{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129945"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129945","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"MojoFrame: dataframe library in Mojo language","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2025-10-20 without embargo terms","The student, Arthur Huang, accepted the attached license on 2025-07-15 at 17:44.","The student, Arthur Huang, submitted this Thesis for approval on 2025-07-15 at 18:03.","This Thesis was approved for publication on 2025-07-18 at 14:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22590 on 2025-10-20 at 20:15:18","Mojo is an emerging programming language built on MLIR (Multi-Level Intermediate Representation) and JIT compilation. It enables transparent optimizations with respect to the underlying hardware (e.g., CPUs, GPUs), while allowing users to express their logic using Python-like user-friendly syntax. Mojo has been shown to offer great performance in tensor operations; however, its performance has not been tested for relational operations (e.g., filtering, join, and group-by), which are common in data science workflows. To date, no dataframe implementation exists in the Mojo ecosystem. In this work, we introduce the first Mojo-native dataframe library, called MojoFrame, that supports core relational operations and user-defined functions (UDFs). MojoFrame is built on top of Mojo’s tensor to achieve fast operations on numeric columns, while utilizing a cardinality-aware approach to effectively integrate non-numeric columns for flexible data representation. To achieve high efficiency, MojoFrame takes significantly different approaches than existing libraries. MojoFrame supports all operations for TPC-H queries, and achieves up to 2.97× speedup versus existing dataframe libraries in other programming languages. Nevertheless, there remain optimization opportunities for MojoFrame (and the Mojo language), particularly in data loading and dictionary operations."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["MojoFrame: dataframe library in Mojo language"]}]}],"canonical_facts":{"dc:contributor":["Park, Yongjoo"],"dc:creator":["Huang, Arthur"],"dc:date":["2025-07-18","2025-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. 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To date, no dataframe implementation exists in the Mojo ecosystem. In this work, we introduce the first Mojo-native dataframe library, called MojoFrame, that supports core relational operations and user-defined functions (UDFs). MojoFrame is built on top of Mojo’s tensor to achieve fast operations on numeric columns, while utilizing a cardinality-aware approach to effectively integrate non-numeric columns for flexible data representation. To achieve high efficiency, MojoFrame takes significantly different approaches than existing libraries. MojoFrame supports all operations for TPC-H queries, and achieves up to 2.97× speedup versus existing dataframe libraries in other programming languages. 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