Abstract: Relevance optimized representative content associated with a data storage system is disclosed. One example is a system including a data summarization module, a clustering module, and a representative content selection module. The data summarization module associates, via a processor, each data object in a storage system with a derived data object. The clustering module determines clusters of similar data objects based on a similarity between associated derived data objects, and selects a representative data object for each determined cluster. The representative content selection module selects representative content associated with the storage system, where the representative content is based on the data objects, the derived data objects, and the representative data objects, and relevance optimizes of the selected representative content to an analytics application.

Abstract: In an example, a distributed data access system compares a first lineage of a first analytics application with a second lineage of a second analytics application. The first analytics application may be run in a first container hosted by a first computing node and the second analytics application may run in a second container hosted by a second computing node. Based on the comparison, the distributed data access system determines whether a first insight generated by the first analytics application can be used in a processing of the second analytics application.

Abstract: A source system generates snapshots of collected data. The snapshots have respective associated time references. Responsive to a request from a target system for data collected over a time interval, the source system generates a subset of the data collected by determining a start snapshot and an end snapshot. The start snapshot and the end snapshot are determined as a pair of snapshots that have respective associated time references that are most closely spaced and are inclusive of the time interval. The source system determines a difference in the data included in the end snapshot and the start snapshot and provides the subset of the data as the difference in the data included in the end snapshot and the start snapshot.

Abstract: Example implementations relate to partial file system instances. In an example, a subset of objects of a source file system instance on a source system are replicated to a target system to form a partial file system instance on the target system comprised of the subset of objects. Each of the objects of the source file system instance is identified by a signature based on content of each of the objects and the objects exhibit a hierarchical relationship to a root object in the file system instance. An unmaterialized object is dynamically added to the partial file system instance by replicating the corresponding object from the source file system instance. The target system is asynchronously updated from the source file system instance based on a comparison of the partial file system instance to the source file system instance.

Abstract: Example aspects for transformation of video streams include searching for a first signature of a segment of a video stream in an index comprising a first level signature of each of a plurality of stored segments. In response to identifying a first set of similar segments from the stored segments, a second signature of the segment may be determined. In response to identifying a second set of similar segments from the first set of similar segments based on the second signature, a matching segment may be ascertained from the second set of similar segments. The matching segment may be provided for being stored in place of the segment in a storage medium.

Abstract: Example implementations relate to performing computations using provenance data. An example implementation includes storing first lineage data of a first dataset and provenance data of an application operating on the first dataset in a storage system. A computing resource may determine whether second lineage data of a second dataset meets a similarity criterion with the first lineage data of the first dataset. A computation on the second dataset may be performed using the provenance data of the application, and an insight of the second dataset may be generated from the performed computation.

Abstract: Ternary content addressable memory (TCAM) structures and methods of use are disclosed. The memory architecture includes one or more ternary content addressable memory (TCAM) fields, and control logic that applies progressively discriminating data-masking and scores a closeness of a match based on matched and mismatched bits.

Abstract: Examples disclosed herein relate to data provisioning for an analytical process based on lineage metadata. In an example, a value of a parameter related to execution of an analytical process on a remote hub device may be determined based on lineage metadata stored on an edge device, wherein the analytical process is part of an analytical workflow that may be implemented at least in part on the edge device and the remote hub device. In response to a determination that the value of the parameter is above a predefined threshold, the edge device may provide to the remote hub device, input data for a future execution of the analytical process in advance of execution of the analytical process on the remote hub device without a request for the input data by the remote hub device.

Abstract: Ternary content addressable memory (TCAM) structures and methods of use are disclosed. The memory architecture includes one or more ternary content addressable memory (TCAM) fields, and control logic that applies progressively discriminating data-masking and scores a closeness of a match based on matched and mismatched bits.

