1816 results about "List" patented technology

A computerized method for representing a data record comprising: querying a data source to obtain data selected from the group consisting of a data element in a record, and metadata concerning the record; presenting in a display a record handle for manipulation of the record; presenting in the display a data item wherein the data item is a list of data items or a reference to another record; and, optionally, presenting on the display the metadata above the data item. In some embodiments, the method includes the step of retrieving one or more heterogeneous records from a plurality of databases for display and manipulation. The invention is also a grid control programmed to implement a disclosed method and is a computer-readable medium having computer-executable instructions for performing a disclosed method. The invention links the grid control of the invention with automatic query generation using hierarchical data schema trees. Both the trees and the grid records represent relational foreign keys as extra reference columns. In the grid control, these reference columns are additional embedded record handles.

A method, a device and computer program products for protecting privacy of users from web-trackers

The method comprising:

• • capturing and removing a public unique identifier set by a Website (300) in a computing device (100D) of a user (100);
  • monitoring, during a first time-period, web-requests the user (100) makes to obtain a web-behavioral profile of the user (300), and storing the obtained web-behavioral profile as a first vector;
  • tracking, during a second time-period, the web-requests to examine the effect each web-request has on assisting the de-anonymization of the user (100), obtaining a second vector;
  • classifying, the obtained second vector taking into account a computed similarity score parameter;
  • creating and mapping, a corresponding private unique identifier for said captured public identifier; and
  • executing, based on said mapping between the private and the public unique identifiers, an intervention algorithm for said web-tracker, that considers a configured intervention policy.

Disclosed are a computer-readable code, system and method for combining texts to form novel combinations of texts related to a desired target concept, where the concept is represented in the form of a natural-language text or a list of descriptive word and/or word-group terms. The system operates to find primary and secondary groups of texts having highest term match scores with a first and second subset of terms in the concept, respectively. It then generates pairs of texts containing a text from each of the primary and secondary groups of database texts, and selects for presentation to the user, those pairs of texts having highest overlap scores as determined from one or more of (i) term overlap, (ii) term coverage, (iii) feature-specific cross-correlation, (iv) attribute-specific correlation, and (v) citation score of one or both texts in the pair.

A computer program and method are disclosed for matching a potential positionee and potential positionor. The potential positionee is provided with a positionee information entry interface for entering positionee information comprising the potential positionee's actual qualifications. The positionee information is stored in a database. Matching is accomplished by providing the potential positionor with a positionor information entry interface for electronically entering positionor information comprising at least one target qualification for a position, the positionor information being stored in the database. Matching is based on determining whether the positionee information correlates with the positionor information. A correlated information list is created. The correlated information is presented for review. Code sections of the computer program preferably accomplish these and other tasks.

Determining one or more concepts associated with a query is disclosed. A query is received. A list of concepts and associated scores is received. The concepts fit within a concept hierarchy. A density function is used to evaluate the received concepts. One or more concepts are associated with the query based at least in part on the results of the density function.

An optimization technique that reorders outerjoins and antijoins with inner joins in a bottom-up optimizer of a relational database management system (RDBMS). Each join predicate is associated with a normal eligibility list (NEL) that includes tables that are referenced in the join predicate and an extended eligibility list (EEL) that includes additional tables that are referenced in conflicting join predicates. An EEL includes all the tables needed by a predicate to preserve the semantics of the original query. During join enumeration, the optimizer determines whether a join predicate's EEL is a subset of all the tables in two subplans to be merged, i.e., whose EEL is covered. If so, the two subplans are combined using the join predicate. Otherwise, the two subplans cannot be joined. Two approaches are used to reordering: without compensation and with compensation. The "without compensation" approach only allows join reorderings that are valid under associative rules. Thus, the optimizer will not combine subplans using a join predicate whose EEL is not covered. The "with compensation" approach allows two subplans to be combined using the join predicate, when a join predicate's EEL is not covered, as long as the join predicate's NEL is covered. Compensation is performed through nullification and best match. Multiple compensations may be merged and performed at any time.

A method of ranking workers for an incoming task includes recording a list of completed tasks in a computer data structure, extracting first attributes from the list for the tasks that were completed during a pre-determined period, generating a first feature vector for each task and worker from the first extracted attributes, training a Support Vector Machine (SVM) based on the feature vector to output a weight vector, extracting second attributes from an incoming task, generating a second feature vector for each worker based on the second extracted attributes, and ranking the workers using the second feature vectors and the weight vector. The first attributes may be updated during a subsequent period to re-train the SVM on updated first feature vectors to generate an updated weight vector. The workers may be re-ranked based on the second feature vectors and the updated weight vector. Accordingly, the feature vectors are dynamic.

A computer-parsing system based upon using vectors (lists) to represent natural-language elements, providing a robust, distributed way to score grammaticality of an input string by using as a source material a large corpus of natural-language text. The system uses recombining of asymetric associations of syntactically similar strings to form an the vectors. The system uses equivalence lists for your the organization subparts of the string to build equivalence lists for our the province longer strings in an order controlled by the potential these/parse to be scored. The power of recombination of Entries from: vector elements in building longer strings provides a means of representing collocational complexity. Grammaticality scoring is based upon the number and similarity of the vector elements.

The invention relates to a vector data space indexing method base on hbase and geohash. The method comprises the following steps: firstly, vector data to be stored is subjected to grid grading; secondly, grid path is calculated; then, the structure of a grid list is designed and indexing codes are established; lastly, the space range to be inquired is subjected to space indexing. According to the vector data space indexing method, two-dimensional vector data such as dots, lines and surfaces are mapped into a one-dimensional character string type rowkey index, so that a huge amount of vector data can be stored through the adoption of the hbase, and the high-performance space inquiry analysis service is provided.

A preferred embodiment of the invention pertains to a method of comparing first and second databases (DB1, DB2) that are each comprised of a plurality of entities (A to E) having one or more characteristics, said entities being grouped into a plurality of data classes in each said database each representative of a particular entity type; the method comprising: (i) for each said data class of said first and second databases, compiling a list representative of the entities occurring within that class and the attributes for each said entity; (ii) identifying and comparing corresponding data classes for each of said first and second databases; and (iii) identifying on the basis of said comparison differences between corresponding entities of said corresponding data classes. A computer program, and storage medium are also disclosed.

This invention relates to one code message get method and device based on means prediction, which comprises the following steps: unit cutting step to divide the input requires into meanings prepare units set; unit codes knowledge database establishing to expend the prepare units in original common dictionary and to form unit code knowledge database; meaning prediction based on the prediction method to get relative times of the inquire meanings; effective page getting step to get the relative inquire items set by use of the combination times and to get effective page through index; prepare evaluating to get effective page to get prepare file list on codes.

A method for performing entropy coding comprising the steps of (A) receiving an input stream and side information, (B) analyzing the side information to determine all constraints associated with the side information and (C) replacing the input stream with an index to a list of the number of valid input streams that satisfy all constraints associated with each specific type of the side information.

A method for evaluating data includes defining a list of data categories, determining a relative sensitivity to each data category, determining one or more classifiers for each of the data categories, receiving a plurality of data items to be valued, determining one of the data categories for each of said plurality of data items according to the one or more classifiers, and determining a respective sensitivity for each of said plurality of data items.