This demonstration explores the novel and unconventional idea of implementing an analytical RDBMS in pure JavaScript so that it runs completely inside a browser with no external dependencies. Our prototype, called Afterburner, generates compiled query plans that exploit two JavaScript features: typed arrays and asm.js. On the TPC-H benchmark, we show that Afterburner achieves comparable performance to MonetDB running natively on the same machine. This is an interesting finding in that it shows how far JavaScript has come as an efficient execution platform. Beyond a mere technical curiosity, we demonstrate how our techniques can support interactive data exploration by automatically generating materialized views from a backend that is then shipped to the browser to facilitate subsequent interactions seamlessly and efficiently.

In this paper, we solve the following data summarization problem: given a multi-dimensional data set augmented with a binary attribute, how can we construct an interpretable and informative summary of the factors affecting the binary attribute in terms of the combinations of values of the dimension attributes? We refer to such summaries as explanation tables. We show the hardness of constructing optimally-informative explanation tables from data, and we propose effective and efficient heuristics. The proposed heuristics are based on sampling and include optimizations related to computing the information content of a summary from a sample of the data. Using real data sets, we demonstrate the advantages of explanation tables compared to related approaches that can be adapted to solve our problem, and we show significant performance benefits of our optimizations.

Automatic physical database design tools rely on "what-if" interfaces to the query optimizer to estimate the execution time of the training query workload under different candidate physical designs. The tools use these what-if interfaces to recommend physical designs that minimize the estimated execution time of the input training workload. In this paper, we argue that minimizing estimated execution time alone can lead to designs with inherent problems. In particular, if the optimizer makes an error in estimating the execution time of some workload queries, then the recommended physical design may actually harm the workload instead of benefiting it. In this sense, the physical design is risky. Moreover, if the production queries are slightly different from the training queries, the recommended physical design may not benefit them at all. In this sense, the physical design is not general. We define Risk and Generality as two new metrics to evaluate the quality of a proposed physical database design, and we show one way of extending the objective function being optimized by a generic physical design advisor to take these measures into account. We have implemented a physical design advisor in PostgreSQL, and we use it to experimentally demonstrate the usefulness of our approach. We show that our two new metrics result in physical designs that are more robust, which means that the user can implement them with a higher degree of confidence. This is particularly important as we move towards truly zero-administration database systems in which there is not the possibility for a DBA to vet the recommendations of the physical design tool before applying them.

The typical approach to data integration is to start by defining a common mediated schema, and then to map the data sources being integrated to this schema. In Internet-scale data integration tasks, where there may be hundreds or thousands of data sources providing data of relevance to a particular domain, a better approach is to allow the user to discover the mediated schema and the set of sources to use through an iterative exploration of the space of possible schemas and sources. In this paper, we present μBE, a data integration tool that helps in this iterative exploratory process by automatically choosing the data sources to include in a data integration system and defining a mediated schema on these sources. The data integration system desired by the user may depend on several subjective and objective criteria, and the user guides μBE towards finding this system by iteratively solving a series of constrained non-linear optimization problems, and modifying the parameters and constraints of the problem in the next iteration based on the solution found in the current iteration. Our formulation of the optimization problem is designed to make it easy for the user to provide such feedback. A simple, intuitive user interface helps the user in this process. We experimentally demonstrate that μBE is efficient and finds high-quality data integration solutions.

The typical approach to data integration is to start by defining a common mediated schema, and then to map the data sources being integrated to this schema. In Internet-scale data integration tasks, where there may be hundreds or thousands of data sources providing data of relevance to a particular domain, a better approach is to allow the user to discover the mediated schema and the set of sources to use through an iterative exploration of the space of possible schemas and sources. In this paper, we present μBE, a data integration tool that helps in this iterative exploratory process by automatically choosing the data sources to include in a data integration system and defining a mediated schema on these sources. The data integration system desired by the user may depend on several subjective and objective criteria, and the user guides μBE towards finding this system by iteratively solving a series of constrained non-linear optimization problems, and modifying the parameters and constraints of the problem in the next iteration based on the solution found in the current iteration. Our formulation of the optimization problem is designed to make it easy for the user to provide such feedback. A simple, intuitive user interface helps the user in this process. We experimentally demonstrate that μBE is efficient and finds high-quality data integration solutions.