Abstract: The present invention describes a method and system for optimizing a test flow within each ATE (Automated Test Equipment) station. The test flow includes a plurality of test blocks. A test block includes a plurality of individual tests. A computing system schedule the test flow based one or more of: a test failure model, test block duration and a yield model. The failure model determines an order or sequence of the test blocks. There are at least two failure models: independent failure model and dependant failure model. The yield model describes whether a semiconductor chip is defective or not. Upon completing the scheduling, the ATE station conducts tests according to the scheduled test flow. The present invention can also be applied to software testing.

Abstract: A method for selecting an analysis procedure for a value series, including displaying a value series on a computer display monitor, receiving one or more sequences of user provided annotations, where the annotations overlay at least a sub-interval of the value series on the computer display monitor, using the sequences of user provided annotations to select an optimal value series analysis method from a set of value series analysis methods, where selecting an optimal value series analysis method includes determining parameter values for the optimal value series analysis method, and presenting the selected optimal value series analysis method and parameters, and the optimal reconstruction of the annotation sequences to the user.

Abstract: Energy usage can be monitored within at least one building having a plurality of energy consuming components. A database can be generated that contains values for a set of data points corresponding to data received from the plurality of energy consuming components. A change in a configuration can be detected for the plurality of energy consuming components based upon a change in values received from plurality of energy consuming components relative to the database. Based upon the change, an additional data point can be added to the set of data points in the database. Based upon the values for the set of data points, a probability can be determined that a rule for the additional data point is valid. A message can then be generated that includes the determined probability.

Abstract: The present invention describes a method and system for optimizing a test flow within each ATE (Automated Test Equipment) station. The test flow includes a plurality of test blocks. A test block includes a plurality of individual tests. A computing system schedule the test flow based one or more of: a test failure model, test block duration and a yield model. The failure model determines an order or sequence of the test blocks. There are at least two failure models: independent failure model and dependant failure model. The yield model describes whether a semiconductor chip is defective or not. Upon completing the scheduling, the ATE station conducts tests according to the scheduled test flow. The present invention can also be applied to software testing.

Abstract: Software that uses actual, historic building management system (BMS) data and/or machine logic to estimate an amount of energy that would have been consumed (and saved) had the BMS been operated under the control of an energy management system (EMS). The software performs the following steps: (i) receiving first actual BMS information relating to operation of a first set of appliance(s) located in a first building during a first time interval; (ii) selecting a first proposed EMS rule set; and (iii) determining a first contrafactual energy resource use value based, at least in part, on the first actual BMS information, with the first contrafactual use value corresponding to an amount of energy resources that would have been consumed by the first set of appliance(s) if the BMS had operated the first set of appliance(s) under control of the first proposed EMS rule set.

Abstract: Embodiments of the disclosure include a method for journey planning including receiving a journey planning request. The request includes an origin and a destination in a transportation network. The method also includes calculating an optimized journey plan based on the journey planning request and a route map corresponding to the transportation network and identifying a potential event in the transportation. The method further includes monitoring the transportation network for the potential event and based on detecting an execution of the potential event, calculating an improved journey plan.

Abstract: Techniques for iterative feature extraction using domain knowledge are provided. In one aspect, a method for feature extraction is provided. The method includes the following steps. At least one query to predict at least one future value of a given value series based on a statistical model is received. At least two predictions of the future value are produced fulfilling at least the properties of 1) each being as probable as possible given the statistical model and 2) being mutually divert (in terms of numerical distance measure). A user is queried to select one of the predictions. The user may be queried for textual annotations for the predictions. The annotations may be used to identify additional covariates to create an extended set of covariates. The extended set of covariates may be used to improve the accuracy of the statistical model.

Abstract: A method for selecting an analysis procedure for a value series, including displaying a value series on a computer display monitor, receiving one or more sequences of user provided annotations, where the annotations overlay at least a sub-interval of the value series on the computer display monitor, using the sequences of user provided annotations to select an optimal value series analysis method from a set of value series analysis methods, where selecting an optimal value series analysis method includes determining parameter values for the optimal value series analysis method, and presenting the selected optimal value series analysis method and parameters, and the optimal reconstruction of the annotation sequences to the user.

Abstract: A method for selecting an analysis procedure for a value series, including displaying a value series on a computer display monitor, receiving one or more sequences of user provided annotations, where the annotations overlay at least a sub-interval of the value series on the computer display monitor, using the sequences of user provided annotations to select an optimal value series analysis method from a set of value series analysis methods, where selecting an optimal value series analysis method includes determining parameter values for the optimal value series analysis method, and presenting the selected optimal value series analysis method and parameters, and the optimal reconstruction of the annotation sequences to the user.

Abstract: Techniques for securely and adaptively delivering multimedia content are disclosed in which a set of alternate access units for each time slot is obtained. Then, the encryption stream index of each access unit from the set of alternate access units of the previous time slot are obtained. An encryption stream index is then assigned to each access unit in the set of alternate access units in the current time slot, such that the encryption index increases over time. Thus, the invention overcomes the problem of encrypting a multimedia stream that may have multiple access units for each time slot by selecting the encryption index for each access unit such that the encryption index increases, regardless of which access unit the delivery system (e.g., server) selects for transmission.

Abstract: A method for accelerating time series data base queries includes segmenting an original time series of signal values into non-overlapping chunks, where a time-scale for each of the chunks is much less than the time scale of the entire time series, representing time series signal values in each chunk as a weighted superposition of atoms that are members of a shape dictionary to create a compressed time series, storing the original time series and the compressed time series into a database, determining whether a query is answerable using the compressed time series or the original time series, and whether answering the query using the compressed time series is faster. If answering the query is faster using the compressed representation, the query is executed on weight coefficients of the compressed time series to produce a query result, and the query result is translated back into an uncompressed representation.

