Abstract: Techniques for similarity searching are provided. Structural data in a database is searched against one or more structural queries. A desired minimum degree of similarity between the one or more queries and the structural data in the database is first specified. One or more indices are then used to exclude from consideration any structural data in the database that does not share the minimum degree of similarity with one or more of the queries.

Abstract: Techniques for classifying structural data with skewed distribution are disclosed. By way of example, a method classifying structural input data comprises a computer system performing the following steps. Multiple classifiers are constructed, wherein each classifier is constructed on a subset of training data, using one or more selected composite features from the subset of training data. A consensus among the multiple classifiers is computed in accordance with a voting scheme such that at least a portion of the structural input data is assigned to a particular class in accordance with the computed consensus. Such techniques for structured data classification are capable of handling skewed class distribution and partial feature coverage issues.

Abstract: A system has a processor coupled to access a document database that indexes keywords and instances of entities having entity types in a plurality of documents. The processor is programmed to receive an input query including one or more keywords and one or more entity types, and search the database for documents having the keywords and entities with the entity types of the input query. The processor is programmed for aggregating a respective score for each of a plurality of entity tuples across the plurality of documents. The aggregated scores are normalized. Each respective normalized score provides a ranking of a respective entity tuple, relative to other entity tuples, as an answer to the input query. The processor has an interface to a storage or display device or network for outputting a list including a subset of the entity tuples having the highest normalized scores among the plurality of entity tuples.

Abstract: Embodiments of the invention relate to automated system problem diagnosing. An index is created with problem description information of previously diagnosed problems, a diagnosis for each problem, and a solution to each diagnosis. System states, traces and logs are extracted from a source system with a new problem. The problem diagnosis system generates problem description information of the new problem from the system states, traces and logs. Problem description information of the new problem is compared with problem description information in the problem description index. A search score is computed for each document in the problem description index. The search score is a measure of similarity between each document in the index and the description of the new problem. A matching score is assigned to each previously diagnosed problems based on the search score. The matching score is a measure of similarity between the new problem and each previously diagnosed problem.

Abstract: Embodiments of the invention relate to automated system problem diagnosing. An index is created with problem description information of previously diagnosed problems, a diagnosis for each problem, and a solution to each diagnosis. System states, traces and logs are extracted from a source system with a new problem. The problem diagnosis system generates problem description information of the new problem from the system states, traces and logs. Problem description information of the new problem is compared with problem description information in the problem description index. A search score is computed for each document in the problem description index. The search score is a measure of similarity between each document in the index and the description of the new problem. A matching score is assigned to each previously diagnosed problems based on the search score. The matching score is a measure of similarity between the new problem and each previously diagnosed problem.

Abstract: Techniques for graph indexing are provided. In one aspect, a method for indexing graphs in a database, the graphs comprising graphic data, comprises the following steps. Frequent subgraphs among one or more of the graphs in the database are identified, the frequent subgraphs appearing in at least a threshold number of the graphs in the database. One or more of the frequent subgraphs are used to create an index of the graphs in the database.

Abstract: Techniques for classifying structural data with skewed distribution are disclosed. By way of example, a method classifying structural input data comprises a computer system performing the following steps. Multiple classifiers are constructed, wherein each classifier is constructed on a subset of training data, using one or more selected composite features from the subset of training data. A consensus among the multiple classifiers is computed in accordance with a voting scheme such that at least a portion of the structural input data is assigned to a particular class in accordance with the computed consensus. Such techniques for structured data classification are capable of handling skewed class distribution and partial feature coverage issues.

Abstract: A system has a processor coupled to access a document database that indexes keywords and instances of entities having entity types in a plurality of documents. The processor is programmed to receive an input query including one or more keywords and one or more entity types, and search the database for documents having the keywords and entities with the entity types of the input query. The processor is programmed for aggregating a respective score for each of a plurality of entity tuples across the plurality of documents. The aggregated scores are normalized. Each respective normalized score provides a ranking of a respective entity tuple, relative to other entity tuples, as an answer to the input query. The processor has an interface to a storage or display device or network for outputting a list including a subset of the entity tuples having the highest normalized scores among the plurality of entity tuples.

Abstract: Techniques for similarity searching are provided. In one aspect, a method of searching structural data in a database against one or more structural queries comprises the following steps. A desired minimum degree of similarity between the one or more queries and the structural data in the database is first specified. One or more indices are then used to exclude from consideration any structural data in the database that does not share the minimum degree of similarity with one or more of the queries.

Abstract: Techniques for graph indexing are provided. In one aspect, a method for indexing graphs in a database, the graphs comprising graphic data, comprises the following steps. Frequent subgraphs among one or more of the graphs in the database are identified, the frequent subgraphs appearing in at least a threshold number of the graphs in the database. One or more of the frequent subgraphs are used to create an index of the graphs in the database.