UCI Database Group

Quality Aware Sensor Database (QUASAR) Project**

Sharad Mehrotra

Department of Information and Computer Science

University of California, Irvine

**Supported in part by a collaborative NSF ITR grant entitled “real-time data capture, analysis, and querying of dynamic spatio-temporal events” in collaboration with UCLA, U. Maryland, U. Chicago

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Talk Outline

• Quasar Project– motivation and background– data collection and archival components – query processing – tracking application using QUASAR framework– challenges and ongoing work

• Brief overview of other research projects– MARS Project - incorporating similarity retrieval and refinement

over structured and semi-structured data to aid interactive data analysis/mining

– Database as a Service (DAS) Project - supporting the application service provider model for data management

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Emerging Computing Infrastructure…

Instrumented wide-area spaces

In-body, in-cell, in-vitro spaces • Generational advances to computing infrastructure– sensors will be everywhere

• Emerging applications with limitless possibilities– real-time monitoring and control,

analysis

• New challenges – limited bandwidth & energy – highly dynamic systems

• System architectures are due for an overhaul– at all levels of the system OS,

middleware, databases, applications

Immediate vicinity area boundary(single-hop)

Roadside Base station

To the fixed Infrastructure ( Internet)

Ad hoc (802.11) link

Cellular (CDPD?) link

Immediate vicinity area boundary(single-hop)

Roadside Base station

To the fixed Infrastructure ( Internet)

Ad hoc (802.11) link

Cellular (CDPD?) link

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Impact to Data Management …

• Traditional data management– client-server architecture– efficient approaches to data storage & querying – query shipping versus data shipping– data changes with explicit update

• Emerging Challenge– data producers must be considered as “first class”

entities• sensors generate continuously changing highly dynamic

data• sensors may store, process, and communicate data

Data/query request

Data/query result clientserverData producers

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Data Management Architecture Issues

• Where to store data?– Do not store -- stream model

• not suitable if we wish to archive data for future analysis or if data is too important to lose

– at the producers• limited storage, network, compute resources

– at the servers• server may not be able to cope with high data production

rates. May lead to data staleness and/or wasted resources

• Where to compute?– At the client, server, data producers

Data/query request

Data/query result client

serverData producers

producer cache

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Quasar Architecture

• Hierarchical architecture– data flows from producers to

server to clients periodically– queries flow the other way:

• If client cache does not suffices, then

• query routed to appropriate server• If server cache does not suffice, then

access current data at producer

– This is a logical architecture-- producers could also be clients.

server

clientClient cache

Server cache & archive

producer cache

dat

a fl

ow

Qu

ery

flow

producer

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Quasar: Observations & Approach

• Applications can tolerate errors in sensor data– applications may not require exact answers:

• small errors in location during tracking or error in answer to query result may be OK

– data cannot be precise due to measurement errors, transmission delays, etc.

• Communication is the dominant cost – limited wireless bandwidth, source of major energy drain

• Quasar Approach– exploit application error tolerance to reduce communication between

producer and server

– Two approaches • Minimize resource usage given quality constraints • Maximize quality given resource constraints

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Quality-based Data Collection Problem

• Let P = < p[1], p[2], …, p[n] > be a sequence of environmental

measurements (time series) generated by the producer, where

n = now

• Let S = <s[1], s[2], …, s[n]> be the server side representation of

the sequence

• A within- quality data collection protocol guarantees that

for all i error(p[i], s[i]) <

is derived from application quality tolerance

Sensor time series…p[n], p[n-1], …, p[1]

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Simple Data Collection Protocol

• sensor Logic (at time step n)

Let p’ = last value sent to server

if error(p[n], p’) >

send p[n] to server

• server logic (at time step n)

If new update p[n] received at step n

s[n] = p[n]

Else

s[n] = last update sent by sensor

– guarantees maximum error at server less than equal to

Sensor time series…p[n], p[n-1], …, p[1]

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Exploiting Prediction Models

• Producer and server agree upon a prediction model (M, )

• Let spred[i] be the predicted value at time i based on (M, )

• sensor Logic (at time step n)

if error(p[n], spred[n] ) >

send p[n] to server

• server logic (at time step n)

• If new update p[n] received at step n

s[n] = p[n]

Else

s[n] = spred[n] based on model (M, )

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Challenges in Prediction

• Simple versus complex models?• Complex and more accurate models require more parameters (that will

need to be transmitted).

