Lecture 1.1
2026/09/23
Big Data Infrastructure I. The Scale-Out Paradigm & Distributed Storage — vertical vs. horizontal scaling, the Google File System (GFS) and HDFS.
M.Sc. in Computer Science · Sapienza University of Rome
Academic Year 2026/27 — 6 CFU
The course introduces big data computing as an advanced structural paradigm of modern computing platforms,
at the intersection of distributed computing architectures, high-dimensional data
representations, and scalable machine learning systems. Students learn how the
scale-out model handles volume, velocity and variety of data, and the methodological abstractions needed
where classical exact computation becomes intractable.
COURSE START: The first lecture is scheduled for Wednesday, September 23.
Enrolling on the course's Google Classroom page is highly recommended to stay up to date on all course-related communications.
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The course introduces big data computing as an advanced structural paradigm of modern computing platforms, at the intersection of distributed computing architectures, high-dimensional data representations, and scalable machine learning systems. Students learn how the scale-out model handles volume, velocity and variety of data, and the methodological abstractions needed where classical exact computation becomes intractable.
Required
Recommended
Free online
Mining of Massive Datasets — Leskovec, Rajaraman & Ullman (freely available online)
Foundations of Data Science — Blum, Hopcroft & Kannan (freely available online)
Research Seminar — mandatory
A critical seminar on a recent, high-impact research paper, chosen autonomously by the student and approved by the instructor, from top-tier venues (e.g., SIGMOD, VLDB, OSDI, SOSP, NeurIPS, ICML, KDD). Honors (30 cum laude) require exceptional mastery, deep critical insight, and flawless academic communication.
Big Data Infrastructure I. The Scale-Out Paradigm & Distributed Storage — vertical vs. horizontal scaling, the Google File System (GFS) and HDFS.
Big Data Infrastructure II. The MapReduce Execution Model — Map, Shuffle, Sort and Reduce phases; straggler tracking and fault tolerance.
Big Data Infrastructure III. Apache Spark & Dataflow Graph Architectures — RDDs, memory-centric execution, lazy evaluation, DAG scheduling.
High-Dimensional Representations I. Geometry of High-Dimensional Spaces — volume concentration, distance concentration, breakdown of Euclidean metrics.
High-Dimensional Representations II. Dimensionality Reduction & Random Projections — the Johnson–Lindenstrauss Lemma.
High-Dimensional Representations III. Vector Representations of Text & Embeddings — sparse TF-IDF vs. dense embeddings, Word2Vec.
High-Dimensional Representations IV. Graph Representations & Embeddings — power-law graphs, adjacency decomposition, node-to-vector mappings.
Scaling Classical Tasks I. Web-Scale Search — The Inverted Index and Query Processing.
Scaling Classical Tasks I. Web-Scale Search — Distributed Search at Scale.
Scaling Classical Tasks II. Link Analysis: Page Rank.
Scaling Classical Tasks III. Similarity Search — MinHashing.
Material and announcements from previous editions of the course.