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Theorem 4: (10), individually, as one situation : "" element contents. Theorem 5: (11), (12), (13), the three cases can be classified one situation: . Theorem 67: the cases of (15) and (10) are the same, as a judgement event: element content. Theorem 7: (16), (17), (18), the three cases as a judgement event class: . Theorem 8: (19) as a judgement event alonely: According to the 7 judgement events summarized in this article, and then expand the SAX API, then we can create the SCTP index. Through the analysis above ,the algorithm process of building CSTP as follows: Input: XML and encoding table Output: CSTP Compressed index Begin: Start with SAX //parse sequencely, new (Node3)// to build the first node; s = new (Node1); push (S3, s); if (xml root tag has property value) { p = new (Node3); s.attribute = p; P-> left = property value; } while (* PP) / / there is also property { q = new (Node3); p->right = q; p = q; q->left = property value; } p->right = NULL; while (xml document did not be finished) { Case 1: / / read the string form: < ... ... Case 2: / / read the string form : < ... ... Case 3: //the third case: contents ... ... Case 4: / /,be similar to Case 3 ... ... Case 5: / / ... ... Case 6: / / read the string form : ... ... Case 7: / / read the string form: ( must be Children) ... ... } There is another way to build an index: build nodes using the contents of judgement event, rather than the previous one, then it’s different to categorize the 25 original events. Of course, the specific implementation of the algorithm is also different from the process above, don’t give it’s detail here. Ⅲ. THE QUERY ALGORITHM BASED ON CSTP A. Tthe Analysis of The Process and Character of Query Query process of Xpath is similar to Xquery’s, but the latter’s is more complicated to achieve. Only discuss the XPath query at this. Analyse the query process and the character of it on the CSTP index before discussing the XPath query, as follows: For simple path query: I : t / t / t 1 2 L m .The t 1 may be the root element node of tree-graph of CSTP, that may be not. If it is not the root element node, such as a third layer node in the tree-graph of CSTP, when users submit query expression, then the computer would begin to visit differrent chain on the second layer of the CSTP treegraph. If there is no t 1 , then visit the the differrent chain constituted with the children of the second node on the second layer (the second node may be in the same name item). Continue to do until find t 1 , we can see that the computer will compare many times. Suppose, there are N elements nodes on the second layer of the CSTP, and there are M elements nodes in the different chain, each node on the second layer has on average N 2 child nodes of different chain, then such a query’s time complexity is O( N1 + N2 ⋅ N ) ≈ O( N2 ⋅ N ) . To a mass of data, N is a rather large number. And if the expression t 1 supplied by user does not exist, which will make computer visit through CSTP tree-graph. Time cost is obviously very high. Of course, if additional information such as start layer can be specified, and new a head node on each layer of CSTP tree-graph, so that can significantly reduce the query time, but this would obviously lead to an unnecessary number of additional work. Therefore, suppose, t 1 is on the first layer of , this path expression is called a full path expression. If the path expression submitted by the user is not full, we can predefined certain rules, then generate the full path expression automatically when the user query. B. Analysis of Algorithm and Efficiency Based on XPath Query According to the analysis above, we can query efficiently in the compressed structure. At the same time, in accordance with coding table generated by DTD ,we can design decompression algorithms at the process of query. Because of the complexity of XML itself and CSTP index, only give the qualitative analysis and comparison at this. Basing on the high repeatability of XML tag data this paper introduce TP relation to convert the original tree structure of XML into CSTP structure in this paper. And the introduction of the same name items and different 56
chains can greatly improve efficiency of query. The query algorithm that is partial decompression designed by this article can skip a number of unnecessary intermediate nodes to query and compare. For example, there are N 1 same name items starting from the root node of the CSTP index, the number of average of tag nodes of each same name item on the first layer is N 2 , then the time complexity of general query is O( N = N 1 ⋅ N 2 ) , but the time complexity of this method is O ( N = N 1 + N 2 ) . It is Obvious N1 + N 2
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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ISBN 978-952-5726-09-1 (Print) Proc
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model that can deal with time serie
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Page 17 and 18: training for the Wushu competition
Page 21 and 22: information, called weak uncertain
Page 23 and 24: Student side Student side Student s
Page 27 and 28: If we define element of student as
Page 31 and 32: QoS, each frame data is divided int
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Page 39 and 40: A. Profiling&following control algo
Page 41 and 42: Research and Realization about Conv
Page 43 and 44: PDF document structure is a tree st
Page 47 and 48: support 10 Mb / s. But ENC28J60 onl
Page 51 and 52: condition the first byte output of
Page 55 and 56: According to maximum membership deg
Page 59 and 60: ubber according to the mass ratio o
Page 61 and 62: Ⅲ. AN IMPROVED DNA ALGORITHM FOR
Page 63 and 64: Ⅴ.CONCLUSION REMARKS DNA computer
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Page 69 and 70: II. RELATED WORK A. Mobile Service
Page 71 and 72: special services. SOAP is used to b
Page 73 and 74: detection methods of DDoS attacks m
Page 75 and 76: Step4 calculate the new subordinate
Page 77 and 78: Figure 1. An analysis of the partit
Page 81 and 82: The preceding three formulas can be
Page 85 and 86: And the decay speed of buffer seque
Page 89 and 90: distance of view point. Given that
Page 93 and 94: Ⅳ. EVALUATION OF BLENDED LEARNING
Page 97 and 98: symmetric with respect to the origi
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Page 105 and 106: A. Data Preparing To generate our t
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indirect causes, and the logical re
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egression. Granger causality test i
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B. Evaluation We have evaluated thi
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used as a source and neighboring ce
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Figure 3. Drainage networks generat
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Combinational logic unit failures i
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Tabal.1 Combinational fault logic t
