A data pre-processing architecture may include an interface and a pruning logic configured to receive, via the interface, at least one filter value from a query processor; use the at least one filter value to scan rows or columns of a data table stored in a memory; generate a selection indicator identifying a set of rows or columns of the data table where the at least one filter value resides; and provide to the query processor a filtered output based on the selection indicator.

A raw policy set is received for the network processor and a dimension bitmap corresponding to the raw policy set. From the raw policy set, a policy tree builder generates a policy tree image from a set of recursive operations on the raw policy set including selecting boundaries of the raw policy set from cuts on a given dimension of the raw policy set, the dimension cut based on a dimension selection and a partition number selection for the raw policy set. Network processor hardware is configured according to the policy tree image including at least one set of registers, at least one set of tables, and at least one sequence of instructions. At runtime, the network processor applies the optimized policy set to processing of the packet session from the data communication network by the network processor hardware.

Data investigations are performed by querying a plurality of data sources. A system receives an investigation input and queries a plurality of data sources in accordance with the received input. The system receives, in response to the querying, response data from the plurality of data sources, and generates and stores a data structure representing relationships between the first investigation input and the first response data. The data structure may be in the form of a knowledge graph. The system may generate and display a visualization of the data structure. The system may generate and store a record of investigation steps used to generate the data structure, such that the investigation steps may be applied in future instances, for example using different inputs, to generate new data structures.

Techniques for ordering predicates in column partitioned databases for query optimization. Predicates on a single CP table within a query are organized to predicate-CP nodes with various sets of column partitions. The predicates within each predicate-CP node and the predicate-CP nodes as a whole are ordered in ascending order of cost which is determined by CPU/IO cost and predicate selectivity. The reorganized query is then executed.

A method comprising receiving, as input, a data stream representing events; receiving a complex event pattern (CEP) specification representing an occurrence of a CEP in said data stream based on a set of conditions and set of event attributes; applying an algorithm to generate a current CEP plan configured to determine said occurrence of said CEP, wherein said current CEP plan comprises of a series of execution steps; optimize said current CEP plan by, iteratively: (i) executing said current CEP plan, (ii) identifying, with respect to each of said execution steps, one of said conditions whose verification causes said execution step to be included in said current CEP plan, and (iii) performing a re-verification of all of said identified conditions using said updated current values; and re-applying said algorithm when said re-verification fails with respect to at least one of said identified conditions, to generate an updated CEP plan.

A computer-implemented method for access path optimization is provided according to embodiments of the present disclosure. In the method, a plurality of real values of an access path factor may be collected during a specified time period. One of the real values may be generated when a query is executed on a first access path. Then, at least one second access path may be generated for the query based on the plurality of real values of the access path factor. Moreover, an optimal access path for the query may be identified from the first access path and the at least one second access path.

A method of performing a data search in a data source by which an operator of a data search pipeline is just-in-time optimized and compiled, using an operator optimization module which optimizes and compiles an intermediate representation of the operator, considering runtime information, and optimization rules, to produce an operator that is optimized for the data search being performed. The method can be applied with one operator or with many operators applied in any sequence or tree structure according to a query plan, as determined by runtime information and optimization rules.

A transaction processing method and system, and a server. The method includes: after a first node obtains at least one statement corresponding to a first transaction, the first node may classify each statement, and send the at least one statement to at least one second node according to the type of each statement, respectively, so that the at least one second node generates an execution plan corresponding to the received statement according to the statement; then the first node may process the first transaction according to the received execution plan which corresponds to the at least one statement of the first transaction and is sent by the at least one second node. In this way, embodiments can process statements of multiple types at the same time, thereby effectively improving the processing efficiency of the system, reducing the operating burden of the system, and ensuring the transactionality of the system.

A system (100) for Partial Unstructured Information Processing, constituting storing, indexing, querying and retrieval of partially specified unstructured data, the system (100) comprising: Quantum Clustering Algorithm (101) that partitions data records in different clusters such that the data in each cluster can be indexed efficiently, a Compressed Ternary Tree (111, 112, 113) that replaces all conceivable indices for each cluster thereby solving the Unthinkable Query Problem (110) for each cluster, and a Virtual Query Processor (120) that converts traditional data base queries to raw Compressed Ternary Tree queries and appropriate filters (121, 122, 123).

A machine-learning driven Database Management System (DBMS) is provided. One or more machine-learning algorithms are trained on the database constructs and execution plans produced by a database optimizer for queries. The trained machine-learning algorithms provide predictors when supplied the constructs and plans for a given query. The predictors are processed by the DBMS to make resource, scheduling, and Service Level Agreement (SLA) compliance decisions with respect to the given query.