By Amuda James Abu, Steven Gordon (auth.), Borworn Papasratorn, Wichian Chutimaskul, Kriengkrai Porkaew, Vajirasak Vanijja (eds.)
At the varsity of knowledge expertise, KMUTT, we think that info te- nology is an important driving force of economic climate and social improvement. it might probably - capable larger productiveness, in addition to assisting us to save lots of assets. it truly is giving upward push to a brand new around of business and company revolution. We now may have items and s- vices that after have been believed to be past succeed in. with no IT, it's most unlikely for individuals to achieve their complete power. companies all over the world are harnessing the ability of broadband verbal exchange, for you to have a profound and confident influence at the financial, social devel- ment, schooling, and just about all points of our existence. This new period of unified commu- cation provides us with new demanding situations. the reason is, we should always interact extra heavily to reinforce the alternate of information concerning powerful program of broadband verbal exchange and IT. it truly is my honest desire that every one contributions to the 3rd overseas convention on Advances in details expertise (IAIT 2009) increases our knowing of ways we will have successfully follow this rising know-how for the advantage of every person all over the global. i'm hoping IAIT 2009 also will result in extra examine which can contr- ute to a greater method for IT functions within the period of unified verbal exchange. i'm very thankful to all our keynotes audio system for coming the entire technique to Thailand.
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Additional resources for Advances in Information Technology: Third International Conference, IAIT 2009, Bangkok, Thailand, December 1-5, 2009. Proceedings
Sample text
For sake of readability, we use “semantic relationship of a search term” to denote “semantic relationship between a search term and the document containing it”. Consider the search request that finds the purchase orders taken by the salesperson whose name is Tanaka. This request cannot be fulfilled by these tools because they cannot identify (i) the documents which are purchase order, and (ii) the search term “Tanaka” which is regarded as “the name of the salesperson” of the documents. As the result, traditional search of these tools is both under- and overinclusive, insofar as the query result may miss many critical documents or captures many irrelevant documents.
After the test graph is generated, we can run a scheduling simulator on this task graph using different scheduling strategies. To evaluate these scheduling strategies, we ran all of the considered strategies on variation architecture as shown in Table 2. All the scheduling strategies in our simulator have been written in Java programming language. 00 GB memory and hard disk 140 GB. The operating system used is Windows Vista. Table 2. Multi-core variation architecture Total number of cores 64 32 16 8 # of machines 4 8 16 32 2 4 8 16 1 2 4 8 1 2 4 8 # of per machines 16 8 4 2 16 8 4 2 16 8 4 2 8 4 2 1 An Impact of Scheduling Strategy to Parallel FI-Growth Data Mining Algorithm 45 Fig.
Top-to-bottom (T-B): Under this priority, a scheduling list from task graph in Figure 2 is T0, T1, T5, T9, T2, T6, T10, T3, T7, T11, T4, T8, T12, T13, T17, T21, T14, T18, T22, T15, T19, T23, T16, T20, T24, T25. 2 Strategies for Task Mapping We considered three criterions for mapping tasks to processing unit. Earliest start-time: Allocate the task to a processing unit (core) which allows the earliest start-time first. Largest size of data transfer: Tasks are allocated by considering the size of data transfer from its parent nodes.