IDTS4102 - Distributed Data Processing
Overview
This course provides a rigorous, foundational introduction to the organizational systems, core concepts, and architectural paradigms of distributed data computing, structurally aligned with the classic textbook Introduction to Distributed Data Processing by Harry Katzan. It shifts the computing paradigm from isolated centralized mainframes to cooperative networks of autonomous data nodes.
Students will explore the fundamental concepts of distributed intelligence, investigating the operational trade-offs among centralized, decentralized, and truly distributed topographies. The curriculum traces the engineering requirements for systemic data communications, hardware facility arrangements, distributed database management systems (DDBMS), and multi-processor job control scheduling. Critical systems problems—including structural data partitioning, synchronization errors, Deadlock prevention, master-replica network configurations, and nodal task allocation mechanics—are thoroughly developed.
The course also explores critical algorithmic governance parameters, highlighting performance evaluation boundaries (ROC/AUC analysis, rare-class imbalances, cost-sensitive matrices), feature selection techniques, profiling bias auditing, and data privacy masking compliance under regulatory frameworks like the Indonesian UU PDP and GDPR. Through hands-on programming labs, students will develop the professional capacity to design, optimize, and defend scalable classification solutions for complex corporate and research applications.
Learning Hours Composition:
- 42 hours face to face, consisting of 14 sessions of lecturing, tutorial or workshop.
- 28 hours of Structured Learning and Guided Group Discussions
- 65 hours of Independent, Project-Based Learning
Managing Faculty:
Credit Point:
3 Credit(s)
Learning Hours:
45 hours x 3 CPs = 135 hours
