Present invention discloses method and system for generating knowledge graph and sub-graph clusters to perform a root cause analysis. Method comprising extracting at least one of objects, data entities, links between the objects and the data entities, or relationships between the objects and the data entities from input content. Thereafter, method comprising generating a knowledge graph from the extracted data and sub-graphs from the knowledge graph using an unsupervised ML technique and extracting graph data structure information for each sub-graph. Subsequently, method comprising generating root cause model based on the sub-graphs and the graph data structure information and generating at least one sub-graph cluster and corresponding probabilistic graphical model using the root cause model and the knowledge graph. Generated Knowledge graph, root cause model and at least one sub-graph cluster and corresponding probabilistic graphical model are used to determine a root cause for an issue from an issue content.

A host, a storage system, and an operating method of the host are provided. The host includes a host memory configured to store a tree structure including a leaf node and an index node, an index management module configured to manage an index based on the tree structure and generate a first log corresponding to the leaf node based on a first update request corresponding to a first key-value entry included in the leaf node, and a device driver configured to generate a first write command corresponding to the first log and transmit the generated first write command to a key-value storage device, so as to store the first log in the key-value storage device. The index management module is configured to generate a first new key-value entry, the first new-key value entry including a first value updated based on the first update request, as the first log.

A URL and a categorization associated with the URL are received. A key associated with the received URL is determined. An operation is performed on a database using the determined key. Examples of such operations include inserting the categorization into the database, changing a value associated with the key in the database, removing a key-value pair from the database, and querying the database.

Techniques are disclosed for identifying hypocrisy in text. A computer system creates, from fragments of text, a syntactic tree that represents syntactic relationships between words in the fragments. The system identifies, in the syntactic tree, a first entity and a second entity. The system further determines that the first entity is opposite to the second entity. The system further determines a first sentiment score for a first fragment comprising the first entity and a second sentiment score for a second fragment comprising the second entity. The system, responsive to determining that the first sentiment score and the second sentiment score indicate opposite emotions, identifies the text as comprising hypocrisy and providing the text to an external device.

System and method of compiling a program having a mixture of host code and device code to enable Profile Guided Optimization (PGO) for device code execution. An exemplary integrated compiler can compile source code programmed to be executed by a host processor (e.g., CPU) and a co-processor (e.g., a GPU) concurrently. The compilation can generate an instrumented executable code which includes: profile instrumentation counters for the device functions; and instructions for the host processor to allocate and initialize device memory for the counters and to retrieve collected profile information from the device memory to generate instrumentation output. The output is fed back to the compiler for compiling the source code a second time to generate optimized executable code for the device functions defined in the source code.

Various embodiments set forth techniques for free space management in a block store. The techniques include receiving a request to allocate one or more blocks in a block store, accessing a sparse hierarchical data structure to identify an allocator page identifying a region of a backing store having a greatest number of free blocks, and allocating the one or more blocks.

A software voting or prediction system iteratively solicits participant preferences between members of a set, with a binary tree built used to minimize the number of iterations required. As each member of the set is considered, it is pairwise-compared with select members represented by nodes already in the binary tree, with iterations beginning at a root node of the tree and continuing to a leaf node. The newly considered member is placed as a new leaf node, and the tree is height-rebalanced as appropriate. Red-black tree coloring and tree rotation rules are optionally used for this purpose. Yes/no preference tallies are kept for each member of the set throughout the tree-building process and are ultimately used for scoring. Height-rebalancing of the tree helps minimize the number of iterations needed to precisely score each member of the set relative to its alternatives.

A system, product and process for building a package dependencies data structure. The method comprises determining a package instance identifier of the package to be processed. The package instance identifier is determined based on a unique identifier of the package to be processed and based on a unique identifier of a dependency sub-tree of the package to be processed in the package instance identifier. The package instance identifier is utilized to determine if a node representing the package to be processed exists or not. In case the node does not exist already in the data structure, the node is created and added.

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.

Crux detection in search definition sets is provided herein. Crux detection can include generating a hierarchical representation of a search definition set. The hierarchical representation can be reduced by removing search definitions unrelated to a target search definition. The search definitions in the reduced hierarchical representation can be focused by removing inapplicable elements or operations in the search definitions. Focusing the search definitions in the reduced hierarchical representation can include removing context-irrelevant elements and/or removing search-definition-irrelevant elements. The focused reduced hierarchical representation can be analyzed to identify implementation cruxes in the search definitions. Cruxes can include performance-impacting elements of the search definitions. The cruxes can be tagged and provided for redevelopment or analysis.