Abstract: A search apparatus causes a first learning process using a first sample size and a first hyperparameter value to be executed, and causes a second learning process using the first sample size and a second hyperparameter value to be executed. When a third learning process using a second sample size larger than the first sample size and the first hyperparameter value has not been executed, the search apparatus calculates total resources associated with the first sample size based on resources used by the first and second learning processes. If the total resources exceed a threshold, the search apparatus allows the third learning process to be executed. If the total resources are equal to or less than the threshold, the search apparatus withholds the execution of the third learning process, and allows a fourth learning process using the first sample size or smaller and a third hyperparameter value to be executed.

Abstract: A relevance searching method performed by a computer, the relevance searching method includes generating a combined database by combining a plurality of databases each including a plurality of elements and relevance information indicating direct relevance between two elements in the plurality of elements; and searching for relevance between two elements that do not have direct relevance by using the combined database.

Abstract: A machine learning management apparatus identifies a maximum prediction performance score amongst a plurality of prediction performance scores corresponding to a plurality of models generated by executing each of a plurality of machine learning algorithms. As for a first machine learning algorithm having generated a model corresponding to the maximum prediction performance score, the machine learning management apparatus determines a first training dataset size to be used when the first machine learning algorithm is executed next time based on the maximum prediction performance score, first estimated prediction performance scores, and first estimated runtimes.

Abstract: A processor obtains a first measurement value representing prediction performance of a model that has been learned by using a first parameter value and training data of first size. The processor calculates a first expected value and a first variance degree of prediction performance of a model that would be learned by using the first parameter value and training data of second size. The processor also obtains a second measurement value representing prediction performance of a model that has been learned by using a second parameter value and training data of the first size. The processor calculates a second expected value and a second variance degree of prediction performance of a model that would be learned by using the second parameter value and training data of the second size.

Abstract: A relevance searching method performed by a computer, the relevance searching method includes generating a combined database by combining a plurality of databases each including a plurality of elements and relevance information indicating direct relevance between two elements in the plurality of elements; and searching for relevance between two elements that do not have direct relevance by using the combined database.

Abstract: An operation control method includes: storing data used by an application according to a first definition with respect to an interface of data operation when defining a way of starting a container, the container representing a region in which an execution environment of the application is disposed; storing an image file for the execution environment of the application; and registering a second definition into definition information, the second definition indicating the way of starting the container and being obtained by adding data information to information regarding the image file for the execution environment of the application in accordance with a third definition with respect to an interface of container operation, the data information being information regarding data used by the application in the container.

Abstract: A determination method executed by a computer, the determination method includes obtaining a service desired by a user; identifying a resource of the obtained service; when a container to perform the obtained service is not started in the identified resource, calculating, for each of a plurality of candidate containers to be stopped, a degree of influence exerted on the user when a candidate container is stopped, the calculating being performed by using a starting time period of the candidate container, a response time period for the service to be performed, a required resource of a container, and a frequency of a request for the service to be performed; stopping at least one container for securing the identified resource among the plurality of candidate containers based on the calculated degree of influence; and starting the container to perform the obtained service by using a resource for the at least one container.

Abstract: A searching device converts a searching data into an encoded searching data in a first encoding format according to a first encoding rule; and searches the encoded searching data in a target encoded data that has been encoded into the first encoding format by the first encoding rule, or that has been encoded in a second encoding format wherein when a specific encoded data encoded in the second encoding format is detected in the searching, the searching compares the encoded searching data and a converted encoded data, the converted encoded data being the specific encoded data converted into the first encoding format based on a predetermined conversion rule.

Abstract: A searching device converts a searching data into an encoded searching data in a first encoding format according to a first encoding rule; and searches the encoded searching data in a target encoded data that has been encoded into the first encoding format by the first encoding rule, or that has been encoded in a second encoding format wherein when a specific encoded data encoded in the second encoding format is detected in the searching, the searching compares the encoded searching data and a converted encoded data, the converted encoded data being the specific encoded data converted into the first encoding format based on a predetermined conversion rule.

Abstract: A non-transitory computer-readable storage medium stores a data stream processing program that causes a computer to execute a process. The process includes extracting a relationship between properties included in events in a data stream from the data stream itself, definitions of queries for processing the data stream, or both of the data stream and the definitions of the queries; specifying a common partitioning key common to a subset of the queries to be grouped into a partitioning group based on partitioning keys in the definitions of the queries, the relationship between the properties, and a probability of the relationship; and assigning the subset of the queries belonging to the partitioning group to each of parallel nodes provided for parallel distributed processing based on the specified common partitioning key.

