Abstract: A blockchain node obtains first service data. An attribute value of the first service data is determined, where the attribute value is used to represent uniqueness of the first service data. The blockchain node determines whether the first service data has been processed based on stored attribute values of second service data that has been processed and the attribute value of the first service data. In response to determining that the first service data has not been processed, the blockchain node processes the first service data.

Abstract: A system and method for faster access for compressed time series data. A set of blocks are generated based on a table stored in a database of the data platform. The table stores data associated with multiple sources of data provided as consecutive values, each block containing index vectors having a range of the consecutive values. A block index is generated for each block having a field start vector representing a starting position of the block relative to the range of consecutive values, and a starting value vector representing a value of the block at the starting position. The field start vector of the block index is accessed to obtain the starting position of a field corresponding to a first block and to the range of the consecutive values of the first block. The starting value vector is then determined from the block index to determine an end and a length of the field of the first block.

Abstract: Methods, systems, devices and/or apparatus related to finding and sharing information on the Internet. Specifically, the disclosed methods, systems, devices and/or apparatus relate to utilizing reference materials to find and share topical information on the Internet.

Abstract: A system and method include storing a table of time series data in a database of a data platform, the table of time series data representing a set of time series blocks. Each time series block of the set of time series blocks has a time series of equally-incremented time intervals and a run length. Each time interval of the time series is associated with one or more values. The run length has a starting position with at least one starting value and an ending position with at least one ending value. The starting position and the at least one starting value is stored for each time series block in a column store of the database. Then, a compressed index is generated in the column store of the database for each time series block, the compressed index comprising the starting position and the at least one starting value.

Abstract: The subject technology receives an operation to perform on an external data source accessible via a network, the external data source being hosted by an external system separate from a network-based database system. The subject technology determines a set of shards corresponding to the external data source. The subject technology determines a set of offsets of each shard of the set of shards. The subject technology, based on the set of shards and the set of offsets, performs the operation on the external data source. The subject technology provides an indication that the operation is complete.

Abstract: An analysis system is configured to acquire data of a first numeric feature combination by referring to the parent population data. The data of the first numeric feature combination includes a score of a target indicator for each of category combinations. Each category combination of the category combinations is composed of categories of the numeric features of the first numeric feature combination. The analysis system is configured to determine a score at coordinates corresponding to each of the category combinations in a space having each of the numeric features of the first numeric feature combination as an axis, calculate a gradient feature vector representing a gradient for the score in the space, and determine, based on the gradient feature vector, whether to include the first numeric feature combination in numeric feature combinations to be extracted from the parent population data.

Abstract: An approach includes a system implemented in a computer infrastructure including a processor. The approach further includes the processor configured to receive dimensionally aware linkages. The approach further includes the processor configured to retrieve aggregated data from a data set. The approach further includes the processor configured to identify at least one affinity cluster related to the dimensionally aware linkages in at least two dimensions and the retrieved aggregated data by performing at least one lookup linkage which matches at least one dimension of the at least two dimensions of the dimensionally aware linkages with the retrieved aggregated data. The approach further includes the processor configured to provide personalized content to a user having same or similar user preferences as the received dimensionally aware linkages based on the identified at least one affinity cluster.

Abstract: Various embodiments of systems and methods for internet of things (IoT) data analysis are described herein. In an aspect, the method includes receiving user's input for data fields which are configured for retrieving data from a data repository. The data repository stores data related to one or more IoT devices. A structured query language (SQL) statement corresponding to the received user's input is generated. Based upon the generated SQL statement, data from the data repository is retrieved. A visual representation for displaying the retrieved data is identified. The retrieved data is rendered based upon the identified visual representation.

Abstract: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

Abstract: Provided is a method and system for transforming a GUID of a database entry into a reduced identifier. The transformation may be performed by a bijective function. In one example, the method may include one or more of receiving a database entry which includes a global unique identifier (GUID) which uniquely identifies the database entry, identifying a transformation function associated with the database entry, transforming the GUID into a reduced identifier based on the transformation function wherein the reduced identifier has a reduced size with respect to a size of the GUID, and storing the database entry based on the reduced identifier.

Abstract: An approach is provided for storing a snapshot of an execution image of a virtual machine (VM) instance. Based on VM, application, criticality, and change parameters, and based on a recommendation model trained on historical data about effects that changes caused by VM execution had on a capacity of a storage system that stores execution images of VM instances in first datastores, a tier is selected from multiple tiers of the storage system as the tier recommended for storing the snapshot. A datastore is selected for storing the snapshot so that the selected datastore is associated with the selected tier and selected from second datastores dedicated to storing only snapshots. The snapshot is created in the selected datastore and in the selected tier and unused space in a portion of the storage system for storing the first datastores is reduced.

