812 resultados para distributed organization


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The Neolithic chambered tombs of Bohuslan on the west coast of Sweden were built out of locally occurring raw materials. These exhibit a wide variety of colours, textures and mineral inclusions, and all were used to contrive a series of striking visual effects. Certain of these would have been apparent to the casual observer but others would only have been apparent to someone inside the passage or the burial chamber. There is no evidence that the materials were organized according to a single scheme. Rather, they permitted a series of improvisations, so that no two monuments were exactly alike. The effects that they created are compared with those found in megalithic art where the design elements were painted or carved, but in Bohuslan all the designs were created using the natural properties of the rock.

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Phosphorite-filled crustacean burrows associated with a Campanian-age omission surface in the north-western Negev are described. The phosphatic burrow casts weather out displaying scratches (bioglyphs) and two types of local swellings (chambers), which are flattened normal to the course of the burrow. The more abundant chamber type is a flattened spheroid (diameter 45-50 mm) or a flattened, highly prolate ellipsoid of larger dimensions, with bioglyphs. The other type is a flattened spheroid (diameter 45 mm), gently rounded on the upper side and flat on the base. Rings of elevations on the cast (representing moats) form interconnected circlets, each capped by about eight rounded hemispherical tubercles (4 x 4 mm) (pits on original), the whole forming a discrete network. The first type of chamber may have hosted the young (nursery chamber) and/or stored food. The second type of cast replicates a chamber with a pitted floor, which may have formed a brood chamber for 60-70 spherical eggs, each about 3 mm in diameter. Brood chambers in crustacean burrow systems were previously suspected, but only at burrow terminations. The interpreted K-type breeding strategy, brood care and associated functions require a high degree of social organization, none of which has been observed in extant crustaceans, but all occur within social insects.

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In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

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We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.