Example methods, apparatus, and articles of manufacture to capture and compress telematics data are disclosed herein. An example computer-implemented method, executed by a processor, to represent telematics data includes identifying, with the processor, a physical intersection of roads, identifying, with the processor, virtual lines crossing the roads, assigning, with the processor, ordinals to the virtual lines, representing, with the processor, a physical traversal through the physical intersection captured in first telematics data by a pair of the ordinals, and storing the pair of the ordinals in second compressed telematics data.

Example methods, apparatus, and articles of manufacture to capture and compress telematics data are disclosed herein. An example computer-implemented method, executed by a processor, to represent telematics data includes identifying, with the processor, a physical intersection of roads, identifying, with the processor, virtual lines crossing the roads, assigning, with the processor, ordinals to the virtual lines, representing, with the processor, a physical traversal through the physical intersection captured in first telematics data by a pair of the ordinals, and storing the pair of the ordinals in second compressed telematics data.

A processor-implemented method including generating k sub-compressed data streams based on a compressed data stream for a plurality of symbols divided into a plurality of k blocks and count information for each of the plurality of k blocks, generating k sub-symbols by processing each of the k sub-compressed data streams using k decoding engines, metadata about the compressed data stream, and generating an output data stream corresponding to the plurality of symbols based on the k sub-symbols.

A method for compressing flow data, including: generating multiple line segments according to flow data and a predefined maximum error that are acquired; obtaining a target piecewise linear function according to the multiple line segments, where the target piecewise linear function includes multiple linear functions, and an intersection set of value ranges of independent variables of every two linear functions among the multiple linear functions includes a maximum of one value; and outputting a reference data point according to the target piecewise linear function, where the reference data point includes a point of continuity and a point of discontinuity of the target piecewise linear function. In this way, a maximum error, a target piecewise linear function is further determined according to the multiple line segments, and a point of continuity and a point of discontinuity of the target piecewise linear function are used to represent compressed flow data.

A signal encoder divides the signal into segments and uses prediction models to approximate the samples of each segment

Each local prediction model, each applicable to one segment, is applied in its own translated axis system within the segment and the offset is given by the last predicted value for the previous segment. When the signal is reasonably continuous, it alleviates the need to parameterize the offset for each local predictor model as each local predictor model can build on this last predicted sample value of the previous segment.

The encoder as a consequence doesn't suffer from a build up of error even though the offset is not transmitted but instead the last predicted value of the last sample of the previous segment is used. Prediction errors are obtained for the approximated samples and transmitted to the decoder, together with the predictor model parameters and seed value to allow accurate reconstruction of the signal by the decoder.

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.

A method includes receiving, by a first computing entity of a database system, a query request that is formatted in accordance with a generic query format. The method further includes generating, by the first computing entity, an initial query plan based on the query request and a query instruction set. The method further includes determining, by the first computing entity, storage parameters. The method further includes determining, by the first computing entity, processing resources for processing the query request based on the storage parameters. The method further includes generating, by the first computing entity, an optimized query plan from the initial query plan based on the storage parameters, the processing resources, and optimization tools. The method further includes sending, by the first computing entity, the optimized query plan to a second computing entity for distribution and execution of the optimized query plan.

Systems and methods are disclosed for a hybrid image compression technique. The technique may be used to reduce the size of sparse binary images in a lossless fashion for transmission over a low-bandwidth channel and using less power. In embodiments, a received image is partitioned into pixel blocks. Sequences of empty blocks are encoded as a skip counter that indicates the length of the sequence. Non-empty blocks are encoded using one of several block encoding techniques selected based on the length of the resulting block encoding. In embodiments, a loss report may be included in the compressed data stream if blocks are lost due to buffer overflow. In embodiments, the block skip counter is encoded using a variable length code that maps more common skip values to shorter bit widths. In embodiments, the variable length code may be changed periodically to adapt to the characteristics of recent images.

A method of neural network decoding includes receiving a first syntax element in a model parameter set from a bitstream of a compressed neural network representation (NNR) of a neural network. The first syntax element indicates whether a coding tree unit (CTU) block partitioning is enabled for a tensor in an NNR aggregate unit. The method also includes reconstructing the tensor in the NNR aggregate unit based on the first syntax element.

Adaptively compressing an input string (10) comprising a sequence of symbols in order to create a plurality of segment dictionaries D.sub.m, with the steps of: generating a lookup map (110); generating a key value segment S.sub.m,n; searching the lookup map for each symbol received in the input string (120, 130); upon detecting a symbol is not stored in the lookup map, adding the symbol by storing the symbol at a next sequential key index in the lookup map lookup map (135) and assigning a next sequential key value entry to the symbol and adding this key value to the key value segment S.sub.m,n (150); upon detecting the symbol is stored in the lookup map, adding the corresponding key value assigned to this symbol to the next sequential entry of the key value segment S.sub.m,n (150); wherein a new key value segment S.sub.m,n+1 of the lookup map is generated if the number of different symbols equals the number of available key values k=2.sup.n for the opened/current key value segment S.sub.m,n (141, 142), and where-in the lookup map is converted into a segment dictionary D.sub.m if the maximal key value size k.sub.nmax=2.sup.nmax is reached (132, 133, 134), with n being any positive integral number 1 to nmax, nmax denoting the maximal bit size, and m being any positive integral number denoting the consecutive numbering of segment dictionaries D.sub.m.