Abstract: A method for analyzing disaster prevention and mitigation effectiveness of an ecological seawall is provided, including: performing seawall ecologicalization on a target seawall; establishing three-dimensional space hydrodynamic force for the target ecological seawall; simulating wave climbing on a dike body and a wave overtopping on a dike top of the target ecological seawall to obtain a wave overtopping index; calculating wave-flow bottom shear stress of the target ecological seawall, establishing a sediment movement model, and calculating suspended load and bed load sediment transportation volumes; calculating the change index of coastal bed surface according to the suspended load and bed load sediment transportation volumes, and determining a development index of tidal flats in front of dike of the target ecological seawall according to the change index; and calculating the disaster prevention and mitigation effectiveness grade of the target ecological seawall according to the wave overtopping index and t

Abstract: The present disclosure provides a method for reducing noise in AES detection, including steps: obtaining G based on a sub-array {tilde over (Z)} of detection data; for each element in G, forming a set of data using three adjacent elements including the element in a column direction, and sorting the set of data in a descending order to obtain an array {tilde over (D)}; performing normalization processing on the array {tilde over (D)} to obtain an array D; for each element in {tilde over (Z)}, forming a set of data using three adjacent elements including the element in the column direction, and sorting the set of data in a descending order to obtain an array U of m rows by n columns; calculating a noise difference value in the column direction, i.e., an array C of m rows by n?1 columns; formulating a noise array N of m rows by n columns; and constructing a new sub-array PN.

Abstract: A method for dynamic filter pushdown for massive parallel processing databases on the cloud, including acquiring one or more filters corresponding to a query, acquiring statistics information of one or more database tables, determining a selectivity of the one or more database tables based on the statistics information, determining whether the selectivity satisfies a threshold condition, and pushing down the one or more filters to the one or more database tables based on the determination of whether the selectivity satisfies a threshold condition.

Abstract: Embodiments of the present disclosure provide methods and devices for formatting a database. The method can include clustering, into each block, a set of sequence of elements from each column of a database; generating a file storing one or more blocks having the clustered set of sequences of elements; providing a first table storing file schema information that associates the elements with the file; and providing a second table for storing block schema information that associates the columns with the one or more blocks.

Abstract: The present disclosure provides systems and methods for prefetching data in databases. One method for prefetching data in a database comprises receiving a database query on the database, determining one or more sets of adjacent columns access by the database query, and for each set of adjacent columns of the one or more determined sets, prefetching data in the adjacent columns.

Abstract: The present disclosure provides a method for generating a classification model in a database system. The method comprises generating a plurality of database tables by performing data clustering on a database, acquiring a plurality of queries associated with information from the database, generating training data by executing each of the plurality of queries on the plurality of database tables, and training a classification model using the training data, wherein the classification model is used to select a database table for a query based on corresponding performance information associated with the query on the database during executing of the query. The present disclosure also provides a method for executing a query on a database using a classification model. The method comprises obtaining the classification model, obtaining a database query, generating a target table by inputting the database query into the classification model, and executing the database query on the target table.

Abstract: Efficient large column values storage in columnar databases is disclosed, including: providing a database comprising a write storage, a read storage, and an auxiliary storage, and wherein the auxiliary storage is used to store a large column value of a row that is associated with either the write storage or the read storage, wherein the large column value comprises a column value that meets a set of large column value criteria; and operating the database using the write storage, the read storage, and the auxiliary storage.

Abstract: Embodiments of the present disclosure provide methods and devices for inserting data in a database. The method can include caching one or more rows of data in a delta table in response to a request to insert the one or more rows in a destination table, wherein the destination table is one of a group of destination tables of a first database, and wherein the delta table contains requested changes of rows of the destination table, the changes being caused by a data operation; determining a priority of the destination table based on runtime information of the destination table relative to the group of destination tables; and scheduling the merging of the inserted rows from the delta table to the destination table based on the determined priority of the destination table. The destination table is in a format of a file storing one or more blocks, each block having a set of sequence of elements clustered from each column of the destination table.

Abstract: Intelligent merging for efficient updates in columnar databases are disclosed, including: determining a current age associated with a row in a database, wherein the database comprises a write storage and a read storage, wherein the row is located in the write storage; determining a set of one or more merge criteria based at least in part on a modification age distribution, wherein the modification age distribution is dynamically determined based at least in part on previous transactions at the database; determining whether the current age meets the set of merge criteria; and in response to a determination that the current age meets the set of merge criteria, merging the row from the write storage into the read storage.

Abstract: Intelligent merging for efficient updates in columnar databases are disclosed, including: determining a current age associated with a row in a database, wherein the database comprises a write storage and a read storage, wherein the row is located in the write storage; determining a set of one or more merge criteria based at least in part on a modification age distribution, wherein the modification age distribution is dynamically determined based at least in part on previous transactions at the database; determining whether the current age meets the set of merge criteria; and in response to a determination that the current age meets the set of merge criteria, merging the row from the write storage into the read storage.

