38 results about "Schema transformation" patented technology

A method and system for automated knowledge extraction and organization, which uses information retrieval services to identify text documents related to a specific topic, to identify and extract trends and patterns from the identified documents, and to transform those trends and patterns into an understandable, useful and organized information resource. An information extraction engine extracts concepts and associated text passages from the identified text documents. A clustering engine organizes the most significant concepts in a hierarchical taxonomy. A hypertext knowledge base generator generates a knowledge base by organizing the extracted concepts and associated text passages according to the hierarchical taxonomy.

Methods, program products and systems for determining, for a database query that does not represent a snowflake schema, a graph comprising vertices each representing a table joined in the query, a directed edge between each pair of vertices of which a first vertex represents a first table and a second vertex represents a second table that is joined in the query with the first table, each of the edges representing one of an outer join and an inner join. Further determining, for the graph, a directed spanning tree that represents an ordering of joins in the query and includes all outer join edges in the graph.

A system and method for cross-language knowledge searching. The system has a Semantic Analyzer, a natural language user request / document search pattern / semantic index Generator, a user request search pattern Translator and a Knowledge Base Searcher. The system also provides automatic semantic analysis and semantic indexing of natural language user requests / documents on knowledge recognition and cross-language relevant to user request knowledge extraction / searching. System functionality is ensured by Linguistic Knowledge Base as well as by a number of unique bilingual dictionaries of concepts / objects and actions.

The invention discloses an OLAP query processing method based on array storage and vector processing. In the method, array subscript mapping is carried out on major keys and foreign keys according to referential integrity constraint in a data warehouse model, the major keys of a dimension table and the foreign keys of a fact table are directly mapped to be array subscript addresses, the complex model is converted into a virtual materialized table, and a unified data access view is provided for different queries. The OLAP query is simplified to be three stages of predicate processing, grouping processing and gathering processing. In the stage of predicate processing, selection vectors are used for processing a selection result, on the fact table, of a plurality of predicates many times; in the stage of grouping processing, grouping vectors are used for distributing grouping array subscripts for each fact table record for meeting the predicate condition; in the stage of gathering processing, gathering vectors are used for providing an aggregator based on an array for an aggregate function. The OLAP query processing method based on array storage and vector processing simplifies the OLAP query processing process under a complex mode, eliminates the connection operation between the tables, and improves the code execution efficiency when the OLAP query processing is carried out.

A method and apparatus produces documentation from customized extensible mark-up language (XML) by generating a schema-specific Darwin Information Typing Architecture (DITA) specialization. The DITA specialization is generated from XML schemas which define the XML documents. During the process of generating the DITA specialization, a XSL transform is also generated. The XSL transform is applied to the XML file to produce a DITA file that follows the rules of the DITA specialization. Prior art transforms can then be used to convert the DITA file and the DITA specialization to standard documentation. Generating schema-specific DITA specializations may be an automatic process or a dynamic process under the direction of a user.

A database command is received from a user for modifying an existing data structure or creating a new data structure. The database command is used to construct a query that is provided to a query optimizer component where the query is transformed into a first query execution plan including operations that persist operation state. Some data specified in the query is received, and some of the new data structure is constructed by executing some of the first query plan. While receiving some of data specified in the query and constructing some of the new data structure, operation state of execution is persisted. When an interruption of execution of the first query plan occurs, an updated query plan is generated using persisted operation state. External updates occur, before or after the interruption, but before executing the updated query plan. The external updates are transactionally validated.

Embodiments provide automatic representational state transfer application programming interface (“REST API”) document generation for System for Cross-domain Identity Management (“SCIM”) based resources. For each resource, embodiments identify all SCIM based resource type data files and corresponding schema, where each resource type data file includes resource type definition metadata. Embodiments identify available operations based on the resource type definition metadata and apply HyperText Markup Language (“HTML”) operations corresponding to the metadata based on an available Swagger Operation template engine. Embodiments convert SCIM based schema to Swagger schemas by iterating through each available schema and identifying attributes and preparing a Swagger attribute template engine to generate a Swagger based attribute definition to a Swagger template aggregator. Embodiments then generate Swagger JavaScript Object Notation (“JSON”) corresponding to the Swagger schemas.

Light-feedback distometer based on two-frequency laser with Zeeman-birefraction, which belong to laser ranging technology field. The distometer consists of light source, measuring section and signal processing section. Laser outside cavity is tuned to realize laser internal schema transformation and at the same time optical signal feedback detection is exert on laser. The difference of light intensity modulation depth between laser working on longitudinal bimodule and on monomode is relative to outside cavity length. When outside cavity length equals integer multiple laser cavity length modulation depth difference is minimum; when outside cavity length bias from integer multiple laser cavity length, modulation depth enlarges gradually.

