Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for data processing. The method includes: determining, based on sizes of multiple data segments included in data to be processed, a first time required to perform a matching operation for each data segment, wherein the matching operation is used to determine non-duplicate data segments; determining, based on the size of each data segment and a compression level for the data to be processed, a second time required to perform a compression operation for each data segment; and determining, based on the first time, the second time, and a de-duplication rate for the data to be processed, a target mode for processing the multiple data segments from a first mode and a second mode, wherein in the first mode, a compression operation is performed only on the non-duplicate data segments in the multiple data segments, and in the second mode, a compression operation is performed on each of the multiple data segments. In this way, the data processing mode can be dynamically selected according to features of the data to be processed, thereby improving the efficiency of data processing.

Techniques are provided herein for more efficiently storing images that have a common subject, such as product images that share the same product in the image. Each image undergoes an adaptive tiling procedure to split the image into a plurality of tiles, with each tile identifying a region of the image having pixels with the same content. The tiles across multiple images can then be clustered together and those tiles having identical content are removed. Once all duplicate tiles have been removed from the set of all tiles across the images, the tiles are once again clustered based on their encoding scheme and certain encoding parameters. Tiles within each cluster are compressed using the best compression technique for the tiles in each corresponding cluster. By removing duplicative tile content between numerous images of the same subject, the total amount of data that needs to be stored is reduced.

Systems and methods are described for avoiding redundant data transfers using delta coding techniques when reliably and opportunistically communicating data to multiple user systems. According to embodiments, user systems track received block sequences for locally stored content blocks. An intermediate server intercepts content requests between user systems and target hosts, and deterministically chucks and fingerprints content data received in response to those requests. A fingerprint of a received content block is communicated to the requesting user system, and the user system determines based on the fingerprint whether the corresponding content block matches a content block that is already locally stored. If so, the user system returns a set of fingerprints representing a sequence of next content blocks that were previously stored after the matching content block. The intermediate server can then send only those content data blocks that are not already locally stored at the user system according to the returned set of fingerprints.

A data management device includes a persistent storage and a processor. The persistent storage includes an object storage. The processor segments a file into file segments. The processor generates meta-data of the file segments. The processor stores a portion of the file segments in a data object of the object storage. The processor stores a portion of the meta-data of the file segments in a meta-data object of the object storage.

Unsorted sparse dictionary encodings are transformed into unsorted-dense or sorted-dense dictionary encodings. Sparse domain codes have large gaps between codes that are adjacent in order. Unlike spare codes, dense codes have smaller gaps between adjacent codes; consecutive codes are dense codes that have no gaps between adjacent codes. The techniques described herein are relational approaches that may be used to generate sparse composite codes and sorted codes.

A data processing method includes: acquiring, by one or more processors, compressed data generated from data, wherein values of the compressed data are stored at first storage locations, values of the data are stored at second storage locations; acquiring, by the one or more processors, index data includes indices indicative of the first storage locations; acquiring, by the one or more processors, at least two packed indices from the index data, the at least two packed indices being generated from the index data; and inputting, by the one or more processors, the at least two packed indices into at least two selectors.

A computing system may include a server, and a client computing device in communication with the server. The server may be configured to provide a corresponding virtual desktop instance for the client computing device. The computing system may include a local device to be coupled to a given client computing device and to be operable in a given virtual desktop instance associated with the given client computing device, thereby generating client initialization packets. The server may be configured to generate a server mapping table. The given client computing device may be configured to generate a client mapping table, replace a client packet with a client mapping ID number to define compressed client initialization packets, and send the compressed client initialization packets to the server. The server may be configured to replace the client mapping ID number with the client packet in the compressed client initialization packets based upon the server mapping table.

An application is provided as a compound virtual appliance having components to be hosted by virtual machines. Each component includes a set of virtual machine disks. Partial versions of the components are created by removing from each component each virtual machine disk determined to be a duplicate of a virtual machine disk of another component. A compact version of the compound virtual appliance is created by packing together the partial versions of the components and a single copy of each virtual machine disk having been determined to be a duplicate. The compact compound virtual appliance is deployed to a customer site. At the customer site, a complete version of the compound virtual appliance is reconstructed by adding back the single copy of each virtual machine disk having been determined to be a duplicate into each component having had the duplicate virtual machine disk removed.

Unsorted sparse dictionary encodings are transformed into unsorted-dense or sorted-dense dictionary encodings. Sparse domain codes have large gaps between codes that are adjacent in order. Unlike spare codes, dense codes have smaller gaps between adjacent codes; consecutive codes are dense codes that have no gaps between adjacent codes. The techniques described herein are relational approaches that may be used to generate sparse composite codes and sorted codes.

One aspect of the present disclosure relates to a data compression method. The method includes generating, by one or more processors, compressed data from data, wherein the compressed data includes one or more unduplicated values of the data and generating, by the one or more processors, index data from the data, wherein the index data includes indices indicative of storage locations for the unduplicated values.