Sparse Matrix Support for Apache Flink
Organisatsiooni nimi
Data Systems Group
Kokkuvõte
Sparse matrices are very common in data representation, especially for machine learning. When it comes to performing these analytics and/or machine learning on data streams, the constraints on memory footprint and latency are more strict.
Apache Flink is a well-known open source system for distributed large-scale stream processing. Unfortunately, Flink does not give support for efficient handling and access to sparse data. Yet, Flink has been used in several online machine learning research.
This thesis's objective is to build a general purpose sparse matrix library that allows efficient storage and access to sparse data that can be integrated with Flink APIs.
We have an application scenario related to online recommender systems that shall benefit from this sparse matrix implementation.
Apache Flink is a well-known open source system for distributed large-scale stream processing. Unfortunately, Flink does not give support for efficient handling and access to sparse data. Yet, Flink has been used in several online machine learning research.
This thesis's objective is to build a general purpose sparse matrix library that allows efficient storage and access to sparse data that can be integrated with Flink APIs.
We have an application scenario related to online recommender systems that shall benefit from this sparse matrix implementation.
Lõputöö kaitsmise aasta
2021-2022
Juhendaja
Ahmed Awad
Suhtlemiskeel(ed)
inglise keel
Nõuded kandideerijale
Tase
Bakalaureus, Magister
Kandideerimise kontakt
Nimi
Ahmed Awad
Tel
E-mail