Abstract: An Artificial Intelligence (AI)-based data processing system processes current data to determine if the quality of the current data is adequate to be provided to data consumers and if the quality is adequate, the current data is further analyzed to determine if an impacted load including changes to dimension data of the current data or an incremental load including changes to fact data of the current data is to be provided to the data consumers. Depending on the amount of data to be provided to the data consumers, processing units (PUs) may be determined and assigned to carry out the data upload. Various machine learning (ML) models that are used to provide predictions from the current data are analyzed to determine the quality of predictions and if needed, can be automatically retrained by the data processing system.

Abstract: One or more techniques and/or systems are provided for implementing a pipeline used to generate, train, test, and implement a document scoring model for assigning document scores to documents. Features from various sources are combined to create a joined page level feature set, a joined domain level feature set, and a host level feature set. Numerical features and content features are extracted from ground truth documents and random documents. The numerical features are joined with the joined feature sets to create a set of joined features. The document scoring model is trained using the set of joined features and a training technique. A document is scored with a document score using the document scoring model based upon the content features and the set of joined features with document scores obtained during training.

Abstract: Systems for application configuration backup and restore. Techniques establish high frequency restore points of application configuration metadata. Processing commences upon receiving instructions to periodically take snapshots of application configuration metadata. To avoid incurring unnecessary costs to process snapshots of application configuration metadata when the application configuration metadata is slow-changing, the method performs a low-cost check to get the latest update time of the application configuration metadata. That time is compared to the time of the most recent snapshot of the application configuration metadata. When the time of the latest update time of the application configuration metadata is equal to the time of the most recent snapshot of the application configuration metadata, then rather than incurring the cost of generating a duplicate snapshot, the method waits for a duration without generating a new snapshot.

Abstract: A computer-implemented method, a computer program product, and a computer system for measuring relevance of datasets to data science models. One or more servers implement steps: extract keywords in each data science model; determine first relative frequencies of the respective keywords in each data science model, for each source group in the data science models; extract keywords in each dataset; determine second relative frequencies of the respective keywords in each dataset, for each source group in the datasets; determine weights of the keywords; calculate first aggregated relevant scores of the respective keywords in each data science model, based on the first relative frequencies and the weights; calculate second aggregated relevant scores of the respective keywords in each dataset, based on the second relative frequencies and the weights. One or more servers calculate similarity between vectors of the first and second aggregated relevant scores, based on a similarity measure between vectors.

Abstract: Embodiments provide for intelligent auto filing of documents to enterprise content management workspaces. One embodiment of a method includes receiving a document to auto file to a workspace of a content management system; detecting an indicator of an entity from the text of a document, the indicator of the entity corresponding to a value of a workspace attribute; determining a result set of candidate records based on querying a set of workspace data for workspaces with the workspace attribute value corresponding to the indicator; detecting mentions in the document text that match attribute values from the candidate records; generating a score for each candidate record based on the mentions detected in the text that match the attribute values from the candidate record; linking the document to an entity based on the scores for the candidate record; and automatically storing the document to a workspace based on the linking.

Abstract: Described are systems, methods, and techniques for collecting, analyzing, processing, and storing time series data and for evaluating and determining whether and how to include late or delayed data points when publishing or storing the time series data. Maximum delay values can identify a duration for waiting for late or delayed data, such as prior to publication. In some examples, maximum delay values can be dynamically adjustable based on a statistical evaluation process. For late or delayed data points that are received after the maximum delay elapses, some data points can be included in the stored time series data, such as if they are received in the same order that they are generated.

Abstract: Example techniques and systems may obtain one or more search types associated with a geographic location of a computing device. In one example, a technique may include determining, by a mobile computing device, a geographic location of the mobile computing device and obtaining one or more search types associated with the geographic location. Responsive to obtaining the one or more search types, the technique may also include outputting, by the mobile computing device and for display, a graphical user interface comprising at least an indication of the one or more search types associated with the geographic location.

Abstract: The subject technology receives a query directed to a set of source tables, each source table organized into a set of micro-partitions. The subject technology determines a set of metadata, the set of metadata comprising table metadata, query metadata, and historical data related to the query. The subject technology predicts, using a machine learning model, an indicator of an amount of computing resources for executing the query based at least in part on the set of metadata. The subject technology generates a query plan for executing the query based at least in part on the predicted indicator of the amount of computing resources. The subject technology executes the query based at least in part on the query plan.

Abstract: Methods and systems for generating multi-operator data transformation pipelines. An example method includes accessing raw data for transformation; receiving a selection of a target table or target visualization, wherein the target table or target visualization is for data other than the raw data; extracting table properties and target constraints; and based on the extracted table properties and target constraints, synthesizing one or more multi-operator data transformation pipelines for transforming the raw data to a generated table or generated visualization.

Abstract: Systems and methods are disclosed comprising a data transfer and search facility adapted to access content, such as variables and cases, stored on a plurality of disparate computer content storage facilities. The variable data may comprise scientific data. The access of variables may be performed over an encrypted network. A user filter specification may be received and corresponding variables or cases may be identified using a search engine, the user may select which of the identified variables are to be transferred, and the variable data may be transferred from a source storage facility to a destination file. Missing variables values may be identified and a determination is made whether the identified number of missing variables values exceeds a threshold. Encoding of variable data is performed. The variable data may be provided to one or more statistical data analysis and processing applications.

