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ARGUS: Rete + DBMS =3D Efficient Persistent Profile Matching on Large-Volume Data Streams
Chun Jin <= /b>
Language Technologies Institute
School of Computer Science
Carnegie Mellon University
cjin@cs.cmu.edu
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Chun Jin   Carnegie Mellon
2
Stream Processing Model
nStream Processing becomes
n demanding and pre= valent.
Storage
Data Streams
Output
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Chun Jin   Carnegie Mellon
3= 3;
Stream Databases
nStream Database Applications
nNetwork Traffic Analysis and Rou= ter Configuration
nDynamic Internet Services
nSensor Data Analysis =
nAnomaly Detection
nStream Database Projects&= #13;
nSTREAM, TelegraphCQ, Aurora =
nNiagaraCQ, OpenCQ, WebCQ
nGigascope, Tribeca
nTapestry, Alert, Tukwila, etc.&#= 13;
nARGUS
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Chun Jin   Carnegie Mellon
4= 3;
Stream Anomaly Monitoring Systems (SAMS)
nSAMS monitors structured data streams for anomalies or potential hazards.
nMatche= s of queries may be high urgency alerts. Prompt detections are desirable.
nSatisfaction of a SAMS query is often <= /span>rare (very-high-selectivity).
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Chun Jin   Carnegie Mellon
5= 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
6= 3;
Challenges to SAMS
nPersis= tent queries may number in thousa= nds or tens of thousands.
nDaily stream volumes may exceed millions of records.
nPrompt detections are desirable.
n = ;
nVery-high-selectivity Query Property.
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Chun Jin   Carnegie Mellon
7= 3;
Propos= ed ARGUS Approach
nBasic Framework:
nIncremental evaluation schemes (Adapted Rete algorithm)
nRete (Forgy 1982): Incremental Evaluation based on
n Materialized Intermediate Results.
nUpon a traditional DBMS platform
nExploiting Very-High-Selectivity Query Property:
nTransitivity Inference
nConditional Materialization =
nOptimizing Join Order
nComputation Sharing <= /span>
nRelated to Other Applications
nStream Databases
nModern DBMS Query Optimization
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Chun Jin   Carnegie Mellon
8= 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
9= 3;
SQL Query for Example 4
nFROM transaction r1, transaction= r2, transaction r3
nWHERE r2.type_code =3D 1000 AND
n r3.= type_code =3D= 1000 AND
n r1.= type_code =3D= 1000 AND
n r1.= amount > 100= 0000 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
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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Chun Jin   Carnegie Mellon
11= 3;
ReteGe= nerator Architecture
System&= #13;
Catalog=
Transitivity <= /span>
Inferen= ce
SQL Queries
ReteGen= erator
= =
=
= =
Sharing
Module
Join Order
Conditional
Materialization
Optimizer<= /div>
<= span style=3D'font-size:71%'>nCommon Computation Identification
nPredicate Indexing
nExtended Predicate Set Operations
nChoose what and how to share = ;
nRecording and Manipulating Network Topology
nEstimating Sharing Costs<= /span>
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