Abstract: An apparatus comprises at least one processing device configured to collect, from a plurality of storage systems, data patterns for data stored in the plurality of storage systems and to cluster the plurality of storage systems into one or more data pattern sharing clusters based at least in part on the collected data patterns, a given one of the one or more data pattern sharing clusters comprising two or more of the plurality of storage systems. The at least one processing device is also configured to identify, for the given data pattern sharing cluster, a subset of the collected data patterns and to provide, to the two or more storage systems of the given data pattern sharing cluster, the identified subset of the data patterns, wherein the identified subset of the collected data patterns are utilized by the two or more storage systems in performing data deduplication.

Abstract: Devices and techniques are generally described for generating personalized search query suggestions. In some examples, a first search query entered by a user into a search field may be determined. A first keyword associated with the first search query may be determined based at least in part on past interactions of the user with content. In various examples, first string data comprising at least a portion of the first search query and the first keyword may be generated. A data store may be searched using the first string data. A first suggested search string associated with the first string data may be determined. In some examples, the first suggested search string may be displayed on a display in association with the search field.

Abstract: A method of obtaining a response to a query inputted by a user, the method comprising: receiving a user inputted query; representing the user inputted query as a sequence of embedding vectors using a first model; encoding the sequence of embedding vectors to produce a context vector using a second model; retrieving responses with associated response vectors; scoring response vectors against the context vector, wherein the scoring is a measure of the similarity between the context vector and a response vector; and outputting the responses with the closest response vectors, wherein the first model is configured to segment a user inputted query into a sequence of units from a vocabulary of units and represent each unit in the sequence as an embedding vector, wherein at least one of the units in the vocabulary is an incomplete word, and wherein the first model comprises parameters that are stored using eight bits per parameter; and wherein the second model has been trained using corresponding queries and response

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for transition-coded media, measuring engagement of transition-coded media, and distribution of components of transition-coded media. For instance, the method may include: obtaining a media file; determining whether the media file includes transition-coded media; in response to determining the media file includes the transition-coded media, setting up a dynamic media environment; detecting whether a user is interacting with the transition-coded media; and in response to detecting the user is interacting with the transition-coded media, executing the transition-coded media with the dynamic media environment.

Abstract: In some embodiments, a method includes extracting metadata of a set of assets and providing the metadata to a machine learning model to define a tree data structure including a leader subtree and a follower subtree that is dependent upon a portion of a hierarchical classification structure of the leader subtree. The method further includes matching the metadata to attributes assigned to classification nodes within the tree data structure to map the set of assets into data nodes of the tree data structure. The method further includes parsing a query to traverse the tree data structure to locate an asset based at least in part on the query attribute and at least one of the attributes assigned to the classification nodes, and, after and/or in response to parsing the query, sending a signal representing the asset and/or a location of the asset.

Abstract: A partial group by operator is a group by operator that implements a fallback mechanism. The fallback mechanism is triggered whenever memory pressure reaches a certain threshold. When the fallback mechanism is triggered, a row is included in an output of the partial group by operator without including an aggregation value for a grouping value for the row to an aggregation data structure. A final group by operator computes a final aggregate value of all results, including pre-grouped results and passed through results, from the partial group by operator.

Abstract: Systems and methods for classifying job search queries for improved precision using a machine-trained classifier are provided. In example embodiments, a network system receives a job search query including one or more terms from a device of a user. The network system extracts one or more features from the job search query, whereby the one or more features are derived from the one or more terms. Based on the one or more features, a machine-learned model of the classifier determines whether to use a title field search process or a compound search process. Based on the determining, the network system formats the job search query into a corresponding machine-language format and performs the job search query to derive results. The network system causes presentation of the results on the device of the user.

Abstract: Various embodiments are provided for intelligent management of data flows in a computing environment by a processor. One or more data transformation in time-series data applications templates may be created and managed according to concepts, one or more instances of the concepts, relationships between the concepts, and a mapping of the concepts to one or more data sources.

Abstract: Techniques for a context preservation during server restart are described. A restart of a database server having at least one open communications session with a database client is initiated. The database server can be executed by a compute instance hosted by a computer system of a provider network. A restarted database server is launched. The restarted database server obtains session state data to resume the at least one open communications session with the database client, the session state data including one or more session variables. The restarted database server receives a transaction from the database client.

Abstract: Embodiments relate to a system, computer program product, and method to merge two or more heterogeneous datasets. Seed attributes of each dataset that is the subject of the merge are identified. The seed attributes are derived from candidate attributes of the respective datasets. A correlation is assessed to create a set of mergeable attributes and a set of non-mergeable attributes. A cohesiveness characteristic is leveraged to iteratively identify one or more attributes from the set of non-mergeable attributes, and to amend the set of mergeable attributes with the one or more attributes identified in the set of non-mergeable attributes. A merged dataset based on the amended set of mergeable attributes and representing non-trivial similarities between the first and second dataset is formed as output.