• Goal is to minimize communication not necessarily best prediction

• How is a model M generated?• static -- one out of a fixed set of models

• dynamic -- dynamically learn a model from data

• When should a model M or parameters be changed?

• immediately on model violation:

– too aggressive -- violation may be a temporary phenomena

• never changed:

– too conservative -- data rarely follows a single model

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Challenges in Prediction (cont.)

• who does the model update?

• Server

– Long-haul prediction models possible, since server maintains history

– might not predict recent behavior well since server does not know exact

S sequence; server has only samples

– extra communication to inform the producer

• Producer

– better knowledge of recent history

– long haul models not feasible since producer does not have history

– producers share computation load

• Both

– server looks for new models, sensor performs parameter fitting given

existing models.

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Archiving Sensor Data

• Often sensor-based applications are built with only the real-time

utility of time series data.

– Values at time instants <<n are discarded.

• Archiving such data consists of maintaining the entire S sequence,

or an approximation thereof.

• Importance of archiving:

– Discovering large-scale patterns

– Once-only phenomena, e.g., earthquakes

– Discovering “events” detected post facto by “rewinding” the time series

– Future usage of data which may be not known while it is being collected

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Problem Formulation

• Let P = < p[1], p[2], …, p[n] > be the sensor time series

• Let S = < s[1], s[2], …, s[n] > be the server side representation

• A within archive quality data archival protocol guarantees that

error(p[i], s[i]) < archive

• Trivial Solution: modify collection protocol to collect data at quality guarantee of min(archive , collect)

– then prediction model by itself will provide a archive quality data stream that can be archived.

• Better solutions possible since – archived data not needed for immediate access by real-time or forecasting applications

(such as monitoring, tracking) – compression can be used to reduce data transfer

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Data Archival Protocol

• Sensors compresses observed time series p[1:n] and sends a lossy compression to the server

• At time n :

– p[1:n-nlag] is at the server in compressed form s’ [1:n-nlag] within-

archive

– s[n-nlag+1:n] is estimated via a predictive model (M, )

• collection protocol guarantees that this remains within- collect

– s[n+1:] can be predicted but its quality is not guaranteed

(because it is in the future and thus the sensor has not observed

these values)

…p[n], p[n-1], .. compress

Sensor memory buffer

Sensor updates for data collection

Compressed representation for archiving

processing at sensor exploited to reduce communication cost and hence battery drain

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Piecewise Constant Approximation (PCA)

• Given a time series Sn = s[1:n] a piecewise constant approximation

of it is a sequence

PCA(Sn) = < (ci, ei) >

that allows us to estimate s[j] as:

scapt [j] = ci if j in [ei-1+1, ei]

= c1 if j<e1

Time

Value

e1 e2 e3 e4

c1

c2

c3

c4

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Online Compression using PCA

• Goal: Given stream of sensor values, generate a within-archive PCA

representation of a time series

• Approach (PMC-midrange)

– Maintain m, M as the minimum/maximum values of observed samples

since last segment

– On processing p[n], update m and M if needed

• if M - m > 2archive , output a segment ((m+M )/2, n)

Time

Value

Example: archive = 1.5

1 2 3 4 5

23

4

2.5

6

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Online Compression using PCA

• PMC-MR …

– guarantees that each segment compresses the corresponding

time series segment to within-archive

– requires O(1) storage

– is instance optimal

• no other PCA representation with fewer segments can meet the

within-archive constraint

• Variant of PMC-MR

– PMC-MEAN, which takes the mean of the samples seen thus far instead

of mid range.