Abstract: A computer program product for creating models comprises a computer readable storage medium having stored thereon first program instructions executable by a processor to cause the processor to receive the modeling tasks each having a target variable and at least one covariate, the target variable and the at least one covariate being the same for all of the modeling tasks, a relationship between the target variable and the at least one covariate being different for all of the modeling tasks, and second program instructions executable by the processor to cause the processor to generate, for each of the modeling tasks, a model including a transfer function for approximating the relationship between the target value and the at least one covariate of the modeling task in a manner that at least two of the models share an identical transfer function and the models satisfy an accuracy condition.

Abstract: A method for generating models for a plurality of modeling tasks is disclosed. The method comprises receiving, with a processing device, the modeling tasks each having a target variable and at least one covariate. The target variable and at least one covariate are the same for all of the modeling tasks. A relationship between the target variable and at least one covariate is different for all of the modeling tasks. For each of the modeling tasks, generating a model including a transfer function for approximating the relationship between the target value and at least one covariate of the modeling task in a manner that at least two of the models share at least one identical transfer function and the models satisfy an accuracy condition.

Abstract: A method for accelerating time series data base queries includes segmenting an original time series of signal values into non-overlapping chunks, where a time-scale for each of the chunks is much less than the time scale of the entire time series, representing time series signal values in each chunk as a weighted superposition of atoms that are members of a shape dictionary to create a compressed time series, storing the original time series and the compressed time series into a database, determining whether a query is answerable using the compressed time series or the original time series, and whether answering the query using the compressed time series is faster. If answering the query is faster using the compressed representation, the query is executed on weight coefficients of the compressed time series to produce a query result, and the query result is translated back into an uncompressed representation.

Abstract: The present invention describes a method and system for optimizing a test flow within each ATE (Automated Test Equipment) station. The test flow includes a plurality of test blocks. A test block includes a plurality of individual tests. A computing system schedule the test flow based one or more of: a test failure model, test block duration and a yield model. The failure model determines an order or sequence of the test blocks. There are at least two failure models: independent failure model and dependant failure model. The yield model describes whether a semiconductor chip is defective or not. Upon completing the scheduling, the ATE station conducts tests according to the scheduled test flow. The present invention can also be applied to software testing.

Abstract: A method, system, and computer program product are disclosed for guiding users in optimization-based planning under uncertainty. In one embodiment, the invention provides a method comprising identifying one or more characterizations of a specified uncertainty in a defined process; generating a set of plans based on the uncertainty characterization; and finding a new plan based on the existing set of plans, including identifying an added constraint, and finding a new plan that satisfies this added constraint. The new plan is analyzed to determine whether the new plan satisfies defined criteria; and when the new plan satisfies the defined criteria, the new plan is added to the set of plans. One of the plans is identified as a recommended plan for the defined process. In an embodiment, the recommended plan is identified based on a trade-off analysis of the plans using at least two defined aspects of the plans.

Abstract: Processing test results from a plurality of individual semiconductor testers by analyzing each test result at an adaptive test engine. A centralized system jointly analyzes all the test results from the plurality of individual semiconductor testers. The adaptive test engine or the centralized system identifies, based on the analysis of each test result or the joint analysis of all the test results, one or more of: a test environmental issue, a tester variability issue, a tester calibration issue, a product variability issue, and a manufacturing process variability issue. The adaptive test engine or the centralized system determines whether one or more of the plurality of individual semiconductor testers causes one or more of the identified issues or whether semiconductor products tested by the plurality of individual semiconductor testers causes one or more of the identified issues.

Abstract: Processing test results from a plurality of individual semiconductor testers by analyzing each test result at an adaptive test engine. A centralized system jointly analyzes all the test results from the plurality of individual semiconductor testers. The adaptive test engine or the centralized system identifies, based on the analysis of each test result or the joint analysis of all the test results, one or more of: a test environmental issue, a tester variability issue, a tester calibration issue, a product variability issue, and a manufacturing process variability issue. The adaptive test engine or the centralized system determines whether one or more of the plurality of individual semiconductor testers causes one or more of the identified issues or whether semiconductor products tested by the plurality of individual semiconductor testers causes one or more of the identified issues.

Abstract: Techniques for iterative feature extraction using domain knowledge are provided. In one aspect, a method for feature extraction is provided. The method includes the following steps. At least one query to predict at least one future value of a given value series based on a statistical model is received. At least two predictions of the future value are produced fulfilling at least the properties of 1) each being as probable as possible given the statistical model and 2) being mutually divert (in terms of numerical distance measure). A user is queried to select one of the predictions. The user may be queried for textual annotations for the predictions. The annotations may be used to identify additional covariates to create an extended set of covariates. The extended set of covariates may be used to improve the accuracy of the statistical model.

Abstract: Techniques for iterative feature extraction using domain knowledge are provided. In one aspect, a method for feature extraction is provided. The method includes the following steps. At least one query to predict at least one future value of a given value series based on a statistical model is received. At least two predictions of the future value are produced fulfilling at least the properties of 1) each being as probable as possible given the statistical model and 2) being mutually divert (in terms of numerical distance measure). A user is queried to select one of the predictions. The user may be queried for textual annotations for the predictions. The annotations may be used to identify additional covariates to create an extended set of covariates. The extended set of covariates may be used to improve the accuracy of the statistical model.