Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (“ADAS”), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (ADAS), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (ADAS), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (ADAS), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (ADAS), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (ADAS), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: A closed-loop service, referred to as an Adaptive Data Analytics Service (ADAS), characterizes the performance of a system or systems by providing information describing how users or agents are operating the system, how the system components interact, and how these respond to external influences and factors. The ADAS then builds models and/or defines relationships that can be used to optimize performance and/or to predict the results of changes made to the system(s). Subsequently, this learning provides the basis for administering, maintaining, and/or adjusting the system(s) under study. Measurement can be ongoing, even after the operating parameters or controls of a system under the administration or monitoring of the ADAS have been adjusted, so that the impact of such adjustments can be determined.

Abstract: This invention is designed to offer ready access to an athlete's contact and health information for emergency responders at sporting events while providing improved security constraints to preserve the confidentiality of the athlete's information. The system offers new flexibility in the types of data fields that are collected, the duration of data availability, as well as to whom the data access privileges are granted.

Abstract: This invention is a method and a system for accessing medical records of an injured party by an emergency responder through a secure website, utilizing a portable emergency access card provided with at least one item of information of the victim, while offering safeguards for the confidentiality of the victim's information and records.

Abstract: This invention is designed to offer ready access to an athlete's contact and health information for emergency responders at sporting events while providing improved security constraints to preserve the confidentiality of the athlete's information. The system offers new flexibility in the types of data fields that are collected, the duration of data availability, as well as to whom the data access privileges are granted.

Abstract: We present a data-driven system that seamlessly integrates the compilation of a diverse set of patient electronic medical records, family history, and environmental risk factor databases, a program that determines correlations and assigns weights through a neural network between illness and the state of symptoms, a program to create disease-specific questionnaires and content, a program alert that informs users of trends and potential illness outbreaks due to anomalous changes in regional or national symptom trends, and a program that enables medical practitioners to view patient-generated records and manage an online referral system. The automated diagnoses and alerts allow users to make informed and rapid medical treatment decisions.

Abstract: This invention is a method and a system for accessing medical records of an injured party by an emergency responder through a secure website, utilizing a portable emergency access card provided with at least one item of information of the victim, while offering safeguards for the confidentiality of the victim's information and records.

Abstract: An cost based optimizer optimizes access to at least a portion of hierarchically-organized documents, such as those formatted using eXtensible Markup Language (XML), by estimating a number of results produced by the access of the hierarchically-organized documents. Estimating the number of results comprises computing the cardinality of each operator executing query language expressions and further computing a sequence size of sequences of hierarchically-organized nodes produced by the query language expressions. Access to the hierarchically-organized documents is optimized using the structure of the query expression and/or path statistics involving the hierarchically-organized data. The cardinality and the sequence size are used to calculate a cost estimation for execution of alternate query execution plans. Based on the cost estimation, an optimal query execution plan is selected from among the alternate query execution plans.