Abstract: Described herein are technologies relating to computing resource allocation among multiple tenants. Each tenant may have a respective absolute reservation for rate-based computing resources, which is independent of computing resource reservations of other tenants. The multiple tenants vie for the rate-based computing resources, and tasks are scheduled based upon which tenants submit the tasks and the resource reservations of such tenants.

Abstract: A set expansion system is described herein that improves precision, recall, and performance of prior set expansion methods for large sets of data. The system maintains high precision and recall by 1) identifying the qualify of particular lists and applying that quality through a weight, 2) allowing for the specification or negative examples in a set of seeds to reduce the introduction of bad entities into the set, and 3) applying a cutoff to eliminate lists that include a low number of positive matches. The system may perform multiple passes to first generate a good candidate result set and then refine the set to find a set with highest quality. The system may also apply Map Reduce or other distributed processing techniques to allow calculation in parallel. Thus, the system efficiently expands large concept sets from a potentially small set of initial seeds from readily available web data.

Abstract: Architecture that enables a Database-as-a-Service (DaaS) to auto-scale container sizes on behalf of tenants. An abstraction is provided that enables tenants to reason about monetary budget and query latency, rather than resource provisioning. An auto-scaling module automatically determines a container size for a subsequent billing interval based on telemetry that comprises latencies (e.g., waits), resource utilizations, and available budget, for example. A set of robust signals are derived from database engine telemetry and combined to significantly improve accuracy of resource demand estimation for database workloads. In a more specific implementation, resource demands can be estimated for arbitrary SQL (structured query language) workloads in a relational database management system (RDBMS).

Abstract: A plurality of indicators representing a plurality of respective candidate database configurations may be obtained, each of the candidate database configurations including a plurality of database queries and a plurality of candidate database indexes associated with a database table. A portion of the candidate database indexes included in the plurality of database indexes may be selected based on skyline selection. An enumeration of the portion of the plurality of the candidate database indexes may be determined based on a greedy algorithm.

Abstract: Various technologies that facilitate performance of a data finding data (DFD) search are described herein. A user specifies entities, for example, by entering the entities into a query field, selecting the entities from a computer-executable application, or the like. The user further specifies an attribute of the entities that is of interest. A query is constructed based upon the entities and the attribute, and a search for tables is performed based upon the entities and the attribute. Values of the attribute for the selected entities are identified in a table, and the values of the attribute are returned.

Abstract: Described herein are technologies relating to computing resource allocation among multiple tenants. Each tenant may have a respective absolute reservation for rate-based computing resources, which is independent of computing resource reservations of other tenants. The multiple tenants vie for the rate-based computing resources, and tasks are scheduled based upon which tenants submit the tasks and the resource reservations of such tenants.

Abstract: This patent application relates to granular and workload driven database index defragmentation techniques. These techniques allow for defragmenting individual index ranges, performing benefit analysis to estimate the impact of defragmenting indexes or index ranges, and leveraging such benefit analysis to provide automated workload-driven recommendations of index(es) or index range(s) to defragment.

Abstract: A plurality of items included in a catalog may be obtained, each item associated with an item category. Brand indicators may be obtained, each brand indicator associated with the item category. Brand indicators associated with each of the items may be determined, and the each item may be assigned to a partition group associated with the brand indicator that is associated with the each item. Correlated string tokens that are correlated, greater than a predetermined correlation threshold value, with the brand indicator associated with the partition group that is associated with the each one of the items, the correlated string tokens associated with the each one of the plurality of items, may be determined. A dictionary hierarchy may be generated based on the one or more correlated string tokens.

Abstract: A set expansion system is described herein that improves precision, recall, and performance of prior set expansion methods for large sets of data. The system maintains high precision and recall by 1) identifying the qualify of particular lists and applying that quality through a weight, 2) allowing for the specification or negative examples in a set of seeds to reduce the introduction of bad entities into the set, and 3) applying a cutoff to eliminate lists that include a low number of positive matches. The system may perform multiple passes to first generate a good candidate result set and then refine the set to find a set with highest quality. The system may also apply Map Reduce or other distributed processing techniques to allow calculation in parallel. Thus, the system efficiently expands large concept sets from a potentially small set of initial seeds from readily available web data.

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: A plurality of indicators representing a plurality of respective candidate database configurations may be obtained, each of the candidate database configurations including a plurality of database queries and a plurality of candidate database indexes associated with a database table. A portion of the candidate database indexes included in the plurality of database indexes may be selected based on skyline selection. An enumeration of the portion of the plurality of the candidate database indexes may be determined based on a greedy algorithm.

