International Business Machines Corporation

Yanyan Han

Sheng Hua Bao

Xiaoyang Gao

Brian Scott Dreher

William Scott Spangler

Feng Wang

A computer system accesses a storage device. Contents of an object of a request are sorted. The contents of an object of a request are sorted. A hash key is generated to access information in the storage device based on the sorted contents of the object, wherein objects with non-critical differences are mapped to the same hash key. The information in the storage device is accessed based on the generated hash key to produce a response to the request. Embodiments of the present invention further include a method and program product for accessing a storage device in substantially the same manner described above.

Accessing cached data using hash keys

A computer system processes a group of inputs. A group of entities that is input for processing is intercepted. The intercepted group is expanded into individual entities. Each of the individual entities is processed to produce results for each individual entity. The results for each individual entity are intercepted and merged to produce results for the group of entities. Embodiments of the present invention further include a method and program product for processing a group of inputs in substantially the same manner described above.

Processing entity groups to generate analytics

According to embodiments of the present invention, similarity metrics or measures of similarity may be combined using an adaptive weighting scheme. A subset of entities from a first set of entities that have a known relationship is randomly selected. The subset is combined with a second set of entities that have an unknown relationship to each other and/or to the first set of entities. At least two different measures of similarity (similarity metrics) between the first set and the combined second set (including the subset) is determined for each entity in the second set. For each entity in the second set, the at least two different measures of similarity are compared, and a weight is assigned adaptively to each measure of similarity based on the magnitude of the measure of similarity. The weighted measures of similarity are combined to determine an aggregate adaptively weighted similarity score for each entity.

Adaptive weighting of similarity metrics for predictive analytics of a cognitive system

Methods, systems, and computer program products are provided for processing a request regarding relationships among instances of entities. A graphical representation of instances of entities is generated and includes one or more source nodes, each representing an instance of an input entity of a request, and one or more related nodes, each representing an instance of a second entity related to one or more corresponding instances of the input entity and associated with a corresponding confidence score for the relationship. Each of the one or more related nodes associated with a confidence score satisfying a threshold is identified. One or more supplemental nodes are added to the graphical representation, each of which represents a corresponding instance of a third entity with a relationship to a corresponding instance of the second entity. The graphical representation is traversed to identify relationships between instances of entities and produce results for the request.

Searching for and determining relationships among entities

Methods, systems and computer readable media are provided for accessing data utilizing a multi-level table comprising generating a plurality of levels of the multi-level table, wherein a first level of the multi-level table includes a hyper-table with a plurality of hyper-cells each hyper-cell including information for a group of cells from an initial base table, wherein intermediate levels of the multi-level table each include a plurality of hyper-tables comprising hyper-cells with each hyper-table linked to and providing information for a corresponding hyper-cell of a hyper-table of a prior level, and wherein a plurality of tables of a terminal level includes information from cells of the initial base table with each table linked to and providing information for a corresponding hyper-cell. Data from the multi-level table is accessed by traversing links between the hyper-tables of the plurality of levels to access data within the tables of the terminal level.

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San Jose, CA, United States of America

Yanyan Han