Abstract: A computer-implemented method for analysis of data from multiple data sources and a computing system therefor is provided. The method includes receiving a query instance. The query instance may be processed to generate one or more relational query instances based, at least in part, on the query instance. The generated one or more relational query instances may be distributed among one or more nodes. Each of the generated one or more relational query instances may be processed by at least one node of the one or more nodes to extract data from at least one data source of multiple data sources in communication therewith corresponding to the one or more relational query instances. One or more insights may be generated based on the extracted data. The one or more insights generated may be stored in relevant cache to be retrieved therefrom.

Abstract: A computer-implemented method for analysis of data from multiple data sources and a computing system therefor is provided. The method includes receiving a query instance. The query instance may be processed to generate one or more relational query instances based, at least in part, on the query instance. The generated one or more relational query instances may be distributed among one or more nodes. Each of the generated one or more relational query instances may be processed by at least one node of the one or more nodes to extract data from at least one data source of multiple data sources in communication therewith corresponding to the one or more relational query instances. One or more insights may be generated based on the extracted data. The one or more insights generated may be stored in relevant cache to be retrieved therefrom.

Abstract: A computer-implemented method for analysis of data from multiple data sources and a computing system therefor is provided. The method includes receiving a query instance. The query instance may be processed to generate one or more relational query instances based, at least in part, on the query instance. The generated one or more relational query instances may be distributed among one or more nodes. Each of the generated one or more relational query instances may be processed by at least one node of the one or more nodes to extract data from at least one data source of multiple data sources in communication therewith corresponding to the one or more relational query instances. One or more insights may be generated based on the extracted data. The one or more insights generated may be stored in relevant cache to be retrieved therefrom.

Abstract: A computer-implemented method for analysis of data from multiple data sources and a computing system therefor is provided. The method includes receiving a query instance. The query instance may be processed to generate one or more relational query instances based, at least in part, on the query instance. The generated one or more relational query instances may be distributed among one or more nodes. Each of the generated one or more relational query instances may be processed by at least one node of the one or more nodes to extract data from at least one data source of multiple data sources in communication therewith corresponding to the one or more relational query instances. One or more insights may be generated based on the extracted data. The one or more insights generated may be stored in relevant cache to be retrieved therefrom.

Abstract: Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user's level of exposure to email.

Abstract: Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user's level of exposure to email.

Abstract: Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user's level of exposure to email.

Abstract: Emboiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user's level of exposure to email.

Abstract: Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user's level of exposure to email.

Abstract: Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user's level of exposure to email.

Abstract: Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user's level of exposure to email.

Abstract: Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user's level of exposure to email.

Abstract: Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user's level of exposure to email.

Abstract: Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user's level of exposure to email.

Abstract: Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user's level of exposure to email.

Abstract: Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user's level of exposure to email.

Abstract: Methods and apparatus include presenting an initial set of names to a user. The user selects a set of names from those presented. An Interactive Evolutionary Algorithm (IEA) extracts features of each selected name from a database of names and features to form a feature set. The IEA forms a set of match features that are chosen from the feature set according to a priority function and/or weighting of the features, either of which may vary in succeeding iterations. The IEA searches the database to obtain a candidate set of names, where each name has features matching the match features. One or more names is chosen from the candidate set and added into a presentation set of names. The IEA may repeat the formation of the match features, candidate set, and selection of one or more names from the candidate set until the new presentation set is complete.

Abstract: Methods and systems for designing new products such as perfumes comprise having existing products in a product field rated according to product attributes; assigning each of the existing products a location in a multi-dimensional space according to the ratings; locating the existing products in a second multi-dimensional space; choosing reference products, which sample the second space, from among the existing products; having a consumer survey of the reference products conducted; creating a map of consumer responses in the second multi-dimensional space; and designing the new product based on the map of consumer responses.

Abstract: A computer implemented method for interactive advertising can include displaying on a display a context-specific interactive text cloud containing a plurality of displayed topics that are topically associated with a common context. An advertisement can be displayed that is topically associated with one or more displayed topics of the plurality of displayed topics, with the common context of the context-specific interactive text cloud, or both. The step of displaying an advertisement can be initiated by an interaction by a user with a first displayed topic of the plurality of displayed topics.

Abstract: Methods and systems for interactive evolutionary computing may include generating a set of candidate molecules based on an evolutionary scheme in which an objective function is a priori mathematically unexpressed, presenting data based on the set of candidate molecules to one or more users, receiving at least one input from the user(s), the input(s) based on the user(s)'s evaluation of the presented set of candidate molecules, and, based on the input(s), using at least the evolutionary scheme and the input(s) to generate an updated set of candidate molecules, and repeating the presenting and receiving.