Abstract: Efficient large column values storage in columnar databases is disclosed, including: providing a database comprising a write storage, a read storage, and an auxiliary storage, and wherein the auxiliary storage is used to store a large column value of a row that is associated with either the write storage or the read storage, wherein the large column value comprises a column value that meets a set of large column value criteria; and operating the database using the write storage, the read storage, and the auxiliary storage.

Abstract: The present disclosure provides systems and methods for an indexing system in databases. One method for locating a row in a row-group columnar storage database comprises: receiving a query to locate a target row in the row-group columnar storage database that comprises rows of data divided into one or more blocks of data having columns of data that correspond to the rows of data, and one or more block information tables having column information corresponding to the columns of data in the one or more blocks of data, each block information table of the one or more block information tables being associated with a corresponding block of data of the one or more blocks of data; and locating the target row using the column information from the one or more block information tables.

Abstract: The present disclosure provides systems and methods for prefetching data in databases. One method for prefetching data in a database comprises receiving a database query on the database, determining one or more sets of adjacent columns access by the database query, and for each set of adjacent columns of the one or more determined sets, prefetching data in the adjacent columns.

Abstract: Embodiments of the present disclosure provide methods and devices for inserting data in a database. The method can include caching one or more rows of data in a delta table in response to a request to insert the one or more rows in a destination table, wherein the destination table is one of a group of destination tables of a first database, and wherein the delta table contains requested changes of rows of the destination table, the changes being caused by a data operation; determining a priority of the destination table based on runtime information of the destination table relative to the group of destination tables; and scheduling the merging of the inserted rows from the delta table to the destination table based on the determined priority of the destination table. The destination table is in a format of a file storing one or more blocks, each block having a set of sequence of elements clustered from each column of the destination table.

Abstract: The present disclosure provides a method for compressing numeric data. The method comprises receiving a data set having a plurality of numeric values; for each numeric value of the plurality of numeric values of the data set, dividing a numeric value into a plurality of arrays arranged according to a specific location of the numeric value, wherein the plurality of arrays include a first array and a second array; grouping, across the plurality of numeric values, first arrays; grouping, across the plurality of numeric values, second arrays; and compressing the group of first arrays and the group of second arrays. The present disclosure also provides a method for decompressing numeric data. The method comprises receiving a data buffer comprising compressed numeric values; decompressing the compressed numeric values into groups of arrays; aligning the groups of arrays according to their relative positions from decimal points; and reconstructing numeric values according to the aligned groups of arrays.

Abstract: Embodiments of the present disclosure provide methods and devices for formatting a database. The method can include clustering, into each block, a set of sequence of elements from each column of a database; generating a file storing one or more blocks having the clustered set of sequences of elements; providing a first table storing file schema information that associates the elements with the file; and providing a second table for storing block schema information that associates the columns with the one or more blocks.

Abstract: The present disclosure provides a method for generating a classification model in a database system. The method comprises generating a plurality of database tables by performing data clustering on a database, acquiring a plurality of queries associated with information from the database, generating training data by executing each of the plurality of queries on the plurality of database tables, and training a classification model using the training data, wherein the classification model is used to select a database table for a query based on corresponding performance information associated with the query on the database during executing of the query. The present disclosure also provides a method for executing a query on a database using a classification model. The method comprises obtaining the classification model, obtaining a database query, generating a target table by inputting the database query into the classification model, and executing the database query on the target table.

Abstract: A method for dynamic filter pushdown for massive parallel processing databases on the cloud, including acquiring one or more filters corresponding to a query, acquiring statistics information of one or more database tables, determining a selectivity of the one or more database tables based on the statistics information, determining whether the selectivity satisfies a threshold condition, and pushing down the one or more filters to the one or more database tables based on the determination of whether the selectivity satisfies a threshold condition.

Abstract: Systems, apparatus, methods and articles of manufacture provide for reviewing the management of one or more business files. According to some embodiments, methods may include determining a plurality of file review criteria, determining a plurality of file review questions, determining a respective maximum potential review score for each file review question, determining respective responses for the file review questions, adjusting (if necessary) one or more of the maximum potential review scores, and/or determining at least one review score for each file review question answered. Some embodiments may provide for determining a respective review score for a file for each of one or more predetermined file review criteria.

Abstract: Systems and methods for optimizing outcomes in view of various business scenarios are based on a unique quantification of work and estimate of task duration, which may be used to develop a measure of the work required to complete a task. This measure may be compared to forecasted work and used to allocate resources accordingly. Additionally, optimal outcomes may be identified subject to any classification, such as by class of worker, type of task, location of task, and/or size of work unit.