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\begin{abstract}
The database community has developed a plethora of tools and techniques for data curation and exploration, from declarative languages, to specialized techniques for data repair, and more.  
Yet, there is currently  no consensus on how to best expose these powerful tools to an analyst in a simple, intuitive, and above all, flexible way.
Thus, analysts continue to rely on tools such as spreadsheets, imperative languages, and notebook style programming environments like Jupyter for data curation.
In this work, we explore the %intersection 
integration of spreadsheets, notebooks, and relational databases.
We focus on a key advantage that both spreadsheets and imperative notebook environments have over classical relational databases: ease of exception.  
By relying on set-at-a-time operations, relational databases sacrifice the ability to easily define singleton operations, exceptions to a normal data processing workflow that affect query processing for a fixed set of explicitly targeted records.  
In comparison, a spreadsheet user can easily change the formula for just one cell, while a notebook user can add an imperative operation to her notebook that alters an output ``view''.  
We believe that enabling such idiosyncratic manual transformations in a classical relational database is critical for curation, as curation operations that are easy to declare for individual values can often be extremely challenging to generalize.  
We explore the challenges of enabling singletons in relational databases, propose a hybrid spreadsheet/relational notebook environment for data curation, and present our vision of \sysname, a system that exposes data curation through such an interface.  
\end{abstract}


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