Abstract: A platform that integrates and collates the data points from students, employers, schools, and industry into an ecosystem which allows for customers (students, employers, schools, and industry) to model ‘what-if’ scenarios based on their industry parameters. By using a design algorithm based on automated reasoning, game theory, and knowledge mining, within a neural network, the platform can predict, model, and build the journey. The decision modeling neural learning platform may be used to augment or replace the need for guidance counselors in schools, along with assisting industry and immigration liaisons.

Abstract: A system for providing intervention information to a subject may be configured to perform a method including: receiving, via the processor, genetic information of the subject, the genetic information including a plurality of sets, and each set comprising one or more genes; assigning, via the processor, a value to each respective set; assigning, via the processor, a respective classification to the subject in one or more categories, wherein: each category corresponds to a respective set; and the assignment of the respective classification is based on the respective values assigned to the respective sets; obtaining, via the processor, health profile information and wellness intervention information associated with the assigned classifications of the one or more categories; and outputting, via the processor, the health profile information and the wellness intervention information to a user.

Abstract: Systems and method for performing a genotype-based analysis of an individual are discussed. An exemplary method may include: causing an interactive interface of an assessment application operating on a user device to prompt for responses to one or more phenotype interrogatories from an individual; receiving, from the user device, responses to the one or more phenotype interrogatories from the individual, entered via the interactive interface; using a relational model, determining at least one genotype classification for the individual based on the received responses to the one or more phenotype interrogatories; and causing the interactive interface to output information associated with the at least one genotype classification.

Abstract: A anatomical phantom is disclosed containing a pathological abnormality having fluorescing properties to simulate fluorescence training and/or the simulation of medical procedures, such as fluorescence-based methods for intraoperative visualization and image-guidance. The phantom is made of materials that mimic anatomical biomechanical properties and wherein the pathological abnormality contains fluorescing properties to fluoresce in responsive to UV light excitation. The phantom may be a standalone model or may be used in a complementary simulation kit to provide a more comprehensive training approach.

Abstract: A platform that integrates and collates the data points from students, employers, schools, and industry into an ecosystem which allows for customers (students, employers, schools, and industry) to model ‘what-if’ scenarios based on their industry parameters. By using a design algorithm based on automated reasoning, game theory, and knowledge mining, within a neural network, the platform can predict, model, and build the journey. The decision modeling neural learning platform may be used to augment or replace the need for guidance counselors in schools, along with assisting industry and immigration liaisons.

Abstract: A anatomical phantom is disclosed containing a pathological abnormality having fluorescing properties to simulate fluorescence training and/or the simulation of medical procedures, such as fluorescence-based methods for intraoperative visualization and image-guidance. The phantom is made of materials that mimic anatomical biomechanical properties and wherein the pathological abnormality contains fluorescing properties to fluoresce in responsive to UV light excitation. The phantom may be a standalone model or may be used in a complementary simulation kit to provide a more comprehensive training approach.

Abstract: A method for creating a joined data set from a join input data set is disclosed. The method starts by categorizing the join input data set into a high-skew data set and a low-skew data set. The low-skew data set is distributed to the plurality of CPUs using a first distribution method. The high-skew data set is distributed to the plurality of CPUs using a second distribution method. The plurality of CPUs process the high-skew data set and the low-skew data set to create the joined data set.

Abstract: A method of optimizing a query is provided herein. The method includes determining a cost estimate for a query. The method further includes determining a budget for optimizing the query based on the cost estimate. Additionally, the method includes determining a complexity of the query based on the budget. The method also includes determining a strategy based on the complexity. The strategy specifies a limit to a search space enumerated during optimization of the query. Further, the method includes optimizing the query based on the strategy.

Abstract: An enterprise data warehouse or business intelligence system having a plurality of processors and memory resources. The system includes at least a database server, a workload management system, a query compiler, and a query executor. The database server configured to receive a database query from an application at a database client system. The query compiler configured to prepare the execution plan for the query and compute the number of executive server processes (ESPs) in each ESP layer of the query. The workload management system is configured to generate an affinity value, and the query executor is configured to execute the query. As disclosed herein, placement of the executive server process layers of the query onto processors of the computing system is determined using the affinity value. Other embodiments, aspects and features are also disclosed.

