The Web offers a corpus of over 100 million tables [6], but the meaning of each table is rarely explicit from the table itself. Header rows exist in few cases and even when they do, the attribute names are typically useless. We describe a system that attempts to recover the semantics of tables by enriching the table with additional annotations. Our annotations facilitate operations such as searching for tables and finding related tables. To recover semantics of tables, we leverage a database of class labels and relationships automatically extracted from the Web. The database of classes and relationships has very wide coverage, but is also noisy. We attach a class label to a column if a sufficient number of the values in the column are identified with that label in the database of class labels, and analogously for binary relationships. We describe a formal model for reasoning about when we have seen sufficient evidence for a label, and show that it performs substantially better than a simple majority scheme. We describe a set of experiments that illustrate the utility of the recovered semantics for table search and show that it performs substantially better than previous approaches. In addition, we characterize what fraction of tables on the Web can be annotated using our approach.

Google Fusion Tables is a cloud-based service for data management and integration. Fusion Tables enables users to upload tabular data files (spreadsheets, CSV, KML), currently of up to 100MB. The system provides several ways of visualizing the data (e.g., charts, maps, and timelines) and the ability to filter and aggregate the data. It supports the integration of data from multiple sources by performing joins across tables that may belong to different users. Users can keep the data private, share it with a select set of collaborators, or make it public and thus crawlable by search engines. The discussion feature of Fusion Tables allows collaborators to conduct detailed discussions of the data at the level of tables and individual rows, columns, and cells. This paper describes the inner workings of Fusion Tables, including the storage of data in the system and the tight integration with the Google Maps infrastructure.

It has long been observed that database management systems focus on traditional business applications, and that few people use a database management system outside their workplace. Many have wondered what it will take to enable the use of data management technology by a broader class of users and for a much wider range of applications.

The rapid growth of Web communities has motivated many solutions for building community data portals. These solutions follow roughly two approaches. The first approach (e.g., Libra, Citeseer, Cimple) employs semi-automatic methods to extract and integrate data from a multitude of data sources. The second approach (e.g., Wikipedia, Intellipedia) deploys an initial portal in wiki format, then invites community members to revise and add material. In this paper we consider combining the above two approaches to building community portals. The new hybrid machine-human approach brings significant benefits. It can achieve broader and deeper coverage, provide more incentives for users to contribute, and keep the portal more up-to-date with less user effort. In a sense, it enables building "community wikipedias", backed by an underlying structured database that is continuously updated using automatic techniques. We outline our ideas for the new approach, describe its challenges and opportunities, and provide initial solutions. Finally, we describe a real-world implementation and preliminary experiments that demonstrate the utility of the new approach.

When integrating data from multiple sources, a key task that online communities often face is to match the schemas of the data sources. Today, such matching often incurs a huge workload that overwhelms the relatively small set of volunteer integrators. In such cases, community members may not even volunteer to be integrators, due to the high workload, and consequently no integration systems can be built. To address this problem, we propose to enlist the multitude of users in the community to help match the schemas, in a Web 2.0 fashion. We discuss the challenges of this approach and provide initial solutions. Finally, we describe an extensive set of experiments on both real-world and synthetic data that demonstrate the utility of the approach.

Current approaches to develop information extraction (IE) programs have largely focused on producing precise IE results. As such, they suffer from three major limitations. First, it is often difficult to execute partially specified IE programs and obtain meaningful results, thereby producing a long debug loop. Second, it often takes a long time before we can obtain the first meaningful result (by finishing and running a precise IE program), thereby rendering these approaches impractical for time-sensitive IE applications. Finally, by trying to write precise IE programs we may also waste a significant amount of effort, because an approximate result -- one that can be produced quickly -- may already be satisfactory in many IE settings.To address these limitations, we propose iFlex, an IE approach that relaxes the precise IE requirement to enable best-effort IE. In iFlex, a developer U uses a declarative language to quickly write an initial approximate IE program P with a possible-worlds semantics. Then iFlex evaluates P using an approximate query processor to quickly extract an approximate result. Next, U examines the result, and further refines P if necessary, to obtain increasingly more precise results. To refine P, U can enlist a next-effort assistant, which suggests refinements based on the data and the current version of P. Extensive experiments on real-world domains demonstrate the utility of the iFlex approach.

Entity matching (a.k.a. record linkage) plays a crucial role in integrating multiple data sources, and numerous matching solutions have been developed. However, the solutions have largely exploited only information available in the mentions and employed a single matching technique. We show how to exploit information about data sources to significantly improve matching accuracy. In particular, we observe that different sources often vary substantially in their level of semantic ambiguity, thus requiring different matching techniques. In addition, it is often beneficial to group and match mentions in related sources first, before considering other sources. These observations lead to a large space of matching strategies, analogous to the space of query evaluation plans considered by a relational optimizer. We propose viewing entity matching as a composition of basic steps into a "match execution plan". We analyze formal properties of the plan space, and show how to find a good match plan. To do so, we employ ideas from social network analysis to infer the ambiguity and related-ness of data sources. We conducted extensive experiments on several real-world data sets on the Web and in the domain of personal information management (PIM). The results show that our solution significantly outperforms current best matching methods.

The rapid growth of distributed data at enterprises and on the WWW has fueled significant interest in building data integration systems. Such a system provides users with a uniform query interface (called mediated schema) to a multitude of data sources, thus freeing them from manually querying each individual source. To address some problems in the MOBS (Mass Collaboration to Build Systems) project at the University of Illinois, we develop solutions that learn from the multitude of users in the integration environment to improve the accuracy of integration tools. The improved accuracy in turn can significantly reduce the workload of the system builder. In developing MOBS we address the following key challenges: (i) obtaining user participation, (ii) learning from user participation, and (iii) combining user answers.