Dagstuhl Seminar 28221
Simplicity and Efficiency of Data Structures
( May 28 – Jun 02, 2028 )
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Organizers
- Michael A. Bender (Stony Brook University, US)
- Ioana Oriana Bercea (KTH Royal Institute of Technology - Stockholm, SE)
- László Kozma (TU Dresden, DE)
- Eva Rotenberg (IT University of Copenhagen, DK)
Contact
- Marsha Kleinbauer (for scientific matters)
- Jutka Gasiorowski (for administrative matters)
Data structures describe and control how data is stored and manipulated in computing systems. Data structures affect the efficiency of algorithms and directly influence the performance of computing infrastructure, including file systems, search engines, routers, and databases.
Accordingly, data structures research is one of the core areas of computer science, with a rich interplay between theory and practice. A usual focus is the study of abstract prototypical problems in idealized computational models, aiming to obtain strong (asymptotic) bounds on the running times and space usage in these settings. Once theoretically sharp results are obtained, a line of investigation is often considered closed, with further research seen as unnecessary. Yet, in our rush to obtain tight asymptotic (worst-case) bounds, we often lose sight of the actual assumed implementation of our data structures, leaving parts of the design space unexplored, and leading to a gap between theory and practice.
In this Dagstuhl Seminar we aim to address this gap from the point of view of simplicity. Simplicity is increasingly recognized as a primary concern in the field of algorithms, and most of the lasting data structural contributions of the past decades have always been characterized by simplicity and elegance. Innovation in data structures often manifests itself in “doing less” to achieve the same goal. Simpler data structures tend to be easier to understand, implement, and debug, are often fast in practice, and are easier to build upon and adapt to changing assumptions.
With this seminar we aim to study different aspects of simplicity in data structures: simplicity of design, simplicity of analysis, and simplicity of models. We encourage participants to revisit known data-structural settings, and investigate whether simpler strategies can achieve state-of-the-art results. We also ask whether known analyses of data structures can be obtained in simpler ways, more transparently, or using fewer assumptions. Finding suitable computational models as abstractions of the underlying computing environment is a core issue of data structures research, and inherently an act of simplification.
Participants are expected to highlight results that are remarkable through their simplicity and to identify open problems in settings where lack of simplicity is an obstacle. Relevant topics include core data structural settings, as well as applications for graphs, geometric objects, strings, data streams, and other areas.
This Dagstuhl Seminar is part of a successful series begun in 1991. The series has contributed to shaping trends in data structures research. We aim to bring together leading researchers in classical data structures with those with expertise in applied areas, with a joint focus and interest in simplicity. By exposing the participants to diverse viewpoints, we aim to inspire new directions and collaborations, make progress on difficult problems, and continue advancing the state-of-the-art in data structures research.
Creative Commons BY 4.0
Michael A. Bender, Ioana Oriana Bercea, László Kozma, and Eva Rotenberg
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Classification
- Data Structures and Algorithms
Keywords
- data structures
- algorithms
- simplicity
- computational models
- big data
