refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-complex-event-processing-cep
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title: Complex Event Processing (CEP)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [CEP, Event Stream Processing, 복합 이벤트 처리]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.88
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verification_status: applied
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tags: [cep, streaming, event-driven, flink, esper]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: java
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framework: flink
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---
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# Complex Event Processing (CEP)
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## 매 한 줄
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> **"매 stream of simple events → meaningful complex pattern"**. David Luckham (Stanford, 2002) 가 정의한 paradigm. 2026 현재 Apache Flink CEP, Kafka Streams, Esper NEsper 가 main implementation; fraud detection, IoT anomaly, algorithmic trading 의 backbone.
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## 매 핵심
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### 매 개념
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- **Event**: timestamped 의 fact (transaction, sensor reading, click).
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- **Pattern**: temporal/causal relationship 의 events (A followed by B within 5s).
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- **Window**: sliding/tumbling/session 시간 frame.
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- **Aggregation**: count, sum, avg over window.
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- **Correlation**: 다중 stream 매 join (e.g., trades + market data).
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### 매 pattern operator
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- **Sequence**: A → B → C (in order).
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- **Conjunction**: A AND B (any order, in window).
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- **Negation**: A NOT followed by B.
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- **Iteration**: A repeated N times.
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- **Within**: temporal constraint.
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### 매 응용
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1. Fraud detection — card swipes 매 different countries within 1h.
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2. IoT — sensor reading exceeds threshold for 3 consecutive readings.
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3. Trading — bid/ask spread anomaly detection.
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4. Network security — port scan pattern (many SYN, few ACK).
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5. SLA monitoring — 5xx error rate spike correlated with deploy event.
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## 💻 패턴
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### Flink CEP — 3 failed login pattern
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```java
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Pattern<LoginEvent, ?> failedLogins = Pattern
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.<LoginEvent>begin("first")
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.where(e -> !e.success)
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.next("second").where(e -> !e.success)
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.next("third").where(e -> !e.success)
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.within(Time.minutes(5));
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CEP.pattern(loginStream.keyBy(e -> e.userId), failedLogins)
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.select(match -> new Alert(match.get("first").get(0).userId))
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.addSink(alertSink);
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```
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### Esper EPL — fraud detection
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```sql
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-- swipe in different countries within 1 hour
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SELECT a.cardId, a.country, b.country
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FROM pattern [
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every a=Swipe -> b=Swipe(cardId=a.cardId, country!=a.country)
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where timer:within(1 hour)
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];
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```
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### Kafka Streams — sliding window aggregation
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```java
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KStream<String, Click> clicks = builder.stream("clicks");
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clicks.groupByKey()
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.windowedBy(SlidingWindows.ofTimeDifferenceWithNoGrace(Duration.ofMinutes(5)))
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.count()
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.filter((k, count) -> count > 1000)
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.toStream()
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.to("anomalies");
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```
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### Flink — session window
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```java
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stream.keyBy(e -> e.userId)
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.window(EventTimeSessionWindows.withGap(Time.minutes(30)))
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.aggregate(new SessionStats())
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.addSink(...);
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```
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### Pattern with negation (NO heartbeat in 30s)
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```java
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Pattern.<HeartbeatEvent>begin("start")
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.notFollowedBy("missing")
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.where(e -> true)
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.within(Time.seconds(30));
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```
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### Modern: Materialize / RisingWave (SQL-native streaming)
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```sql
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CREATE MATERIALIZED VIEW fraud_alerts AS
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SELECT user_id, COUNT(*) as failed_count
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FROM logins
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WHERE success = false
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AND ts > NOW() - INTERVAL '5 minutes'
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GROUP BY user_id
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HAVING COUNT(*) >= 3;
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Java/JVM, complex patterns | Flink CEP |
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| Kafka-centric, simple aggregation | Kafka Streams |
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| SQL-first, low ops | Materialize / RisingWave |
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| In-process, low-volume | Esper |
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| Cloud-native, serverless | AWS Kinesis Data Analytics |
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**기본값**: Flink CEP for complex patterns, Materialize for SQL-native streaming.
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## 🔗 Graph
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- 부모: [[Event-Driven Architecture]] · [[Stream-Processing-Architectures|Stream Processing]]
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- 변형: [[Event Sourcing]] · [[CQRS]]
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- Adjacent: [[Apache Flink]]
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## 🤖 LLM 활용
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**언제**: pattern definition 매 natural language → EPL/Flink translation, alert explanation.
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**언제 X**: micro-second latency hot path (LLM 매 too slow).
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## ❌ 안티패턴
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- **Unbounded state**: window 없이 group-by → memory blowup.
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- **Wall-clock instead of event-time**: out-of-order event 매 wrong result.
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- **Pattern explosion**: NFA state count 매 exponential, pattern 너무 복잡.
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- **No watermark**: late event 매 silently lost.
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## 🧪 검증 / 중복
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- Verified (Luckham 2002 *Power of Events*, Apache Flink CEP docs 2026).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full content with Flink CEP, Esper, Materialize |
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