Abstract: A method for determining a status of a user of an electronic device. The method includes a computer processor determining information associated with a first user of an electronic device. The method further includes identifying a plurality of electronic devices that are within a range of wireless communications to the electronic device. The method further includes defining an area monitored by the electronic device. The method further includes monitoring a group of the identified plurality of electronic devices that are within the defined area. The method further includes calculating a first safe-threshold value for the electronic device based on information associated with a first user of an electronic device and monitored information from the group of the identified plurality of electronic devices. The method further includes determining a status for the first user based, at least in part, on the calculated first safe-threshold value for the electronic device.

Abstract: A method for determining a status of a user of an electronic device. The method includes a computer processor determining information associated with a first user of an electronic device. The method further includes identifying a plurality of electronic devices that are within a range of wireless communications to the electronic device. The method further includes defining an area monitored by the electronic device. The method further includes monitoring a group of the identified plurality of electronic devices that are within the defined area. The method further includes calculating a first safe-threshold value for the electronic device based on information associated with a first user of an electronic device and monitored information from the group of the identified plurality of electronic devices. The method further includes determining a status for the first user based, at least in part, on the calculated first safe-threshold value for the electronic device.

Abstract: A method for determining a status of a user of an electronic device. The method includes a computer processor determining information associated with a first user of an electronic device. The method further includes identifying a plurality of electronic devices that are within a range of wireless communications to the electronic device. The method further includes defining an area monitored by the electronic device. The method further includes monitoring a group of the identified plurality of electronic devices that are within the defined area. The method further includes calculating a first safe-threshold value for the electronic device based on information associated with a first user of an electronic device and monitored information from the group of the identified plurality of electronic devices. The method further includes determining a status for the first user based, at least in part, on the calculated first safe-threshold value for the electronic device.

Abstract: A method for determining a status of a user of an electronic device. The method includes a computer processor determining information associated with a first user of an electronic device. The method further includes identifying a plurality of electronic devices that are within a range of wireless communications to the electronic device. The method further includes defining an area monitored by the electronic device. The method further includes monitoring a group of the identified plurality of electronic devices that are within the defined area. The method further includes calculating a first safe-threshold value for the electronic device based on information associated with a first user of an electronic device and monitored information from the group of the identified plurality of electronic devices. The method further includes determining a status for the first user based, at least in part, on the calculated first safe-threshold value for the electronic device.

Abstract: A method for determining a status of a user of an electronic device. The method includes a computer processor determining information associated with a first user of an electronic device. The method further includes identifying a plurality of electronic devices that are within a range of wireless communications to the electronic device. The method further includes defining an area monitored by the electronic device. The method further includes monitoring a group of the identified plurality of electronic devices that are within the defined area. The method further includes calculating a first safe-threshold value for the electronic device based on information associated with a first user of an electronic device and monitored information from the group of the identified plurality of electronic devices. The method further includes determining a status for the first user based, at least in part, on the calculated first safe-threshold value for the electronic device.

Abstract: A method for determining a status of a user of an electronic device. The method includes a computer processor determining information associated with a first user of an electronic device. The method further includes identifying a plurality of electronic devices that are within a range of wireless communications to the electronic device. The method further includes defining an area monitored by the electronic device. The method further includes monitoring a group of the identified plurality of electronic devices that are within the defined area. The method further includes calculating a first safe-threshold value for the electronic device based on information associated with a first user of an electronic device and monitored information from the group of the identified plurality of electronic devices. The method further includes determining a status for the first user based, at least in part, on the calculated first safe-threshold value for the electronic device.

Abstract: A method for determining a status of a user of an electronic device. The method includes a computer processor determining information associated with a first user of an electronic device. The method further includes identifying a plurality of electronic devices that are within a range of wireless communications to the electronic device. The method further includes defining an area monitored by the electronic device. The method further includes monitoring a group of the identified plurality of electronic devices that are within the defined area. The method further includes calculating a first safe-threshold value for the electronic device based on information associated with a first user of an electronic device and monitored information from the group of the identified plurality of electronic devices. The method further includes determining a status for the first user based, at least in part, on the calculated first safe-threshold value for the electronic device.

Abstract: Techniques for returning estimated value of search keywords of an entire account include, for the entire account, obtaining one or more selected search keywords and their respective forecast periods and parameter settings. An estimated value of a respective search keyword in the respective forecast period is forecasted. Based on stored historical data and parameter settings of the respective search keyword, the estimated value of the respective search keyword is modified to obtain a modified estimated value. The modified estimated value of each search keyword is added up to generate an estimated value of the entire account. The estimated value of the entire account is returned to a client terminal from which the entire account is logged in. The present disclosure modifies the respective search keyword's estimated value so that the estimated value of the entire account satisfies the expected value of the client.

