Siemens Aktiengesellschaft

Siemens Corporation

Christoph Kuhmuench

Andre Benjamin Bondi

Christoph Wienands

Michael W Hasling

Systems and methods are presented for dynamically controlling role-based access to enterprise applications. The access includes both a user's ability to access a requested functionality (hereinafter referred to as 'features') in an enterprise applications, as well as the user's ability to access the specific data (and request filtering of the data) within the enterprise applications. The systems and methods provide dynamic control by utilizing a number of separate tables for identifying each element (user, role and feature), with join-tables used to define, on an active/customized basis, the association of each user with respect to a particular role (user_role join-table) and association of each feature with the listing of roles (feature_role join-table). The join-tables and specific element tables may be modified during runtime to modify any of the associations or listings.

Dynamic authorization to features and data in JAVA-based enterprise applications

A reading aid is provided for large documents or images on space-constrained touch-sensitive screens of devices, such as mobile phones and personal digital assistants (PDAs). A visual scrolling reference pattern such as a grid is projected over a document during scrolling operations on a touch screen. The scrolling reference pattern correlates information presented in different parts of large documents that are not visible at the same time.

Method and system for overlaying space-constrained display with a reference pattern during document scrolling operations

A method for automatic identification of execution phases in load test data includes receiving load test data indicating processor utilization for threads over a period of time divided into a plurality of intervals. For each pair of proximate intervals, it is determined whether thread-wise processor utilization for a first interval is statistically indistinguishable from that of a second interval. The pair of proximate intervals is combined when it is determined that the processor utilization for the first interval is statistically indistinguishable from that of the second interval. Each of the pair of proximate intervals is divided into subintervals when it is determined that the first interval is not statistically indistinguishable the second interval. One or more execution phases are automatically identified as occurring between proximate intervals that are not substantially equivalent.

Automatic identification of execution phases in load tests

A method for automatic identification of execution phases in load test data includes receiving load test data. A period of time of the load test data is divided into a plurality of intervals. For each pair of proximate intervals of the plurality of intervals, a first interval is compared with a second interval to determine whether application execution characteristics for each of the pair of proximate intervals are substantially equivalent. The pair of proximate intervals is combined into a single interval when it is determined that the pair is substantially equivalent. Each of the pair of proximate intervals is divided into subintervals when it is determined that the pair of proximate intervals is not substantially equivalent. One or more execution phases are automatically identified as occurring between proximate intervals that are not substantially equivalent.

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Monmouth Junction, NJ, United States of America

Christoph Kuhmuench