A transform-by-pattern (TBP) system is configured to proactively suggest relevant TBP programs based on inputted source dataset and target dataset without requiring users typing in examples. The TBP system has access to multiple TBP programs, each of which includes a combination of a source pattern, a target pattern, and a transformation program that is configured to transform data that fits into the target pattern into data that fits into the source pattern. When a source dataset and a target dataset are received from a user, the TBP system identifies a subset of the source dataset and a subset of the target dataset as related data. The TBP system then identifies one or more applicable TBP programs amongst the multiple TBP programs, and suggest or apply at least one of the one or more applicable TBP programs.

According to an embodiment of the present invention, a system provides a graph that identifies data mapping rules for a data transformation type and comprises: a display interface configured to present a plurality of distinct node types, each with a distinct location and is displayed at corresponding a first display location, a second display location and a third display location on the display surface, a first distinct node type is a pure data type with one or more value output ports; a second distinct node type is a data mapping type with one or more value input ports and at least one output port; and a third distinct node type is a sequence instruction type with one or more stream input ports and at least one output data transform port; a computer processor that generates a rules configuration representing a graph construct; and a memory that stores the rules configuration.

The invention discloses a line loss fault positioning implementation method for a power system. The line loss fault positioning implementation method is implemented by line loss business data simulation modeling, line loss simulation calculation fault positioning analysis, fault information base construction, and fault information library-based operation. The line loss fault positioning implementation method comprises the steps of: extracting fault features, determining condition attributes and decision attributes of corresponding objects, exploring fault modes through association rules, positioning fault sections, and converting the fault modes into simple text description and output the simple text description. According to the line loss fault positioning implementation method, the faultand line loss linkage of a virtual technology is combined, the fault diagnosis information processing of workers and retrograde line loss calculation is accelerated, the fault positioning speed is increased, the fault processing efficiency is improved, the fault is accurately positioned, and a reliable guarantee is provided for continuous loss reduction.

The invention discloses a business data distribution server and a business data distribution method of the business data distribution server. The method comprises: when a server monitors that the concurrent number of a service request exceeds first threshold value, wherein a processing mode of the service request is a first processing mode, business data need to be distributed of an intelligent terminal which is referred by a business distribution strategy is confirmed according to the pre-formulated business distribution strategy which aims at the business request, attribute information of the intelligent terminal and attribute information of the business data need to be distributed of the intelligent terminal are stored correspondingly; and then, the processing mode of the business request is changed from the first processing mode to a second processing mode. Due to the fact that a large amount of business requests of the first processing mode can save a lot of memory spaces, when the concurrent number of the service request exceeds the first threshold value is monitored, the business request is changed to a business request of the second processing mode, the server can directly search the business data which need to be distributed and the business data are fed back to the intelligent terminal, and therefore the business requests of users can be responded timely.

According to an embodiment of the present invention, a computer implemented method and system comprises: receiving, via an electronic input, a transaction having a serialized structure; identifying, using a computer processor, a transaction type for the transaction; identifying, using the computer processor, a schema for the transaction type; retrieving, from a memory, a schema correlated transform process wherein the schema correlated transform process transforms the serialized structure to a hierarchical structure; and parsing, using the computer processor, the transaction into a hierarchical format based on the schema.

Technologies are described herein for changelog transformation and correlation in a multi-tenant cloud service. Components within the multi-tenant cloud service generate changelogs that describe changes made to hardware or software components within the multi-tenant cloud service. The changelogs are received and transformed from different schemas into a common schema. A central change management service ("CCMS") exposes a network service application programming interface ("API"), or other type of interface, through which other network services can obtain the changelogs that have been transformed into the common schema. For example, services can obtain changelogs in order to correlate changes to anomalies or other events taking place in the multi-tenant cloud service, to identify upstream or downstream components that might be impacted by a change, to provide a user interface for viewing the changelogs, the correlation, or the potential impact of a change, and / or to perform other typesof functions.

An artificial intelligence apparatus includes an input processor configured to convert input information to patterns, an analyzer configured to analyze the input information, a recorder configured to record the information, a controller configured to perform at least one of a development of a process according to a type of a sentences and an intention, a search for information and a logic development to solve a problem, an execution of a process and activating a program, a generalization of information and a procedure, an update to a better knowledge and a logic, a search and an arrangement of information about an interesting field and an item, recording and updating of information, connective relations and relationship, and a transition control between information to a goal and an output processor configured to convert the patterns to information or control signals.

The invention discloses an SELinux strategy optimization method based on a knowledge base, and relates to the SELinux strategy optimization method based on the knowledge base. The invention aims to solve the problem that the existing SELinux strategy file can cause low security of the system. The method comprises the following steps of: 1, obtaining a preprocessed strategy set, an audit log, a mapping relationship between attributes and types and a mapping relationship data file between the types and a file full path; 2, constructing a knowledge base; 3, obtaining a classification result of the unknown access mode; 4, converting the newly identified access mode into a strategy rule form of an SELinux system strategy set, and performing conflict detection with a known access mode or rule; when the conflict does not occur, merging the newly identified access mode into the database, and executing the step 3 again; and when a conflict occurs, resolving the conflict, merging the resolved newly identified access mode into the database, and executing the step 3 again. The method is applied to the technical field of information security.

