35 results about "Mashup" patented technology

A method, apparatus, computer readable medium, computer system, wireless or wired network, or system to provide an online and / or mobile security of a user's privacy and / or security method of internet or mobile access or system, apparatus, computer readable medium, or system using encryption technologies and / or filters to access data, encrypt and / or decrypt data, sync data, secure data storage and / or process data using cloud technology across many different networks and / or fiber optic communications from an endpoint accessed through multiple devices, browsers, operating systems, networks, servers, storage, software, applications or services integrated in a public cloud or a private cloud within an enterprise, a social network, big data analytics or electronic surveillance tracking or some mashup of two or more to prevent the unauthorized collecting, tracking and / or analysis of a user's personal data by a third party and / or for generating relevant advertising, mobile, internet social messaging, internet posted promotions or offers for products and / or services.

The invention relates to a Web service recommendation method based on a CNN and LSTM. At present, a traditional collaborative filtering technology dominates an application system, but still has the problem of data sparsity. The Web service recommendation method based on the CNN and LSTM effectively combines the CNN with the LSTM to construct a deep learning model to realize an optimal recommendation result, and uses implicit feedback information, which is the historical behavior of a user, to extract the preference of the user when computing the preference characteristics of the user. A BERT-based language representation model word vectorization method is used for training natural language attributes of a user, an API and Mashup to obtain a feature matrix of each attribute, constructs a score prediction model based on the CNN and LSTM, and inputs each feature matrix into the score prediction model to obtain a prediction score of the Web service by the user, and a recommendation strategy selects the Web service with the user score Top_3 to generate recommendation for the user. The method is applied to the field of the Internet.

The invention discloses an API recommendation method based on a knowledge graph and collaborative filtering. The method comprises the following steps: constructing a service knowledge graph; embedding API entities in the knowledge graph into a low-dimensional space, and calculating the similarity Sim1 between APIs; obtaining APIs used by the target Mashup, and enabling similar APIs of the APIs to form a recommendation set RS1 based on Sim1; extracting functions between the Mashups according to the Mashup description, and calculating the similarity Sim2 between the target Mashup and other Mashups through the extracted functions; obtaining a similar Mashup of the target Mashup based on the Sim2, and combining APIs used by the similar Mashup into a recommendation set RS2; constructing a usage matrix of the Mashup and a usage matrix of the API based on a historical usage relationship between the Mashup and the API, and calculating the similarity Sim3 between the Mashup matrixes and the similarity Sim4 between the API matrixes; obtaining a similar Mashup of the target Mashup based on the Sim3, and enabling APIs used by the similar Mashup to form a recommendation list RS3; obtaining APIs used by the target Mashup, and enabling similar APIs of the APIs to form a recommendation list RS4 based on Sim4; and finally, obtaining a final API recommendation result according to the RS1, the RS2, the RS3 and the RS4.

The invention discloses a building engineering project construction information data network transmission platform, which comprises a PC (Personal Computer) and a BIM5 visual cloud platform, and is characterized in that the BIM5 visual cloud platform is downloaded to a server of the PC through a communication network; and a signal output port of the BIM5 visual cloud platform is connected with a data mashup advanced analysis module, a building information management module, a video security module, an electromechanical equipment management module and an equipment detection system. According to the building engineering project construction information data network transmission platform, a BIM5 visual cloud platform is adopted as an overall research and development architecture, and BIM sub-models dynamically constructed in all stages can be integrated to form an intelligent building data center by building a building full-life-cycle BIM data platform and a database; and conversion and integration from the BIM construction model to the BIM operation and maintenance model are supported.

Embodiments of the present invention are directed to a communication system that provides various automated operations, including searches of public and private resources for third party information and presentation of the located third party information by a mashup and preparation of a mashup to combine different communication modes or modes in a common session.

