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Today, data analysts largely rely on intuition to determine whether missing or withheld rows of a dataset significantly affect their analyses. We propose a framework that can produce automatic contingency analysis, i.e., the range of values an aggregate ...

Distance measures are core building blocks in time-series analysis and the subject of active research for decades. Unfortunately, the most detailed experimental study in this area is outdated (over a decade old) and, naturally, does not reflect recent ...

Many applications schedule queries before all data is ready. To return fast query results, database systems can eagerly process existing data and incrementally incorporate new data into prior intermediate results, which often relies on incremental view ...

Databases can be corrupted with various errors such as missing, incorrect, or inconsistent values. Increasingly, modern data analysis pipelines involve Machine Learning, and the effects of dirty data can be difficult to debug.Dirty data is often sparse, ...

Recent advances in differential privacy make it possible to guarantee user privacy while preserving the main characteristics of the data. However, most differential privacy mechanisms assume that the underlying dataset is clean. This paper explores the ...

R is a popular statistical programming language with a number of extensions that support data processing and machine learning tasks. However, interactive data analysis in R is usually limited as the R runtime is single threaded and can only process data ...

Spark SQL is a new module in Apache Spark that integrates relational processing with Spark's functional programming API. Built on our experience with Shark, Spark SQL lets Spark programmers leverage the benefits of relational processing (e.g. ...

"Next generation" data acquisition technologies are allowing scientists to collect exponentially more data at a lower cost. These trends are broadly impacting many scientific fields, including genomics, astronomy, and neuroscience. We can attack the ...

The rise of data-intensive "Web 2.0" Internet services has led to a range of popular new programming frameworks that collectively embody the latest incarnation of the vision of Object-Relational Mapping (ORM) systems, albeit at unprecedented scale. In ...

Modern query engines are increasingly being required to process enormous datasets in near real-time. While much can be done to speed up the data access, a promising technique is to reduce the need to access data through data skipping. By maintaining ...

In emerging Big Data scenarios, obtaining timely, high-quality answers to aggregate queries is difficult due to the challenges of processing and cleaning large, dirty data sets. To increase the speed of query processing, there has been a resurgence of ...

The Database Community has a unique perspective on the challenges and solutions of long-term management of data and the value of data as a resource. In current computer science curricula, however, these insights are typically locked up in the context of ...

Latency unpredictability in a database system can come from many factors, such as load spikes in the workload, inter-query interactions from consolidation, or communication costs in cloud computing or geo-replication. High variance and high latency ...

Developers of rapidly growing applications must be able to anticipate potential scalability problems before they cause performance issues in production environments. A new type of data independence, called scale independence, seeks to address this ...

In the cloud services industry, a key issue for cloud operators is to minimize operational costs. In this paper, we consider algorithms that elastically contract and expand a cluster of in-memory databases depending on tenants' behavior over time while ...

Shark is a new data analysis system that marries query processing with complex analytics on large clusters. It leverages a novel distributed memory abstraction to provide a unified engine that can run SQL queries and sophisticated analytics functions (...

The development of crowdsourced query processing systems has recently attracted a significant attention in the database community. A variety of crowdsourced queries have been investigated. In this paper, we focus on the crowdsourced join query which ...

A large body of recent work has proposed analytical and empirical techniques for quantifying the data consistency properties of distributed data stores. In this demonstration, we begin to explore the wide range of new database functionality they enable, ...

Shark is a research data analysis system built on a novel coarse-grained distributed shared-memory abstraction. Shark marries query processing with deep data analysis, providing a unified system for easy data manipulation using SQL and pushing ...

In the database community, work on information extraction (IE) has centered on two themes: how to effectively manage IE tasks, and how to manage the uncertainties that arise in the IE process in a scalable manner. Recent work has proposed a ...