Algorithmic Techniques for Modern Data Models

• Describe an algorithm or a data structure in a comprehensible manner, i.e., accurately, concise, and unambiguous.
• Prove correctness of algorithms and data structures in different data models, such as the streaming and parallel models.
• Analyze, evaluate, and compare the performance of algorithms and data structures in different data models, such as the streaming, parallel, and external memory models.
• Analyse, evaluate, and compare the suitability of different data models in a given setting.
• Apply and extend relevant algorithmic techniques (e.g. sketching, map-reduce, compressed computation) in modern data models.
• Design algorithms that solve a given problem in a given modern data model.
• Systematically identify and analyse problems and make informed choices for solving the problems based on the analysis.
• Argue clearly for the choices made when solving a problem.
• Express oneself in writing at a scientific level.

State-of-the-art algorithmic techniques for modern data models, such as probabilistic, time-dependent and approximate summary techniques, distributed and massively parallel computation techniques, and compressed computation techniques.

02110, Basic courses in algorithms and data structures (comparable to 02105/02326 + 02110). Mathematical maturity.

Lectures and exercises.