Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collecting data regarding resource views. In one aspect, a method includes generating a tag that includes instructions that, upon execution of the instructions, cause a device to perform operations including identifying content located in at least one particular portion of a structured resource. The particular portion can be specified by the instructions. The operations may include generating resource view data that includes parameters identifying the device and at least a portion of the identified content. The operations may include sending the resource view data to a server. A request for the tag may be received. The tag may be provided in response to the request.

Abstract: An integrated data pipeline can take advantage of a streaming service, which can handle tasks such as automated redelivery, as well as a processing service, which can allocate workers on a task- or event-specific basis. Event data is aggregated and compressed for delivery by the streaming service. The streaming service can deliver the data asynchronously to the processing service, which can disaggregate and decompress the data to obtain the original data records. The type of event for each record can be determined to determine whether the data should be processed using online and/or offline processing. For online processing the appropriate fields are determined and data extracted to be passed to the online processing services. For offline processing the record data is concatenated sequentially into mini-batches, then compacted into larger batch files that are stored for subsequent offline processing.

Abstract: A plurality of log messages may be clustered into a plurality of clusters. For each of the plurality of log messages, the log message may be partitioned into a series of substrings. At least two of the plurality of clusters may be selected. For each one of the at least two selected clusters, a parsing rule may be generated corresponding to a plurality of substrings each of which are at a given location of a respective one of the log messages of the plurality of log messages in the one of the selected cluster.

Abstract: A container group is created using a database deployment infrastructure (DI) administrator (HA). API privileges for the container group are granted, using the HA, to a container group administrator (GA). API privileges for a container created in the container group using the GA are granted, using the GA, to a container administrator (CA). API privileges for the container are granted, using the CA, to a container developer (CD). Schema privileges for the container are granted, using the CA, to a container consumer (CC). API privileges for the container group are revoked, using the HA, from the GA. The container group is dropped using the HA.

Abstract: An online system receives a search query from a user. In response to the request, the online system obtains search results matching the search query and identifies a set of attributes describing a context of the search query. The online system generates a data structure that includes a plurality of search coefficients. The search coefficients are selected based on the identified set of attributes. Some of the search coefficients have conflicting values. The online system traverses the data structure to identify the search coefficients having conflicting values. For each search coefficient having conflicting values, the online system resolves conflicts and determines a value of the search coefficient. The online system ranks search results based on the resolved values of the search coefficients.

Abstract: Systems and methods for just-in-time data quality assessment of best records created during data migration are disclosed. A data steward includes tools for creating and editing a best record creation strategy that defines how records from multiple systems will be integrated into target systems. At design time, the data steward can generate best record creation and validation rules based on the best record creation strategy. The data steward can apply the best record creation and validation rules to a sample of matched records from multiple data sources to generate a sample set of best records. The efficacy of the best record creation rules can be evaluated by assessing the number of fields in the sample set that fail the validation rules. During review, the validation rules can be applied to edits to the best records received from a human reviewer to ensure compliance with the best record creation strategy.

Abstract: The present teaching relates to rewriting a query and providing search results. In one example, a plurality of queries is obtained. For each of the plurality of queries, one or more search results are identified. The one or more search results have been obtained in response to the query and have been previously selected by a user submitting the query. A plurality of titles is obtained. Each of the titles corresponds to one of the one or more search results with respect to one of the plurality of queries. A model is generated based on the plurality of queries and the plurality of titles. The model is to be used for rewriting a query.

Abstract: A process for extracting and recognizing named entities from a short unstructured chat-style text input. The process may tokenize an inbound electronic message, and use a combination of entity specific classifiers and databases comprising known named entities such as gazetteer(s) to identify one or more named entities within the inbound electronic message. The identified named entities are then compiled as response message and transmitted to the user.

Abstract: Systems, methods, and computer-readable media are disclosed for an improved database. The systems, methods, and computer-readable media described herein may enhance the response time of databases and improve user experiences. In an example method described herein, a database may receive a search request for data stored in the database. The database may be stored in association with a b-tree to facilitate searching and retrieving data from the database. The method may include allocating a portion of a first memory for read ahead operations and performing the read ahead operations, wherein the read ahead operations comprise loading, into the portion of the first memory, data associated with one or more siblings of a current node, wherein the one or more siblings of the current node are one or more nodes that have a same number of parent nodes as the current node.

Abstract: Storage virtualization techniques allow files and directories to be stored remotely, for example, by a cloud storage provider, but in a manner that appears to a user or application running on a local computing device as if the files are stored locally—even though the data of those files and directories may not be resident on the local computing device. That is, the contents of files and directories that may exist in the cloud look and behave as if they were stored locally on a computing device. The level of hydration of placeholders may be monitored and operations may be performed to reduce the impact of excessive hydration on the resources of the computing device.

