Abstract: Techniques to store graph information in a database are disclosed. In various embodiments, each node in a graph may be modeled as a micro b-tree. Node identity, attribute, edge, and edge attribute data may be stored in one or more pages modeled on page formats typically used to store index data for a relational database index. Data associated with a plurality of nodes and edges, each of said edges representing a relationship between two or more of said nodes, may be received. For each node, one or more pages of data may be created, each corresponding to a prescribed page size associated with a storage device in which said one or more pages are to be stored, and each page having a data structure that includes a variable-sized set of fixed length data slots and a variable-sized variable length data region.

Abstract: A computing device for interpreting a data model algorithm includes an object searcher, an interpreter, and a translator. The object searcher is configured to search for attributes within datasets generated from at least one method of an instantiation of the data model algorithm in a development mode workflow. The interpreter is configured to evaluate the attributes, identify attributes having a use type, identify the type information of the identified attribute, and create data schema using the identified attributes and type information. The use type can be determined based on attribute values or an interface type associated with an identified attribute. The translator is configured to compare the data schema with another data schema in response to selecting the data model algorithm for inclusion in a production mode workflow.

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: Embodiments are described for a system and method to analyze data at a plurality of data sources. A data analytic workflow may be received. The data analytic workflow may include at least one operation to be performed on a plurality of data sets stored at a plurality of data sources. Instructions may be created based on the operation to be performed and a type of platform that operates the data sources. Furthermore, the instructions may be transmitted to the data sources such that the data sources may execute the operations on the data sets stored at the data sources.

Abstract: In various aspects, a method, system and computer program product provides for concurrent changes to data of a document or a file by a plurality of users at a plurality of user devices. The documents or file may be rendered in JavaScript™ Object Notation (JSON) format. The documents may be patched or changed by employing new functional operations for applying metadata for elements of the arrays associated with the document or file, and to merge changes from made among the various users. The disclosure provides for maintaining and producing new versions of the document or the file, the changes controlled so that data of the arrays are merged atomically and the updates do not fail permitting simultaneous changes to the document or file by multiple users.

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 for estimating analytics and interactive exploration of big data, stored and/or streaming, using approximate query processing is presented. The apparatus comprises a model constructor and a sampler. The model constructor identifies important predictors variables in big data using feature selection, predictor variables, and outcome variables and partitions the important predictor variables into one or more stratifications based either the identified interactions or identified relationships. The sampler generates a subset of data by querying the big data using a query constructed based on at least one stratification. The subset of data can be fed into an analytics generator. The analytics generator generates analytics data for the outcome variables based on the subset of data and an analytics algorithm and a visualization, e.g. an interactive visualization, comprising the outcome variables, the important predictor variables, the stratification, the subset of data, and the analytics data.

Abstract: Techniques are disclosed to operate binary objects across private address spaces. In various embodiments, a private shared memory segment is allocated for two non-privileged address spaces, the first comprising a home address space and the second comprising a target address space. One or more executable modules are loaded in a private address space of the home address space. One or more program call routines and an environment to schedule system request blocks (SRB) are built in the home address space. The environment to schedule system request blocks is configured to be used to schedule an SRB into the target address space, the SRB comprising information configured to cause the target address space to cause an associated one of the executable modules to execute.

Abstract: A system for scheduling and dispatching workload items between tasks can include an aggregated file structure and a task scheduler. The aggregated file structure comprises at least one logical partition with each partition having a secondary operating system and address spaces and a plurality of tasks executable therein. The task scheduler is executable from the primary operating system and can receive a service request from a task requesting an action from at least one other task and writing the action, a first task identifier, and at least one other task identifier to at least one private memory address associated with the other task or tasks. The service request action can be a start service request, a stop service request, a suspend service request, a resume service request, change the order of execution service request, or a change priority of executing tasks service request.

Abstract: Techniques to correlate event data are disclosed. In various embodiments, an aggregation engine is used to correlate event data received from one or more source systems based on one or more correlation rules. An event group comprising at least a portion of said correlated event data is generated programmatically and is asserted as a fact in a working memory of a Rete engine configured to apply one or more Rete rules to facts in the working memory.

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 method of application programming interface (API) documentation generation includes receiving an API interaction, determining at least one interaction parameter based on the API interaction, and automatically generating the documentation based on the at least one interaction parameter. The generated documentation documents an action associated with the API interaction.

Abstract: Techniques to provide recommended data visualizations or analytical operations are disclosed. In various embodiments, an indication is received of a set of data for which a data visualization or analytical operation is to be provided. The set of data is analyzed to determine one or more data attributes of the set of data. A data visualization or analytical method to be recommended is selected for the set of data, based at least in part on the determined one or more data attributes.

Abstract: Data mapping techniques are disclosed. A representation of a first destination data field to which data is to be mapped is provided via a displayed user interface. One or more other destination data fields are not displayed at the same time as the first destination data field is displayed. A user input comprising a selection of a source data input field to be used to determine a data value for the first destination field is received. The displayed user interface is updated to include a representation of the selected source data input field and a control, wherein user interface code with which the displayed user interface is associated is responsive to provide via the displayed user interface a set of structurally valid options to further define how the data value for the first destination field is to be determined.

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.

Abstract: A computing device for interpreting a data model algorithm includes an object searcher, an interpreter, and a translator. The object searcher is configured to search for attributes within datasets generated from at least one method of an instantiation of the data model algorithm in a development mode workflow. The interpreter is configured to evaluate the attributes, identify attributes having a use type, identify the type information of the identified attribute, and create data schema using the identified attributes and type information. The use type can be determined based on attribute values or an interface type associated with an identified attribute. The translator is configured to compare the data schema with another data schema in response to selecting the data model algorithm for inclusion in a production mode workflow.

Abstract: A system, method, and computer-readable medium for performing a variable and interaction detection operation. In certain embodiments, the variable and interaction detection operation detects important variables and as well as interactions among certain important variables in big data. In certain embodiments, the variable and interaction detection operation includes a robust automated process flow (i.e., an analytic recipe) which identifies important variables and their interactions in large data sets.

Abstract: Techniques to manage virtual memory are disclosed. In various embodiments, a time domain page access signal of a page is transformed to a frequency domain to obtain an access frequency. The access frequency is used to manage storage of the page in a page cache in memory. The access frequency may be used to evict the page from the page cache or, in some embodiments, to predictively load the page into the page cache.

Abstract: A system, method, and computer-readable medium for performing an auto-query construction operation for use with a distributed analytics operation. More specifically, in certain embodiments, the auto-query construction operation provides automatically generates SQL code instructions via an auto-query construction user interface (UI) settings in a computational system, such as the Dell Statistica computational system. The auto-query construction operation allows a user to interact with a common interface to provide query information including decision variables, parameters of an analysis and convergence criteria. The query information provided via the UI is automatically transformed to database queries and subsequent computation system operations. Thus, the user experience remains intact whether the analytics is performed in database or within the computation system.

Abstract: Techniques to provide and deploy a business process are disclosed. A business process source filed stored in a memory is received. The business process source file, as stored in memory, is provided as an input to an in-memory compiler. A business process class file stored in the memory is received as output from the in-memory compiler. The business process class file, as stored in memory, is provided to a business process engine class loader.