International Business Machines Corporation

Brent Paulovicks

Vadim Sheinin

Petros Zerfos

Ligang Lu

Michael Peter Perrone

Thomas A Horvath

Methods are provided for enabling secure big data analytics in the cloud. A method includes reading a secure file, by a Secure Distributed File System in a public cloud service provider. The reading step includes computing a hash of a name of the secure file to obtain a hashed file name, finding metadata for the secure file using the hashed file name, retrieving a sharing policy identifier from the metadata, and obtaining authorization from an external entity to decrypt the secure file. The reading step further includes extracting a security key and encrypted data file names from the metadata using the sharing policy identifier, requesting one or more encrypted data files that form the secure file from a node of the public cloud service provider, and at least one of decrypting and reconstructing plaintext data for the secure file from the one or more encrypted data files.

Enabling secure big data analytics in the cloud

A computer readable medium is provided embodying instructions executable by a processor to perform a method for caching video data in a two-dimensional cache. The method includes storing the video data in the two-dimensional cache, addressing stored video data in the two-dimensional cache using a first tag for referencing video data of a first dimension, addressing the stored video data in the cache in terms of a second tag for referencing video data of a second dimension, and retrieving and outputting a portion of the stored video data from the two-dimensional cache according to one of the first tag and the second tag.

Two dimensional memory caching apparatus for high definition video

Techniques are disclosed for parallel computing of a line of sight (LoS) map (e.g., view-shed) in a parallel computing system. For example, a method for computing an LoS map comprises the following steps. Data representing at least one image is obtained. An observation point in the at least one image is identified. A portion of the data that is associated with a given area in the image is partitioned into a plurality of sub-areas. The plurality of sub-areas are assigned to a plurality of processor elements of a parallel computing system, respectively, such that the data associated with each one of the plurality of sub-areas is processed independent from the data associated with each other of the plurality of sub-areas, wherein results of the processing by the processor elements represents the LoS map. The parallel computing system may be a multicore processor.

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Danbury, CT, United States of America

Brent Paulovicks