The last few years have witnessed a spate of data protection regulations in conjunction with an ever-growing appetite for data usage in large businesses, which presents significant challenges for businesses to maintain compliance. To address this conflict, we present Data Guard - a fine-grained, purpose-based access control system for large data warehouses. Data Guard enables authoring policies based on semantic descriptions of data and purpose of data access. Data Guard then translates these policies into SQL views that mask data from the underlying warehouse tables. At access time, Data Guard ensures compliance by transparently routing each table access to the appropriate data-masking view based on the purpose of the access, thus minimizing the effort of adopting Data Guard in existing applications. Our enforcement solution allows masking data at much finer granularities than what traditional solutions allow. In addition to row and column level data masking, Data Guard can mask data at the sub-cell level for columns with non-atomic data types such as structs, arrays, and maps. This fine-grained masking allows Data Guard to preserve data utility for consumers while ensuring compliance. We implemented a number of performance optimizations to minimize the overhead of data masking operations. We perform numerous experiments to identify the key factors that influence the data masking overhead and demonstrate the efficiency of our implementation. Data Guard is deployed inside LinkedIn's production data warehouses and ensures compliance of more than 20,000 table accesses each day across different data processing engines.

This paper presents the first Datalog evaluation engine for executing graph analytics over BSP-style graph processing engines. Building on recent advances in Datalog that support efficient evaluation of aggregates functions, it is now easy for data scientists to author many important graph algorithms succinctly. Without the burden of low-level parallelization and optimization, data scientists can avoid programming to the quirks of the latest high-performance distributed computing framework. Where prior approaches build bespoke evaluation engines or modify generalized dataflow processing engines to achieve performance, this work shows how to efficiently evaluate Datalog directly on BSP-style graph processing engines such as Giraph. Datalography incorporates both traditional Datalog optimizations, such as semi-naive evaluation, and new evaluation algorithms and optimization techniques for efficient distributed evaluation of Datalog queries on graph processing engines. In particular we develop evaluation techniques that take advantage of super vertices, eager aggregation, and asynchronous execution to optimize graph processing on Pregel-like systems. We implement our algorithms on top of Apache Giraph and our results indicate that Datalography competes with native, tuned implementations, with some analytics running up to 9 times faster.

There is a growing need for methods that can represent and query uncertain graphs. These uncertain graphs are often the result of an information extraction and integration system that attempts to extract an entity graph or a knowledge graph from multiple unstructured sources [25], [7]. Such an integration typically leads to identity uncertainty, as different data sources may use different references to the same underlying real-world entities. Integration usually also introduces additional uncertainty on node attributes and edge existence. In this paper, we propose the notion of a probabilistic entity graph (PEG), a formal model that uniformly and systematically addresses these three types of uncertainty. A PEG is a probabilistic graph model that defines a distribution over possible graphs at the entity level. We introduce a general framework for constructing a PEG given uncertain data at the reference level and develop efficient algorithms to answer subgraph pattern matching queries in this setting. Our algorithms are based on two novel ideas: context-aware path indexing and reduction by join-candidates, which drastically reduce the query search space. A comprehensive experimental evaluation shows that our approach outperforms baseline implementations by orders of magnitude.

There is a growing interest in methods for analyzing data describing networks of all types, including biological, physical, social, and scientific collaboration networks. Typically the data describing these networks is observational, and thus noisy and incomplete; it is often at the wrong level of fidelity and abstraction for meaningful data analysis. This demonstration presents GrDB, a system that enables data analysts to write declarative programs to specify and combine different network data cleaning tasks, visualize the output, and engage in the process of decision review and correction if necessary. The declarative interface of GrDB makes it very easy to quickly write analysis tasks and execute them over data, while the visual component facilitates debugging the program and performing fine grained corrections.

There is increasing interest in analyzing networks of all types including social, biological, sensor, computer, and transportation networks. Broadly speaking, we may be interested in global network-wide analysis (e.g., centrality analysis, community detection) where the properties of the entire network are of interest, or local ego-centric analysis where the focus is on studying the properties of nodes (egos) by analyzing their neighborhood sub graphs. In this paper we propose and study ego-centric pattern census queries, a new type of graph analysis query, where a given structural pattern is searched for in every node's neighborhood and the counts are reported or used in further analysis. This kind of analysis is useful in many domains in social network analysis including opinion leader identification, node classification, link prediction, and role identification. We propose an SQL-based declarative language to support this class of queries, and develop a series of efficient query evaluation algorithms for it. We evaluate our algorithms on a variety of synthetically generated graphs. We also show an application of our language in a real-world scenario for predicting future collaborations from DBLP data.