Abstract: Content planning techniques are provided that recommend content features based on the investment interest of advertisers in various audience segments and historical audience measurements. An exemplary method comprises obtaining historical data comprising content preferences indicating a performance metric for each pair of a plurality of content items and audience segment, wherein the content items comprise a plurality of content features indicating characteristics of a corresponding content item; obtaining, for each of a plurality of advertisers, a weight indicating a future interest of a given advertiser in a given audience segment; calculating a pairwise similarity between a vector of the content preferences and a vector of the weights for the plurality of the audience segments to obtain a ranked list of the content items sorted by the pairwise similarity; and generating a summarization of the content features to be used in future content items based on the ranked list.

Abstract: Techniques are provided for allocation of shared computing resources using source code feature extraction and machine learning techniques. An exemplary method comprises obtaining source code for execution in a shared computing environment; extracting a plurality of discriminative features from the source code; obtaining a trained machine learning model; and generating a prediction of an allocation of one or more resources of the shared computing environment needed to satisfy one or more service level agreement requirements for the source code. The generated prediction is optionally adjusted using a statistical analysis of an error curve, based on one or more error boundaries obtained by the trained machine learning model.

Abstract: Methods and apparatus are provided for domain-tailored detection of outliers, patterns, and/or events in data streams. An exemplary method comprises obtaining a domain-dependent definition of (i) data outliers based on predefined outlier criteria; (ii) data patterns based on predefined pattern criteria; and/or (iii) data events based on predefined event criteria; obtaining time series measurement data from a plurality of sensors; determining, substantially simultaneously with the obtaining, whether individual samples satisfy the domain-dependent definitions of the data outliers, data patterns and/or data events; and storing the individual samples with an indication of whether the individual samples satisfy the domain-dependent definitions of the data outliers, data patterns and/or data events. The domain-dependent definitions are optionally specified using a declarative command language.

Abstract: Methods and apparatus are provided for automatic combination of sub-process simulation results and heterogeneous data sources. An exemplary method comprises obtaining, for a process comprised of a sequence of a plurality of sub-processes, an identification of relevant input and output features for each sub-process; obtaining an execution map for each sub-process, wherein each execution map stores results of an execution of a given sub-process; and, in response to a user query regarding a target feature and a user-provided initial scenario: composing a probability distribution function for the target feature representing a simulation of the process based on a sequence of the execution maps, by matching input features of each execution map with features from the initial scenario or the output of previous execution maps; and processing the probability distribution function to answer the user query.