Abstract: Systems and methods are provided for generating training data for a machine-learning classifier. A knowledge representation synthesized based on an object of interest is used to assign labels to content items. The labeled content items can be used as training data for training a machine learning classifier. The labeled content items can also be used as validation data for the classifier.

Abstract: Systems and methods are provided for generating training data for a machine-learning classifier. A knowledge representation synthesized based on an object of interest is used to assign labels to content items. The labeled content items can be used as training data for training a machine learning classifier. The labeled content items can also be used as validation data for the classifier.

Abstract: Systems and methods are provided for classifying at least one unlabeled content item with a machine-learning classifier. A knowledge representation synthesized based on an object of interest is used as a source of features for which training data is evaluated. The machine learning classifier is trained based on features based on the attributes and the training data.

Abstract: Systems and methods are provided for modifying a knowledge representation based on a machine-learning classifier. The knowledge representation is synthesized based on an object of interest. The machine-learning classifier is applied to predict relevance of validation data items. The knowledge representation is modified based on the results of the machine-learning classifier and the validation data. The modified knowledge representation can be used in subsequent applications of the classifier.

Abstract: Systems and methods are provided for generating training data for a machine-learning classifier. A knowledge representation synthesized based on an object of interest is used to assign labels to content items. The labeled content items can be used as training data for training a machine learning classifier. The labeled content items can also be used as validation data for the classifier.

Abstract: A method, system, and computer program product for managing database statistics are provided. The method, system, and computer program product provide for receiving a query for optimizing, collecting statistics specific to the query prior to generating any access plans for executing the query, and generating an access plan for executing the query based on the collected statistics.

Abstract: A workload to be handled by a database system can be identified. The workload can include at least one query that the database system is to handle. A set of at least one candidate statistical views (statviews) to be utilized when optimizing the workload can be enumerated. A benefit value and a cost value of the each of the enumerated candidate statistical views relative to the entire workload can be computed. The cost value can reflect a cost of constructing and collecting statistics on the associated statistical view. A set of the candidate views most beneficial for handling the workload can be determined based upon the computed benefit values and computed cost values. A generalization phase that augments the candidate view set with higher value candidate views for consideration during the recommendation phase. The optimum subset of views from the determined set of candidate views can be recommended, which can cause them to be constructed and utilized by a database optimizer.

Abstract: A method, system, and computer program product for managing database statistics are provided. The method, system, and computer program product provide for receiving a query for optimizing, collecting statistics specific to the query prior to generating any access plans for executing the query, and generating an access plan for executing the query based on the collected statistics.

Abstract: A system and a priori method of discovering dependencies between relational database column pairs and application of discoveries to query optimization is provided. For each candidate column pair remaining after simultaneously generating column pairs, pruning pairs not satisfying specified heuristic constraints, and eliminating pairs with trivial instances of correlation, a random sample of data values is collected. A candidate column pair is tested for the existence of a soft functional dependency (FD), and if a dependency is not found, statistically tested for correlation using a robust chi-squared statistic. Column pairs for which either a soft FD or a statistical correlation exists are prioritized for recommendation to a query optimizer, based on any of: strength of dependency, degree of correlation, or adjustment factor; statistics for recommended columns pairs are tracked to improve selectivity estimates.

Abstract: A compilation time estimator provides a quantified estimate of the optimizer compilation time for a given query optimizer. The estimator automates the optimizer to choose the right level of optimization in commercial database systems. The estimator reuses an optimizer's join enumerator to obtain actual number of joins, but bypasses plan generation to save estimation overhead, and maintains a small number of interesting physical properties to estimate the number of plans by using a linear regression model. The estimator uses the number of generated plans to estimate query compilation time.