Abstract: In a mechanism for deleting blocks from a blockchain, a retention period (T), a minimum number of blocks (L), and a hash of the oldest block are defined. A participant node creates the new block including a block header. The participant node determines whether a timestamp in an oldest block in the blockchain is older than a current time plus T, and whether a current number of blocks in the blockchain is greater than or equal to L. When both are true, the participant node deletes an oldest block in the blockchain. The participant node adds a hash of an oldest remaining block and an original number of blocks of the blockchain to the block header of the new block. The participant node broadcasts a message announcing the new block to remaining participant nodes of the plurality of participant nodes.

Abstract: A method includes acquiring one or a plurality of first tables including a first data group regarding a first item, acquiring a second table corresponding to a result of executing a program with the one or the plurality of first tables set as an input, the second table including a second item and a second data group, generating, when a first name of the first item and a second name of the second item have a relationship in which the first name partially matches the second name, first information indicating the relationship between the first name and the second name while defining parts that partially match, registering the first information in a memory as metadata for the program, the metadata indicating a relationship between the one or the plurality of first tables set as the input and the second table set as an output.

Abstract: Methods, systems, apparatus, including computer programs encoded on computer storage medium, for predicting an outcome in response to a detected event. In one aspect, the method includes actions of maintaining a database of data records with a knowledge graph, receiving a stream of real-time data that includes data records, and from time-to-time as the real-time data is received: identifying a set of one or more attributes associated with a real-time event represented by one of the data records received, identifying a data record in the knowledge graph representing an event-outcome pair, determining that an event of the event-outcome pair has historically been associated with triggering an outcome of the event-outcome pair, and, executing a set of rules defining logic associated with the event-outcome pair specifying that the outcome will occur at a time that is subsequent to a time in which the one of the data records is received.

Abstract: Techniques for discovery and management of physical components are disclosed. A push notification from a physical component is received. The push notification includes an announcement that the physical component has been physically coupled with a system. Responsive to receiving the push notification, the system registers the physical component. The system receives push notifications from physical component. Based on the push notifications, the system determines an operational status of the physical component.

Abstract: A data structure used in memory-based file system, method and apparatus using thereof. The data structure comprising: a tree of the nodes comprising tree nodes and leaf nodes, each tree node points to at least one node, each leaf node is associated with a plurality of data unit elements each of which representing a data unit, wherein each data unit element is associated with two pointers, wherein at least one of the two pointers is capable of pointing to a data unit or to a data unit element; and a cyclic linked list of data unit elements representing identical clones of a data unit, wherein the cyclic linked list comprises a first element pointing directly to the data unit, wherein from each element in the cyclic linked list, the data unit can be reached in time complexity of O(1).

Abstract: The disclosed computer-implemented method for evaluating and storing data items may include (i) receiving a data item to be evaluated and stored, (ii) evaluating the data item by comparing the data item with a set of rules used to determine properties of data items, (iii) storing, in connection with the data item, (a) at least one determined property of the data item and (b) contextual details about a state of at least one rule used to determine the property at a point in time at which the rule was used, and (iv) after the data item has been stored, performing an action on the data item based on the stored contextual details. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The present disclosure relates to management of cloned objects and corresponding snapshot objects in file systems by a file server. Aspects may relate to one or more processes for managing file system objects of a file system in a data storage system comprising a file system server, wherein the file system server comprises: a computer system including a processor configured to execute an operating system and further software components executed on the operating system, and a programmable logic device interfacing with the computer system and including a file system management unit configured to manage a file system.

Abstract: A first file and a second file are retrieved from a database, in which the first and second files include an unstructured text stream. Metadata of the first and second files are extracted. The extracted metadata include a description category, entity source, geographic region, and a set of sub-files linked to the file. A data structure indicative of relationship between the first and second files is generated. Weighting factor is applied to the generated data structure. which indicates a degree of relationship between the first file and the second file. The relationship and the degree of the relationship are determined based on the extracted metadata of the first and second files. In response to a user requesting the first file, it is determined whether the second file should be provided in conjunction with the first file based on the weighting factor as applied to the data structure.

Abstract: A storage controller coupled to a multi-tenant storage array receives a request from a client device to write a data block to a volume resident on the storage array, wherein the client device is associated with a tenant of the storage array. The storage controller determines a tenant identifier associated with the tenant, generates a hash value for the data block based at least in part on the data block and the tenant identifier, and performs at least one data deduplication operation on the data block using the hash value by determining whether the hash value matches with any of the plurality of previous hash values that are identified in a deduplication map. Responsive to determining that the hash value does not match with any of the plurality of previous hash values that are identified in the deduplication map, the hash value is stored in the deduplication map.

Abstract: The present embodiments relate generally to master image management for a virtualization environment such as a virtual desktop infrastructure. Some embodiments are directed toward using image snapshot technology to identify differences between master images. In these and other embodiments, a methodology includes indexing the contents of snapshots of the same master image taken at different points in time to determine the delta between the snapshots, and thus the states of the master image at the different points in time.

Abstract: Disclosed are systems, methods and computer program products for data deduplication during a backup using at least two LSM trees. An example method includes calculating, for a first data block, a first hash value associated with the first data block and determining a reduced hash value based on the first hash value. The method includes determining whether the first data block contains data duplicative of an existing data block in a prior backup based on whether the reduced hash value occurs in a first log-structured merge (LSM) tree. If so, the method includes comparing the first hash value to one or more hash values in a second LSM tree to identify a matching hash value, and writing a first segment identifier (ID) corresponding to the matching hash value in an archive, the first segment ID referencing the existing data block in a segment store.

Abstract: In an example embodiment, for each of a plurality of different titles in a social network structure, the title is mapped into a first vector having n coordinates, while kills are mapped into a second vector having n coordinates. The first and second vectors are stored in a deep representation data structure. One or more objective functions are applied to at least one combination of two or more of the vectors in the deep representation data structure. Then, an optimization test on each of the at least one combination is performed using a corresponding objective function output for each of the at least one combination of two or more of the vectors, and, for any combination that did not pass the optimization test, one or more coordinates for the vectors in the combination are altered so that the vectors in the combination become closer together within an n-dimensional space.

Abstract: In an example embodiment, for each of a plurality of different entities in a social network structure, the entity is mapped into a vector having n coordinates. The vector for each of the plurality of different entities is stored in a deep representation data structure. One or more objective functions are applied to at least one combination of two or more of the vectors in the deep representation data structure. Then, an optimization test on each of the at least one combination of two or more of the vectors is performed using a corresponding objective function output for each of the at least one combination of two or more of the vectors, and, for any combination that did not pass the optimization test, one or more coordinates for the vectors in the combination are altered so that the vectors in the combination become closer together within an n-dimensional space.