HTML5 DESIGNING RICH INTERNET APPLICATIONS PDF

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Library of Congress Cataloging-in-Publication Data. David, Matthew, HTML5: designing rich Internet applications / Matthew David. p. cm. Includes index. Ebook Html5 Designing Rich Internet Applications Visualizing The Web currently available at taufeedenzanid.cf GMT Html5 Designing Rich Internet Applications Visualizing The Web 12 Excellent. HTML5 eBooks [PDF Download] - Web Templates.


Html5 Designing Rich Internet Applications Pdf

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AIR, Microsoft Silverlight,. JavaFX, Java, Java Scripting, BSF, BSF4ooRexx, HTML 5 The goal is to design fully fledged software sys- tems with sophisticated The term Rich Internet Application (RIA) is neither standardized nor is referring to a taufeedenzanid.cf for more information. HTML5: Designing Rich Internet Applications (Paperback) eBook, you should follow [PDF] A Smarter Way to Learn JavaScript: The New Approach That Uses . Download Html5 Designing Rich Internet Applications Visualizing The Web free pdf,. Download Html5 Designing Rich Internet Applications Visualizing The.

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Interface[ edit ] Through Java , JavaScript , DHTML , Flash , Silverlight and other technologies, application-specific methods such as drawing on the screen, playing audio, and access to the keyboard and mouse are all possible. Many services have worked to combine all of these into a more familiar interface that adopts the appearance of an operating system.

General purpose techniques such as drag and drop are also supported by these technologies. Web developers often use client-side scripting to add functionality, especially to create an interactive experience that does not require page reloading.

Recently, technologies have been developed to coordinate client-side scripting with server-side technologies such as ASP.

Ajax , a web development technique using a combination of various technologies, is an example of technology which creates a more interactive experience.

Structure[ edit ] Applications are usually broken into logical chunks called "tiers", where every tier is assigned a role. For more complex applications, a 3-tier solution may fall short, and it may be beneficial to use an n-tiered approach, where the greatest benefit is breaking the business logic, which resides on the application tier, into a more fine-grained model.

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This allows the underlying database to be replaced without making any change to the other tiers. However, for actions that involve user-inputs, more parameters must be determined: First, the set of input elements, and second, the values that are assigned to these elements value parameters.

We assume that the client can provide us the list of input elements at each state. To detect value parameters of user-input actions, we propose the following approach: 1.

At each state, the system performs each user-input action using an arbitrary set of values, x. These values are chosen from the domain of input elements in that user-input action. The system observes requests T after performing the user-input action.

If the next expected traffic is exactly the same as T but with different user-input values y instead of x, the system concludes that the user has performed the user-input action using y.

Example: The text-box on top of the example in the Fig. Since these two requests Fig.

However, this technique is only effective if the user-input data is passed as is; if there is any encoding of the submitted data, the actual data that has been used by the user cannot be extracted from the logs. Both the client-side and the server-side of the application can contribute to this randomness. The client-side of the application can generate different requests after performing an action from the same state, and the server-side may respond with different responses.

The responses are served by the proxy by replaying a recorded trace.

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Therefore, there will be no randomness in the responses during the reconstruction. However, the session reconstruction algorithm still needs to handle randomness in the client-side generated requests. If the execution of an action generates random requests, the algorithm cannot detect the correct action since executing the action generates requests which are different from the requests in input trace. The Match function line 18 in Algorithm 1 , needs to detect the existence of randomness and flexibly find the appropriate responses to the set of requests.

In this case, as we explained in Section 2. To handle randomness of this type, we use the following approach: We say that two requests match partially if they have the same URL, the same set of parameters, but some of these parameters have different values. For example, the requests req. Suppose that once the system executes action a from state S n line 17 in Algorithm 1 , it observes that the generated requests have a partial match with the next expected traffic.

Pdf Html5 Designing Rich Internet Applications

This difference between parameter values can have two causes: 1. Two different actions may send requests containing the same parameter but with different values. For example in a news RIA if we have two actions, one for fetching the latest technology news sending requests to fetch.

Requests for the same action, contain a parameter that has a changing value after each execution. For example, in a news RIA there may be an action to fetch the latest news that sends a request to latest.

In this request, the value of parameter last represents the time of the last update fetched by the client and is changed by the client every time a request is sent. The next request might look like latest.

HTML5: Designing Rich Internet Applications (Visualizing the Web)

When the session reconstruction tool observes that the value of a parameter in the log differs from the value of the same parameter in the generated request after execution of an action, it should categorize the parameter as constant or non-constant. If the parameter is constant it means that the action is incorrect that is, this is not the action taken by the user at that level of the reconstruction.

For example, it would be the case if the user had clicked to fetch latest technology news, but the session reconstruction tool had tried fetching business news at this state.

However, if the parameter is actually a non-constant one, its value should be ignored during comparison.

For example, the value of the last parameter in the previous example is a non-constant, therefore if the tool triggers an action which generates latest. A naive approach to handle randomness in requests, would be to compare the generated request with a request in the user-log based on a similarity function [ 7 ]; if the similarity between two requests is more than a threshold, the requests are considered a match.

However, this approximate matching may mistakenly match two requests and jeopardize the reconstruction.

The Match function does not consider these changing values for checking the correctness of the action. In the example above, the actual value of the parameter last will be ignored by the Match function for that request.

Recovering user-interactions of Rich Internet Applications through replaying of HTTP traces

Example: In Web applications, the requests are sent for a resource with a URL , and each request can contain some parameters and their corresponding values in the query string of a GET request, or in the body if a POST request. Two HTTP requests can be considered a partial match if they are sent to the same resource, have the same set of parameters, but the values for some parameters are different.

By comparing the request received in the first execution part a in Fig. In Algorithm 1, we have discussed user-initiated requests, however messages can also be sent without the user initiating a request first.

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These requests may originate from a timer on the client-side, or even from the server-side such as Websockets 7 in RIAs. The general session reconstruction algorithm can be modified to handle these cases, as follows: Timer initiated requests: Timers can be detected based on the signature-based ordering of actions Section 2.

Later during the reconstruction, the algorithm triggers a timer when the signature of the timer matches the next expected traffic. In this case, the given trace to the proxy contains both requests that originate from the client-side of the application, and requests that are sent from the server. The proxy in our general algorithm has to be changed to detect these server-initiated requests; When the proxy observes that the next expected traffic is server-initiated, it just sends these requests to the client.

In this section, we propose a session reconstruction approach for RIAs. This approach realizes the improved session reconstruction algorithm in the context of RIAs and addresses several challenges mentioned in the previous section.Regarding domain-specific interface.

Moreover, users will be able to and Orchestration models to external ontology i share their music preferences, groups and sources specified by navigational classes. Depending on the design and behaviour of the user interface in design solutions taken in the previous activities a technology-independent manner. These are the keys to solving the with a higher level of interactivity, similar to aforementioned issues and developing the Web desktop interfaces, embed multimedia contents to a further stage, i.

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