rγ : denote the original media resource in a page.
Trγ : denote the transcoded media resource.
Input: a UR with corresponding MPset and LOij. Output: a transcoding content version, XHTML.
Step 1: for each media resource, rγ , in a page pi.
Step 1.1: apply MPγ∈MPset, to transcode the rγ into the trγ. Step 2: transform the original HTML into XHTML format Step 3: replace all rγ by trγ into the XHTML.
Step 4: replace all unshown media resources by useful annotation.
Step 5: output the XHTML with associated transcoded media resources.
Chapter 6
Implementation and Experimental Results
The ACDM prototypical system is developed based on Apache Server and PHP, Perl and C Language. Except screenshots of current implementation, the experimental results are good.
6.1 The System of ACDM
The ACDM Web Server has built up with SCORM-based learning object repository to provide mobile devices viewing learning object online. Figure 6.1 shows the main flow of ACDM Web Server.
Figure 6.1: Main flow for a user to retrieve a learning object
Figure 6.2 shows the content adaptation results comparison when meeting the
different bandwidth setting. The user also specifies the picture format’s ordering (PFO). ACDM Web Server will do content adaptation according to the preferences and suppress the sizes of the media objects to meet the delivery time condition.
Figure 6.2: Content Adaptation
Figure 6.3 shows the adaptation result can be generated in accordance with the switch settings of user preference. If the user switches off the WAV file, BACKGROUND, ICON, and BAR pictures, they would not be displayed. There are two influences of the result. First, the disabled media object will be substituted with the annotation one and be transformed to a hyperlink. Second, the redundant traffic size will be split into other media objects to improve their display qualities. For example, the F1 picture has been upgraded from 77Kbytes to 112Kbytes.
Figure 6.3: Preference-based Transformation
Figure 6.4 shows the effect of “progressive adaptation”. We assume there is a previous retrieval that the user tries to get the learning object with turning off the background. When next user with the same user preference except for turning on the background tries to get the same learning object, the adaptation result is still the same with previous one (no background). It is because the similarities between two preferences are high enough to adopt the previous adaptation rule. At the same time, ADP invokes CAMS and Content Synthesizer to do the real transformation with the true condition which the user has. Therefore, when the other learner tries to retrieve the same learning object again, the background will be shown.
Figure 6.4: Progressive Adaptation
The final screenshot is the administrative interface for the administrator to maintain or observe the ACDM Web Server conditions. It provides two buttons to trigger the task of model construction. The first button is “Assign cluster label”. It first runs UPCALgo to get the new rule clusters and assign the cluster label to each rule. The second button is “Rebuild decision tree”. It triggers CAMS to redo classification with hardware profile and saves the result into memory. The rest of the components in Figure 6.5 show the adaptation rules, decision tree, and the detail of rule clusters. Figure 6.5 shows the ACMD Web Server Monitoring.
Figure 6.5: ACDM Web Server Monitoring
6.2 Experiments
Several experiments have been conducted to prove the ACDM system well.
Assume there is a learning object which contains a WAV file with 660,768 bytes and six pictures with 1,016,392 bytes. The original size is about 1.8 Mbytes. The user specified the maximum tolerable delivery time to 5 seconds. We observe the transmission result with the various bandwidth settings.
In the first experiment, we assume there are no prior connections existing before.
When the first user retrieves the learning object, it could take about 3 seconds to do the content adaptation (by Content Synthesizer), and the later connections, the content could be transmit immediately; hence the transmission time is in the control of 5
seconds. This experiment also shows ACDM always keeps the delivery time controlled with various bandwidth settings. Figure 6.7 shows the result. Figure 6.8 shows the total transmission size variation.
Delivery Time Comparison between First/Later Connection in the same bandwidth
0
Figure 6.7: Exp 1 – Delivery Time with various bandwidth settings
Delivered data size and Bandwidth
Figure 6.8: Exp 1-B – Observations of total transmission size
The second experiment is conducted to compare the transmission time between Traditional Web Server and ACDM-based Web Server. In virtue of the simplicity of the traditional web server, since it sends whole content without any adaptations, the transmission time is long when the bandwidth is low. ACDM can overcome this suffering.
Delivery Time Comparison between traditional web server and ACDMMD Web Server
0
Figure 6.9: Exp 2 – Comparison with traditional web server
The third experiment compares Annotation-based Web Server and ACDM-based Web Server. In Annotation-based Web Server, there are several content versions of learning object defined in the authoring stage. Hence it may have various versions of content to deliver. For example, there are three existing content versions and they are suitable of bandwidth settings with 140Kbytes/sec, 170Kbytes/sec and 200Kbytes/sec.
The experiment result shows the Annotation-based Web Server suffers a long delivery time when the bandwidth is < 100k. It is because when the bandwidth is very low, sending the content version of 140Kbytes/sec causes a long delivery time.
Delivery Time Comparison between Annotation-based web server and ACDMMD Web Server
Figure 6.10: Exp 3 – Comparison with Annotation-based Server
The fourth experiment shows that ACDMMD Web Server has the capability to do bandwidth estimation dynamically. It overcomes the problem when network characteristic is changed. The first connection may suffer long delivery time because the bandwidth user specified differs from the actual condition. After the transmission is done, the bandwidth value is re-calculated automatically. The later connection may regain the delivery time in control. The following experiment assumes the user specified the maximum tolerable delivery time to 5 seconds.
Table 6.1: Dynamic bandwidth re-estimation
Bandwidth (User) Bandwidth (Actually) Transmission Time Remark
200Kb 200K 5.3 seconds
200Kb 150K 7 seconds *
150Kb (Auto Update) 150K 5.2 seconds
150Kb 100K 7.8 seconds *
100Kb (Auto Update) 100 5.4 seconds
100Kb 50K 10.5 seconds *
50Kb (Auto Update) 50K 5.2 seconds
z : Time excessive.
Chapter 7 Conclusions
In this paper, we propose an Adaptive Content Delivery Mechanism, called ACDM, which can efficiently manage a large number of historical user requests, and intelligently deliver a proper adaptive content with higher fidelity from LOR to users directly and then prepare a transcoded content version for next similar request. The ACDM includes Adaptation Data Format Definition Phase and Adaptive Content Delivery Phase. The former defines an adaptation data format, called Content Adaptation Rule (CAR), based upon CC/PP, UAProf, etc. In order to efficiently deliver the suitable content with associated learning resources to users in accordance with their user preferences, hardware capabilities, and variable wireless bandwidth, the latter consists of 1) Content Adaptation Management Scheme (CAMS): applies clustering approach and decision tree approach successively to create a Content Adaptation Decision Tree (CADT), which can be used to predict the appropriate adaptive contents from the LOR, 2) Adaptation Decision Process (ADP): proposes an Adaptation Decision Process Algorithm to intelligently determine a suitable version of the existing adaptive content based on the CADT, and 3) Content Synthesizer: transcodes the content if necessary. For evaluating our proposed approach, an ACDM prototypical system is developed. Furthermore, the experimental results show that the ACDM is workable and beneficial. In the near future, we will also deploy the ACDM system on the general web server, not only the SCORM learning object repository. Besides, the management scheme will be enhanced to efficiently maintain the huge number of adapted content versions in the ACDM storage.
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