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Improving PMC using Prediction• Observation: Prediction models guarantee a within- collect version of the time

series at server even before the compressed time series arrives from the

producer.

• Can the prediction model be exploited to reduce the overhead of compression.

– If archive> collect no additional effort is required for archival --> simply archive the

predicted model.

• Approach:

– Define an error time series E[i] = p[i]-spred[i]

– Compress E[1:n] to within-archive instead of compressing p[1:n]

– The archive contains the prediction parameters and the compressed error time

series

– Within-archive of E[I] + (M, ) can be used to reconstruct a within- archive version of p

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Combing Compression and Prediction (Example)

-5

0

5

10

15

20

25

30

0 10 20 30 40 50 60

Predicted Time Series

Actual Time Series

-5

0

5

10

15

20

25

0 10 20 30 40 50 60

Actual Time Series

Compressed Time Series

(7 segments)

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

Compressed Error

(2 segments)

Error =

Actual – Predicted

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Estimating Time Series Values

• Historical samples (before n-nlag) is maintained at the server within-archive

• Recent samples (between n-nlag+1 and n) is maintained by the sensor

and predicted at the server.

• If an application requires q precision, then:

– if q collect then it must wait for time in case a parameter refresh is en route

– if q archive but q < collect then it may probe the sensor or wait for a

compressed segment

– Otherwise only probing meets precision

• For future samples (after n) immediate probing not available as an

option

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Experiments

• Data sets:

– Synthetic Random-Walk

• x[1] = 0 and x[i]=x[i-1]+sn where sn drawn uniformly from [-1,1]

– Oceanographic Buoy Data

• Environmental attributes (temperature, salinity, wind-speed, etc.) sampled at 10min intervals

from a buoy in the Pacific Ocean (Tropical Atmosphere Ocean Project, Pacific Marine

Environment Laboratory)

– GPS data collected using IPAQs

• Experiments to test:

– Compression Performance of PMC

– Benefits of Model Selection

– Query Accuracy over Compressed Data

– Benefits of Prediction/Compression Combination

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Compression Performance

K/n ratio: number of segments/number of samples

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Query Performance Over Compressed Data

“How many sensors have values >v?” (Mean selectivity = 50)

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Impact of Model Selection

K/n ratio: number of segments/number of samples. pred is the localization tolerance in meters

• Objects moved at approximately constant speed (+ measurement noise)

•Three models used:• loc[n] = c• loc[n] = c+vt• loc[n] = c+vt+0.5at2

•Parameters v, a were estimated at sensor over moving-window of 5 samples

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Combining Prediction with Compression

K/n ratio: number of segments/number of samples

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QUASAR ClientTime Series

GPS Mobility Data from Mobile Clients (iPAQs)

Latitude Time Series: 1800 samples

Compressed Time Series (PMC-MR, ICDE 2003)Accuracy of ~100 m130 segments

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Query Processing in Quasar

• Problem Definition– Given

• sensor time series with quality-guarantees captured at the server• A query with a specified quality-tolerance

– Return• query results incurring least cost

• Techniques depend upon – nature of queries – Cost measures

• resource consumption -- energy, communication, I/O• query response time

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Aggregate Queries

9

6

3

8

27

Q

S

minQ = 2

maxQ = 7

countQ = 3

sumQ = 2+7+6 = 15

avgQ = 15/3 = 5

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Processing Aggregate Queries (minimize producer probe)

• MIN Query– c = minj(si.high)

– b = c - query

– Probe all sensors where sj.low < b• only s1 and s3 will be probed

• Sum Query – select a minimal subset S’ S such that

si in S’ (jpred) >= si in S(j

pred)- query

– If query = 15, only s1 will be probed

Let S = <s1,s2, …,sn> be set of sensors that meet the query criteria

si.high = sipred[t] + j

pred

sj.low = sipred[t] - j

pred

a b c

s1s2

sn

s3

s1s2

s5

s3 s4

10

5

25

3

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Minimizing Cost at Server

• Error tolerance of queries can be exploited to reduce processing at server.