Abstract: A method for creating a joined data set from a join input data set is disclosed. The method starts by categorizing the join input data set into a high-skew data set and a low-skew data set. The low-skew data set is distributed to the plurality of CPUs using a first distribution method. The high-skew data set is distributed to the plurality of CPUs using a second distribution method. The plurality of CPUs process the high-skew data set and the low-skew data set to create the joined data set.

Abstract: One example discloses a database management system that can comprise a memory for storing computer executable instructions and a processing unit for accessing the memory and executing the computer executable instructions. The computer executable instructions can comprise a compiler to amortize the execution resource cost of searching a search space corresponding to a received query over a plurality of searches.

Abstract: A method for creating a joined data set from a join input data set is disclosed. The method starts by categorizing the join input data set into a high-skew data set and a low-skew data set. The low-skew data set is distributed to the plurality of CPUs using a first distribution method. The high-skew data set is distributed to the plurality of CPUs using a second distribution method. The plurality of CPUs process the high-skew data set and the low-skew data set to create the joined data set.

Abstract: A method of optimizing a query is provided herein. The method includes determining a cost estimate for a query. The method further includes determining a budget for optimizing the query based on the cost estimate. Additionally, the method includes determining a complexity of the query based on the budget. The method also includes determining a strategy based on the complexity. The strategy specifies a limit to a search space enumerated during optimization of the query. Further, the method includes optimizing the query based on the strategy.

Abstract: A system and method for join order optimization in a query optimizer is disclosed. The method includes receiving a query having a plurality of join operators, including at least one multi-way join between relational operators in the query tree. The join operators include at least one outer-join and/or semi-join. The multi-way-join is transformed to a multi-join operator with a plurality of join back bone children representing the relational operators. The dependencies that occur between the join back bone children are tracked. Join order validity is evaluated based on the tracked dependencies. One or more multi-join rules are applied to the multi-join operator sufficient to generate at least one join subtree when at least one join subtree is determined to have a valid join order.

Abstract: An enterprise data warehouse or business intelligence system having a plurality of processors and memory resources. The system includes at least a database server, a workload management system, a query compiler, and a query executor. The database server configured to receive a database query from an application at a database client system. The query compiler configured to prepare the execution plan for the query and compute the number of executive server processes (ESPs) in each ESP layer of the query. The workload management system is configured to generate an affinity value, and the query executor is configured to execute the query. As disclosed herein, placement of the executive server process layers of the query onto processors of the computing system is determined using the affinity value. Other embodiments, aspects and features are also disclosed.

Abstract: Methods and systems receive a normalized query tree and analyze the tree to collect information about join operators and their children, and tables in an associated query. This information is then made available to a rule based optimizer that is configured to produce, from the normalized query tree, an execution plan. In addition, in at least some embodiments, an extensible framework is provided for join order optimization via the use of a multi-join operator and multi-join rules as part of the general framework of a query optimizer.

Abstract: A method for creating a joined data set from a join input data set is disclosed. The method starts by categorizing the join input data set into a high-skew data set and a low-skew data set. The low-skew data set is distributed to the plurality of CPUs using a first distribution method. The high-skew data set is distributed to the plurality of CPUs using a second distribution method. The plurality of CPUs process the high-skew data set and the low-skew data set to create the joined data set.

Abstract: Methods and systems receive a normalized query tree and analyze the tree to collect information about join operators and their children, and tables in an associated query. This information is then made available to a rule based optimizer that is configured to produce, from the normalized query tree, an execution plan. In addition, in at least some embodiments, an extensible framework is provided for join order optimization via the use of a multi-join operator and multi-join rules as part of the general framework of a query optimizer.

Abstract: A system that provides compressed histogram data comprises a compiler histogram interface that obtains histogram data about a table in a database, and a dynamic histogram compressor that receives output from the compiler histogram interface, input regarding a database query and table information to produce compressed histogram data for use by an optimizer.