Window aggregates are ubiquitous in stream processing. In Azure Stream Analytics (ASA), a stream processing service hosted by Microsoft's Azure cloud, we see many customer queries that contain aggregate functions (such as MIN and MAX) over multiple correlated windows (e.g., tumbling windows of length five minutes and ten minutes) defined on the same event stream. In this paper, we present a cost-based optimization framework for optimizing such queries by sharing computation among multiple windows. In particular, we introduce the notion of factor windows, which are auxiliary windows that are not in the input query but may nevertheless help reduce the overall computation cost, and our cost-based optimizer can produce rewritten query plans that have lower costs than the original query plan by utilizing factor windows. Since our optimization techniques are at the level of query (plan) rewriting, they can be implemented on any stream processing system that supports a declarative, SQL-like query language without changing the underlying query execution engine. We formalize the shared computation problem, present the optimization techniques in detail, and report evaluation results over both synthetic and real datasets. Our results show that, compared to the original query plans, the rewritten plans output by our cost-based optimizer can yield significantly higher (up to 16.8×) throughput.

We investigate the utility of a novel form of prior, namely the accuracies with which humans categorize briefly displayed images. Such information reflects the complexity of an image for the visual system and carries information about the features important for categorization. We incorporate the prior in an SVM framework, by biasing the decision boundary towards examples difficult for humans, and by learning a suitable kernel. We focus on the task indoors vs. outdoors using a variety of histogram and interest point features. We observe improvement in classification especially for the indoor class when gist features are used.

Many polyvariant program analyses have been studied in the 1990s, including k -CFA, polymorphic splitting, and the cartesian product algorithm. The idea of polyvariance is to analyze functions more than once and thereby obtain better precision for each call site. In this paper we present an equivalence theorem which relates a co-inductively-defined family of polyvariant flow analyses and a standard type system. The proof embodies a way of understanding polyvariant flow information in terms of union and intersection types, and, conversely, a way of understanding union and intersection types in terms of polyvariant flow information. We use the theorem as basis for a new flow-type system in the spirit of the λ CIL -calculus of Wells, Dimock, Muller and Turbak, in which types are annotated with flow information. A flow-type system is useful as an interface between a flow-analysis algorithm and a program optimizer. Derived systematically via our equivalence theorem, our flow-type system should be a good interface to the family of polyvariant analyses that we study.

Structural coverage criteria are often used as an indicator of the thoroughness of testing, but complete satisfaction of a criterion is seldom achieved.When a software product is released with less than 100% coverage, testers are explicitly or implicitly assuming that executions satisfying the remaining test obligations (the residue) are either infeasible or occur so rarely that they have negligible impact on quality.Violation of this assumption indicates shortcomings in the testing process.Monitoring in the deployed environment, even in the beta test phase, is typically limited to error and sanity checks.Monitoring the residue of test coverage in actual use can provide additional useful information, but it is unlikely to be accepted by users unless its performance impact is very small.Experience with a prototype tool for residual test coverage monitoring of Java programs suggests that, at least for statement coverage, the simple strategy of removing all probes except those corresponding to the residue of coverage testing reduces execution overhead to acceptably low levels.

Many polyvariant program analyses have been studied in the 199Os, including k-CFA, poly-k-CFA, and the Cartesian product algorithm. The idea of polyvariance is to analyze functions more than once and thereby obtain better precision for each call site. In this paper we present the first formal relationship between polyvariant analysis and standard notions of type. In the spirit of Nielson and Nielson, we study a parameterized flow analysis which can be instantiated to the analyses of Agesen, Schmidt, and as a simple case also 0-CFA. Extended with safety checks, the flow analysis accepts and rejects programs, much like a type checker. We prove that if a program can be safety-checked by a finitary instantiation of the flow analysis, then it can also be typed in a type system with intersection types, union types, subtyping, and recursive types, but no universal or existential quantifiers. This provides a framework for designing and understanding combinations of flow analyses and type systems.