Microblogging platforms such as Twitter provide low cost access to an immense reserve of authoritative professionals, opinion leaders and hobbyists for a wide range of topics. Yet, as microposts are short and incredibly diverse, many of these experts are hidden. In this paper, we present e#, a system to retrieve experts automatically for a given set of keywords. Our design targets exhaustivity: e# can detect previously undetectable experts. The core idea is to enhance a state-ofthe-art expert detection algorithm with a graph of expertise domains. Our system produces this graph from hundreds of Gigabytes of Web search query logs and behavioral data, processed in a distributed, parallel fashion. We provide a detailed description of our architecture, including an original SQL-based community detection algorithm. We then benchmark our system with 750 queries, using crowdsourcing. We observe that e# finds many more experts than a state-of-the-art baseline.

We live in the golden age of distributed computing. Public cloud platforms now offer virtually unlimited compute and storage resources on demand. At the same time, the Software-as-a-Service (SaaS) model brings enterprise-class systems to users who previously could not afford such systems due to their cost and complexity. Alas, traditional data warehousing systems are struggling to fit into this new environment. For one thing, they have been designed for fixed resources and are thus unable to leverage the cloud's elasticity. For another thing, their dependence on complex ETL pipelines and physical tuning is at odds with the flexibility and freshness requirements of the cloud's new types of semi-structured data and rapidly evolving workloads. We decided a fundamental redesign was in order. Our mission was to build an enterprise-ready data warehousing solution for the cloud. The result is the Snowflake Elastic Data Warehouse, or "Snowflake" for short. Snowflake is a multi-tenant, transactional, secure, highly scalable and elastic system with full SQL support and built-in extensions for semi-structured and schema-less data. The system is offered as a pay-as-you-go service in the Amazon cloud. Users upload their data to the cloud and can immediately manage and query it using familiar tools and interfaces. Implementation began in late 2012 and Snowflake has been generally available since June 2015. Today, Snowflake is used in production by a growing number of small and large organizations alike. The system runs several million queries per day over multiple petabytes of data.

Today's data integration systems must be flexible enough to support the typical iterative and incremental process of integration, and may need to scale to hundreds of data sources. In this work we present a novel data integration system that offers great flexibility and scalability. Our approach to data integration is unique in that it executes mapping rules at query runtime using annotations. On top, we have built the People People People application. It allows users to search for people, display information about people, and browse through a network of related people, where the data is integrated from local and remote data sources. The demo presents all features of our underlying data integration engine through a set of motivating scenarios.

Cloud storage solutions promise high scalability and low cost. Existing solutions, however, differ in the degree of consistency they provide. Our experience using such systems indicates that there is a non-trivial trade-off between cost, consistency and availability. High consistency implies high cost per transaction and, in some situations, reduced availability. Low consistency is cheaper but it might result in higher operational cost because of, e.g., overselling of products in a Web shop. In this paper, we present a new transaction paradigm, that not only allows designers to define the consistency guarantees on the data instead at the transaction level, but also allows to automatically switch consistency guarantees at runtime. We present a number of techniques that let the system dynamically adapt the consistency level by monitoring the data and/or gathering temporal statistics of the data. We demonstrate the feasibility and potential of the ideas through extensive experiments on a first prototype implemented on Amazon's S3 and running the TPC-W benchmark. Our experiments indicate that the adaptive strategies presented in the paper result in a significant reduction in response time and costs including the cost penalties of inconsistencies.