Graphs are a natural way to model real-world entities and relationships between them, ranging from social networks to data lineage graphs and biological datasets. Queries over these large graphs often involve expensive sub-graph traversals and complex analytical computations. These real-world graphs are often substantially more structured than a generic vertex-and-edge model would suggest, but this insight has remained mostly unexplored by existing graph engines for graph query optimization purposes. In this work, we leverage structural properties of graphs and queries to automatically derive materialized graph views that can dramatically speed up query evaluation. We present Kaskade, the first graph query optimization framework to exploit materialized graph views for query optimization purposes. Kaskade employs a novel constraint-based view enumeration technique that mines constraints from query workloads and graph schemas, and injects them during view enumeration to significantly reduce the search space of views to be considered. Moreover, it introduces a graph view size estimator to pick the most beneficial views to materialize given a query set and to select the best query evaluation plan given a set of materialized views. We evaluate its performance over real-world graphs, including the provenance graph that we maintain at Microsoft to enable auditing, service analytics, and advanced system optimizations. Our results show that Kaskade substantially reduces the effective graph size and yields significant performance speedups (up to 50X), in some cases making otherwise intractable queries possible.

Many database columns contain string or numerical data that conforms to a pattern, such as phone numbers, dates, addresses, product identifiers, and employee ids. These patterns are useful in a number of data processing applications, including understanding what a specific field represents when field names are ambiguous, identifying outlier values, and finding similar fields across data sets.One way to express such patterns would be to learn regular expressions for each field in the database. Unfortunately, existing techniques on regular expression learning are slow, taking hundreds of seconds for columns of just a few thousand values. In contrast, we develop XSYSTEM, an efficient method to learn patterns over database columns in significantly less time.We show that these patterns can not only be built quickly, but are expressive enough to capture a number of key applications, including detecting outliers, measuring column similarity, and assigning semantic labels to columns (based on a library of regular expressions). We evaluate these applications with datasets that range from chemical databases (based on a collaboration with a pharmaceutical company), our university data warehouse, and open data from MassData.gov.

Model diagnosis is the process of analyzing machine learning (ML) model performance to identify where the model works well and where it doesn't. It is a key part of the modeling process and helps ML developers iteratively improve model accuracy. Often, model diagnosis is performed by analyzing different datasets or intermediates associated with the model such as the input data and hidden representations learned by the model (e.g., [4, 24, 39,]). The bottleneck in fast model diagnosis is the creation and storage of model intermediates. Storing these intermediates requires tens to hundreds of GB of storage whereas re-running the model for each diagnostic query slows down model diagnosis. To address this bottleneck, we propose a system called MISTIQUE that can work with traditional ML pipelines as well as deep neural networks to efficiently capture, store, and query model intermediates for diagnosis. For each diagnostic query, MISTIQUE intelligently chooses whether to re-run the model or read a previously stored intermediate. For intermediates that are stored in MISTIQUE, we propose a range of optimizations to reduce storage footprint including quantization, summarization, and data de-duplication. We evaluate our techniques on a range of real-world ML models in scikit-learn and Tensorflow. We demonstrate that our optimizations reduce storage by up to 110X for traditional ML pipelines and up to 6X for deep neural networks. Furthermore, by using MISTIQUE, we can speed up diagnostic queries on traditional ML pipelines by up to 390X and 210X on deep neural networks.

The use of Java for distributed systems and highavailable applications demands the validation of their fault tolerance mechanisms to avoid unexpected behavior during execution. We present an extension of FIONA (Fault Injector Oriented to Network Applications), a fault injection environment to experimentally validate the dependability of distributed Java applications. The main features of this extension are: its distributed architecture, which allows centralized configuration of multiple faults scenarios, and the support for a wider fault model associated with distributed systems, which includes network partitioning. For monitoring, FIONA supports the collection of log information and includes a helper application to integrate this information in a global log for post-mortem dependability analysis. FIONA is simple to operate, and we expect it will facilitate the conduction of validation experiments by application developers and testers.