Abstract: The present disclosure involves systems, software, and computer implemented methods for generating a hash identifier. One example method includes: identifying a record to include in a table, the record associated with two or more primary key fields that are concatenated to create a concatenated key, wherein the table includes one or more hash columns for storing hash identifiers; applying a hash function to the concatenated key to create a new hash value; determining whether a record in the table has a hash value matching the new hash value; in response to determining that a hash value of a record matches the new hash value and the concatenated key of the identified record does not match the concatenated key of any existing record, adding a counter to the new hash value to generate a unique hash ID; and storing the record, including the unique hash ID, in the table.

Abstract: The present disclosure involves systems, software, and computer implemented methods for generating a hash identifier. One example method includes: identifying a record to include in a table, the record associated with two or more primary key fields that are concatenated to create a concatenated key, wherein the table includes one or more hash columns for storing hash identifiers; applying a hash function to the concatenated key to create a new hash value; determining whether a record in the table has a hash value matching the new hash value; in response to determining that a hash value of a record matches the new hash value and the concatenated key of the identified record does not match the concatenated key of any existing record, adding a counter to the new hash value to generate a unique hash ID; and storing the record, including the unique hash ID, in the table.

Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for receiving a probability distribution function model, the probability distribution function model representing an uncertain time series and providing a plurality of probability distribution functions, each probability distribution function corresponding to respective values at a respective time in the uncertain time series, generating a visualization based on the probability distribution function model to create a transparency gradient, the transparency gradient including a plurality of points graphically representing the plurality of probability distribution functions, and displaying the visualization including the transparency gradient, wherein a visual characteristic of each point of the plurality of points of the transparency gradient is a function of a probability of the respective value associated with the respective point.

Abstract: Implementations include generating a first plurality of univariate distributions based on known data stored in a computer-readable database, each univariate distribution of the plurality of distributions comprising an arbitrary distribution, processing the univariate distributions and the known data to generate an empirical ACR provided as a multi-dimensional histogram, storing the empirical ACR in the computer-readable database, retrieving, from computer-readable memory, a second plurality of univariate distributions, each univariate distribution in the second plurality of univariate distributions being associated with a respective set of uncertain values that are to be correlated to a respective set of uncertain values associated with one or more other univariate distributions in the second plurality of univariate distributions, processing the empirical ACR and the second plurality of univariate distributions to generate a correlation histogram that represents a correlation between the respective sets of un

Abstract: Implementations of the present disclosure include receiving user input, the user input indicating a distribution type and a correlation factor, providing the distribution type and correlation factor for identifying an approximate correlation representation (ACR) histogram from a plurality of ACR histograms based on the distribution type and the correlation factor, receiving the ACR histogram, retrieving a first distribution associated with a first uncertain value and a second distribution associated with a second uncertain value from computer-readable memory, processing the ACR histogram, the first distribution and the second distribution to generate a correlation histogram that represents a correlation between the first uncertain value and the second uncertain value, and displaying the correlation histogram on a display.

Abstract: Implementations of the present disclosure include receiving user input, the user input indicating a distribution type and a correlation factor, providing the distribution type and correlation factor for identifying an approximate correlation representation (ACR) histogram from a plurality of ACR histograms based on the distribution type and the correlation factor, receiving the ACR histogram, retrieving a first distribution associated with a first uncertain value and a second distribution associated with a second uncertain value from computer-readable memory, processing the ACR histogram, the first distribution and the second distribution to generate a correlation histogram that represents a correlation between the first uncertain value and the second uncertain value, and displaying the correlation histogram on a display.