Abstract: Examples disclosed herein relate to re-execution of an analytical process based on lineage metadata. In an example, a determination may be made on a hub device that an analytical process previously executed on a remote edge device is to be re-executed on the hub device, wherein the analytical process is part of an analytical workflow that is implemented at least in part on the hub device and the remote edge device. In response to the determination, a storage location of input data for re-executing the analytical process may be identified based on lineage metadata stored on the hub device, and input data may be acquired from the storage location.

Abstract: Example aspects for transformation of video streams include searching for a first signature of a segment of a video stream in an index comprising a first level signature of each of a plurality of stored segments. In response to identifying a first set of similar segments from the stored segments, a second signature of the segment may be determined. In response to identifying a second set of similar segments from the first set of similar segments based on the second signature, a matching segment may be ascertained from the second set of similar segments. The matching segment may be provided for being stored in place of the segment in a storage medium.

Abstract: In an example, a distributed data access system compares a first lineage of a first analytics application with a second lineage of a second analytics application. The first analytics application may be run in a first container hosted by a first computing node and the second analytics application may run in a second container hosted by a second computing node. Based on the comparison, the distributed data access system determines whether a first insight generated by the first analytics application can be used in a processing of the second analytics application.

Abstract: A scoped search engine is disclosed. The scoped search engine includes a memory unit storing reference data records. The scoped search engine also includes a data comparison unit that searches the reference data records using different searches. The scoped search engine further includes a match analysis unit that combines result data from the different searches and determines a scope for a subsequent search based on the combined result data.

Abstract: Example techniques of data streaming between datacenters are described. In an example, a delta data may be replicated at a specific time interval from a first filesystem of a first stream-processing platform implemented at a first datacenter or source datacenter to a second filesystem of a second stream-processing platform implemented at a second datacenter or a target datacenter. The delta data indicates modifications to data of the data stream stored in the first filesystem, during the specific time interval.

Abstract: Example techniques of data management in a network environment are described. In an example, a semantic pattern in a data stream transmitted from a source device to an edge device in the network environment is determined. The semantic pattern indicates relevance of data samples in the data stream for analysis of the data stream. The data stream is processed based on the semantic pattern, for storage and transmission in the network environment.

Abstract: Examples include bypassing a portion of an analytics workflow. In some examples, execution of an analytics workflow may be monitored upon receipt of a raw data and the execution may be interrupted at an optimal bypass stage to obtain insights data from the raw data. A similarity analysis may be performed to compare the insights data to a stored insights data in an insights data repository. Based, at least in part, on a determination of similarity, a bypass operation may be performed to bypass a remainder of the analytics workflow.

Abstract: Ternary content addressable memory (TCAM) structures and methods of use are disclosed. The memory architecture includes one or more ternary content addressable memory (TCAM) fields, and control logic that applies progressively discriminating data-masking and scores a closeness of a match based on matched and mismatched bits.

Abstract: Example implementations relate to a storage memory direct access (SMDA) provider. The SMDA provider may pin a storage memory region to a memory address of a consumer machine, the storage memory region corresponding to a storage range of a storage device requested by the consumer machine. The SMDA provider may atomically commit data in the storage memory region accessed by the consumer machine via the memory address.

Abstract: Ternary content addressable memory (TCAM) structures and methods of use are disclosed. The memory architecture includes one or more ternary content addressable memory (TCAM) fields, and control logic that applies progressively discriminating data-masking and scores a closeness of a match based on matched and mismatched bits.

Abstract: A system comprises a hashing logic, which executes instructions to convert raw data into a first logical address and payload data, where the first logical address describes metadata about the payload data. A hardware translation unit executes instructions to translate the first logical address into a first physical address on a storage device. A hardware load/storage unit stores the first logical address and the payload data at the first physical address on the storage device. A content addressable store is associated with a reference to the logical addressed data in this location addressable high dimensional store, where the content addressable store is searched for the desired content word using at least one temporal attribute to retrieve the corresponding references with low latency. A hardware exclusive OR (XOR) unit compares two logical address vectors to derive a Hamming distance between the two logical address vectors.