Abstract: Techniques for processing relational data with a user-defined function (UDF) are provided. Relational input data being requested by the UDF, from within a relational database system, is intercepted and normalized. The UDF is called with the normalized input data and as the UDF produces output data in response to the normalized input data that output data is captured and normalized. In an embodiment, the normalized output data is used to dynamically update a data model within the relational database for the input data.

Abstract: In accordance with embodiments, there are provided mechanisms and methods for performing transparent object migration across storage tiers. In an embodiment and by way of example, a method for appending data to large data volumes is provided. The method embodiment includes a) setting a CustomEntityOption bit that determines (at object creation time) where the object is stored, either in the relational or the non-relational data store portion, b) loading the CustomEntityOption bit in a cached CustomEntityDefinition, c) showing the CustomEntityOption bit as EntityInfo, and d) allowing custom object definition and Metadata API functionality when the bit is shown.

Abstract: A mechanism is provided for determining distinct values of an attribute in a data table including the attribute and data records stored in a set of data blocks. Attribute value information is provided for each data block that comprises information indicating distinct values of the attribute in the data blocks. At least part of a current data block of the data table is scanned to determine current distinct values of the attribute. The current distinct values of the attribute of the current data block are added to a list of current distinct values of the data table. The scanning and adding are repeatedly performed for a further data block of the data table if the attribute value information of the further data block indicates at least one distinct value that is not present in the list. The list of current distinct values are then provided.

Abstract: In various embodiments, methods and systems for implementing distributed data object management are provided. The distributed data object management system includes a local metadata-consensus information store and one or more remote metadata-consensus information stores for metadata-consensus information and a local data store and one or more remote data stores for erasure coded fragments. For a write operation, corresponding metadata writes and data writes are performed in parallel using a metadata write path and a data write path, respectively, when writing to the local metadata-consensus information store and the one or more remote metadata-consensus information stores and the local data store and the one or more remote data stores. And, for a read operation, corresponding metadata reads and data reads are performed in parallel using a metadata read path and a data read path, respectively, when reading from the metadata-consensus information stores and the data stores.

Abstract: Certain example embodiments relate to a computer system for performing a map reduce sequence. Nodes therein include at least one processor and memory and are divided into at least mapper and reducer nodes. Each mapper node executes a map function on input to generate intermediate output elements. Each said intermediate output element includes a first key-value pair. Each element key includes associated map and reduce task identifiers. Each element value includes substantive data, organized as another key-value pair. The intermediate output elements are stored to memory. Each reducer node: retrieves at least intermediate output element values from the memory of a given mapper node, using specified map and reduce task identifiers; stores the retrieved element values to its memory; executes a reduce function on the retrieved element values, in order; and outputs a result from the reduce function for the map reduce sequence. Disk operations advantageously are reduced or eliminated.

Abstract: Certain embodiments involve ranking search results from an information retrieval system using user query data to provide relevant search results to users of the information retrieval system. For example, a system determines a weight factor associated with a first user that provides a query to the information retrieval system based on a type or role of the first user. The system further determines a boost factor associated with the first user based on the weight factor and a number of consecutive search queries provided by the user. The system uses the boost factor to automatically tune a ranking algorithm to adjust a rank of a search result item resulting from a search query provided by a second user.

Abstract: Exemplary embodiments of the present disclosure provide for cleansing data generated by one or more servers in response to database interactions resulting from an automated software robot interacting with the one or more servers via a telecommunications network. Log entries in usage logs corresponding to events during a session can be analyzed to determine relationships between events and the usage logs can be classified based on the relationships as either corresponding to human behavior or automated software robot behavior. Usage logs corresponding to automated software robot behavior can be removed from further analysis.

Abstract: Methods, including computer programs encoded on a computer storage medium, for indexing native application data. In one aspect, a method includes: receiving a partial search query, determining that the partial search query is classified as a symptom query, in response to determining that the partial search query is classified as a symptom query, identifying one or more differential diagnosis terms, generating a suggested search query based at least on (i) the partial search query, and (ii) one or more of the differential diagnosis terms, and providing the suggested search query in response to the partial search query.

Abstract: A messaging system provides recommendations of content that account holders of the messaging system might be interested in engaging with. In order to determine what to recommend, the messaging system generates a model of account holder engagement behavior organized by type of engagement. The model parameters are trained on differences between expected engagement behavior based on past data and actual engagement behavior, and include a set of common factor matrices that are trained using data from more than on engagement type. As a consequence, engagement behavior of other account holders with respect to other types of engagements different than the one sought to be recommended serves as a partial basis for determining what engagements of the sought-after type are recommended.