Abstract: Some implementations generate logical queries from a canonical query, where the logical queries each reflect a modified scope of the canonical query. Implementations receive, via a personalized analytics system, a canonical query that is associated with a user. The canonical query can be analyzed to determine an intent of the canonical query. In turn, one or more implementations generate, based on the intent an anecdotal information associated with the user, a logical query that reflects a modified scope of the canonical query. In implementations multiple logical queries are generated and are processed to remove a duplicate logical query. A logical query can be used to extract data from a database associated with the personalized analytics system based on a modified scope.

Abstract: Techniques described herein provide intelligent context-based testing. One or more implementations receive an image that includes content. In turn, some implementations process the image to extract test information from the content, such as questions, answers, learning material, and so forth. By analyzing the test information, various implementations determine one or more characteristics associated with the test information, and dynamically generate new test information based on the determined one or more characteristics. As one example, some implementations obtain new content by searching for content that includes the one or more characteristics and generate new test information based on the new content and the extracted test information.

Abstract: Some implementations generate logical queries from a canonical query, where the logical queries each reflect a modified scope of the canonical query. Implementations receive, via a personalized analytics system, a canonical query that is associated with a user. The canonical query can be analyzed to determine an intent of the canonical query. In turn, one or more implementations generate, based on the intent an anecdotal information associated with the user, a logical query that reflects a modified scope of the canonical query. In implementations multiple logical queries are generated and are processed to remove a duplicate logical query. A logical query can be used to extract data from a database associated with the personalized analytics system based on a modified scope.

Abstract: Some implementations generate logical queries from a canonical query, where the logical queries each reflect a modified scope of the canonical query. Implementations receive, via a personalized analytics system, a canonical query that is associated with a user. The canonical query can be analyzed to determine an intent of the canonical query. In turn, one or more implementations generate, based on the intent an anecdotal information associated with the user, a logical query that reflects a modified scope of the canonical query. The logical query can be used to extract data from a database associated with the personalized analytics system based on the modified scope.

Abstract: Techniques described herein provide intelligent context-based testing. One or more implementations receive an image that includes content. In turn, some implementations process the image to extract test information from the content, such as questions, answers, learning material, and so forth. By analyzing the test information, various implementations determine one or more characteristics associated with the test information, and dynamically generate new test information based on the determined one or more characteristics. As one example, some implementations obtain new content by searching for content that includes the one or more characteristics and generate new test information based on the new content and the extracted test information.

Abstract: Some implementations generate logical queries from a canonical query, where the logical queries each reflect a modified scope of the canonical query. Implementations receive, via a personalized analytics system, a canonical query that is associated with a user. The canonical query can be analyzed to determine an intent of the canonical query. In turn, one or more implementations generate, based on the intent an anecdotal information associated with the user, a logical query that reflects a modified scope of the canonical query. The logical query can be used to extract data from a database associated with the personalized analytics system based on the modified scope.

Abstract: Methods and systems for managing an item assortment from among a collection of heterogeneous items are disclosed. One method includes receiving item data associated with the collection of heterogeneous items that defines values for a plurality of item attributes, and calculating a score for a degree of substitutability between items. A community detection algorithm is applied to edge weights that are based on the scores between items, to identify substitution groups among the items. Preferred attributes common to items within the substitution groups are found, and an item assortment is updated based on a determination of substitutability among items in at least one of the substitution groups.

Abstract: Systems and methods (300) for offline/online performance monitoring of batch processes (BPs) involving obtaining archived data (AD) obtained during runs of BP and including information defining a batch quality attribute for each run. The method also involves forming clusters by classifying AD for the runs into classes based on the batch quality attribute(s) and building a first multivariate statistical model (MSM) using AD. The method can further involve building a wavelet analysis based feature matrix (FM) using AD, forming a first projection (1200) by projecting FM onto a first MSM, building a second MSM (1300) using information obtained from the first projection, and computing centroids (C902, . . . , C918) and boundary profiles for the clusters (902, . . . , 918). The method can involve performing an online/offline performance monitoring (700/800) using an integrated version of the first and second MSM, a classification algorithm, centroids, and boundary profiles.

Abstract: A method (300, 400, 500, 1200) for offline/online monitoring of batch processes. The method involves (312) decomposing a time domain of a batch process run (BPR) into several blocks and (334) building multivariate statistical models (MSMs) for each of them using archived data for a batch process (ABPD). ABPD comprises stored data obtained during BPRs. The method also involves (506, 1204) retrieving recently stored data (RSD) for a recent fully performed BPR run (FPRNEW) or current BPR run. The method further involves (520, 1210) building a feature vector matrix (FVM) using RSD. FVM contains feature vectors representing statistical measures of wavelet coefficients determined for variables (v0, . . . , vJ). A projection (1100, 1150, 1190) is formed by projecting feature vectors onto at least one MSM or a combined multivariate statistical model (CMSM). CMSM is a weighted average of at least two MSMs.

Abstract: Systems and methods (300) for offline/online performance monitoring of batch processes (BPs) involving obtaining archived data (AD) obtained during runs of BP and including information defining a batch quality attribute for each run. The method also involves forming clusters by classifying AD for the runs into classes based on the batch quality attribute(s) and building a first multivariate statistical model (MSM) using AD. The method can further involve building a wavelet analysis based feature matrix (FM) using AD, forming a first projection (1200) by projecting FM onto a first MSM, building a second MSM (1300) using information obtained from the first projection, and computing centroids (C902, . . . , C918) and boundary profiles for the clusters (902, . . . , 918). The method can involve performing an online/offline performance monitoring (700/800) using an integrated version of the first and second MSM, a classification algorithm, centroids, and boundary profiles.

Abstract: A method (300, 400, 500, 1200) for offline/online monitoring of batch processes. The method involves (312) decomposing a time domain of a batch process run (BPR) into several blocks and (334) building multivariate statistical models (MSMs) for each of them using archived data for a batch process (ABPD). ABPD comprises stored data obtained during BPRs. The method also involves (506, 1204) retrieving recently stored data (RSD) for a recent fully performed BPR run (FPRNEW) or current BPR run. The method further involves (520, 1210) building a feature vector matrix (FVM) using RSD. FVM contains feature vectors representing statistical measures of wavelet coefficients determined for variables (v0, . . . , vJ). A projection (1100, 1150, 1190) is formed by projecting feature vectors onto at least one MSM or a combined multivariate statistical model (CMSM). CMSM is a weighted average of at least two MSMs.

Abstract: A method for performing diagnosis on an engine includes the steps of obtaining data for a plurality of variables pertaining to the engine, transforming the data with respect to each of the plurality of variables using a wavelet transformation, to thereby generate initial coefficients for each of the plurality of variables, and aggregating the initial coefficients for each of the plurality of variables, to thereby generate an aggregate set of coefficients for the plurality of variables.