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under the endorsement of both the m
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TABLE I. DESCRIPTION OF PROPOSITION
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Figure 2. The process of the invers
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Figure 9. (a)the original image.(b)
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ISBN 978-952-5726-09-1 (Print) Proc
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In order to reduce to the number of
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ISBN 978-952-5726-09-1 (Print) Proc
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that studying being going to be to
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Reference[10] analyzed the evolutio
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module, communication module, apper
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After the comprehensive performance
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system testing can be seen that the
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III. AUTONOMIC RESOURCE ALLOCATION
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The QoE i (T i ) is the ith user’
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ISBN 978-952-5726-09-1 (Print) Proc
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nodes will be formed one cluster, t
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ISBN 978-952-5726-09-1 (Print) Proc
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Where n is the number of data point
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ISBN 978-952-5726-09-1 (Print) Proc
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Keyboard event Application User mod
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ISBN 978-952-5726-09-1 (Print) Proc
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SQL Azure will eventually include a
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ISBN 978-952-5726-09-1 (Print) Proc
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The nodes in the suffix tree are dr
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[3] Y. Li, S. M. Chung, and J. D. H
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some drilling fluid produces hydrog
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"normalization", whose membership b
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and minimum structural elements in
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esults of the spatial transform par
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B. Research on protocol actions Com
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ACKNOWLEDGMENT This work is funded
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A. Problem Description In a small b
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[4] Huang, Hung, and J. Y. jen Hsu.
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Further by calculating, the followi
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TABLE II. THE CONCENTRATION BETWEEN
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private key that obtained by using
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ISBN 978-952-5726-09-1 (Print) Proc
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ontology, and the domain dictionary
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ISBN 978-952-5726-09-1 (Print) Proc
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Eq.4 is NP-hard and can be solved b
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ISBN 978-952-5726-09-1 (Print) Proc
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Where: G i is the selection field o
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If there is X j in a generation, wh
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Theorem 2.4([10]). Let L 1 and L 2
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The relation between the lattice im
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Figure 2. Example of single-step de
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evocation and the fourth group stor
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focused mainly on rule-based forms
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Ⅵ. CONCLUSION Figure 6. The Flask
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EDCF in comparison with DCF, has so
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B. Simulation results and analysis
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ISBN 978-952-5726-09-1 (Print) Proc
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in routing table. When search resou
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ISBN 978-952-5726-09-1 (Print) Proc
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others through the selective emotio
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such as weather information, techno
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the opening window, a related page
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1) Process context storage areas. I
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V. CONCLUSION In this thesis, sCPU-
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main types of horizontal search env
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Enterprise Portal security provides
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() t = [ S () t S () t ] T N S ,...
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[2] Kumaravel, N., and Kavitha, V.,
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output, so BP network has been wide
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input to the artificial neural netw
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(2) In a process (tokens from outsi
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The reduction process consists of t
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all eight normal vectors are identi
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is B object , and the number of emp
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xi, j y' = εα ( (1 + tanh( )) −
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Error 8000 7000 6000 5000 4000 3000
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Architecture (AMBA) a new bus archi
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In order to ensure the smooth proce
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In this paper, we adopt two Sobel o
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four layers.The far right of the gr
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ISBN 978-952-5726-09-1 (Print) Proc
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TABLE II. OPERATIONAL EMPLOYEE TABL
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A. Object-relational Type To audit
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to execution time. Also, DBA would
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Liping Chen .......................
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