56 results about "Relation discovery" patented technology

Methods and systems for searching over a large corpus of data to discover relevant information artifacts based on similar content and/or relationships are disclosed. Improvements over simple keyword and phrase based searching over Internet scale data are shown. A search query may be modified or relaxed based on the search terms and a contextual relationship therebetween. The search results may be ranked based on both a data ranking corresponding to the data entries in the corpus and a query ranking corresponding to the search query and/or the modified or relaxed search query. In this manner, the accuracy and relevance of the search results is improved.

Embodiments of the present invention provide systems and methods for relationship discovery. According to one embodiment of the present invention, arbitrarily complex relationship discovery rules can be applied to components in a data model to determine if a relationship exists between the components. If the components satisfy a relationship discovery rule, a relationship can be established for the components. Because the relationships are established based on the application of relationship discovery rules rather than through enforcement of a database schema, new relationships and relationship types can be established without having to alter the underlying database schema. Additionally, because rules can be repetitively applied, relationships can be established, deleted and updated as the system being modeled changes.

The invention discloses a topology discovery system of a next generation Internet based on the IPv6 (Internet Protocol Version 6), comprising a detection list input module, an equipment and connection relation discovery module, an equipment and subnet topology discovery module, an analysis integration module, a topology structure information storage module, a database and a topology structure information display module. The invention also discloses a realizing method of the system, comprising the following steps: (S1) forming a detection node list by the detection list input module; (S2) sending a network path detection packet by the equipment and connection relation discovery module to acquire a router marked by an IPv6 address and a connection relation thereof, and exchanging network topology information between the equipment and subnet topology discovery module and the router on the network to acquire the network topology information; and (S3) analyzing by the analysis integration module to form an integrated topology structure diagram. The invention has the advantages of enhanced stability, better performance, alias removal, and the like and effectively improves the accuracy and the integrity of topology discovery.

The present invention discloses a processing method and system of big data. The method comprises the steps of exacting big data from a plurality of data sources, and performing rule conversion on the big data; performing data processing on the big data after rule conversion; and establishing a database according to the big data after data processing. Various information is analyzed from multi-source heterogeneous data and the database is established, and therefore data support is provided for various data analysis, behavior analysis, user figure analysis and relation discovery.

The invention provides an application topological relation discovery method, relating to the IT technical field and particularly relating to a discovery method, device and system for the application topological relation between virtual machines. The method records the API call information during the message transmission. A topology discovery server determines whether there is interaction between the virtual machines in the virtual machine cluster by analyzing the collected API call information. Using the above analysis, the topology discovery server can then determine the frequency of interaction between the virtual machines involved in the collected API call information, and the application topological relation between the virtual machines is determined according to the frequency of interaction between virtual machines. The above method gives a method to find the application topological relation between the virtual machines in virtualized scenarios, by analyzing the API call information generated during the message transmission, the application topological relation between the virtual machines is determined, and the complexity of the scheme is low.

The invention discloses an optimization method based on random walk relationship discovery, belongs to the technical field of knowledge maps, and relates to a relationship discovery problem in the knowledge map. The invention provides the random walking optimization method under a defined radius based on the random walk relationship discovery. The method is characterized in that aiming at a largescale knowledge base, the magnitude of knowledge map nodes is excessive along with the base, a local submap obtaining the defined radius under a breadth-first search is combined, and a large amount oftime and storage space consumed when preprocessing is conducted are accelerated by adopting the obtained local submap but not the whole map. Compared with a tradition random walk algorithm, random walking is conducted under the local submap so that the preprocessing time and the magnitude of space complicity can be greatly reduced to shorten random walking time on the basis of ensuring the accuracy of the relationship discovery.

The invention discloses a relation discovery method and device and a computer readable storage medium, and the method comprises the steps: converting event data into personnel relation data, the personnel relation data comprising n sets, and each set comprising m * (m- 1)/2 subsets, each subset comprising two persons in an event, n being the number of events, and m being the number of persons in each set; Converting the personnel relationship data into graph structure data G = (V, E), a set V of vertexes in the graph G being all personnel in the n sets, and a set E of sides being a connectionline between two personnel belonging to one subset; And in the graph structure data, mining a frequent subgraph with the frequency meeting a preset minimum frequency threshold value, and obtaining a partner relationship among the personnel according to the mined frequent subgraph. According to the method, the event data is converted into the graph structure data, the frequent subgraphs are mined according to the minimum frequency threshold value, the granularity of relation discovery is better adjusted, and therefore the hidden relation in the event data is more accurately obtained.

The invention provides a customer relation discovery method based on data vectorization spatial analysis, and mainly relates to the technical field of data processing and data mining. The customer relation discovery method based on data vectorization spatial analysis is mainly implemented through vectorization processing of customer attributes. The method has high universality. Through uniform data processing and geographic spatial analysis methods, the problem that in the prior art, a clustering analysis method and a classifying analysis method cause analysis difference under different data sets is effectively solved.

