A method and system for identifying garbage data, an electronic device, and a storage medium. The method includes: uploading an object to a distributed object storage system; acquiring a head object identifier in an index storage pool of the distributed object storage system; querying a data storage pool for a target data group corresponding to the head object identifier; marking a tail object corresponding to a tail object identifier in the target data group as a target tail object; and marking tail objects in the data storage pool other than the target tail object as garbage data.

Implementations described herein relate to methods, systems, and computer-readable media to automatically compute features from a data stream. In some implementations, a method includes receiving a data stream comprising a plurality of data fields. The method further includes storing at least three copies of data in the data stream in a database, wherein each copy is stored on a corresponding hardware storage device. The method further includes performing, one or more rollup operations on a first copy of the data to compute one or more aggregate features and storing the one or more aggregate features on each of the hardware storage devices. The method further includes receiving a request for a particular aggregate feature and providing the particular aggregate feature in response to the request.

Provided is a method for executing a query in a DBMS. The method may include: receiving a query statement for requesting BLOB data serialized to a binary large object (BLOB) type and stored in a pivot table; determining name information for one or more columns generated as a result of a pivot operation included in respective pivot columns of the pivot table by querying a pivot name table for the pivot table; determining internal storage type information of the pivot table by querying a pivot meta table for the pivot table; and generating output information for the BLOB data corresponding to the query statement based on the determined name information and internal storage type information.

Provided is a method for executing a query in a DBMS. The method may include: receiving a query statement including a pivot operator—the query statement being associated with an original table; executing a pivot operation for the original table in response to reception of the query statement; generating BLOB data by serializing at least a part of an execution result of the pivot operation to a BLOB type; and storing the generated BLOB data in a pivot table.

System, method, and various embodiments for providing a hybrid data storage and load system are described herein. An embodiment operates by receiving a lookup request comprising a row identifier (ROWID) corresponding to a row of a table of a database. Which of a first set of pages corresponds to a sorted position of the ROWID is identified. The identified page of the first set of pages is loaded from the disk storage to the memory storage. A sorted row position of the ROWID is identified from a memory block of the loaded page from the first set of pages. One of a second set of pages that includes the sorted row position is identified. The identified one of the second set of pages is loaded from the disk storage into memory. The loaded identified one of the second set of pages is searched for the unsorted position of the ROWID.

Disclosed is a method, a device, and/or a system of ephemeral control of a data resource according to a use policy. In one embodiment, a method for ephemeral control of a data resource includes receiving a request from a device to use a protected resource stored in a non-hierarchical data structure. A use policy defining an authorized context for which the device can use the protected resource based on one or more contextual value is extracted from a data node. A set of use terms is generated and returned to the device. The protected resource is then streamed to the device, where the device includes a process to enforce ephemerality of the protected resource by maintaining a ledger comprising data identifying the protected resource that is in an active use by the device and a corresponding instance of the use terms associated with the authorized use of the protected resource.

A method includes receiving data objects, determining a predicted lifespan of each data object, and instantiating multiple shard files. Each shard file has an associated predicted lifespan range. The method also includes writing each data object into a corresponding shard file having the associated predicted lifespan range that includes the predicted lifespan of the respective data object and storing the shard files in a distributed system. The method also includes determining whether any stored shard files satisfy a compaction criteria based on a number of deleted data objects in each corresponding stored shard file. For each stored shard file satisfying the compaction criteria, the method also includes compacting the stored shard file by rewriting the remaining data objects of the stored shard file into a new shard file.

Methods and systems for serverless data deduplication are disclosed. A blob of data is received at a cloud services platform, where the blob of data includes incremental data. The blob of data is used to create an object in a first object store included in the cloud services platform. A function as a service (FaaS) function is triggered when the object is created. The FaaS function deduplicates the object to generate a deduplicated object. The deduplicated object is stored in a second object store included in the cloud services platform.

A system and method to capture and interact with a comprehensive digital record of an individual's biographical history and produce a synthetic model of their personality. The captured biographical history is a detailed record of this individual's actions, interactions, and experiences over a period which may span decades of their lifetime. The biographical history is indexed by areas of data variability and neural network confidence variability to identify points of likely human interest. A synthetic personality model is generated as a representation of the individual's personality structure, biases, sentiments, and traits. The synthetic personality can be interacted with through a digital interface and demonstrates the interaction patterns, triggers, and habits of the original individual. The functioning and the performance of the system over an individual's lifespan are optimized through data synthesis and disposition.

A geographic database may include binary data including list portions and/or non-list portions. A computing device may identify a list in a first binary large object (BLOB) for navigation data including road segments and road attributes. The first BLOB is segmented based on the byte list portion into a plurality of elements each having a predetermined size. The computing device may define a series of operations for the multiple elements to transform the first BLOB to a second BLOB.