Wall Street's trading engines are complex database applications written for time series databases like kdb+ that uses the query language Q to perform real-time analysis. Extending the models to include other data sources, e.g., historic data, is critical for backtesting and compliance. However, Q applications cannot run directly on SQL databases. Therefore, financial institutions face the dilemma of either maintaining two separate application stacks, one written in Q and the other in SQL, which means increased IT cost and increased risk, or migrating all Q applications to SQL, which results in losing the inherent competitive advantage on Q real-time processing. Neither solution is desirable as both alternatives are costly, disruptive, and suboptimal. In this paper we present Hyper-Q, a data virtualization plat- form that overcomes the chasm. Hyper-Q enables Q applications to run natively on PostgreSQL-compatible databases by translating queries and results on the fly. We outline the basic concepts, detail specific difficulties, and demonstrate the viability of the approach with a case study.

The performance of analytical query processing in data management systems depends primarily on the capabilities of the system's query optimizer. Increased data volumes and heightened interest in processing complex analytical queries have prompted Pivotal to build a new query optimizer.

There is currently a tug-of-war going on surrounding data releases. On one side, there are many strong reasons pulling to release data to other parties: business factors, freedom of information rules, and scientific sharing agreements. On the other side, concerns about individual privacy pull back, and seek to limit releases. Privacy technologies such as differential privacy have been proposed to resolve this deadlock, and there has been much study of how to perform private data release of data in various forms. The focus of such works has been largely on the data owner: what process should they apply to ensure that the released data preserves privacy whilst still capturing the input data distribution accurately. Almost no attention has been paid to the needs of the data user, who wants to make use of the released data within their existing suite of tools and data. The difficulty of making use of data releases is a major stumbling block for the widespread adoption of data privacy technologies.

Discovering frequent graph patterns in a graph database offers valuable information in a variety of applications. However, if the graph dataset contains sensitive data of individuals such as mobile phone-call graphs and web-click graphs, releasing discovered frequent patterns may present a threat to the privacy of individuals. Differential privacy has recently emerged as the de facto standard for private data analysis due to its provable privacy guarantee. In this paper we propose the first differentially private algorithm for mining frequent graph patterns.

Differential privacy has recently emerged as the de facto standard for private data release. This makes it possible to provide strong theoretical guarantees on the privacy and utility of released data. While it is well-understood how to release data based on counts and simple functions under this guarantee, it remains to provide general purpose techniques to release data that is useful for a variety of queries. In this paper, we focus on spatial data such as locations and more generally any multi-dimensional data that can be indexed by a tree structure. Directly applying existing differential privacy methods to this type of data simply generates noise. We propose instead the class of "private spatial decompositions'': these adapt standard spatial indexing methods such as quad trees and kd-trees to provide a private description of the data distribution. Equipping such structures with differential privacy requires several steps to ensure that they provide meaningful privacy guarantees. Various basic steps, such as choosing splitting points and describing the distribution of points within a region, must be done privately, and the guarantees of the different building blocks composed to provide an overall guarantee. Consequently, we expose the design space for private spatial decompositions, and analyze some key examples. A major contribution of our work is to provide new techniques for parameter setting and post-processing the output to improve the accuracy of query answers. Our experimental study demonstrates that it is possible to build such decompositions efficiently, and use them to answer a variety of queries privately with high accuracy.

Uncertainties in data can arise for a number of reasons: when data is incomplete, contains conflicting information or has been deliberately perturbed or coarsened to remove sensitive details. An important case which arises in many real applications is when the data describes a set of possibilities, but with cardinality constraints. These constraints represent correlations between tuples encoding, e.g. that at most two possible records are correct, or that there is an (unknown) one-to-one mapping between a set of tuples and attribute values. Although there has been much effort to handle uncertain data, current systems are not equipped to handle such correlations, beyond simple mutual exclusion and co-existence constraints. Vitally, they have little support for efficiently handling aggregate queries on such data. In this paper, we aim to address some of these deficiencies, by introducing LICM (Linear Integer Constraint Model), which can succinctly represent many types of tuple correlations, particularly a class of cardinality constraints. We motivate and explain the model with examples from data cleaning and masking sensitive data, to show that it enables modeling and querying such data, which was not previously possible. We develop an efficient strategy to answer conjunctive and aggregate queries on possibilistic data by describing how to implement relational operators over data in the model. LICM compactly integrates the encoding of correlations, query answering and lineage recording. In combination with off-the-shelf linear integer programming solvers, our approach provides exact bounds for aggregate queries. Our prototype implementation demonstrates that query answering with LICM can be effective and scalable.