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Using Customer Profiling and Analytics to Incorporate Customer Data/Information by Time Series into Visual Test Workload Execution Modeling

IP.com Disclosure Number: IPCOM000247082D
Publication Date: 2016-Aug-02
Document File: 6 page(s) / 242K

Publishing Venue

The IP.com Prior Art Database


Disclosed is a method for incorporating/integrating customer workload time series data into Visual Test Workload Execution Modeling as a means of providing the test team/organization with valuable insights. Through effective Customer Workload Profiling and analytics, this method provides visualization of customer workload/application transaction time series data and incorporates this time series information and predictive and cognitive analytics for test workload/application transaction data comparison into the Visual Test Workload Execution Modeling tooling.

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Using Customer Profiling and Analytics to Incorporate Customer Data/Information by Time Series into Visual Test Workload Execution Modeling

One of the main focuses of designing and deploying high quality and highly effective test workloads within a company (and beyond that) is to focus limited workload resources on the functional areas that the customer actually exercises/uses (i.e., functional coverage/intersection) and at the activity/stress levels that the customer attains. This workload functional coverage as well as corresponding activity/stress levels can significantly vary over time, including between and within the traditional online transaction processing (OLTP) and batch workloads, as well as cloud, analytics, mobile, security, and social (CAMSS) workloads. Test workloads must not only understand but also emulate the sequence of events in which key customer workloads change within the same day, as well as over the course of weeks, including when different components change both in relation to and independent of each other.

Test personnel may possess intuitive ideas of the time sequence(s) in which specific functional areas and the corresponding activity/stress activity levels are exercised by the customer, as well as relationships between multiple data points. Incorporating actual customer profiling workload time series data into the Test Workload Modeling process, however, provides the actual visual customer workload timelines and data point relationships, resulting in greater understanding and capabilities. Possible misconceptions of when customer workload functional areas are exercised, when

activity/stress levels are attained, and perceived relationships between data points are replaced with actual time series empirical data.

Enhancing this visualized customer empirical data with time series data enables the tester to design a test workload execution run that is more reflective of the target individual customer or customer set. This is in a subset to all targeted transaction/workload characteristics, dependent on the attainment goals of the specific test workload run in order of occurrence. In addition, the test workload run model information can be saved in a database repository for future reference and historical comparison purposes (e.g., to determine in what ways the customer workload sequence of events may have changed/evolved over time) and applied for continuous improvement.

Emulating the workload transaction sequences of events and data points relationships through time series transactions is important for understanding cause and effect and finding potential product defects. Time series data also provides the capability of determining/discovering existing correlations between data points. In addition, it provides predictive correlations and cognitive analytics between data points, which

support greater test plan variation and coverage.

The novel contribution is a method for incorporating/integrating customer...