• Key Idea– Use a multi-resolution index structure (MRA-tree) for

processing aggregate queries at server.– An MRA-Tree is a modified multi-dimensional index trees (R-

Tree, quadtree, Hybrid tree, etc.)– A non-leaf node contains (for each of its subtrees) four

aggregates {MIN,MAX,COUNT,SUM} – A leaf node contains the actual data points (sensor models)

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MRA Tree Data Structure

S1 S2 S3

S4

S5

S6

S7

S8

Spatial View

A

B

C

D

E

F

G

S7S2 S3 S4 S5 S6S1 S8

A

B C

D E F G

Tree Structure View

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min

max

count

sum

2

3

9

4

4

2

9

5

1

4

4

2

6

1

6

6

Non-Leaf Node

Disk Page Pointers(each costs 1 I/O)

Leaf Node

M1

1

M2

2

M3

3

Probe “Pointers”(each costs 2 messages)

MRA-Tree Node Structure

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QN

disjoint

contains

QNQN

is contained

QN

partially overlaps

• Two sets of nodes: – NP (partial contribution to the query)– NC (complete contribution)

Node Classification

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Aggregate Queries using MRA Tree

• Initialize NP with the root• At each iteration: Remove one node N from NP and for each

Nchild of its children

– discard, if Nchild disjoint with Q

– insert into NP if Q is contained or partially overlaps with Nchild

– “insert” into NC if Q contains Nchild (we only need to maintain aggNC)

– compute the best estimate based on contents of NP and NC

QN

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MIN (and MAX)

3

9

4

5

Interval

minNC = min { 4, 5 } = 4

minNP = min { 3, 9 } = 3

L = min {minNC, minNP} = 3

H = minNC = 4

hence, I = [3, 4]

Estimate

Lower bound:

E(minQ) = L = 3

Traversal

Choose N NP:

minN = minNP

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MRA Tree Traversal

S7S2 S3 S4 S5 S6S1 S8

A

B C

D E F G

• Progressive answer refinement until NP is exhausted• Greedy priority-based local decision for next node to be explored based on:

– Cost (1 I/O or 2 messages)– Benefit (Expected Reduction in answer uncertainty)

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Adaptive Tracking of mobile objects in sensor networks

Track visualization

Base station 1

Base station 2 Base station 3

ServerShow me the approximate track of the object with precision

Wireless Sensor Grid

object

Wireless link

Tracking Architecture A network of wireless acoustic sensors arranged as a grid transmitting via a base station to server

A track of the mobile object generated at the base station or server

Objective

Track a mobile object at the server such that the track deviates from the real trajectory within a user defined error threshold track with minimum communication overhead.

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Sensor Model

Wireless sensors : battery operated, energy constrained

Operate on the received acoustic waveforms

Signal attenuation of target object given by : Is(t) = P /4 r2

P : source object power

r= distance of object from sensor

Is(t) = intensity reading at time t at ith sensor

Ith : Intensity threshold at ith sensor

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Sensor States

• S0 : Monitor ( processor on, sensor on, radio off )

– shift to S1 if intensity above threshold

• S1 : Active state ( processor on, sensor on, radio on)

– send intensity readings to base station.

– On receiving message from BS containing error tolerance shift to S2

• S2 : Quasi-active (processor on, sensor on, radio intermittent)

– send intensity reading to BS if error from previous reading exceeds error threshold

Quasar Collection approach used in Quasi-active state

S0

(Initial state)

S2

S1

Receive BS message

Ii < I th

Ii > I th

Ii < I th

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Server side protocol

Server maintains:

list of sensors in the active/ quasi-active state

history of their intensity readings over a period of time

Server Side Protocol

convert track quality to a relative intensity error at sensors

Send relative intensity error to sensor when sensor state = S1(

quasi- active state)

Triangulate using n sensor readings at discrete time intervals.

