Data Publishing

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www.informatik.uni-trier.de/~ley/db/indices/a-tree/h/Hadjieleftheriou:Marios.html

www.sigmod.org/dblp/db/indices/a-tree/h/Hadjieleftheriou:Marios.html

Outsourced Database for Better Query Services

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Servers that are close to local clients and maintained by local business partners

Company with headquarters in US

Publishing Data and Outsourcing Query Service

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NetworkNetwork

Gigascope:analysis tool by

IP Traffic Streamcoming from

0 1 1 0 0 1 … 1 1 0 …

statistics

Results

Data Publishing Model [HIM02]

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Owner: publish dataServers: host (or monitor) the data and provide query servicesClients: query the owner’s data through servers

ownerserversclients /

H. Hacigumus, B. R. Iyer, and S. Mehrotra, ICDE02

(possibly = owner)

Information Security Issues

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• The third-party (server) cannot be trusted

– Malicious intent

– Compromised equipment

– Unintentional errors (e.g. bugs)

Problem 1: Injection

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SD

Select * from T where 5<A<11

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r1 …

… …

ri-1 4

ri 7

ri+1 9

ri+2 11

A B

r1 …

… …

ri-1 4

ri 7

ri+1 9

ri+2 11

Returns 7, 8, 9

owner

server

client

Problem 2: Drop

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SD

Select * from T where 5<A<11

A B

r1 …

… …

ri-1 4

ri 7

ri+1 9

ri+2 11

A B

r1 …

… …

ri-1 4

ri 7

ri+1 9

ri+2 11

Returns 7

owner

server

client

9ri+1

Query Authentication: Goals

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• Query Correctnessresults do exist in the owner's database

• Query Completenessno records have been omitted from the result

• Query Freshnesslatest available answer (in case of updates)

General Approach

SD

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ownerserversclients

A B

r1 …

… …

ri-1 4

ri 7

Authenticated Structures

Query results

Verification Object (VO)

Merkle Hash Tree[M89]

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m1 m2 m3 m4 m5 m6 m7 m8

h1 h2 h3 h4 h5 h6 h7 h8

h12 h34 h56 h78

h1..4 h5..8

h1..8

Sign(h1..8,SK)

h12=H(h1|h2)

R. C. Merkle. CRYPTO, 1989

m6

h78

h5 h6

m5

h56

h5..8h1..4

h1..8

Ver(h1..8, ,pK)=valid?

Collision resistant hash function any change in the tree will lead to a different hash value for the rootDigital signature of the root no one except the ownercould produce the signatureHash function is publicly knownSingle signature to sign many messages

Merkle B(MB) Tree: Natural Extension for Range Query

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• Use a B+-tree instead of a binary search tree:

… Ki…

250 320 410 600 720

410 720

t1 t2 t3 t4 t5

…

Extend it with hash information:

hi=H(ti) Kj hj=H(tj) leaf node

Merkle B(MB) Tree: Natural Extension for Range Query

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h0 p1 k1p0 h1 … pf kf hf

h10 p11 k11p10 h11h1=H(h10|…|h1f)

For root node, =Sign(h0|…|hf)

h1

h10 h11

How about Streaming Data?Outsourced stream model: stock trading

monitoring

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Provider: A stock broker

Servers(bloomberg)

Q

Register Queries: Sliding window query and/or

One shot query

Clients

And Other Model?Revisiting the CISCO – AT&T Example

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NetworkNetworkGigascopeIP Traffic Stream

0 1 1 0 0 1 … 1 1 0 …

statistics

lawyers: sign the trust agreementCould we help? (computer scientists)

CISCO owns the Network Traffic Data: He is both the data owner and the client!

Outline

• Background• My Research Focus and Experience• Some Problems I have worked on• Current Interest and Activity

– Uncertainty Database and Data Cleaning , Talk to me if you’d like to learn more..

• My Experience as a PhD student• Q&A

My Lessons

• Have a strong motivation– You are doing a PhD for yourself, not for FSU, not

for your advisor

• Find a topic that attracts you the most– PhD could be frustrating and boring + You are

almost broke as a student.. So why not do sth that you have the greatest interest at?

My Lessons

• Meet your advisor as often as possible– He is almost the only one that really cares about

your PhD and knows what you are doing…

• Make connections– Whenever possible, conferences, industry labs etc

and work on your communication skills (including writing the papers)

My Lessons

• Read Papers– As much as you can! It’s never enough

• Finally– Work hard , enjoy your life and good luck!!

Questions?

• Thank you!

Back to COUNT Sketches

• The COUNT sketches of [FM'85] are equivalent to the paintball process.– Start with a bit-vector of all

zeros.– For each item,

• Use its ID and a hash function for coin flips.

• Pick a bit-vector entry.• Set that bit to one.

• These sketches are duplicate-insensitive:

1 0 0 0 0{x}

0 0 1 0 0{y}

1 0 1 0 0{x,y}

A,B (Sketch(A) XOR Sketch(B)) = Sketch(A B)