Spreadsheets are one of the most popular data analysis tools, wherein users can express computation as formulae alongside data. The ensuing dependencies are tracked as formula graphs. Efficiently querying and maintaining these formula graphs is critical for interactivity across multiple settings. Unfortunately, formula graphs are often large and complex such that querying and maintaining them is time-consuming, reducing interactivity. We propose TACO, a framework for efficiently compressing formula graphs, thereby reducing the time for querying and maintenance. The efficiency of TACO stems from a key spreadsheet property: tabular locality, which means that cells close to each other are likely to have similar formula structures. We leverage four such tabular locality-based patterns, and develop algorithms for compressing formula graphs using these patterns, directly querying the compressed graph without decompression, and incrementally maintaining the graph during updates. We integrate TACO into an open-source spreadsheet system and show that TACO can significantly reduce formula graph sizes. For querying formula graphs, the speedups of TACO over a baseline implemented in our framework and a commercial spreadsheet system are up to 34,972 × and 632 ×, respectively.

Spreadsheet tools are ubiquitous for interactive adhoc data management and analysis. With increasing dataset sizes, spreadsheet tools fall short-they freeze during heavy computation within the sheet (interactivity); they are hard to navigate when datasets go beyond a certain size (navigability); they only support cell-at-a-time computation, severely limiting analysis capabilities (expressiveness). We have been developing DATASPREAD to holistically unify databases and spreadsheets to leverage the benefits of both, with a spreadsheet-like front-end and a database-like backend. We demonstrate three key features of DATASPREAD to address the aforementioned spreadsheet scalability challenges in interactivity, navigability, and expressiveness1. Our demonstration will let attendees perform typical analysis tasks on Microsoft Excel and DATASPREAD side-by-side, providing a clear understanding of the improvements offered by DATASPREAD over traditional spreadsheet tools.

Spreadsheet systems enable users to store and analyze data in an intuitive and flexible interface. Yet the scale of data being analyzed often leads to spreadsheets hanging and freezing on small changes. We propose a new asynchronous formula computation framework: instead of freezing the interface we return control to users quickly to ensure interactivity, while computing the formulae in the background. To ensure consistency, we indicate formulae being computed in the background via visual cues on the spreadsheet. Our asynchronous computation framework introduces two novel challenges: (a) How do we identify dependencies for a given change in a bounded time? (b) How do we schedule computation to maximize the number of spreadsheet cells available to the user over time? We bound the dependency identification time by compressing the formula dependency graph lossily, a problem we show to be NP-Hard. A compressed dependency table enables us to quickly identify the spreadsheet cells that need recomputation and indicate them as such to users. Finding an optimal computation schedule to maximize cell availability is also NP-Hard, and even merely obtaining a schedule can be expensive-we propose an on-the-fly scheduling technique to address this. We have incorporated asynchronous computation in DataSpread, a scalable spreadsheet system targeted at operating on arbitrarily large datasets on a spreadsheet frontend.

Interest in collaborative dataset versioning has emerged due to complex, ad-hoc, and collaborative nature of data science, and the need to record and reason about data at various stages of pre-processing, cleaning, and analysis. To support effective collaborative dataset versioning, one critical operation is differentiation : to succinctly describe what has changed from one dataset to the next. Differentiation, or diffing, allows users to understand changes between two versions, to better understand the evolution process, or to support effective merging or conflict detection across versions. We demonstrate DataDiff, a practical and concise data-diff tool that provides human-interpretable explanations of changes between datasets without reliance on the operations that led to the changes.