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Basic Triangulation Algorithm (using 3 sensor readings)

(x1, y1) (x2, y2)

(x3, y3)

P: source object power, Ii = intensity reading at ith

sensor

(x-x1)2 + (y- y1)2 = P/4 I1

(x-x2)2 + (y- y2)2 = P/4 I2

(x-x3)2 + (y- y3)2 = P/4 I3

Solving we get (x, y)=f(x1,x2,x3,y1,y2,y3, P,I1, I2 , I3, )

(x, y)

More complex approaches to amalgamate more than three sensor readings possible

Based on numerical methods -- do not provide a closed form equation between sensor reading and tracking location !

Server can use simple triangulation to convert track quality to sensor intensity quality tolerances and a more complex approach to track.

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Adaptive Tracking : Mapping track quality to sensor reading

Intensity ( I1 )

time

Intensity ( I2 )

time

Intensity ( I3 )

time

t i t( i+1 )

t i t( i+1 )

t i t( i+1 )

X (m)

Y (m)

Claim 1 (power constant)

Let Ii be the intensity value of sensor

If then, track quality is guaranteed to be within track

where and C is a constant derived from the known locations of the sensors and the power of the object.

Claim 2 (power varies between [Pmin , Pmax ])

If then

track quality is guaranteed to be within track

where C’ = C/ P2 and is a constant .

The above constraint is a conservative

estimate. Better bounds possible

)ξI /(1ξI|IΔ| i2ii

track

][|| max'

22

2max

min PIC

IP

PI i

trackii

Ctrack /2

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Communication overhead further reduced by exploiting the predictability of the object being tracked

Static Prediction : sensor & server agree on a set of prediction models

only 2 models used: stationary & constant velocity Who Predicts: sensor based mobility prediction protocol

Every sensor by default follows a stationary model

Based on its history readings may change to constant velocity model (number of readings limited by sensor memory size)

informs server of model switch

Adaptive Tracking: prediction to improve performance

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Actual Track versus track on Adaptive Tracking (error tolerance 20m)

• A restricted random motion : the object starts at (0,d) and moves from one node to another randomly chosen node until it walks out of the grid.

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Energy Savings due to Adaptive Tracking

total energy consumption over all sensor nodes for random mobility model with varying track or track error.

significant energy savings using adaptive precision protocol over non adaptive tracking ( constant line in graph)

for a random model, prediction does not work well !

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Energy consumption with Distance from BS

total energy consumption over all sensor nodes for random mobility model with varying base station distance from sensor grid.

As base station moves away, one can expect energy consumption to increase since transmission cost varies as d n ( n =2 )

adaptive precision algorithm gives us better results with increasing base station distance

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Challenges & Ongoing Work

• Ongoing Work:– Supporting a larger class of SQL queries– Supporting continuous monitoring queries – Larger class of sensors (e.g., video sensors)– Better approaches to model fitting/switching in prediction

• In the future:– distributed Quasar architecture– optimizing quality given resource constraints– supporting applications with real-time constraints– dealing with failures

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The DAS Project**Goals:

Support Database as a Service on the Internet

Collaboration:

IBM (Dr. Bala Iyer)

UCI (Gene Tsudik)

** Supported in part by NSF ITR grant entitled “Privacy in Database as a Service” and by the IBM Corporation

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Software as a Service

• Get …– what you need

– when you need

• Pay …– what you use

• Don’t worry …– how to deploy, implement, maintain, upgrade

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Software As a Service: Why?

