An appropriate set of indexes can result in orders of magnitude better query performance. Index management is a challenging task even for expert human administrators. Fully automating this process is of significant value. We describe the challenges, architecture, design choices, implementation, and learnings from building an industrial-strength auto-indexing service for Microsoft Azure SQL Database, a relational database service. Our service has been generally available for more than two years, generating index recommendations for every database in Azure SQL Database, automatically implementing them for a large fraction, and significantly improving performance of hundreds of thousands of databases. We also share our experience from experimentation at scale with production databases which gives us confidence in our index recommendation quality for complex real applications.

This paper considers scalable and unobtrusive activity recognition using on-body sensing for context awareness in wearable computing. Common methods for activity recognition rely on supervised learning requiring substantial amounts of labeled training data. Obtaining accurate and detailed annotations of activities is challenging, preventing the applicability of these approaches in real-world settings. This paper proposes new annotation strategies that substantially reduce the required amount of annotation. We explore two learning schemes for activity recognition that effectively leverage such sparsely labeled data together with more easily obtainable unlabeled data. Experimental results on two public data sets indicate that both approaches obtain results close to fully supervised techniques. The proposed methods are robust to the presence of erroneous labels occurring in real-world annotation data.