36 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.

An internet search utility that combines and presents search results from disparate data sources to the user in a multi-view format. The search terms are disambiguated and a series of prioritized Views is displayed to the user in a View Mix interface. Each View includes a unique interface, processing widgets, and a unique combination of data sources. A View Picker determines, based on specific criteria, which Views are relevant and prioritizes the Views for the View Mix. As the user considers which View to select, the Pre-Caching Module conducts a background search, and preloads snapshots of top URLs for display. Selecting a particular View initiates a search per the instructions of that view. The resulting data is displayed in the View format. Views may be created by third parties or end users to reflect any particular preference thus yielding a multitude of unique views from which to consume search results.

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.

Disclosed are methods, systems, and non-transitory computer-readable medium for providing application programming interface (API) mashups. For instance, the method may include hosting a plurality of certified FMS micro-services associated with a plurality of FMS APIs; hosting an API mashup generator to perform an API mashup process and an API mashup recommendation process, the API mashup process generating combinations of APIs that include one or more APIs from the plurality of FMS APIs, other avionics APIs, and / or third party APIs; and hosting a service mesh to process a user request from a user device for the API mashup recommendation process or an invoke micro-service process.

The invention discloses a Web API recommendation method based on topic model clustering. The method comprises the following steps: calculating semantic weight information of words according to context information so as to obtain a document-word semantic weight information matrix D; counting word co-occurrence information so that SPPMI matrix information is calculated; based on the obtained word frequency information matrix D of the words of the Mashup service document and the context SPPMI matrix M of the words, acquiring a word embedding information matrix by decomposing the M, combinding the two kinds of information , and calculating theme information of service; taking the obtained Mashup service theme features as spectral clustering input for clustering, segmenting a graph formed by all data points, wherein the sum of edge weights between different subgraphs after graph segmentation is made as low as possible, the sum of edge weights in the subgraphs is made as high as possible, and the clustering purpose is achieved; and combining GBDT and FM methods to predict and recommend the Web API service. Web API recommendation is effectively realized.

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.

Embodiments of the present invention provide a method, system and computer program product for the on-demand generation of correlated collections of mashable data from distributed, heterogeneous data sources. In an embodiment of the invention, a method for on-demand generation of correlated collections of mashable data from distributed, heterogeneous data sources is provided. The method includes receiving a request for a data feed from a widget in a mashup rendered in a content browser executing in memory of a computer. The method further includes inspecting a virtual database of data aggregated from different data sources over a computer communications network to locate data requisite to returning the data feed to the widget in the mashup. The method yet further includes querying the data of the virtual database to produce the data feed. Finally, the method includes returning the data feed in the format specified to the widget in the mashup.

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.

A new semantic mashup architecture with modular design can comprise separate Semantic Mashup Profiles (SMPs), Virtual Semantic Mashup Resources (VSMRs), and Semantic Mashup Results (SMRSs). This kindof modular design greatly improves the reusability of SMPs, VSMRs, and SMRSs. In addition, this new mashup architecture leverages semantics during each mashup process, which increases the interoperability. Moreover, this new architecture essentially realizes new Semantic Mashup Service (SMS) at the service layer and consequently improves system efficiency.

The invention discloses a task unloading method in mobile edge computing based on service mashup. According to the MEC network based on equipment, two unloading modes of local unloading and MEC serverunloading are considered in a network, and a user side workload target function is constructed; the weight sum of the real-time delay and the energy consumption is calculated, the constraints of themaximum service number and the maximum computing resource of the server side are considered at the same time in the problem, the workload of the user side is reduced through the distributed delay acceptance algorithm, and the stability of the system is also improved through the distributed algorithm.

The invention relates to an API (Application Program Interface) recommendation method based on a heterogeneous information network element path, which comprises the following steps of: acquiring description information of an API and a Mashup application thereof, and constructing a heterogeneous information network surrounding the API; generating initial low-dimensional embedding of an API (Application Program Interface) and a Mashup; generating context meta-path embedding based on API and Mashup interaction; initial low-dimensional embedding of the API and the Mashup and context meta-path embedding based on interaction of the API and the Mashup are fused, a neural network model is trained, and API recommendation is achieved. According to the method and the device, a user can be helped to quickly find a proper API from a huge API library through accurate recommendation when developing a Mashup application, so that the development efficiency of the application is improved.

The invention belongs to the field of recommendation, and particularly relates to recommendation of web API services meeting user requirements according to the requirements input by a user, so that the accuracy and individuation of recommendation are improved. The recommendation mainly comprises the following four steps: A, Mashup clustering; B, a User link prediction algorithm; C, a collaborativefiltering algorithm based on link prediction; and D, prediction of the popularity of the web API; and E, a Web API recommendation model. According to the method, link prediction and collaborative filtering are combined, recommendation individuation is improved while recommendation accuracy is improved, and recommendation results better meet actual requirements of users.

A text topic mining method based on word semantic weight of Internet service comprises the following steps: 1, performing part-of-speech tagging on words in a Mashup service description document by using a natural language toolkit in Python; 2, counting word frequency information, and calculating TF-IDF information; 3, extracting Mashup service label information wherein the semantic weight of each word in the Mashup service description document is recalculated on the basis of the noun set Nset and the TF-IDF value; and 4, solving Mashup theme features through an NMF model. On the basis of TF-IDF, in combination with service label information and context word information, weights of words are recalculated, and weight values of key words are increased, so that Mashup service modeling and service document theme confirmation are effectively performed.