• Advantages– reduced cost to client

• pay for what you use and not for hardware, software infrastructure or personnel to deploy, maintain, upgrade…

– reduced overall cost• cost amortization across

users

– Better service• leveraging experts

across organizations

• Driving Forces– Faster, cheaper, more accessible

networks– Virtualization in server and storage

technologies– Established e-business

infrastructures

• Already in Market– ERP and CRM (many examples)– More horizontal storage services,

disaster recovery services, e-mail services, rent-a-spreadsheet services etc.

– Sun ONE, Oracle Online Services, Microsoft .NET My Services etc

Better Service for Cheaper

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Database As a Service

40%

51%

51%

57%

58%

0 10 20 30 40 50 60 70

% of respondents (Source: InfoWeek Research)

Platform Independence

Qualified Programmers

Compatibility

Qualified Administrators

Ease of Administration

Most Significant DB Execution Problems

• Why?– Most organizations need DBMSs– DBMSs extremely complex to deploy, setup, maintain– require skilled DBAs with high cost

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What do we want to do?

• Database as a Service (DAS) Model – DB management transferred to service provider for

• backup, administration, restoration, space management, upgrades etc.

– use the database “as a service” provided by an ASP• use SW, HW, human resources of ASP, instead of your own

User

Application Service Provider (ASP)

Server

Internet

BUT….

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Challenges

• Economic/business model?– How to charge for service, what kind of service guarantees can

be offered, costing of guarantees, liability of service provider.

• Powerful interfaces to support complete application development environment– User Interface for SQL, support for embedded SQL

programming, support for user defined interfaces, etc.

• Scalability in the web environment– overheads due to network latency (data proxies?)

• Privacy and Security – Protecting data at service providers from intruders and attacks.– Protecting clients from misuse of data by service providers– Ensuring result integrity

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Data privacy from service provider

User Encrypted User Database

User Data

The problem is we do not trust “the service provider” for sensitive information!

Fact 1: Theft of intellectual property due to database vulnerabilities costs American businesses $103 billion annually

Fact 2: 45% of those attacks are conducted by insiders! (CSI/FBI Computer Crime and Security

Survey, 2001) encrypt the data and store it but still be able to run queries over the encrypted data do most of the work at the server

Server

Application Service ProviderUntrusted

Server Site

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System Architecture

Encrypted User

Database

Query Translator

Server Site

Temporary Results

Result Filter

MetadataOriginal Query

Server Side Query

Encrypted Results

Actual Results

Service Provider

User

Client Site

Client Side Query ?

? ?

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NetDB2 Service

• Developed in collaboration with IBM

• Deployed on the Internet about 2 years ago– Been used by 15

universities and more than 2500 students to help teaching database classes

• Currently offered through IBM Scholars Program

4

2

3

1

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MARS Project**

Goals: integration of similarity retrieval and query refinement over structured and semi-structured databases to help interactive data analysis/mining

**Supported in part by NSF CAREER award, NSF grant entitled “learning digital behavior” and a KDD grant entitled “Mining events and entities over large spatio-temporal data sets”

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Similarity Search in Databases (SR)

Alice

Honda sedan, inexpensive,

after 1994,around LA

Used Car Catalog

Year Model Mileage

TransmissionLocation

Color Price...

Bob

Honda sedan, inexpensive,after 1994,around LA

M3975LA90K94Honda Accord

A3500LA150K95Honda Accord

Similarity search (Bob – location more important)

Similarity is Subjective:

results reflect personal interpretation of

`around’,`inexpensive’, and relative importance

Exact Search semantics (unranked)

Similarity search (Alice – price more important)

.8A6000LA50K95Honda Prelude

.7A6500LA30K98Honda Accord

Honda Accord

Honda Accord

Honda Accord

94

95

94

60K

150K

90K

Irvine

LA

LA

5000

3500

3975

A

A

M

.5

1

1

1M3975LA90K94Honda Accord

.8A3500Malibu100K94Toyota Camry

Honda Accord

Honda Accord

Honda Accord

94

94

95

70K

60K

150K

San Diego

Irvine

LA

4500

5000

3500

A

A

A

.6

.7

1

MARS-QLselect * from user_car_catalogwhere model ~= Honda Accord, year >= 1994, price <= 4K, location ~= LA