Abstract: An operation control method includes: storing data used by an application according to a first definition with respect to an interface of data operation when defining a way of starting a container, the container representing a region in which an execution environment of the application is disposed; storing an image file for the execution environment of the application; and registering a second definition into definition information, the second definition indicating the way of starting the container and being obtained by adding data information to information regarding the image file for the execution environment of the application in accordance with a third definition with respect to an interface of container operation, the data information being information regarding data used by the application in the container.

Abstract: A search apparatus causes a first learning process using a first sample size and a first hyperparameter value to be executed, and causes a second learning process using the first sample size and a second hyperparameter value to be executed. When a third learning process using a second sample size larger than the first sample size and the first hyperparameter value has not been executed, the search apparatus calculates total resources associated with the first sample size based on resources used by the first and second learning processes. If the total resources exceed a threshold, the search apparatus allows the third learning process to be executed. If the total resources are equal to or less than the threshold, the search apparatus withholds the execution of the third learning process, and allows a fourth learning process using the first sample size or smaller and a third hyperparameter value to be executed.

Abstract: A data processing method is executed by a computer to perform distributed processing of a first query and a second query. The data processing method includes calculating a first value indicating a dependency between a first partitioning key and a second partitioning key, the first partitioning key specifying a categorical attribute extracted from syntax of the first query, and the second partitioning key specifying a numerical attribute holding numerical values associated with the second query; generating a query group made of the first query and the second query based on the first value; and performing distributed processing of the first query and the second query based on the query group when executing the first query and the second query.

Abstract: A slave computer reads a plurality of input files that have different formats and generates, for each of the input files, an intermediate file that has added thereto, as a join key, data in a type of column that is common to the input files. The slave computer sorts data in each intermediate file in accordance with the join key. The slave computer extracts, from each intermediate file that has data sorted therein, data that has the format of each of the input files so as to generate a plurality of output files that have data sorted therein with respect to each of the input files. The slave computer inputs the plurality of output files generated to a join program that joins data.

Abstract: When a container generated on a server is started up, an apparatus generates related information indicating that the container has been generated on the server while referring to environmental information of the server that is stored on the server. When causing a file system that retains topology information indicating connection relationship among a plurality of servers and network devices to perform registration processing of the container, the apparatus causes the topology information to be updated to include connection relationship between the container that has been generated based on the related information and the server.

Abstract: A data arrangement apparatus calculates evaluation values for individual relocation processes generated based on an access pattern. Each of the relocation processes indicates a relocation of some of a plurality of unit data pieces in a storage device. The data arrangement apparatus selects, amongst influence range levels whose ranges in relation to unit data pieces to be affected by the relocation differ in extent of influence, one influence range level based on the evaluation values and coefficients associated one-to-one with the influence range levels. Based on the extent of influence indicated by the selected influence range level, the data arrangement apparatus determines one or more relocation processes to be executed amongst the generated relocation processes. Based on access status after the determined relocation processes are executed, the data arrangement apparatus updates a coefficient associated with the selected influence range level.

Abstract: A non-transitory computer-readable recording medium storing a program causing a processor to execute a process, the process includes detecting input of data into a memory to which data is inputted in sequence, the data being a processing object of first processing; allocating the first processing, of which a processing object is the data, with respect to any node in a communicable node group; determining whether or not the data is provided with tail information, the tail information indicating tail data of a series of data that are processing objects of the first processing, when detecting input of the data; and allocating second processing, of which a processing object is a processing result of the first processing that is executed with respect to each piece of data of the series of data, to any node of the node group when determining that the data is provided with the tail information.

Abstract: A code converting unit encodes input text data based on an code assignment table stored in a storage device that defines a conversion rule for encoding text data, wherein; the code assignment table being generated by assigning a part of character strings assigned to a 1-byte region of a first code assignment table to a 2-byte region of the code assignment table, and by assigning one or more codes each having two or more bytes to at least a part of character strings assigned to the 2-byte region of the code assignment table.

Abstract: A machine learning management apparatus identifies a maximum prediction performance score amongst a plurality of prediction performance scores corresponding to a plurality of models generated by executing each of a plurality of machine learning algorithms. As for a first machine learning algorithm having generated a model corresponding to the maximum prediction performance score, the machine learning management apparatus determines a first training dataset size to be used when the first machine learning algorithm is executed next time based on the maximum prediction performance score, first estimated prediction performance scores, and first estimated runtimes.

Abstract: A machine learning management apparatus identifies a maximum prediction performance score amongst a plurality of prediction performance scores corresponding to a plurality of models generated by executing each of a plurality of machine learning algorithms. As for a first machine learning algorithm having generated a model corresponding to the maximum prediction performance score, the machine learning management apparatus determines a first training dataset size to be used when the first machine learning algorithm is executed next time based on the maximum prediction performance score, first estimated prediction performance scores, and first estimated runtimes.