A Web API recommendation method based on a Mashup service neighborhood in a Web open environment comprises the following steps: step 1, constructing a demand Mashup semantic feature vector, and matching a Mashup service class cluster; step 2, collecting Web API data called by Mashup service in a neighborhood, and constructing a Web API neighborhood; step 3, according to the Web API function classification constructed in the neighborhood, performing function category division on the remaining Web APIs in the data set; and step 4, calculating popularity and co-occurrence degree, and ranking to obtain a final recommendation list. According to the method, the required function types are preliminarily judged, the screening range of the Web APIs is defined, then function type division is conducted on all the Web APIs in the data set, finally recommendation is conducted according to ranking of popularity and average co-occurrence, and recommendation accuracy and diversity are high.

The invention discloses a clustering method based on Mashup service function feature representation and density peak detection. The clustering method comprises the following steps: 1, preprocessing all Mashup service data needing feature representation; 2, carrying out function noun extraction operation; 3, performing semantic association calculation on the semantic weight of each functional noun;4, in combination with a TF-IDF algorithm and a Word2Vec model, performing expression of Mashup semantic feature vectors; 5, calculating density information of all Mashup semantic feature vectors participating in clustering; 6, screening out candidate points of the clustering center from all the Mashup semantic feature vectors; and 7, further screening out the most suitable K initial clustering centers, and carrying out K-means clustering. According to the method, the functional characteristics of the Mashup service can be effectively expressed, and the clustering performance of the Mashup service is enhanced.

The invention provides a Web service package recommendation method and system based on combined feature extraction. The method comprises: a step 1, performing semantic feature extraction; a step 2, according to the extracted semantic features, training a deep neural network and extracting combined features, and predicting the probability whether Web service pairs can be used for Mashup to be developed at the same time or not; and a step 3, recommending the Web service package according to the probability adopted by the Mashup to be developed. According to the method, the comprehensive functionformed by combining Web services can be extracted, so that a group of complementary Web services can be recommended to developers, and the Mashup requirements are completely covered.

The invention discloses a Mashup service feature representation method based on functional semantic association calculation. The method comprises the following steps: 1, preprocessing all Mashup service data needing feature representation; 2, based on the preprocessed Mashup service data, performing a function noun extraction operation; 3, for the function noun set FS of each Mashup service, carrying out semantic association calculation on the semantic weight of each function noun; and 4, based on the semantic weight calculation result in the step 3, combining a TF-IDF algorithm and a Word2Vecmodel to express a Mashup semantic feature vector. According to the invention, the matching precision of Mashup services and the service search efficiency can be effectively improved.

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.

The invention relates to a graph embedding enhanced Web API recommendation method and system, and the method comprises the steps: obtaining ID embedding vectors and text embedding vectors of a Mashup node and a Web API node, and calculating ID embedding vectors and text embedding vectors of neighbor nodes of the Mashup node and the Web API node; fusing the ID embedding vector and the text embedding vector, and the ID embedding vectors and the text embedding vectors of all neighbor nodes to obtain a fused embedding vector EM and a fused embedding vector EA; based on the EM and the EA, the matching degree of the Mashup node and the Web API node is calculated, and a Web API recommendation result is obtained. According to the method, the problem of poor recommendation precision caused by the problems of data sparsity and cold start when Web API recommendation is carried out by a method for generating an ID embedding vector purely based on a graph embedding method in a traditional Web API recommendation method is solved.

The invention discloses an improved K-means service clustering method based on topic modeling. The improved K-means service clustering method comprises the following steps: 1, preprocessing Mashup service data needing feature representation; 2, based on the preprocessed Mashup service data, performing a function noun extraction operation; 3, for the function noun set FS of each Mashup service, utilizing a topic model to express a Mashup feature vector; 4, calculating density information of all Mashup feature vectors participating in clustering; step 5, based on the density information calculated in the step 5, screening out candidate points of the clustering center from all Mashup feature vectors; and step 6, for the clustering center candidate points obtained in the step 5, further screening out the most appropriate K initial clustering centers, and performing K-means clustering. According to the method, the final effect of Mahsup service clustering is improved.

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 service modeling method fusing word embedding and non-negative matrix factorization technologies in a cloud computing mode comprises the following steps of 1, counting word frequency information, namely the number of occurrence times of words, in each Mashup service and creating a document-word frequency relation matrix D; 2, counting word co-occurrence information so as to calculate SPPMI matrix information; and 3, based on the first step and the second step, obtaining a word frequency information matrix D of the words of the Mashup service document and a context SPPMI matrix M of the words, decomposing the M to obtain a word embedding information matrix, further combining the two kinds of information, and calculating theme information of the service. Organic unification of the model and non-negative matrix factorization can be realized, and the problem of sparse feature information of the Mashup service can be relieved by introducing word embedding information, so that the Mashup service is effectively modeled.

The invention relates to the software optimization technology and especially relates to a cost-performance-driven Mashup construction method. The Mashup construction method comprises the following steps: S101) selecting an optimal Web service and cloud platform for each task in Mashup to be constructed by utilizing a GA4MC algorithm; S102) carrying out service combination on the Web services obtained in the step S101) to obtain the Mashup; and S103) deploying the Mashup obtained through construction to the cloud platform. Compared with the prior art, the technical scheme has the following advantages: 1) the provided cost-performance-driven Mashup construction method can construct the Mashup, the cost performance of which is optimal, from the aspect of cost performance; 2) the provided cost-performance-driven Mashup construction method takes price association relationship and response time association relationship into consideration to improve the cost performance of the Mashup; and 3) the provided cost-performance-driven Mashup construction method not only considers service selection, but also considers platform deployment of the Mashup, and the cost performance of the Mashup is further improved through selection of the deployment platform.

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.