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Refined Results

Query Refinement (QR)

select * from user_car_catalogwhere model ~= Honda Accord, year >= 1994, price <= 4K, location ~= LA,

mileage~=60K

Refined Query

1M3975LA90K94Honda Accord–

.8A3500Malibu100K94Toyota Camry

Honda Accord

Honda Accord

Honda Accord

94

94

95

70K

60K

150K

San Diego

Irvine

LA

4500

5000

3500

A

A

A

.6

.7

1

Results

Mileage also important

.6A3500Malibu100K94Toyota Camry

.6A4500San Diego

70K94Honda Accord

.8A5000Irvine60K94Honda Accord

.9M3975LA90K94Honda Accord

Honda Accord 93 80K San Diego

4500 A .5

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Why are SR and QR important?

• Most queries are similarity searches – Specially in exploratory data analysis tasks (e.g., catalog search)– Users have only a partial idea of their information need

• Existing Search technologies (text retrieval, SQL) do not provide appropriate support for SR and (almost) no support for QR.– Users must artificially convert similarity queries to keyword-searches

or exact-match queries

– Good mappings difficult or not feasible• Lack of good knowledge of the underlying data or its structure• Exact-match may be meaningless for certain data types (e.g., images, text, multimedia)

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Similarity Access and Interactive Mining Architecture Search Client

Query SessionManager

Query LogManager/Miner

SimilarityQuery

Processor

Feedback-basedRefinement

Method

ORDBMS

SimilarityOperators

Types

FeedbackTable

Database

QueryLog

AnswerTable

ScoresTable

RefinementManager

History-basedRefinement

Method

Query/Feedback Ranked Results

InitialQuery

Feedback

RankedResults

Query Results

Schemes

RankingRules

Legend:--- logging__ Process

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MARS Challenges...

• Learning queries from– user interactions– user profiles– past history of other users

• Efficient implementation of – similarity queries– refined queries

• Role of similarity queries in– OLAP– interactive data mining

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Query-Session Manager

-parse the query- check query validity-generate schema for support tables - maintain sessions registry

Similarity Query Processor

-executes query on ORDBMS- ranks results (e.g. can exclude already see tuples, etc) - logs query(query or Top-k)

Refinement Manager

- maintains a registry of query refinement policies (content/collaborative)- generates the scores table- identifies and invokes intra-predicate refiners.

Query Log Manager/Miner

- maintains query log . Initial-Final pair . Top-K results . Complete trajectory - Query-query similarity (can have multiple policies) - Query clustering

UCI Database Group

Text Search Technologies (Altavista, Verity, Vality, Infoseek)

Strengths

support ranked retrieval

can handle missing data, synonyms, data entry errors

Approach

convert enterprise structured data into a searchable text index.

Limitations

cannot capture semantics of relationships in data

cannot capture semantics of non-text fields (e.g., multimedia)

limited support for refinement or preferences in current systems

cannot express similarity queries over structured or semi-structured data (e.g., price, location)

Honda accord near LA

approx. $4000

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MoviesActors

Directors

Al Pacino acted in a

movie directed by Francis

Ford Coppola

UCI Database Group

SQL-based Search TechnologiesOracle, Informix, DB2, Mercado

Approach

translate similarity query into exact SQL query.

Strengths

support structured as well as semi-structured data

support for arbitrary data types

Scalable attribute-based lookup

Limitations

translation is difficult or not possible

difficult to guess right ranges

causes near misses

not feasible for non-numeric fields

cannot rank answers based on relevance

does not account for user preference or query refinement

1994 Honda accord near LA approx.

$4000

select *from user_car_catalogwhere model = Honda Accord and 1993 year 1995 and dist(90210) 50 and price < 5000