Effektive algoritmer og datastrukturer

Effektive algoritmer og datastrukturer har stor betydning inden for big data, hvor der udføres komplekse beregninger på meget store datasæt. Ofte er algoritmisk effektivitet afgørende for analysekvaliteten (eller for, om analysen overhovedet kan gennemføres). Modellering af beregningsinfrastrukturer (som f.eks. komplekse hukommelsesniveauer, GPU’er og klient/server-arkitekturer) samt udvikling af algoritmer og datastrukturer til disse modeller/devices får også større og større betydning.

Formålet med denne workstream er at øge vores grundlæggende forståelse af effektive algoritmer og datastrukturer for at kunne håndtere udfordringerne inden for big data samt at øge Danmarks styrkeposition og kapacitet inden for algoritmer og datastrukturer.

Eftersom effektive algoritmer og datastrukturer også er vigtige – og ofte afgørende – på andre felter inden for datalogi (som beskrevet under temaerne om kunstig intelligens og data management) samt i applikationer, er der store muligheder for at skabe synergi mellem algoritmeforskere og andre forskere i projektet. Et andet vigtigt formål er at sikre, at resultaterne inden for algoritmeforskningen anvendes i praksis og på tværs af fagligheder.

Relaterede projekter

SciTech project

Online Algorithms with Predictions

Our focus is on improving optimization algorithms in online decision-making. Using techniques from online algorithms for solving optimization problems, we can provide worst-case guarantees, but normal (averagecase) behavior may not be satisfactory. Using techniques from machine learning, we can often provide good behavior in practice, but guarantees are lacking, and in particular missing for situations not captured by the training data. We aim at combining the best features from these two areas.

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SciTech project

Machine Learning Algorithms Generalisation

AI is radically changing society and the main driver behind new AI methods and systems is machine learning. Machine learning focuses on finding solutions for, or patterns in, new data by learning from relevant existing data. Thus, machine learning algorithms are often applied to large datasets and then they more or less autonomously find good solutions by finding relevant information or patterns hidden in the data.

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SciTech project

Benefit and Bias of Approximate Nearest Neighbor Search for Machine Learning and Data Mining

The search for nearest neighbors is an emerging and increasingly vital component in data analysis tasks, for example using vector embedding databases. Typically, the search is the bottleneck in terms of efficiency. Approximate nearest neighbor (ANN) search methods are often employed to speed up the application. However, different methods for ANN search come with different biases that can be positive or negative for the downstream application. In this project, the bias of different ANN methods and its impact on different applications will be studied.

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Workstreamleder

Riko Jacob

Associate Professor

IT University of Copenhagen
Computer Science Department
E: rikj@itu.dk

Tilknyttede personer

Aarhus Universitet
Institut for Datalogi

Philip Bille

Professor

Danmarks Tekniske Universitet
Institut for Matematik og Computer Science

Mikkel Thorup

Professor

Københavns Universitet
Datalogisk Institut

Joan Boyar

Professor

Syddansk Universitet
Institut for Matematik og Datalogi

IT-Universitetet i København
Institut for Datalogi

Aalborg Universitet
Institut for Datalogi

Jan Damsgaard

Professor

Copenhagen Business School
Institut for Digitalisering