3,797 research outputs found

    Extension to UML-B Notation and Toolset

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    The UML-B notation has been created as an attempt to combine the success and ease of use of UML, with the verification and rigorous development capabilities of formal methods. However, the notation currently only supports a basic diagram set. To address this we have, in this project, designed and implemented a set of extensions to the UML-B notation that provide a much fuller software engineering experience, critically making UML-B more appealing to industry partners. These extensions comprise five new diagram types, which are aimed at supplying a broader range of design capabilities, such as conceptual Use-Case design and future integration with the ProB animator tool

    Learning to Infer Graphics Programs from Hand-Drawn Images

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    We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of \LaTeX. The model combines techniques from deep learning and program synthesis. We learn a convolutional neural network that proposes plausible drawing primitives that explain an image. These drawing primitives are like a trace of the set of primitive commands issued by a graphics program. We learn a model that uses program synthesis techniques to recover a graphics program from that trace. These programs have constructs like variable bindings, iterative loops, or simple kinds of conditionals. With a graphics program in hand, we can correct errors made by the deep network, measure similarity between drawings by use of similar high-level geometric structures, and extrapolate drawings. Taken together these results are a step towards agents that induce useful, human-readable programs from perceptual input

    Performance comparison of point and spatial access methods

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    In the past few years a large number of multidimensional point access methods, also called multiattribute index structures, has been suggested, all of them claiming good performance. Since no performance comparison of these structures under arbitrary (strongly correlated nonuniform, short "ugly") data distributions and under various types of queries has been performed, database researchers and designers were hesitant to use any of these new point access methods. As shown in a recent paper, such point access methods are not only important in traditional database applications. In new applications such as CAD/CIM and geographic or environmental information systems, access methods for spatial objects are needed. As recently shown such access methods are based on point access methods in terms of functionality and performance. Our performance comparison naturally consists of two parts. In part I we w i l l compare multidimensional point access methods, whereas in part I I spatial access methods for rectangles will be compared. In part I we present a survey and classification of existing point access methods. Then we carefully select the following four methods for implementation and performance comparison under seven different data files (distributions) and various types of queries: the 2-level grid file, the BANG file, the hB-tree and a new scheme, called the BUDDY hash tree. We were surprised to see one method to be the clear winner which was the BUDDY hash tree. It exhibits an at least 20 % better average performance than its competitors and is robust under ugly data and queries. In part I I we compare spatial access methods for rectangles. After presenting a survey and classification of existing spatial access methods we carefully selected the following four methods for implementation and performance comparison under six different data files (distributions) and various types of queries: the R-tree, the BANG file, PLOP hashing and the BUDDY hash tree. The result presented two winners: the BANG file and the BUDDY hash tree. This comparison is a first step towards a standardized testbed or benchmark. We offer our data and query files to each designer of a new point or spatial access method such that he can run his implementation in our testbed

    Proceedings for the ICASE Workshop on Heterogeneous Boundary Conditions

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    Domain Decomposition is a complex problem with many interesting aspects. The choice of decomposition can be made based on many different criteria, and the choice of interface of internal boundary conditions are numerous. The various regions under study may have different dynamical balances, indicating that different physical processes are dominating the flow in these regions. This conference was called in recognition of the need to more clearly define the nature of these complex problems. This proceedings is a collection of the presentations and the discussion groups

    A query processing system for very large spatial databases using a new map algebra

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    Dans cette thĂšse nous introduisons une approche de traitement de requĂȘtes pour des bases de donnĂ©e spatiales. Nous expliquons aussi les concepts principaux que nous avons dĂ©fini et dĂ©veloppĂ©: une algĂšbre spatiale et une approche Ă  base de graphe utilisĂ©e dans l'optimisateur. L'algĂšbre spatiale est dĂ©fini pour exprimer les requĂȘtes et les rĂšgles de transformation pendant les diffĂ©rentes Ă©tapes de l'optimisation de requĂȘtes. Nous avons essayĂ© de dĂ©finir l'algĂšbre la plus complĂšte que possible pour couvrir une grande variĂ©tĂ© d'application. L'opĂ©rateur algĂ©brique reçoit et produit seulement des carte. Les fonctions reçoivent des cartes et produisent des scalaires ou des objets. L'optimisateur reçoit la requĂȘte en expression algĂ©brique et produit un QEP (Query Evaluation Plan) efficace dans deux Ă©tapes: gĂ©nĂ©ration de QEG (Query Evaluation Graph) et gĂ©nĂ©ration de QEP. Dans premiĂšre Ă©tape un graphe (QEG) Ă©quivalent de l'expression algĂ©brique est produit. Les rĂšgles de transformation sont utilisĂ©es pour transformer le graphe a un Ă©quivalent plus efficace. Dans deuxiĂšme Ă©tape un QEP est produit de QEG passĂ© de l'Ă©tape prĂ©cĂ©dente. Le QEP est un ensemble des opĂ©rations primitives consĂ©cutives qui produit les rĂ©sultats finals (la rĂ©ponse finale de la requĂȘte soumise au base de donnĂ©e). Nous avons implĂ©mentĂ© l'optimisateur, un gĂ©nĂ©rateur de requĂȘte spatiale alĂ©atoire, et une base de donnĂ©e simulĂ©e. La base de donnĂ©e spatiale simulĂ©e est un ensemble de fonctions pour simuler des opĂ©rations spatiales primitives. Les requĂȘtes alĂ©atoires sont soumis Ă  l'optimisateur. Les QEPs gĂ©nĂ©rĂ©es sont soumis au simulateur de base de donnĂ©es spatiale. Les rĂ©sultats expĂ©rimentaux sont utilisĂ©s pour discuter les performances et les caractĂ©ristiques de l'optimisateur.Abstract: In this thesis we introduce a query processing approach for spatial databases and explain the main concepts we defined and developed: a spatial algebra and a graph based approach used in the optimizer. The spatial algebra was defined to express queries and transformation rules during different steps of the query optimization. To cover a vast variety of potential applications, we tried to define the algebra as complete as possible. The algebra looks at the spatial data as maps of spatial objects. The algebraic operators act on the maps and result in new maps. Aggregate functions can act on maps and objects and produce objects or basic values (characters, numbers, etc.). The optimizer receives the query in algebraic expression and produces one efficient QEP (Query Evaluation Plan) through two main consecutive blocks: QEG (Query Evaluation Graph) generation and QEP generation. In QEG generation we construct a graph equivalent of the algebraic expression and then apply graph transformation rules to produce one efficient QEG. In QEP generation we receive the efficient QEG and do predicate ordering and approximation and then generate the efficient QEP. The QEP is a set of consecutive phases that must be executed in the specified order. Each phase consist of one or more primitive operations. All primitive operations that are in the same phase can be executed in parallel. We implemented the optimizer, a randomly spatial query generator and a simulated spatial database. The query generator produces random queries for the purpose of testing the optimizer. The simulated spatial database is a set of functions to simulate primitive spatial operations. They return the cost of the corresponding primitive operation according to input parameters. We put randomly generated queries to the optimizer, got the generated QEPs and put them to the spatial database simulator. We used the experimental results to discuss on the optimizer characteristics and performance. The optimizer was designed for databases with a very large number of spatial objects nevertheless most of the concepts we used can be applied to all spatial information systems."--RĂ©sumĂ© abrĂ©gĂ© par UMI

    RLAP version 1.0 a chip assembly tool

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