Abstract: Systems and methods to implement a model operation application are provided. The method includes defining a plurality of artifacts of a model operation application, where each artifact has an abstract interface, and each artifact is invoked by a corresponding reference. The method also includes organizing references of data inputs and data sinks of the model operation application into a data channel, where the data channel includes one or more data channel artifacts of the plurality of artifacts. The method further includes combining a plurality of processing steps by reference into a scoring flow, wherein the scoring flow is configured to be managed as one or more scoring flow artifacts of the plurality of artifacts. The method further includes attaching the data channel to the scoring flow to form a scoring pipeline, wherein the scoring pipeline comprises one or more scoring pipeline artifacts of the plurality of artifacts.

Abstract: A system for identifying information in high dimensional, low latency streaming data having dynamically evolving data patterns. The system processes, continuously and in real-time, the streaming data. Processing includes filtering the data based on event data to identify diagnostic data points by comparing the event data with an experimental design matrix and performing a modeling operation using the identified diagnostic data points in order to identify efficiently any current and emerging patterns of relationships between at least one outcome variable and predictor variables. The at least one a-priori, pre-designed experimental design matrix is generated based on combinations of the predictor variables and at least one outcome variable. The experimental design matrix is also generated based on at least one of main effects, limitations, constraints, and interaction effects of the predictor variables and combinations.

Abstract: An apparatus comprising an interface and a natural language processor. The interface receives a data retrieval request formatted in a natural language and the natural language processor processes the data retrieval request. Processing the data retrieval request includes identifying database entities, database relations, or any combination thereof based words in the data retrieval request. It can also include identifying database entity criterion, database relation criterion, or any combination thereof based on words in the data retrieval request. It also includes generating a database query based on the database entities, the database relations, the database entity criterion, the database relation criterion, or any combination thereof and causing the database query to be applied to a database. Wherein, processing the data retrieval request includes grammatically tagging the data retrieval request using part-of-speech tagging techniques, e.g.

Abstract: A predictive engine includes an optimizer and a predictor. The optimizer is configured to receive an observed dataset having inputs and multivariate responses and determine latent response variables based on the predictive inputs and the multivariate responses. The optimizer is further configured to select latent response variables, measure dependencies between multivariate responses, estimate coefficients that relate the input predictors to determined latent response variables, and correlate dependencies and coefficients with the latent response variables. The predictor is configured to generate a predictive distribution of probabilities for the latent variables, map the probabilities to multivariate responses, generate a predictive distribution of probabilities for the multivariate responses, and determine at least one optimized input from the multivariate responses.

Abstract: A system for generating algorithmic models comprising a function module to generate a desirability function, an automated machine learning module, and a UI module. The desirability function defines a single desirability value based on an algorithmic model accuracy criteria, criteria for algorithmic model quality, criteria for model fidelity, and criteria for the benefits and cost of model deployment. Specific hard and soft constraints regarding these and other user-defined criteria can also be specified by the user. The automated machine learning module generates an algorithmic model by training the algorithmic model against a model data set, identifying the model with the greatest desirability with respect to all criteria as combined via the desirability function. The UI module generates a user interface to display the overall desirability as well as all model criteria configured by the user. The displayed criteria and desirability are selectable and definable.

Abstract: Policy-based storage and retrieval combined with a distribution algorithm results in automatic and even distribution of policy-based storage structures across a set of nodes and dynamic, automated homing or ownership of policy-based storage structures. Large numbers of policy-based storage structures may be distributed without manual administration, allowing for rapid creation and destruction of storage structures. The overall load may be distributed and balanced across the server pool. Multiple entries having the same key value in a database- or table-like structure allow for distribution of policy-based storage and retrieval by key value and for queue semantics to be utilized for microqueues in the large database- or table-like structure.