A kind of Mahsup service clustering method based on density peak detection, described method comprises the following steps: the first step, for all the characteristic vectors of the Mashup service that participates in clustering, carry out local density, distance between vectors and higher density shortest distance calculation ; The second step, based on the density information calculated in the first step, select the candidate points of the cluster centers from all the Mashup service feature vectors; the third step, further screen out the candidate points of the cluster centers obtained in the second step The most suitable K initial cluster centers are used for K-means clustering. The invention can effectively improve the clustering precision of Mashup services and reduce the service search space.

A Web API recommendation method based on a Mashup service neighborhood in an open Web environment, comprising the following steps: Step 1, constructing a demand Mashup semantic feature vector to match Mashup service clusters; Web API data, build a Web API neighborhood; the third step, according to the functional classification of the Web API built in the neighborhood, classify the remaining Web APIs in the data set; the fourth step, calculate the popularity and co-occurrence, And perform ranking to get the final recommendation list. The present invention first preliminarily determines the functional category of the requirement, clarifies the screening range of Web API, then divides all the Web API in the data set into functional categories, and finally makes recommendations according to the ranking of popularity and average co-occurrence degree, and the accuracy of the recommendation is Higher and better diversity.

A Web API recommendation method based on topic model clustering, calculates the semantic weight information of words according to context information to obtain document-word semantic weight information matrix D; counts word co-occurrence information to calculate SPPMI matrix information; obtains Mashup service based on The word frequency information matrix D of the document word, the context SPPMI matrix M of the word, the word embedding information matrix can be obtained by decomposing M, and the above two kinds of information can be further combined to calculate the topic information of the service; the obtained topic feature of the Mashup service is used as the spectrum The input of the clustering is clustered. By cutting the graph composed of all data points, the edge weights between different subgraphs after the graph are cut as low as possible, and the edge weights in the subgraphs are as high as possible. The purpose of clustering; combining GBDT and FM methods to make prediction recommendations for Web API services. The present invention effectively realizes Web API recommendation.

A deep learning-based social mashup logic implementation system, includes a user interface server configured to store data about a user's personal information, login information, activity information,and the like of web- and mobile-based interfaces, an external link server configured to store data about activities, a situation recognition server configured to sense mental and environmental changes of the user and store data related to the mental and environmental changes of the user, an analysis server configured to generate and store a relationship index obtained by numerically calculating arelationship between the user and the other user with an artificial neural network, and a management server configured to adjust a level of the other user's access to the user's posts and a level ofexposing content uploaded by the user.

A Mashup service feature representation method based on functional semantic association calculation, said method comprising the following steps: the first step, preprocessing all mashup service data that needs feature representation; second step, based on the preprocessed mashup service data , carry out the functional noun extraction operation; the third step, for the functional noun set FS of each mashup service, carry out semantic association calculation on the semantic weight of each functional noun; the fourth step, based on the semantic weight calculation result of the third step, combine TF‑IDF algorithm and Word2Vec model are used to represent Mashup semantic feature vectors. The invention can effectively improve the matching precision of the Mashup service and the service search efficiency.

An improved K-means service clustering method around topic modeling, including the following steps: the first step, preprocessing all mashup service data that needs feature representation; the second step, based on the preprocessed mashup service data , to extract the functional nouns; the third step, for the functional noun set FS of each mashup service, use the topic model to represent the mashup feature vector; the fourth step, for all the mashup feature vectors participating in the clustering, carry out the density information Calculation; the fifth step, based on the density information calculated in the fifth step, select the candidate points of the cluster centers from all the Mashup feature vectors; the sixth step, further screen out the candidate points of the cluster centers obtained in the fifth step The most suitable K initial cluster centers are used for K-means clustering. The present invention improves the final effect of Mahsup service clustering.

A method and apparatus for reliable mashup. The method includes the steps of: intercepting a data update request submitted by a client browser to one or more of a plurality of services for providing mashup page data; performing consistency validation on the data update request using consistency rules; and, in response to a successful validation, forwarding the data update request to the one or more of the plurality of services.