One embodiment of the present invention provides a system for facilitating compression of a Cascading Style Sheet (CSS) file. During operation, the system stores the CSS file in a local storage device and identifies a merge parameter in the CSS file. The merge parameter can be one of: a repeated selector, a repeated property in a selector, and a same value of a property in different selectors. The system then determines whether a merge condition is satisfied for the merge parameter and merges the merge parameter in the CSS file to obtain a compressed merge parameter. The merge condition indicates whether merging of the merge parameter is consistent with original style in the CSS file.

Method, media, and systems for compressing objects, comprising: receiving a request to write a first object including a first key and a first value, wherein the first object is of a given type; receiving a request to write a second object including a second key and a second value, wherein the second object is of the given type; classifying the first object to a compression dictionary according to at least one rule based on a value of the first object and/or the key of the first object; classifying the second object to the compression dictionary according to at least one rule based on a value of the second object and/or the key of the second object; and compressing the first object and the second object based on the compression dictionary.

A computer-implemented method for electronic exchange of data is provided. The method includes the following operations performed by at least one computer processor. These operations include creating source data, identifying data structure from the source data, generating a header file based on the data structure, localizing identical data structure, and storing groups of data that have identical structure in a single data tag.

Stack compression refers to compression of data in one or more dimensions. For uncompressed data blocks that are very sparse, i.e., data blocks that contain many zeros, stack compression can be effective. In stack compression, uncompressed data block is compressed into compressed data block by removing one or more zero words from the uncompressed data block. A map metadata that maps the zero words of the uncompressed data block is generated during compression. With the use of the map metadata, the compressed data block can be decompressed to restore the uncompressed data block.

Herein are fine grained updates to pieces of JavaScript object notation (JSON) documents by database statements that can update, delete, and insert parts of JSON documents. In an embodiment, a computer receives a request that specifies a modification of a JSON document that is stored in a compressed format in persistent storage. The modification adds additional content to the JSON document, and/or replaces an old value in the JSON document with a new value that is not a same size as the old value. The modification is recorded in a change log. The change log is eventually applied to the compressed format of the JSON document in the persistent storage without entirely rewriting the compressed format of the JSON document in the persistent storage.

A system for compressing Controller Area. Network (CAN) messages, the system comprising a processing resource configured to: obtain a CAN messages sequence including a plurality of CAN messages intercepted at a given order by at least one device adapted to monitor messages transmitted via communication channel(s) of a vehicle; group the CAN messages of the CAN messages sequence into MID groups, by a CAN MID field of the CAN messages; for each given MID group of the MID groups split the CAN messages of the MID group into field groups, wherein each field group comprises a respective field of a plurality of to fields of the CAN messages of the MID group; employ at least one compression scheme on at least one of the field groups; generate a data structure comprising the field groups; and compress the data structure using a lossless compression algorithm, giving rise to a. compressed data structure.

An encoding apparatus includes a memory and a processor configured to acquire text data, specify a first dynamic dictionary among a plurality of dynamic dictionaries based on attribute information of a first word included in the text data, register the first word in association with a first dynamic code in the first dynamic dictionary, and encode the first word into the first dynamic code.

A data compression method and a data decompression method for an electronic device, and an electronic device, are provided to make compressed data become smaller, so that overheads caused by data storage and receiving/sending are reduced. Each of one or more matching rules includes one or more matching entries, each matching entry is used to perform matching on one or more pieces of to-be-matched data in a to-be-matched data group, and each matching entry includes: a preset field; a matching rule field. The method includes: receiving a to-be-matched data group, obtaining a target matching rule by performing matching based on the preset field and the matching rule field in each matching entry in the one or more matching rules, and performing processing based on a compression rule field in each matching entry in the target matching rule.

Various of the present embodiments convert structured data to a channel-based representation facilitating pattern recognition by a compression utility. The channels may include a key channel, a number channel, a type channel, a length channel, etc. These channels reflect the same information as the structured data, but reorganized based upon the structured data's hierarchic structure. The encoder and decoder may include rule sets specifying the relations between the channels to deconstruct/construct the JSON file. Memoization, length encoding, and type encoding extensions may also be applied as part of the encoding/decoding process to further improve compression.

Techniques and apparatuses to decompress data that has been stack compressed is described. Stack compression refers to compression of data in one or more dimensions. For uncompressed data blocks that are very sparse, i.e., data blocks that contain many zeros, stack compression can be effective. In stack compression, uncompressed data block is compressed into compressed data block by removing one or more zero words from the uncompressed data block. A map metadata that maps the zero words of the uncompressed data block is generated during compression. With the use of the map metadata, the compressed data block can be decompressed to restore the uncompressed data block.