Abstract: A method, apparatus, and computer-readable storage medium for analyzing component separation/mass spectrometer data for a sample having known characteristic includes analyzing reference ion data for a relationship between ion mass, retention time, and intensity. The analyzed data is added to a repository, wherein each ion therein has an intensity maxima within a characteristic retention time range for a characteristic ion mass. If the reference ion is in the repository, the range is modified according to the characteristic retention time of the reference ion intensity maxima. Based on the known characteristic, an ion expected in the sample is selected from the repository, and sample data is compared to data for the ion selected from the repository to determine whether the ion is present in the sample. The range in the repository is then modified according to the characteristic retention time of the intensity maxima for the ion present in the sample.

Abstract: A method, apparatus, and computer-readable storage medium for analyzing component separation/mass spectrometer data for a sample having known characteristic includes analyzing reference ion data for a relationship between ion mass, retention time, and intensity. The analyzed data is added to a repository, wherein each ion therein has an intensity maxima within a characteristic retention time range for a characteristic ion mass. If the reference ion is in the repository, the range is modified according to the characteristic retention time of the reference ion intensity maxima. Based on the known characteristic, an ion expected in the sample is selected from the repository, and sample data is compared to data for the ion selected from the repository to determine whether the ion is present in the sample. The range in the repository is then modified according to the characteristic retention time of the intensity maxima for the ion present in the sample.

Abstract: A computer-implemented method for selecting a digital content, comprising: receiving a plurality of samples, each comprises a request having a plurality of values of a plurality of attributes, and associated with a success value for a Bernoulli distributed event having a campaign and a bid rate (BR) of the campaign; clustering the plurality of samples in a plurality of homogenous nodes according to respective plurality of values; identifying a group campaign with a highest valuation for each one of the plurality of nodes using triangular approximation of the Bernoulli distribution of events in the node; receiving a query from a device including a plurality of other values of the plurality of attributes; selecting one of the plurality of nodes; selecting a digital content of the group campaign with highest valuation identified for the selected node; and generating a response to the query including the selected content.

Abstract: A computer-implemented method for selecting a digital content, comprising: receiving a plurality of samples, each comprises a request having a plurality of values of a plurality of attributes, and associated with a success value for a Bernoulli distributed event having a campaign and a bid rate (BR) of the campaign; clustering the plurality of samples in a plurality of homogenous nodes according to respective plurality of values; identifying a group campaign with a highest valuation for each one of the plurality of nodes using triangular approximation of the Bernoulli distribution of events in the node; receiving a query from a device including a plurality of other values of the plurality of attributes; selecting one of the plurality of nodes; selecting a digital content of the group campaign with highest valuation identified for the selected node; and generating a response to the query including the selected content.