Abstract: Techniques are provided for simulation-based online workflow optimization.

Abstract: Pattern queries are evaluated in parallel over large N-dimensional datasets to identify features of interest.

Abstract: Lossless content-aware compression and decompression techniques are provided for floating point data, such as seismic data. A minimum-length compression technique exploits an association between an exponent and a length of the significand, which corresponds to the position of the least significant bit of the significand. A reduced number of bits from the significand can then be stored. A prediction method is also optionally previously applied, so that residual values with shorter lengths are compressed instead of the original values. An alignment compression technique exploits repetition patterns in the floating point numbers when they are aligned to the same exponent. Floating point numbers are then split into integral and fractional parts. The fractional part is separately encoded using a dictionary-based compression method, while the integral part is compressed using a delta-encoding method.

Abstract: Data compression with window-based selection from multiple prediction functions is provided. A predefined default predictor and a plurality of other predictors are applied to a floating point number to generate a plurality of predictions. A compression metric over a collection of floating point numbers is evaluated for the default predictor and the plurality of other predictors. Based on the compression metric, (i) the floating point number is encoded using the predefined default predictor, or (ii) the collection of floating point numbers is encoded using one of the other predictors. Stored indexes indicate which predictor was used for the encoding. A set of predictors out of a larger set of predictors can be determined for a specific data set based on a performance-based ranking. The default predictor and the alternate predictors can be represented as ensembles of predictors.

Abstract: Methods and apparatus are provided for compression and decompression of heteroscedastic data, such as seismic data, using Autoregressive Integrated Moving Average (ARIMA)-Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model estimation. Heteroscedastic data is compressed by obtaining the heteroscedastic data; applying the heteroscedastic data to an ARIMA-GARCH model; determining residuals between the obtained heteroscedastic data and the ARIMA-GARCH model; and compressing parameters of the ARIMA-GARCH model and the residuals using entropy encoding, such as an arithmetic encoding, to generate compressed residual data. Parameters of the ARIMA-GARCH model are adapted to fit the obtained heteroscedastic data. The compressed residual data is decompressed by performing an entropy decoding and obtaining the parameters of the ARIMA-GARCH model and the residuals. The ARIMA-GARCH model predicts heteroscedastic data values and then the decompressed residuals are added.

Abstract: Lossless content-aware compression and decompression techniques are provided for floating point data, such as seismic data. A minimum-length compression technique exploits an association between an exponent and a length of the significand, which corresponds to the position of the least significant bit of the significand. A reduced number of bits from the significand can then be stored. A prediction method is also optionally previously applied, so that residual values with shorter lengths are compressed instead of the original values. An alignment compression technique exploits repetition patterns in the floating point numbers when they are aligned to the same exponent. Floating point numbers are then split into integral and fractional parts. The fractional part is separately encoded using a dictionary-based compression method, while the integral part is compressed using a delta-encoding method.

Abstract: Methods and apparatus are provided for compression and decompression of heteroscedastic data, such as seismic data, using Autoregressive Integrated Moving Average (ARIMA)-Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model estimation. Heteroscedastic data is compressed by obtaining the heteroscedastic data; applying the heteroscedastic data to an ARIMA-GARCH model; determining residuals between the obtained heteroscedastic data and the ARIMA-GARCH model; and compressing parameters of the ARIMA-GARCH model and the residuals using entropy encoding, such as an arithmetic encoding, to generate compressed residual data. Parameters of the ARIMA-GARCH model are adapted to fit the obtained heteroscedastic data. The compressed residual data is decompressed by performing an entropy decoding and obtaining the parameters of the ARIMA-GARCH model and the residuals. The ARIMA-GARCH model predicts heteroscedastic data values and then the decompressed residuals are added.