Abstract: An improved process for distributing data objects and a process for reducing skew in groups of data objects to be processed in parallel are provided herein. A request for parallel processing of a plurality of data objects is received. One or more groups for distributing the data objects are generated. Hash value intervals for the one or more groups are determined. Hash values for the plurality of data objects are determined. The plurality of data objects are distributed into the one or more groups based on their respective hash values and the hash value intervals. The plurality of data objects are processed in parallel by the groups comprising the distributed data objects. The processing results of the plurality of data objects are provided in response to the request.

Abstract: Categorical data transformation and clustering techniques and systems are described for machine learning. These techniques and systems are configured to improve operation of a computing device to support efficient and accurate use of categorical data, which is not possible using conventional techniques. In an example, categorical data is received by a computing device that includes a categorical variable having a non-numerical data type for a number of classes. The categorical data is then converted into numerical data based on clustering used to generate a plurality of latent classes.

Abstract: Implementations of the present disclosure are generally directed to linking entities in dynamic knowledge graphs, and more particularly, to linking entities in knowledge graphs based on contextual evolution in one or more dimensions.

Abstract: Many computer applications comprise multiple threads of executions. Some client application requests are fulfilled by multiple cooperating processes. Techniques are disclosed for creating and managing memory namespaces that may be shared among a group of cooperating processes in which the memory namespaces are not accessible to processes outside of the group. The processes sharing the memory each have a handle that references the namespace. A process having the handle may invite another process to share the memory by providing the handle. A process sharing the private memory may change the private memory or the processes sharing the private memory according to a set of access rights assigned to the process. The private shared memory may be further protected from non-sharing processes by tagging memory segments allocated to the shared memory with protection key and/or an encryption key used to encrypt/decrypt data stored in the memory segments.

Abstract: A processor receives statistical information about a data set included in a column of a data table. The processor receives additional information about the data set that indicates a data format utilized by the data set and a type of information represented by the data set. The processor generates a data dictionary for compression of the data set based, at least in part, on the statistical information and the additional information. The data dictionary is created such that the data dictionary is capable of compressing data that is statistically predicted to be received at a future point.

Abstract: Data sets are synchronized between two or more clusters of nodes that support different versions of files (e.g., stub files) within a distributed file storage system. Moreover, the distributed file storage system employs a tiered cloud storage architecture. In one aspect, for stub files having versions that are not commonly supported by the two or more clusters, an application protocol interface (API) is utilized that employs a deep-copy process wherein cloud-backed data referenced by a stub file is retrieved from a cloud storage (e.g., public cloud) and sent by a primary cluster to one or more secondary clusters. Moreover, the API can determine an optimal synchronization process on a per-file basis.

Abstract: Techniques related to real-time distribution and adjustment of content placement are disclosed. A database stores score values respectively corresponding to content items. Computing device(s) are coupled to the database and to digital data storage device(s) storing instructions that cause the computing device(s) to perform a method for adjusting, at runtime, visibility of a particular content item. The method comprises computing a random value for the particular content item based on an identifier for a communication session between the computing device(s) and a client device. The method further comprises generating an adjusted score value for the particular content item based on combining the random value with a particular score value corresponding to the particular content item. Still further, the method comprises sending, to the client device via the communication session, the content items including the particular content item, which is sorted among the content items according to the adjusted score value.

Abstract: Methods and apparatus are provided for reorganizing dynamically loadable namespaces (DLNs). In one exemplary embodiment, a method comprises the steps of, in response to a job submitted by an application, obtaining a DLN portion of a global single namespace of a file system, wherein the DLN is associated with the job and is maintained in a capacity tier of object storage of a storage system; obtaining one or more reordering directives from a user; rearranging one or more files in the DLN into a new directory hierarchy based on the one or more reordering directives to generate a reordered DLN; and dynamically loading the reordered DLN, including the metadata only for the reordered DLN, from the capacity tier of object storage into a performance tier of storage of the storage system for processing by the application. The reordered DLN is merged into the DLN following one or more modifications to the reordered DLN.

Abstract: In an embodiment, a cloud server determines a point in time in a current task, and identifies a corresponding point in time in each of a number of completed tasks retrieved from a first database. User activity data from a second database is used to calculate time spent on the current task up to the identified point in time, a ratio between time spent on each completed task after the corresponding point in time and time spent on the completed task before the corresponding point in time. The cloud server also calculates a similarity score between each completed task and the current task based on a number of parameters and using a predetermined algorithm. A weighted arithmetic mean of the time ratios can be calculated using the similarity scores as weights, and can be used in conjunction with the time spent on the current task up to the identified point in time, to calculate time required to complete the current task.

Abstract: A method, computer program product, and computing system for processing a data portion to divide the data portion into a plurality of data chunks; performing an entropy analysis on each of the plurality of data chunks to generate a plurality of data chunk entropies; and determining an average data chunk entropy from the plurality of data chunk entropies.