Titlebook: Database and Expert Systems Applications; 35th International C Christine Strauss,Toshiyuki Amagasa,Ismail Khalil Conference proceedings 202

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Die Matrix-Steifigkeitsmethode,s (e.g., .). While it scales well for large datasets, characterizing the impact of the small-world construction strategy on the search quality is still an open issue. This paper investigates how result diversification can shed light on that question by adding a parameterless strategy to HNSW that ex

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Das Konzept der Finite-Element-Methode,based text-to-SQL tools, that is, tools that translate Natural Language (NL) sentences into SQL queries using a Large Language Model (LLM). Indeed, their accuracy on RW-RDBs is considerably less than that reported for well-known synthetic benchmarks. This paper then introduces a technique to improve

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https://doi.org/10.1007/978-3-322-92856-6 optimization. However, the task of feature selection and encoding for machine learning in database tasks presents significant challenges. Recently, some representation methods have been proposed that utilize physical plan or SQL query as feature. However, these methods have two limitations. Firstly

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Das Konzept der Finite-Element-Methode,te query results and optimize query execution plans and other tasks. In order to have quick access to the data, the common practice is to create an index, which is often implemented by using B+Trees. Existing state-of-the-art algorithms for random sampling over B+Trees result in a significant perfor

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Ausblick auf Optimierungsstrategien,, it’s crucial to filter out unnecessary tables and columns, focusing the language model on relevant ones. Previous methods have attempted to sort tables and columns based on relevance or directly identify necessary elements, but these approaches suffer from long training times, high costs with GPT-

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https://doi.org/10.1007/978-3-642-60909-1ized the flexibility of serverless computing. The widely used Bulk Synchronous Parallel (BSP) mode has significant resource waste, the parameter server nodes suffer bottleneck pressure from both networking and performance aspects. This paper presents Chorus, a machine learning framework on serverles