Abstract: A database system, computer program product, and a method for evaluating aggregates in database systems includes hashing of aggregation keys on a per bucket basis, and depending on a number of hashed tuples per bucket, sorting said tuples. Additionally, depending on the number of hashed tuples per bucket, the bucket is kept without change. Moreover, depending on the number of hashed tuples per bucket, maintaining a secondary hash table for a particular bucket, map tuples to it, aggregate as you map.

Abstract: A method for performing natural language processing includes receiving a primary text file. The received primary text file is scanned to determine a set of statistics related to a frequency at which various words of the primary text file follow other words of the primary text file. A probabilistic word generator is created based on the determined set of statistics. The probabilistic word generator generates synthetic text exhibiting the determined set of statistics. Synthetic text exhibiting the determined set of statistics is generated using the created probabilistic word generator. Word vectorization is performed on the synthetic text. Results of the performed vectorization are used to perform machine learning tasks.

Abstract: A database system, computer program product, and a method for evaluating aggregates in database systems includes hashing of aggregation keys on a per bucket basis, and depending on a number of hashed tuples per bucket, sorting said tuples. Additionally, depending on the number of hashed tuples per bucket, the bucket is kept without change. Moreover, depending on the number of hashed tuples per bucket, maintaining a secondary hash table for a particular bucket, map tuples to it, aggregate as you map.

Abstract: A method for performing natural language processing includes receiving a primary text file. The received primary text file is scanned to determine a set of statistics related to a frequency at which various words of the primary text file follow other words of the primary text file. A probabilistic word generator is created based on the determined set of statistics. The probabilistic word generator generates synthetic text exhibiting the determined set of statistics. Synthetic text exhibiting the determined set of statistics is generated using the created probabilistic word generator. Word vectorization is performed on the synthetic text. Results of the performed vectorization are used to perform machine learning tasks.

Abstract: A CPU may select a variable from a variable set as a dependent variable. The variable set may be part of the data structure that includes a plurality of vector values, a vector value associated with a variable set of n number of variables, and each variable of the variable set having a variable value. The number of dependent variable steps for the dependent variable may be determined. The number of the vector values in a dependent variable step is determined as being number of independent variables. A function is mapped to a plurality of thread processors, and each thread processor is assigned for the function to be performed on each one of the independent variables for each of the dependent variable steps.

Abstract: A CPU may select a variable from a variable set as a dependent variable. The variable set may be part of the data structure that includes a plurality of vector values, a vector value associated with a variable set of n number of variables, and each variable of the variable set having a variable value. The number of dependent variable steps for the dependent variable may be determined. The number of the vector values in a dependent variable step is determined as being number of independent variables. A function is mapped to a plurality of thread processors, and each thread processor is assigned for the function to be performed on each one of the independent variables for each of the dependent variable steps.

Abstract: Techniques for providing maximal concurrency while ensuring no deadlock in a tree structure are provided. The techniques include accessing a minimum number of one or more nodes to perform an operation.

Abstract: In a virtual machine environment, a method and apparatus for the use of multiple heaps to retain persistent data and transient data wherein the multiple heaps enables a single virtual machine to be easily resettable, thus avoiding the need to terminate and start a new Virtual Machine as well as enabling a single virtual machine to retain data and objects across multiple applications, thus avoiding the computing resource overhead of relinking, reloading, reverifying, and recompiling classes. The memory hierarchy includes a System Heap, a Middleware Heap and a Transient Heap. The use of three heaps enables garbage collection to be selectively targeted to one heap at a time in between applications, thus avoiding this overhead during the life of an application.