Our other publications
Building a Custom BPM Worklist, Oracle InSync 2011 User Group presentation
Performance Tuning for Oracle Business Process Management Suite 10g, Oracle Whitepaper, July 2011
Performance Tuning for Oracle Business Process Management Suite 11g, Oracle Whitepaper, February 2013
Oracle BPM Suite 11g: Advanced BPMN Topics, Packt Publishing, September 2012
Fusion Middleware 12.1.2: Developing Applications with Continuous Integration
Fusion Middleware 12.1.3: Developing Applications with Continuous Integration
Category Archives: Uncategorized
From GraphRAG Demo to AI System: Build a Minimum Viable Knowledge Graph with Oracle AI Database 26ai
Key Takeaways In the first article, we built the mechanics of GraphRAG with Oracle AI Database 26ai. We parsed documents, created chunks, extracted entities and relationships, stored embeddings in VECTOR columns, defined a SQL property graph, and compared … Continue reading
Posted in Uncategorized
Tagged graph, graphrag, hybrid-query, ontology, oracle, taxonomy
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Four kinds of agent memory in Java with LangChain4j and Oracle AI Database
Key Takeaways In the first article, we gave a Java agent durable semantic memory: selected facts stored in Oracle AI Database and retrieved by meaning through LangChain4j. That is a useful starting point, but most agents need more … Continue reading
Posted in Uncategorized
Tagged agent, ai, episodic, graph, langchain4j, oracle, procedural, semantic
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Implementing GraphRAG with Oracle AI Database 26ai: SQL Property Graphs, Vector Search, and Automated Graph Extraction
Key Takeaways Standard vector Retrieval-Augmented Generation, or RAG, retrieves chunks that are semantically similar to a question. That works well when the answer appears in a compact passage whose wording is close to the question. It becomes harder to inspect … Continue reading
Give a Java agent durable memory with LangChain4j and Oracle AI Database
Key Takeaways I like agent memory demos that make one thing obvious: where did the memory actually go? A lot of examples keep memory in a list, a chat window, or a local object. That is fine for learning how … Continue reading
Add Event-Driven Workflows to Your Spring AI App with Oracle (Part 4 of 4)
By the end of Episode 3 (video), the assistant could act. Tool calls let it look up orders, initiate returns, and create support tickets — real backend operations against Oracle, not simulated responses. But every one of those operations happened … Continue reading
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Tagged ai, event-driven, oracle, spring-ai, txeventq, Workflow
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Productionizing Oracle Database Metrics Exporter: Least Privilege, Private Scraping, and Operational Ownership
The local demo worked. /metrics responds. Prometheus scrapes the target. Grafana shows panels. Maybe oracledb_up is 1 in your deployed version, and database-side signals finally sit near application latency, deployment events, queue behavior, and other service telemetry. That is a … Continue reading
Posted in Uncategorized
Tagged exporter, grafana, observability, oracle, production, Prometheus, secure
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From Graph Patterns To Analytics And Visualization
GRAPH_TABLE is the right first tool because it demonstrates the core idea: match a graph pattern and return rows. But graph work often goes one step further. You may want to rank important accounts, find connected groups, trace weighted paths, … Continue reading
Hands-on: Run Oracle Database Metrics Exporter with Prometheus and Grafana
This article uses Oracle Database Metrics Exporter 2.3.1, checked against Oracle’s documentation on 2026-05-10. Container image: container-registry.oracle.com/database/observability-exporter:2.3.1. Default listen address: :9161. Default metrics path: /metrics. In the first article in this series, we looked at why application telemetry stops at … Continue reading
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Tagged exporter, grafana, observability, open-telemetry, oracle, Prometheus
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Detect Fraud Patterns With Graphs
We have tables. We have a SQL property graph named BANK_GRAPH. Now we can ask graph-shaped questions. The key tool is GRAPH_TABLE. GRAPH_TABLE is a SQL table expression. You give it a graph and a pattern to match, and it … Continue reading
Add real business actions to your Spring AI app with Oracle (part 3 of 4)
By the end of Episode 2, the assistant could answer policy questions from Oracle Vector Store and remember conversations across requests. That combination already felt more like a real application than a bare LLM wrapper. But it still could not … Continue reading
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