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users are not aware that their actions are being tracked Therefore, facilitating implicit data collection focuses more on creating proper UI instrumentation rather than additional UI controls For example, go back to Figure 8-7, which is a Silverlight page that shows a video control Each time this page is shown to the user, logging code could run in a background that increments the amount of viewers this particular video has had Other examples of implicitly collecting data include tracking clicks on links, monitoring the frequent zones in an application, timing completion of workflow scenarios, and so on Obviously, monitoring user behavior has been around for several years now Even many firstgeneration web sites contained some forms of logging to track user interactions Furthermore, many pieces of software for web analytics have existed to facilitate this.

In the sample code, we start with the code from Recipe 2-6, which includes a form that saves and loads data from isolated storage. We will save and load data from isolated storage while the background worker thread is executing to prove that the UI is not locked up by the long-running operation. We ll modify the UI to include a button to start the long-running operation as well as a bit of UI work to show what is going on. Figure 2-17 shows the UI.

Boolean Boolean Boolean Boolean Boolean Boolean Boolean Boolean Boolean Boolean Boolean Boolean ServerReport

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How is this different for collective intelligence For the most part, the same best practices for transparent user monitoring apply to collective intelligence However, collective intelligence principles do tweak the instrumentation as they encourage tracking user input that can be displayed back to the users This is a key difference from applying simple logging techniques Previously, instrumentation data on users was kept internally and analyzed by application administrators Applications designers several years ago never thought of sharing this data with their users and closely guarded this data Therefore, instrumentation architecture never planned for easily querying aggregated data that was implicitly collected and surfacing it on the application Collective intelligence systems require instrumentation implementations that not only collect data but also integrate into a queryable system that can present the data back in the application.

Figure 2-17. Recipe 2-11 s test UI To help keep things clean, the code that was copied from Recipe 2-6 is located in #region blocks so that it is not a distraction. There is a bit more code in this recipe, so we will walk through the major code sections. First, we declare a BackGroundWorker object named worker and initialize it in the constructor Page() for the Page class: worker.WorkerReportsProgress = true; worker.WorkerSupportsCancellation = true; worker.DoWork += new DoWorkEventHandler(worker_DoWork); worker.ProgressChanged += new ProgressChangedEventHandler(worker_ProgressChanged); worker.RunWorkerCompleted += new RunWorkerCompletedEventHandler(worker_RunWorkerCompleted);

Back Clear Find FindNext GetPrintDocument PrintDialog Refresh Render RenderStream SaveSnapshot Stop

Furthermore, implicit data collection should remain as unobtrusive as possible with the overall performance of the system The users should not even be able to perceive that data is being collected on their actions How much data should be gathered depends on the requirements of the system If the data is collected properly, then it can be useful in the future You can never have enough information Data attributes that are not useful in current collective intelligence implementations might be important in the future for additional metrics When applying implicit gathering strategies, it is important to not overburden the system..

Goes back if the report is a drill-through report Clears the report Searches the report for the given text Continues the search for the given text from the current search point Returns a print document for printing the report Brings up the Print dialog box and then prints the report Refreshes the report, which reprocesses the report with any new data Processes and renders the report Returns a stream associated with the report Saves a snapshot Stops background processing of the report

We configure the BackgroundWorker to support cancellation and progress reporting so that we can provide a simple UI to give status Next, we wire up the DoWork, ProgressChanged, and RunWorkerCompleted events to handlers The DoWork event contains the code that the BackgroundWorker thread executes This is where the long-running operation goes ProgressChanged and RunWorkerCompleted are events where the UI thread can update status in the UI while the background work is safely executing In our DoWork event, we first check to see if there is a cancel request pending and break out of the loop if there is Otherwise, we call ThreadSleep to delay execution and ReportProgress to provide an updated percentage complete The results of the background worker thread s effort are passed back to the main thread as the value of eResult: eResult = EnvironmentNewLine + "Completed: " + DateTimeNow.

The ReportViewer controls also support events, so your application can listen for events such as page navigation, printing, drill-through, searching, zooming, and other activities in the control.

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