Abstract: Technologies are described for facilitating database system recovery in a distributed database environment having a master node and at least one slave node. The master node maintains an in-memory reference that includes transaction identifiers and their corresponding commit identifiers. When a slave node requests that a commit identifier be assigned to a transaction, the transaction and commit identifiers for the transaction are added to the in-memory reference. The commit identifier is sent by the master node to the slave node. The slave node writes a log entry to a commit log that includes the transaction identifier and the assigned commit identifier. If the database system is interrupted before the slave node writes the commit log entry, the slave node can request the commit identifier for the transaction from the master node, which retrieves the identifier from the in-memory reference.

Abstract: A system and method for performing large-scale data processing using a statistical programming language are disclosed. One or more high-level statistical operations may be received. The received high-level statistical operations may be dynamically translated into a graph of low-level data operations. The unnecessary operations may be removed and operations may be fused or chained together. Operations may then be grouped into distributed data processing operation. The low-level operations may then be run.

Abstract: Data sources provide access to data. The data stored by the data source may be transformed before use by an application. Different data sources support different transformations. A data agent sidecar for the application accepts work orders from the application and submits work orders to data sources. A work order identifies a data source from which data is requested. The work order optionally includes one or more transformations to be applied to the data from the data source. The data agent sidecar determines, for the data source from which data is requested, which transformations can be performed by the data source and which transformations are not supported by the data source. The data transformations that can be performed by the data source are included in the work order to the data source. The remaining data transformations are performed by the data agent sidecar.

Abstract: Methods, systems, and computer-readable media for content importing with discovery in a collaborative environment are disclosed. A content sharing system retrieves a document from a data source using a connector associated with the data source. The document is retrieved using one or more credentials associated with a user. The content sharing system retrieves metadata associated with a plurality of additional documents stored by the data source. The metadata is retrieved using the one or more credentials associated with the user. The content sharing system determines, based at least in part on the metadata associated with the plurality of additional documents, one or more of the additional documents that are anticipated to be relevant to the user. Data descriptive of the one or more of the additional documents is presented to the user via a user interface.

Abstract: Users are most likely to search for documents “around” them, such as documents which are created, read, or modified by other users they work closely or collaborate with, documents on sites the user frequents, and/or documents trending around them, in addition to documents they've created or interacted with themselves. Therefore, embodiments are directed to relevance ranking of query results based on external context. A search service may receive a query and an external context list, determine a relevance value for each entry in the external context list, and perform the query, where a search index comprising items may be returned in response to performance of the query. The search service may then match entries in the external context list against associated properties of the items, compute a score for the items based on the matching, and rank results of the query based on the score computed for the items.

Abstract: A write request is transmitted from a client to a metadata server (“MDS”), wherein the write request comprises an object identifier associated with a data object. An object store location is received for an object store from the MDS. A metadata request is transmitted to the object store using the object store location, wherein the metadata request includes the object identifier. A metadata response is received from the object store. Determine the metadata response contains an object designator. A count associated with a mapping between the object identifier and the object designator is incremented, wherein the mapping resides on an object version manager shared with a second MDS.

Abstract: A device may obtain a location. The device may process the location to form a processed address. The device may obtain, from a plurality of geocoders, a plurality of sets of geographic coordinate candidates for the processed address. The device may obtain confidence information regarding the plurality of sets of geographic coordinate candidates. The device may determine a set of geographic coordinates for the processed address based on the plurality of sets of geographic coordinate candidates and the confidence information regarding the plurality of sets of geographic coordinate candidates. The device may assign a key to the processed address and the set of geographic coordinates for the processed address. The device may store, in a storage device, the key in association with the processed address and the set of geographic coordinates to facilitate access to the processed address or the set of geographic coordinates for the processed address.

Abstract: A system creates and searches knowledge base (KB) articles inside an organization while supporting operation as a SaaS (Software as-a-Service) across a plurality of organizations. The system implements a real-time online learning-to-rank (L2R) algorithm for learning relevance scoring that is customized to each organization. This algorithm incorporates rich lexical features using a query similarity kernel. A scoring function includes a pairwise static module, which may be trained off-line using training data, and a lexical adaptive module, which is trained on-line based on user feedback. The scoring function makes the system easy to deploy, modify, and suitable for handling events that naturally happen over the lifecycle of any KB deployment, without manual training.

Abstract: Load Plan Generator (LPG) is a BIAPPS utility for generating ODI load plans based on desired subset of fact tables for loading BIAPPS Data Warehouse. The tool simplifies the configurations process by minimizing the manual steps and configurations and provides a guided list of configurations steps and checklists. The load plan components can include different sets of load plans that will be stitched together by the load plan generator to create one load plan for loading chosen fact groups in the warehouse sourcing from different transaction systems.

Abstract: A device, system and method for providing descriptions to communication devices using machine learning generated templates is provided. A device replaces given word types in provided text files with corresponding tags to generate corresponding intermediate templates, the provided text files associated with a given topic. The device generates, for the given topic, one or more textual templates that include at least a portion of the corresponding tags, the textual templates in natural language sentences, the generating of the textual templates at least partially based on the corresponding intermediate templates. The device populate the corresponding tags in a textual template, of the textual templates, with corresponding words of a given data file associated with the given topic, to generate a respective description of a given item associated with the given topic, the given data file being specific to the given item.