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------=_NextPart_01C5E889.B8033A30 Content-Location: file:///C:/6539588C/Proposal_files/master05.xml Content-Transfer-Encoding: quoted-printable Content-Type: text/xml; charset="utf-8" ------=_NextPart_01C5E889.B8033A30 Content-Location: file:///C:/6539588C/Proposal_files/pres.xml Content-Transfer-Encoding: quoted-printable Content-Type: text/xml; charset="utf-8" ------=_NextPart_01C5E889.B8033A30 Content-Location: file:///C:/6539588C/Proposal_files/slide0001.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Slide 1
ARGUS: A Prototype Stream Anomaly Monitoring System=
Thesis Proposal
=
Chun Jin
Thesis Committee
Jaime Carbonell (Chair)
Christopher Olston
Jamie Callan
Phil Hayes, DYNAMiX Technologies=
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Chun Jin   Carnegie Mellon
2
Thesis Statement
nStream Anomaly Monitoring System (SAMS) is an important sub-class of stream applications. The difficulty is raised by t= he very-large-volume data and a large number of queries the sy= stem is supposed to handle.
<= span style=3D'font-size:75%'>nPropose an approach for SAMS’s that implements = incremental evaluation schemes with adapted Rete algorithm upon a traditional DBMS platform and exploit SAMS characteristics for query evaluation optimization. =
<= span style=3D'font-size:75%'>nDemonstrate how the approach and the improvements could lead t= o a simple and fast implementation of an effective and efficient SAMS system.
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Chun Jin   Carnegie Mellon
3= 3;
Outlin= e
nMotivation
nMy ARGUS Approach
nCurrent Work Status <= /span>
nCurrent System
nPreliminary Results <= /span>
nProposed Work and Timelin= e
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Chun Jin   Carnegie Mellon
4= 3;
Stream Processing
nStream Processing Applications
nNetwork Traffic Analysis and Rou= ter Configuration
nInternet Services
nSensor Data Analysis =
nAnomaly Detection
nStream Processing Projects
nSTREAM, TelegraphCQ, Aurora =
nNiagaraCQ, OpenCQ, WebCQ
nGigascope, Tribeca
nTapestry, Alert, Tukwila, etc.
------=_NextPart_01C5E889.B8033A30 Content-Location: file:///C:/6539588C/Proposal_files/slide0006.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Slide 1
Chun Jin   Carnegie Mellon
5= 3;
Stream Anomaly Monitoring Systems (SAMS)
nSAMS monitors structured data streams for anomalies or potential hazards.
nContin= uous queries may number in thousa= nds or tens of thousands.
nDaily stream volumes may exceed = millions of records. =
nSatisfaction of a SAMS query is often <= /span>rare (very-high-selectivity).
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Chun Jin   Carnegie Mellon
6= 3;
SAMS Dataflow
Analyst
Stream Anomaly Monitoring System
Storage
3D"Text
Queries
Alerts
Data Streams
FedWire Money Transfers
Patient Records
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Chun Jin   Carnegie Mellon
7= 3;
Query Example 4
= nSuppose for every big transaction of type code 1000, the analyst wants to check if the money stayed in the bank or l= eft within ten days. An additional sign of possible fraud is = <= span lang=3DEN-US style=3D'mso-fareast-font-family:\5B8B\4F53;mso-hansi-font-fa= mily: Tahoma;font-size:88%;mso-fareast-language:ZH-CN'>that transactions invo= lve at least one intermediate bank. The query generates an alarm whenever the receiver of a large transaction (over $1,000,000) transfers at least half of the money further within ten = days of this transacti= on using an intermediate bank.
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Chun Jin   Carnegie Mellon
8= 3;
SQL Query for Example 4
nFROM transaction r1, transaction r2, transaction r3
nWHE= RE r2.type_code =3D 1000 AND
n= r3.type_code =3D 1000 AND
n= r1.type_code =3D 1000 AND
n= r1.amount > 1000000 AND
n= r1.rbank_aba =3D r2.sbank_aba AND =
n= r1.benef_account =3D r2.orig_account AND
n= r2.amount > 0.5 * r1.amount AND
n= r1.tran_date <=3D r2.tran_date AND =
n= r2.tran_date <=3D r1.tran_date + 10 AND
n= r2.rbank_aba =3D r3.sbank_aba AND =
n= r2.benef_account =3D r3.orig_account AND
n= r2.amount =3D r3.amount AND
n= r2.tran_date <=3D r3.tran_date AND =
n= r3.tran_date <=3D r2.tran_date + 10;
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Chun Jin   Carnegie Mellon
9= 3;
ARGUS = as a Prototype SAMS
<= span style=3D'font-size:75%'>nImplement the Adapted Rete Algorithm upon a traditional DBMS platform
nRete (Forgy 1982): Incremental Evaluation based on Materialized Intermediate Results.
nSAMS’s assumption of very-high-selectivity query over very-large-volume data justifies employment of Rete and necessitates some unique improvements.
nTransitivity Inference
nOno/Lohman VLDB90, Pirahesh/Leung/Hasan ICDE97
nPredicate Set Evaluation and Materialization
nPartial Rete (Materialization skipping)
nComplex Common Computation Identification for Sharing
nIntermingled Sharing and Optimization processing
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Chun Jin   Carnegie Mellon
10= 3;
ARGUS System Architecture
Rete Network
Generator
Quer= y
= Rete
Networks
Data Tables
Analyst
Identif= ied Threats
= =
=
=
= Intermediate
Tables
Data Streams
Query <= /div>
Table
Stream
Anomaly
Monitoring
Do_queries<= /div>
Scheduler
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file:///C:/6539588C/Proposal_files/slide0047.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Slide 1
Chun Jin   Carnegie Mellon
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ReteGe= nerator Architecture
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Topology
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Rete Optimizer
ReteGen
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Rewriter
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Transitivity <= /span>
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Counter=
Table
SQL Queries
Check Topology
Register Rete Networks
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History= -based Cost Estimating
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ReteGen= erator
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Chun Jin   Carnegie Mellon
12= 3;
Select= ed ARGUS Topics
nAdapted Rete Algorithm
nReteGenerator translates a query into a Rete network that is wrapped as a stored procedure.
nThe procedure implements the Ada= pted Rete Algorithm accounting for the incremental evaluation
nTransitivity Inference= 3;
nRete Optimization
nComputation Sharing
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Chun Jin   Carnegie Mellon