The invention discloses a network topology discovery method based on MAC information and relates to the technical field of topological relation discovery. The method includes the steps that first, IP and MAC information of each subnet device and an MAC address information table of each subnet device are found and acquired through a network management device; then, the network management device searches for leaf nodes one by one according to all the acquired information; the network management device judges whether an uplink subnet device connected with the leaf nodes exists or not, and a found topological relation is saved; if the uplink subnet device exists, the MAC information of the leaf nodes is deleted, and MAC address information, learnt from the MAC address information tables of other subnet devices, of the leaf nodes is deleted; the leaf nodes are searched for again till no leaf nodes are found, and topological relation searches end. Through the method, complete network topology discovery can be achieved.

The invention discloses a method for finding a path relationship of a graph based on multiple agent routes. The method comprises the following steps of: firstly, respectively acquiring a semantic relationship graph from each knowledge field by using resource description language and a semantic extracting and editing tool based on W3C standard and taking the semantic relationship graph as a bottom-layer knowledge base for finding the correlation relationship; secondly, constructing a multi-agent distributed route framework through a main agent and a plurality of subagents; thirdly, providing a user interaction interface; fourthly, receiving a hypothesis command of a user and then distributing the hypothesis command to subagent groups for executing a query task by the main agent; and fifthly, receiving the query task and then carrying out route query on a correlation relationship path of the bottom layer knowledge base by the subagent groups and feeding back answers to the user by the main agent after the route query is ended. The method has the advantages that a similar vertex incidence matrix with a weight is established between knowledge from every two different fields; and a distributed route support for querying and demonstrating the interdisciplinary knowledge and the like are provided by adopting a multi-agent technology.

The invention discloses a label mining method and device based on user chat records. The method comprises the steps of: preprocessing chat data generated by chat between a voice assistant and a user;extracting user tags from the preprocessed chat data based on a label extraction model and a statistical method; and mining all labels with similarity higher than a set threshold based on a relationship discovery model. According to the method and device of the invention, by means of the machine learning method of the neural network, the model can select a proper label according to semanteme, anda label effect is good; after a system runs for a period of time, more user chat data can be accumulated, labeling training can be carried out again to achieve a better effect, and further optimization can be supported; after early-stage manual labeling, labels can be automatically extracted in the later stage, a large amount of manpower is saved, and the efficiency is improved.

The invention provides a complex network vulnerability and elasticity relation analysis method, and relates to the technical field of information and control. According to the invention, after the relationship between the vulnerability and the elasticity is obtained, one more measurement standard is provided for the performance of the network. Existing research work does not indicate that the vulnerability of the network can be reduced by adding edges to the network, yet a method for reducing the vulnerability of the network is provided through the obtained relation between the vulnerability and the elasticity (edge adding enables the elasticity to be increased, and the vulnerability is reduced). Besides, enhancement of the network elasticity and reduction of the network vulnerability are good indexes for improving the network performance, and the improvement of the performance of the network elasticity and the network vulnerability can be realized by slightly changing the network (namely adding a small number of edges) through obtaining the relationship, which is a good discovery.

The invention discloses a method for determining the proximity relation between user equipment, equipment and a communication system, and is to solve the problem that at present, a solution scheme for finding the proximity relation between mutually-adjacent user equipment is still unavailable. The method of the embodiment in the invention comprises the following steps: the application layer equipment obtaining and storing subscription information of each terminal; after receiving a proximity relation discovery request of a first terminal, determining the first terminal has a function of being capable of finding other terminals based on the subscription information corresponding to the first terminal; and obtaining position information of the first terminal and a second terminal, judging whether the second terminal and the first terminal accord with the proximity principle, and sending the judgment results to the first terminal. The embodiment of the invention provides the scheme for determining the proximity relation between the user equipment; discovery process of the proximity relation between the user equipment is realized; and with the scheme, a network architecture in an existing protocol is modified less, and the scheme is easy to realize.

The invention discloses a financial event-oriented hybrid causal relationship discovery method. The method comprises the following steps of: firstly, extracting a causal relationship of a text event;finding out the change trend of the attention index of the time sequence corresponding to the event on the Baidu index in combination with the keyword corresponding to the text event, i.e., searchingan index curve, and deducing the causal relationship of the time sequence event by using the transfer entropy; finally, using a maximum likelihood estimation method for fusing a causal relationship extraction result and a result obtained through causal relationship inference based on time series data of a search index curve, and generating causal relationship discovery. The method can be used forquickly discriminating and discovering causal relationships contained in financial event pairs.

The invention discloses a task relationship determination method and device and equipment, which are used for automatically determining an association relationship between tasks. In the application, a relationship discovery device can firstly determine sample attributes and corresponding attribute values in each task; then, according to the sample attribute of each task and the corresponding attribute value, the task attribute of each task and the corresponding attribute value is determined; after each task attribute and the corresponding attribute value are determined, the mixed attribute of each task is determined on the basis of the sample attribute and/or the task attribute of each task, and then the associated tasks, namely the association relationship between the tasks, are determined on the basis of the task mixed attribute of each task, so that the whole process does not depend on related personnel; the associated task can be efficiently determined.