Abstract: A plurality of items included in a catalog may be obtained, each item associated with an item category. Brand indicators may be obtained, each brand indicator associated with the item category. Brand indicators associated with each of the items may be determined, and the each item may be assigned to a partition group associated with the brand indicator that is associated with the each item. Correlated string tokens that are correlated, greater than a predetermined correlation threshold value, with the brand indicator associated with the partition group that is associated with the each one of the items, the correlated string tokens associated with the each one of the plurality of items, may be determined. A dictionary hierarchy may be generated based on the one or more correlated string tokens.

Abstract: Technology is described for transformation rule profiling for a query optimizer. The method can include obtaining a database query configured to be optimized by the query optimizer of a database system. An optimized query plan for the database query can be found using a host set of transformation rules. One transformation rule can be removed and checked at a time. Each transformation rule can be checked to determine whether the transformation rule affects an optimal query plan output. A test query plan can be generated after each transformation rule has been removed. The query optimizer can determine whether the test query plan is different than the optimized query plan in the absence of the removed transformation rule. An equivalent set of transformation rules can be created that includes transformation rules where the test query plan generated from the equivalent set of transformation rules is equivalent to the optimized plan.

Abstract: An exact cardinality query optimization system and method for optimizing a query having a plurality of expressions to obtain a cardinality-optimal query execution plan for the query. Embodiments of the system and method use various techniques to shorten the time necessary to obtain the cardinality-optimal query execution plan, which contains the query execution plan when all cardinalities are exact. Embodiments of the system and method include a covering queries technique that leverages query execution feedback to obtain an unordered subset of relevant expressions for the query, an early termination technique that bounds the cardinality to determine whether the processing can be terminate before each of the expressions are executed, and an expressions ordering technique that finds an ordering of expressions that yields the greatest reduction in time to obtain the cardinality-optimal query execution plan.

Abstract: Technology is described for transformation rule profiling for a query optimizer. The method can include obtaining a database query configured to be optimized by the query optimizer of a database system. An optimized query plan for the database query can be found using a host set of transformation rules. One transformation rule can be removed and checked at a time. Each transformation rule can be checked to determine whether the transformation rule affects an optimal query plan output. A test query plan can be generated after each transformation rule has been removed. The query optimizer can determine whether the test query plan is different than the optimized query plan in the absence of the removed transformation rule. An equivalent set of transformation rules can be created that includes transformation rules where the test query plan generated from the equivalent set of transformation rules is equivalent to the optimized plan.

Abstract: This patent application relates to granular and workload driven database index defragmentation techniques. These techniques allow for defragmenting individual index ranges, performing benefit analyses to estimate the impact of defragmenting indexes or index ranges, and leveraging such benefit analyses to provide automated workload-driven recommendations of index(es) or index range(s) to defragment.

Abstract: A database server may be configured to compute distinct page counts of pages accessed to execute operands of respective queries. The queries may be executed against a table comprised of the pages and having an index managed by the database server. The distinct page counts may be obtained by counting, as a part of the executing of the queries, distinct pages accessed during the execution of the queries.

Abstract: An exact cardinality query optimization system and method for optimizing a query having a plurality of expressions to obtain a cardinality-optimal query execution plan for the query. Embodiments of the system and method use various techniques to shorten the time necessary to obtain the cardinality-optimal query execution plan, which contains the query execution plan when all cardinalities are exact. Embodiments of the system and method include a covering queries technique that leverages query execution feedback to obtain an unordered subset of relevant expressions for the query, an early termination technique that bounds the cardinality to determine whether the processing can be terminate before each of the expressions are executed, and an expressions ordering technique that finds an ordering of expressions that yields the greatest reduction in time to obtain the cardinality-optimal query execution plan.

Abstract: Architecture that provides a data profile computation technique which employs key profile computation and data pattern profile computation. Key profile computation in a data table includes both exact keys as well as approximate keys, and is based on key strengths. A key strength of 100% is an exact key, and any other percentage in an approximate key. The key strength is estimated based on the number of table rows that have duplicated attribute values. Only column sets that exceed a threshold value are returned. Pattern profiling identifies a small set of regular expression patterns which best describe the patterns within a given set of attribute values. Pattern profiling includes three phases: a first phases for determining token regular expressions, a second phase for determining candidate regular expressions, and a third phase for identifying the best regular expressions of the candidates that match the attribute values.

Abstract: A database server may be configured to compute distinct page counts of pages accessed to execute operands of respective queries. The queries may be executed against a table comprised of the pages and having an index managed by the database server. The distinct page counts may be obtained by counting, as a part of the executing of the queries, distinct pages accessed during the execution of the queries.