Abstract: Aspects of the current subject matter are directed to providing query results to clients by incorporating pause and/or detach operations into the query execution after a first fetch of results. The pause and the detach operations for query execution consistent with implementations of the current subject matter result in reduced times and conserved resources in providing query results by computing only results necessary to satisfy a first fetch call prior to computing results necessary to satisfy subsequent fetch calls.

Abstract: An electronic device, including a compression subsystem with a comparator, a history buffer, a match detector, and a command generator, performs operations for generating compressed data from original data. The compression subsystem, starting in each cycle of a clock, processes a search string that is copied from original data to generate commands for compressed data. For processing each search string, the comparator compares each of N substrings from the search string with stored data from the history buffer to find matches between the substrings and the stored data. The match detector then determines a longest match for each of the substrings. The command generator next selectively outputs commands for the compressed data based on the longest matches for the substrings.

Abstract: The disclosure discloses a method for analyzing educational big data on the basis of maps. The method includes acquiring educational resource data and storing the educational resource data into databases according to certain data structures; constructing theme map layers for each analysis theme, classifying and indexing data according to the analysis themes, and superimposing the theme map layers onto base maps to form data maps; analyzing data of the theme map layers according to the analysis themes and acquiring theme analysis results; extracting the data of the multiple theme map layers in target regions, fusing the data and acquiring region analysis results; acquiring learning preference of users; combining the learning preference of the users according to content of user requests and searching the region analysis results in response to the user requests. The disclosure further discloses a system for analyzing the educational big data on the basis of the maps.

Abstract: Aspects of the current subject matter are directed to executing queries on tables in which one or more columns contain a single value. Upon execution of a query, columns in which a single value is contained are identified, and a pre-compiled code entry containing relevant identifying information is compiled as part of a query execution plan. The query execution plan is used for subsequent query executions, alleviating the need to access the columns during the subsequent query executions that involve the columns. A fingerprint value may be used to track if changes to relevant tables occur.

Abstract: Categorical data transformation and clustering techniques and systems are described for machine learning using natural language processing. 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 using natural language processing. Data is then generated by the computing device that includes a plurality of latent classes. This is performed by clustering the numerical data into a number of clusters that is smaller than the number of classes in the categorical data.

Abstract: A data management method is provided. To-be-migrated base data in a first service data set is obtained, mirror data that are same as the base data are generated, and the base data to a second service data set are migrated. Incremental data obtained for the base data are recorded in the migration process of the base data. Adding processing is performed on the mirror data by using the incremental data, and the incremental data are migrated to the second service data set, when the migration process of the base data is completed. The mirror data and the incremental data in the first service data set are cleared when the migration process of the incremental data is completed.

Abstract: Implementations are described herein for analyzing existing interactive web sites to facilitate automatic engagement with those web sites, e.g., by automated assistants or via other user interfaces, with minimal effort from the hosts of those websites. For example, in various implementations, techniques described herein may be used to abstract, validate, maintain, generalize, extend and/or distribute individual actions and “traces” of actions that are useable to navigate through various interactive websites. Additionally, techniques are described herein for leveraging these actions and/or traces to automate aspects of interaction with a third party website.

Abstract: Provided is a system and method for reconstructing and visualizing transformation steps that are performed to an optimized database query. In one example, the method may include receiving a database query including an initial set of execution steps, generating a plurality of alternative sets of execution steps for the database query based on transformations to the initial set of execution steps, selecting an alternative set of execution steps from among the plurality of alternative sets of execution steps based on a performance of the alternative set of execution steps, identifying transformations that are used to transform the initial set of execution steps into the selected alternative set of execution steps, and displaying information about the identified transformations via a user interface.

Abstract: Embodiments relate to a computer platform and corresponding process and program code to assess multimedia files with respect to similarity and duplicate media content. Data streams are converted into sequences of events, and object representation within the streams is identified and subject to processing with respect to the event sequences. A similarity assessment is conducted between two or more of the data streams, and a corresponding distance measurement to quantify similarity is produced. Duplicate data is selectively identified in response to the similarity assessment and the produced distance measurement.

Abstract: In an embodiment, a method of sketching using a hybrid quantum-classical system includes creating a set of clustered data sets from a first data set. In an embodiment, the method includes evaluating, using a quantum processor and quantum memory, the set of clustered data sets. In an embodiment, the method includes evaluating, using the quantum processor and quantum memory, a set of quality metrics for the set of clustered data sets. In an embodiment, the method includes reclustering, responsive to at least one of the set of quality metrics failing to meet a quality criterion, the first data set.