Abstract: The present disclosure introduces a method, an apparatus and memory of predicting a user behavior number of a word for reducing the amount and the complexity of operation, saving the consumption of the equipment, and improving the accuracy and reliability of predictions. In an embodiment, a historical data sequence of the user behavior number of a word is converted from a time domain to a frequency domain. Based on the converted frequency domain, each estimated cycle and its effect rate value of the historical data sequence are ascertained. If the historical data sequence is stable, an average value of user behavior numbers of some historical data points before a prediction point is calculated as a user behavior number of the prediction point. Otherwise, the user behavior number is calculated based on a selected main cycle and a selected singularity.

Abstract: Online advertising includes: selecting, among a plurality of advertising sources provided by a seller, a selected plurality of advertising sources that meet a predefined condition; generating a plurality of keyword sets that correspond to the selected plurality of advertising sources; establishing a programming model according to a set of predefined constraints, wherein the programming model represents matches of the selected plurality of advertising sources and the plurality of keyword sets; and determining a substantially optimal match between at least some of the plurality of advertising sources and the plurality of keyword sets by finding a solution for the programming model; wherein the programming model includes an objective function subject to the set of predefined constraints, and finding a solution for the programming model includes searching to find a solution to the objective function.

Abstract: Providing online advertisements includes selecting, among a plurality of advertising sources provided by a seller, a selected plurality of advertising sources that meet a predefined condition; generating a plurality of keyword sets that correspond to the selected plurality of advertising sources; establishing a programming model according to a set of predefined constraints, wherein the programming model represents match of the selected plurality of advertising sources and the plurality of keyword sets; and determining a substantially optimal match between at least some of the plurality of advertising sources and the plurality of keyword sets by solving the programming model.

Abstract: Techniques for returning estimated value of search keywords of an entire account include, for the entire account, obtaining one or more selected search keywords and their respective forecast periods and parameter settings. An estimated value of a respective search keyword in the respective forecast period is forecasted. Based on stored historical data and parameter settings of the respective search keyword, the estimated value of the respective search keyword is modified to obtain a modified estimated value. The modified estimated value of each search keyword is added up to generate an estimated value of the entire account. The estimated value of the entire account is returned to a client terminal from which the entire account is logged in. The present disclosure modifies the respective search keyword's estimated value so that the estimated value of the entire account satisfies the expected value of the client.

Abstract: The present disclosure introduces a method, an apparatus and memory of predicting a user behavior number of a word for reducing the amount and the complexity of operation, saving the consumption of the equipment, and improving the accuracy and reliability of predictions. In an embodiment, a historical data sequence of the user behavior number of a word is converted from a time domain to a frequency domain. Based on the converted frequency domain, each estimated cycle and its effect rate value of the historical data sequence are ascertained. If the historical data sequence is stable, an average value of user behavior numbers of some historical data points before a prediction point is calculated as a user behavior number of the prediction point. Otherwise, the user behavior number is calculated based on a selected main cycle and a selected singularity.

Abstract: Estimating bid prices for keywords is disclosed, including: receiving a request to estimate a bid price associated with a target keyword for a bidder; determining whether the bidder has previously bid on the target keyword; in the event that the bidder has not previously bid on the target keyword, determining an estimated bid price based at least in part on a plurality of historical bid prices associated with the bidder corresponding to keywords other than the target keyword and a plurality of historical bid prices associated with other bidders corresponding to the target keyword; and in the event that the bidder has previously bid on the target keyword, determining the estimated bid price based at least on revising a current bid price associated with the request; and returning the estimated bid price associated with the target keyword.

Abstract: Prediction of cost and income estimates associated with a bid ranking model is disclosed, including: receiving a search keyword estimate prediction request, wherein the request comprises a search keyword, a bid price associated with the search keyword, and a prediction period; determining an average click through rate associated with the search keyword associated with the prediction period for a ranking position; determining a traffic value associated with the search keyword associated with the prediction period; determining an average cost per click associated with the search keyword associated with the prediction period for the ranking position; determining a number of impressions associated with the search keyword for the ranking position; and determining a cost estimate and an income estimate associated with the search keyword associated with the prediction period.

Abstract: The present disclosure discloses a method and system for clustering. The method includes: vectorizing a plurality of readable files to obtain a plurality of file vectors corresponding to the multiple readable files; extracting a total characteristic vector based on the file vectors; and clustering the readable files based on a ranking result of a respective similarity degree between the total characteristic vector and each of the file vectors. The present disclosure also provides a method and system for clustering webpages. An application of the methods or systems described in the present disclosure reduces the number of times of comparison of similarity degrees between file vectors, and further reduces the resulting burden on system resources. This advantageously results in reduced usage of CPU and memory, reduced run time of clustering and improved performance of clustering.