Abstract: Provided are methods and systems for generating physical database design tuning recommendations. Given a workload, the system analyzes the workload to identify and recommend a set of rowstore and columnstore indexes optimal for the performance of the workload. The system is designed to estimate the size of the columnstore index (at the granularity of each column) without actually building the index, estimate the improvement in query performance that each columnstore index would result in when built, and automatically derive the workload used for the physical design tuning task by analyzing stored query execution history data. This automatic workload derivation is orthogonal to columnstores and can be used even when columnstore indexes are not being used.

Abstract: A processing unit can determine a first subset of a data set including data records selected based on measure values thereof. The processing unit can determine an index mapping a predicate to data records associated with that predicate and approximation values of the records. The processing unit can process a query against the first subset to provide a first result and a first accuracy value, determine that the first accuracy value does not satisfy an accuracy criterion, and process the query against the index. In some examples, the processing unit can process the query against a second subset including data records satisfying a predetermined predicate. In some examples, the processing unit can receive data records and determine the first subset. Data records can include respective measure values. Data records with higher measure values can occur in the first subset more frequently than data records with lower measure values.

Abstract: One or more approximations of query output in a data analytics platform are controlled. The one or more approximations are controlled by generating values of error metrics associated with placements of samplers in one or more query execution plans associated with the query, and injecting a plurality of samplers into the query execution plans, using the determined values of the error metrics, in lieu of storing samples of input to the query prior to execution of the query.

Abstract: In general, the knowledge base table composer embodiments described herein provide table answers to keyword queries against one or more knowledge bases. Highly relevant patterns in a knowledge base are found for user-given keyword queries. These patterns are used to compose table answers. To this end, a knowledge base is modeled as a directed graph called a knowledge graph, where nodes represent entities in the knowledge base and edges represent the relationships among them. Each node/edge is labeled with a type and text. A pattern that is an aggregation of subtrees which contain all keywords in the texts and have the same structure and types on node/edges is sought. Patterns that are relevant to a query for a class can be found using a set of scoring functions. Furthermore, path-based indexes and various query-processing procedures can be employed to speed up processing.

Abstract: The claimed subject matter provides a method for providing a progress estimate for a database query. The method includes determining static features of a query plan for the database query. The method also includes selecting an initial progress estimator based on the static features and a trained machine learning model. The model is trained using static features of a plurality of query plans, and dynamic features of the plurality of query plans. Further, the method includes determining dynamic features of the query plan for each of a plurality of candidate estimators. Additionally, the method includes selecting a revised progress estimator based on the static features, the dynamic features and a trained machine learning model for each of the candidate estimators. The method further includes producing the progress estimate based on the revised progress estimator.

Abstract: Described is a fast set intersection technology by which sets of elements to be intersected are maintained as partitioned subsets (small groups) in data structures, along with representative values (e.g., one or more hash signatures) representing those subsets. A mathematical operation (e.g., bitwise-AND) on the representative values indicates whether an intersection of range-overlapping subsets will be empty, without having to perform the intersection operation. If so, the intersection operation on those subsets may be skipped, with intersection operations (possibly guided by inverted mappings or using a linear scan) performed only on overlapping subsets that may have one or more intersecting elements.