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What we have discussed so far regarding views pertains mostly to the standard or datasheet types The Calendar view format contains different settings to generate its display, which is based primarily on beginning and ending dates from list columns that you specify In addition to determining the columns to use for beginning and ending dates, you must select which columns are used to display the text in the calendar entry The view settings call these the Title and Sub Heading of the calendar entry, but you are able to choose any of the list columns to show in the list page Another option for the Calendar view format allows you to choose from three different calendar scopes These scopes determine the time frame shown in the calendar and are month, week, or day displays.

Surfacing collective intelligence data should follow the same principles mentioned in the previous topic of collecting user information. The visuals that are used to surface collective intelligence data should adhere to BI 2.0 best practices and be easy to understand to the casual user. You do not want to overload the user with too much information, and you usually want to convey a few key pieces of insight from the collaboration of all the users. There are many examples of ways to present derived knowledge to casual users. The controls can be visual or text based. Some examples include: Tag clouds: Tag clouds are a great way to provide a list of items that amplify particular attributes. For more information on tag clouds, refer back to 5. Rating controls: These controls provide simple visual cues that users can quickly analyze to relate the information in their heads. KPI visuals: Traffic light visuals have their roots in street traffic lights. Everyone knows what the red, yellow, and green symbols in a street traffic light represent.

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Fixed-width data, which is often used with legacy or embedded systems, is a fine candidate for batch processing. Processing that deals with a resource that s fundamentally nontransactional (e.g., a web service or a file) begs for batch processing, because batch processing provides retry/skip/fail functionality that most web services will not. It s also important to understand what Spring Batch doesn t do. Spring Batch is a flexible but not allencompassing solution. Just as Spring doesn t reinvent the wheel when it can be avoided, Spring Batch leaves a few important pieces to the discretion of the implementor. Case in point: Spring Batch provides a generic mechanism by which to launch a job, be it by the command line, a Unix cron, an operating system service, Quartz (discussed in 18), or in response to an event on an enterprise service bus (for example, the Mule ESB or Spring s own ESB-like solution, Spring Integration, which is discussed in 20). Another example is the way Spring Batch manages the state of batch processes. Spring Batch requires a durable store. The only useful implementation of a JobRepository (an interface provided by Spring Batch for storing runtime data) requires a database, because a database is transactional and there s no need to reinvent it. To which database you should deploy, however, is largely unspecified, although there are useful defaults provided for you, of course.

The option you choose in the view is only the initial display when a user selects the view Once displaying the view, the user is able to change the display by selecting commands from the Scope group on the Calendar Tools - Calendar ribbon or by clicking week rows or specific days in the calendar The only other option that is available when creating a Calendar view is a filter The filter in a Calendar view works identically to the filter in the Standard and Datasheet view settings No sorting, totals, groups, styles, or limits are available in a Calendar view To create a public Calendar view, follow these steps: 1 2 3 On the list page, select the Create View command from the Manage Views group of the List Tools - List ribbon On the Create View screen, click the Calendar View link.

Spring Batch works with a JobRepository, which is the keeper of all the knowledge and metadata for each job (including component parts such as JobInstances, JobExecution, and StepExecution). Each job is composed of one or more steps, one after another. With Spring Batch 2.0, a step can conditionally follow another step, allowing for primitive workflows. These steps can also be concurrent: two steps can run at the same time.

In the previous section, you learned what data visualizations are. But what constitutes good data visualization What are the key characteristics of data visualizations The following subsections cover some basic concepts.

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