In one embodiment, a method comprises receiving a request for a particular user identification (ID) to perform a particular operation on a particular data object. An entitlement cache associates each operation that the particular user ID is entitled to perform with a first encoding of a tuple of a plurality of tuples. An object mapping cache associates each tuple of the plurality of tuples with a second encoding of each tuple of the plurality of tuples. An object mapping is used to determine a first tuple. The object mapping cache is used to determine a first vector of one of more left values based on the first tuple. The entitlement cache is used to determine a second vector of one or more value pairs. In response to identifying a match between the first vector and the second vector, the particular user ID is granted access to the particular data object.

A method for managing big metadata using columnar techniques includes receiving a query request requesting data blocks from a data table that match query parameters. The data table is associated with system tables that each includes metadata for a corresponding data block of the data table. The method includes generating, based on the query request, a system query to return a subset of rows that correspond to the data blocks that match the query parameters. The method further includes generating, based on the query request and the system query, a final query to return a subset of data blocks from the data table corresponding to the subset of rows. The method also includes determining whether any of the data blocks in the subset of data blocks match the query parameters, and returning the matching data blocks when one or more data blocks match the query parameters.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

A query directed to database data stored across a set of files is received. The query includes predicates and each file from the set of files is associated with metadata stored in a metadata store that is separate from a storage platform that stores the set of files. One or more files are removed from the set of files whose metadata does not satisfy a predicate of the plurality of predicates to generate a pruned set of files. One or more predicates are removed that are satisfied by the metadata of the pruned set of files to generate a modified query.

A machine vision device is provided including a camera, a processor, and a device memory including computer program code stored thereon. The computer program code is configured, when executed by the processor, to receive an image, from the camera, including at least one readable digital label associated with communication equipment, determine if an anchor label is present in the image, receive equipment information based on the anchor label and generate a search matrix based on the equipment information and the anchor label The search matrix includes one or more search matrix locations of assets associated with the communication equipment.

Embodiments are generally directed to compression for compression for sparse data structures utilizing mode search approximation. An embodiment of an apparatus includes one or more processors including a graphics processor to process data; and a memory for storage of data, including compressed data. The one or more processors are to provide for compression of a data structure, including identification of a mode in the data structure, the data structure including a plurality of values and the mode being a most repeated value in a data structure, wherein identification of the mode includes application of a mode approximation operation, and encoding of an output vector to include the identified mode, a significance map to indicate locations at which the mode is present in the data structure, and remaining uncompressed data from the data structure.

Systems and methods are provided for master-to-master OT-based artifact peering. A “master-to-master” architecture for artifacts is implemented in a network comprising a plurality of nodes and clients, where no node is designated a “master” or “primary” for a given artifact. A first node receives a subset of remote proposed operations from a second node and determines if a conflict exists between the received subset of remote proposed operations and at least one of a plurality of locally-proposed operations. The first node resolves the conflict based on a total-ordering agreed upon between the first node and the second node. The first node transforms at least one operation, either received or locally-proposed, based on the resolved conflict. The first node than updates a local log to include the transformed operation.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

Methods, apparatuses, or computer program products provide for triggering component workflows within a multi-component system. An update to one or more component metadata records of a component metadata vector associated with a first component identifier may be received. The component metadata vector may include a plurality of records. Each record of the plurality of records may include a unique component metadata record identifier and a component metadata value. The component metadata vector associated with the first component identifier may be traversed after updating the one or more component metadata records. Based at least in part on detecting a component metadata condition associated with a component workflow trigger associated with the first component identifier, a first component workflow action of a first component workflow action series comprising a plurality of component workflow actions may be executed. Furthermore, a component workflow trigger notification may be transmitted to a first computing device.

Adaptive data collections may include various type of data arrays, sets, bags, maps, and other data structures. A simple interface for each adaptive collection may provide access via a unified API to adaptive implementations of the collection. A single adaptive data collection may include multiple, different adaptive implementations. A system configured to implement adaptive data collections may include the ability to adaptively select between various implementations, either manually or automatically, and to map a given workload to differing hardware configurations. Additionally, hardware resource needs of different configurations may be predicted from a small number of workload measurements. Adaptive data collections may provide language interoperability, such as by leveraging runtime compilation to build adaptive data collections and to compile and optimize implementation code and user code together. Adaptive data collections may also provide language-independent such that implementation code may be written once and subsequently used from multiple programming languages.