The invention discloses an address data processing method and device. The method comprises the following steps: converting address data of a plurality of data sources into structured address data; converting the structured address data into standard address data according to a standard mode, and wherein the standard mode comprises all fields forming the standard address data and data types corresponding to all the fields. By adopting the method and the device, the address data of the plurality of data sources can be converted into the address data in the same format, so that the address data can be uniformly managed.

The embodiment of the invention provides a file opening mode conversion method. The method comprises: when a file is opened in a read-only mode, detecting whether a user triggers a mode conversion instruction or not; if the mode conversion instruction is detected, displaying an editing password input dialog box in a read-only mode opening interface of the file; receiving an editing password inputby a user; and when the received editing password is correct, converting the opening mode of the file from the read-only mode to the editing mode. According to the method provided by the embodiment ofthe invention , for a file opened in a read-only mode, a user can directly convert the opening mode of the file into an editing mode from the read-only mode by triggering a mode conversion instruction and inputting an editing password, and the situation that in the prior art, the file is closed firstly, then the file is opened again, and meanwhile the editing password is input, so that the user can enter the editing mode is avoided. Therefore, the problem that in the prior art, when a document opening mode is converted into an editing mode from a read-only mode, operation is complex is solved.

A method, system, and computer program product are provided for generating an external payroll policy for a payroll system. A legacy schema is identified for execution by a legacy payroll system. A set of functions and rules is identified within a processing flow of the legacy schema. The external payroll policy is generated in a selected format type, according to a processing flow of the functions and rules that were identified within the legacy schema.

The present disclosure discloses a dialogue method and system based on free text knowledge, the method includes: receiving free text knowledge and dialogue context; encoding free text knowledge and dialogue context into hidden state representation through knowledge encoder and context encoder respectively ;Decode the hidden state representations of the two through the hybrid decoder; at each time step of decoding, determine the execution mode of the hybrid decoder through the mode converter; after a certain number of decoding time steps, until the results of different execution modes are jointly pressed sequence to synthesize the final reply sequence. This disclosure combines the essence of generative and extractive dialogue systems, and has the ability to generate word by word and extract relevant semantic units from text knowledge. The reply to the system output is significantly enhanced, and fluency and naturalness are guaranteed. , to maximize the user experience.

The invention provides a relational data query optimization method based on data mode conversion and parallel framework, and relates to the technical field of computer databases. This method constructs a new data storage structure by structurally analyzing complex query statements on relational model data, realizes conversion processing on data storage, and transfers the data stored in the relational model database to a new data model storage system. Convert corresponding query statements according to changes in data patterns, generate SPARK tasks based on SPARK SQL queries and execute the tasks. The relational data query optimization method provided by the present invention can accurately convert the data in the relational database according to the query characteristics to the SPARK parallel query processing framework, thereby effectively improving the processing efficiency of some frequently executed complex queries on the relational database.

The embodiment of the invention provides a distributed storage mode control method and device, equipment and a storage medium, and relates to the technical field of data storage. The specific implementation scheme is as follows: determining target user data to be subjected to storage mode control; identifying a first storage mode corresponding to the target user data by using the read-write state information of the target user data in the first historical time period; predicting a second storage mode corresponding to the target user data by using read-write state information of the target user data in a second historical time period; judging whether the first storage mode and the second storage mode are the same or not to obtain a judgment result; determining a to-be-adapted target storage mode of the target user data based on the judgment result; and if the target storage mode is different from the current storage mode of the target user data, carrying out storage mode conversion processing on the target user data based on the target storage mode. Through the scheme, the use efficiency of the storage space can be improved, and the read-write performance of the stored data is considered.

The invention discloses a Markov process-based time series stream data anomaly detection method. The method comprises the following steps of s1, selecting a training data stream; s2, carrying out dimensionality reduction on the training data flow through an LPP algorithm; s3, determining a current mode category through a K-Means clustering algorithm based on an elbow strategy, and performing mode division on the training data stream; s4, constructing a Markov anomaly detection model, and s5, processing to-be-tested data, inputting the processed to-be-tested data into the Markov anomaly detection model, and outputting an anomaly detection result. According to the method, the time series stream data is clustered, and the data is divided into different modes to construct the Markov-based anomaly detection model; in the model, normal conversion between the different modes is identified as concept drift, only mode conversion which cannot occur is identified as anomaly, the distribution rule of the model and the time series flow data is more stable, and the detection accuracy is higher.