14 citations · 40 across the 13 of their papers we have counts for
13 papers
Mind the Data Gap: Bridging LLMs to Enterprise Data Integration
Moe Kayali, Fabian Wenz, Nesime Tatbul +1
Leading large language models (LLMs) are trained on public data. However, most of the world's data is dark data that is not publicly accessible, mainly in the form of private organ…
BEAVER: An Enterprise Benchmark for Text-to-SQL
Peter Baile Chen, Devin Yang, Weiyue Li +6
Existing text-to-SQL benchmarks have largely been constructed from public databases with well-structured schemas and simplistic question-SQL pairs. While large language models (LLM…
Making LLMs Work for Enterprise Data Tasks
Çağatay Demiralp, Fabian Wenz, Peter Baile Chen +3
Large language models (LLMs) know little about enterprise database tables in the private data ecosystem, which substantially differ from web text in structure and content. As LLMs'…
AdaTyper: Adaptive Semantic Column Type Detection
Madelon Hulsebos, Paul Groth, Çağatay Demiralp
Understanding the semantics of relational tables is instrumental for automation in data exploration and preparation systems. A key source for understanding a table is the semantics…
Data Extraction via Semantic Regular Expression Synthesis
Qiaochu Chen, Arko Banerjee, Çağatay Demiralp +2
Many data extraction tasks of practical relevance require not only syntactic pattern matching but also semantic reasoning about the content of the underlying text. While regular ex…
WarpGate: A Semantic Join Discovery System for Cloud Data Warehouses
Tianji Cong, James Gale, Jason Frantz +2
Data discovery is a major challenge in enterprise data analysis: users often struggle to find data relevant to their analysis goals or even to navigate through data across data sou…
VegaProf: Profiling Vega Visualizations
Junran Yang, Alex Bäuerle, Dominik Moritz +1
Domain-specific languages (DSLs) for visualization aim to facilitate visualization creation by providing abstractions that offload implementation and execution details from users t…
Making Table Understanding Work in Practice
Madelon Hulsebos, Sneha Gathani, James Gale +3
Understanding the semantics of tables at scale is crucial for tasks like data integration, preparation, and search. Table understanding methods aim at detecting a table's topic, se…
Sigma Worksheet: Interactive Construction of OLAP Queries
James Gale, Max Seiden, Gretchen Atwood +3
The new generation of cloud data warehouses (CDWs) brings large amounts of data and compute power closer to users in enterprises. The ability to directly access the warehouse data,…
Data Programming by Demonstration: A Framework for Interactively Learning Labeling Functions
Sara Evensen, Chang Ge, Dongjin Choi +1
Data programming is a programmatic weak supervision approach to efficiently curate large-scale labeled training data. Writing data programs (labeling functions) requires, however,…
Leam: An Interactive System for In-situ Visual Text Analysis
Sajjadur Rahman, Peter Griggs, Çağatay Demiralp
With the increase in scale and availability of digital text generated on the web, enterprises such as online retailers and aggregators often use text analytics to mine and analyze…
Sato: Contextual Semantic Type Detection in Tables
Dan Zhang, Yoshihiko Suhara, Jinfeng Li +3
Detecting the semantic types of data columns in relational tables is important for various data preparation and information retrieval tasks such as data cleaning, schema matching,…
Sherlock: A Deep Learning Approach to Semantic Data Type Detection
Madelon Hulsebos, Kevin Hu, Michiel Bakker +5
Correctly detecting the semantic type of data columns is crucial for data science tasks such as automated data cleaning, schema matching, and data discovery. Existing data preparat…