DAGksi

DAGksi is a conceptual framework for constructing and executing workflows that are modeled as directed acyclic graphs (DAGs). In DAGksi, each node represents a task or data transformation, and edges express data or control dependencies between nodes. The acyclic property enforces a well-defined execution order, enabling reproducibility and straightforward reasoning about how inputs propagate to outputs.

The DAGksi architecture typically comprises a graph engine that maintains the DAG, a node runtime for individual tasks, and a scheduler that assigns work to executors. A provenance subsystem records versioned inputs, parameters, and results to support data lineage and auditability. Components are designed to be modular and pluggable, with connectors to storage systems, compute clusters, and data catalogs.

Key features include modular subgraphs for reuse, versioned DAG definitions, and incremental or cached recomputation to avoid re-running unaffected tasks. Fault tolerance is provided through retries, checkpointing, and deterministic replay. The system supports parallel execution of independent branches and includes security and access controls for sensitive data. DAGksi emphasizes declarative pipeline definitions and transparent data flow.

Applications span data engineering pipelines (ETL and data enrichment), machine learning workflows (feature extraction, model training and evaluation), and automated business processes with auditable history. The framework is designed to support reproducible, scalable DAG-based workflows across domains, with a focus on provenance, modularity, and automation.

In practice, DAGksi is described as an open specification with reference implementations and a growing ecosystem of adapters and templates, intended to guide the design of reliable DAG-driven workflows. See also: directed acyclic graph, workflow automation, data lineage, data provenance.