Multi-layer optimizations for end-to-end data analytics

Shaikhha, Amir; Schleich, Maximilian; Ghita, Alexandru; Olteanu, Dan

doi:10.1145/3368826.3377923

Cited by 16 publications

(10 citation statements)

References 61 publications

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“…The tutorial will highlight several recent efforts in this space, in particular on compiling the task of learning specific models over feature extraction queries into efficiently executable low-level code. Such techniques can lead to significant runtime performance improvements, as reported for the AC/DC [47], F-IVM [32,33], LMFAO [42,41], and IFAQ [47,48] prototypes. The tutorial will make the case for such a compilation approach.…”

Section: Engineering Tools Of Db Researchermentioning

confidence: 70%

Machine Learning over Static and Dynamic Relational Data

Kara

Nikolić

Olteanu

et al. 2021

Preprint

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This tutorial overviews principles behind recent works on training and maintaining machine learning models over relational data, with an emphasis on the exploitation of the relational data structure to improve the runtime performance of the learning task.The tutorial has the following parts:1. Database research for data science 2. Three main ideas to achieve performance improvements (2.1) Turn the ML problem into a DB problem (2.2) Exploit structure of the data and problem (2.3) Exploit engineering tools of a DB researcher 3. Avenues for future researchThe starting points of this tutorial are a VLDB 2020 keynote [34] and a short tutorial at SUM 2019 [44]. This tutorial goes beyond these prior tutorials, in particular on learning over dynamic relational data. The interested reader may also want to explore further work on machine learning over databases overviewed in two prior tutorials [23,39].In the following, we briefly overview each part of the tutorial.

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Section: Engineering Tools Of Db Researchermentioning

confidence: 70%

Machine Learning over Static and Dynamic Relational Data

Kara

Nikolić

Olteanu

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The tutorial will highlight several recent e orts in this space, in particular on compiling the task of learning speci c models over feature extraction queries into efciently executable low-level code. Such techniques can lead to signi cant runtime performance improvements, as reported for the AC/DC [47], F-IVM [32,33], LMFAO [41,42], and IFAQ [47,48] prototypes. The tutorial will make the case for such a compilation approach.…”

Section: Engineering Tools Of Db Researchermentioning

confidence: 80%

Machine learning over static and dynamic relational data

Kara

Nikolić

Olteanu

et al. 2021

Proceedings of the 15th ACM International Conference on Distributed and Event-Based Systems

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“…Therefore, we combine the database-centric optimizations in LMFAO with optimizations from the programming language community. We present preliminary results on this extensions in [125].…”

Section: Discussionmentioning

confidence: 93%

Structure-Aware Machine Learning over Multi-Relational Databases

Schleich

2021

Proceedings of the 2021 International Conference on Management of Data

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First and foremost, I would like to express my sincere gratitude to my supervisor Prof. Dan Olteanu for his guidance, invaluable advice, and the many hours we spent discussing the project. His support was a crucial factor of the success of this thesis, and I could not have asked for a better supervisor.A big, heartfelt thank you goes out to my colleagues at relationalAI, in particular Hung Ngo, Mahmoud Abo Khamis, and Long Nguyen, for their contributions to much of the work presented in this thesis. The thesis would not have been possible without their support. I also thank Molham Aref for providing me with the opportunity to spend one summer at relationalAI in Berkeley.I appreciate all the support and companionship my colleagues from the Oxford FDB group both in the lab and outside. Their presence has made the past years significantly more enjoyable.On a more personal note, I owe eternal gratitude to my family, in particular my parents and my brother, for their continuous support and encouragement. I would not have been able to pursue this DPhil without their support.Lastly and most importantly, thank you Julia, for your support, love, and patience. I look forward to a lifetime of adventures with you.

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Multi-layer optimizations for end-to-end data analytics

Cited by 16 publications

References 61 publications

Machine Learning over Static and Dynamic Relational Data

Machine Learning over Static and Dynamic Relational Data

Machine learning over static and dynamic relational data

Structure-Aware Machine Learning over Multi-Relational Databases

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