105results about How to "Easy to parallelize" patented technology

A method and system that utilizes OLAP and supporting data structures for making predictions about business locations. The method includes providing a spatial map and analyzing heterogeneous data having a spatial component to find utilizable data. Relationships are automatically extracted from the utilizable data by employing machine learning. The step of automatically extracting relationships includes generating a composite indicator, which correlates spatial data with unstructured data. The extracted relationships are presented on a spatial map to make a prediction about at least one business location. Preferably, the predictions are presented as a rank-ordered list on the spatial map and a heat map overlays the spatial map to indicate predictions about particular regions.

A system and method are disclosed for parallel compression and decompression of a bitstream. For compression, the bitstream is separated into a plurality of components, and the components are encoded using a compression algorithm. Packets are then constructed from the encoded components. At least one packet is associated with each encoded component and comprises header information and encoded data. The packets are combined into a packetized encoded bitstream. For decompression, the packets are separated from the packetized encoded bitstream using the header information. The packets are then decoded in parallel using a decompression algorithm to recover the encoded data. The plurality of components are reconstructed from the recovered encoded data and combined to-recover the bitstream.

A method generates hardware description language (HDL) code from a model having a plurality of components, including at least one component that processes frame-based input data. A selected preference is received for implementing the frame-based component. The generated HDL code includes a hardware implementation of the frame-based component that satisfies the selected preference.

A system and method for conducting verifiably correct auctions that preserves the secrecy of the bids while providing for verifiable correctness and trustworthiness of the auction is disclosed. Some of the elements of the method and apparatus are that the auction operator accepts all bids submitted and follows the published rules of the auction. In one embodiment, the bids are maintained secret from the auctioneer and all bidders until the auction closes and no bidder is able to change or repudiate her bid. In another embodiment, the auction operator computes the auction results and publishes proofs of the results' correctness. In yet another embodiment, any party can check these proofs of correctness via publicly verifiable computations on encrypted bids.

A method and system for high-resolution and parallelizable data processing, reconstruction, and deconstruction, uses arbitrary frequency-space (FS) or inverse frequency-space (IFS, such as image, audio, or video space) sample points in N dimensions. According to a preferred embodiment of the invention, a subset of optionally pre-processed and / or pre-conditioned N-dimensional FS data (or IFS data) is acquired (102) by a processing device (360), the data is optionally transformed (115) by “region scaling factors”, and the data is optionally reduced (116) in numerical significant digits. A “horizontal key” of data elements is calculated (120) on a processor (361), preferably in parallel, for each of an arbitrary set of x-coordinates in IFS (or FS). IFS “color” data (or FS data) are calculated (130) on a processor (361), preferably in parallel, at the x-coordinates corresponding to the horizontal keys. The IFS coordinates (or the FS coordinates) are arbitrary, and the reconstruction's calculated IFS data (or the deconstruction's calculated FS data) are optionally rotated or transposed (141) (such as for display purposes), and are thus formed (150) in a memory (363) or on an output device (365). The method can be applied to other subsets, such as in the N-dimensional case.