Spreadsheets are a critical and widely-used data management tool. Converting spreadsheet data into relational tables would bring benefits to a number of fields, including public policy, public health, and economics. Research to date has focused on designing domain-specific languages to describe transformation processes or automatically converting a specific type of spreadsheets. To handle a larger variety of spreadsheets, we have to identify various spreadsheet properties, which correspond to a series of transformation programs that contribute towards a general framework that converts spreadsheets to relational tables.

A dictionary --- a set of instances belonging to the same conceptual class --- is central to information extraction and is a useful primitive for many applications, including query log analysis and document categorization. Considerable work has focused on generating accurate dictionaries given a few example seeds, but methods to date cannot obtain long-tail (rare) items with high accuracy and recall.

A key challenge of entity set expansion is that multifaceted input seeds can lead to significant incoherence in the result set. In this paper, we present a novel solution to handling multifaceted seeds by combining existing user-generated ontologies with a novel word-similarity metric based on skip-grams. By blending the two resources we are able to produce sparse word ego-networks that are centered on the seed terms and are able to capture semantic equivalence among words. We demonstrate that the resulting networks possess internally-coherent clusters, which can be exploited to provide non-overlapping expansions, in order to reflect different semantic classes of the seeds. Empirical evaluation against state-of-the-art baselines shows that our solution, EgoSet, is able to not only capture multiple facets in the input query, but also generate expansions for each facet with higher precision.

Spreadsheets contain valuable data on many topics. However, spreadsheets are difficult to integrate with other data sources. Converting spreadsheet data to the relational model would allow data analysts to use relational integration tools. We propose a two-phase semiautomatic system that extracts accurate relational metadata while minimizing user effort. Based on an undirected graphical model, our system enables downstream spreadsheet integration applications. First, the automatic extractor uses hints from spreadsheets' graphical style and recovered metadata to extract the spreadsheet data as accurately as possible. Second, the interactive repair identifies similar regions in distinct spreadsheets scattered across large spreadsheet corpora, allowing a user's single manual repair to be amortized over many possible extraction errors. Our experiments show that a human can obtain the accurate extraction with just 31% of the manual operations required by a standard classification based technique on two real-world datasets.

Spreadsheets have become a critical data management tool, but they lack explicit relational metadata, making it difficult to join or integrate data across multiple spreadsheets. Because spreadsheet data are widely available on a huge range of topics, a tool that allows easy spreadsheet integration would be hugely beneficial for a variety of users. We demonstrate that Senbazuru, a prototype spreadsheet database management system (SSDBMS), is able to extract relational information from spreadsheets. By doing so, it opens up opportunities for integration among spreadsheets and with other relational sources. Senbazuru allows users to search for relevant spreadsheets in a large corpus, probabilistically constructs a relational version of the data, and offers several relational operations over the resulting extracted data (including joins to other spreadsheet data). Our demonstration is available on two clients: a JavaScript-rich Web site and a touch interface on the iPad. During the demo, Senbazuru will allow VLDB participants to search spreadsheets, extract relational data from them, and apply relational operators such as select and join.