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Figure 1. XML Schema for column-based XML auditing of Salary_Audit column in Employee table 2) Row-based XML Auditing To combine auditing information of entity into a single XML tree, row-based XML auditing merges child nodes of every XML trees of the same record of column-based XML auditing into one tree under the same column as shown in Table VIII. An attribute named “column” of “history” element in XML Schema in Fig. 2 identifies the auditing column. TABLE VIII. ROW-BASED XML TABLE FOR AUDITING ID Name DOB Address Hired Date Salary Audit 101 Tom 1980-01-01 New York 2008-01-01 55000 102 Ann 1985-06-16 London 2009-01-01 60000 103 Jack 1975-01-01 2008-01-01 Pros • Auditing data is not retrieved often. Separating it in a column can reduce the overhead of business operational functions. • XML data still requires larger spaces comparing to relational data. Commercial data engines tend to store them separately from the relational data. Therefore, it does not increase the number of data blocks that contain only relational data. • Physically, combining all XML data from different logical columns into a single column would simplify data retrieval. Another word, an Xquery expression can retrieve information from a single source. Cons • Design of auditing data does not divide information logically. • Xquery on row-based XML auditing will be more complex than of column-based. Figure 2. XML Schema for row-based XML auditing of Employee table Our XML approach on temporal data suggests the use of XML elements to demonstrate the auditing data primarily. On the other hand, [3] suggests the use of XML attributes for temporal data. Their data stream management framework for historical XML data proposed XCQL, which is a XQuery Language for Continuous Queries for the temporal extension. Our approach could be simply adapted to their scheme when it is adopted by commercial databases. VI. CONCLUSION AND DISCUSSIONS Table space (or disk space) of auditing table should be separated from the operational table. Database engine could run an auditing query faster than running it against one large table containing both operational and auditing data. Even though, we partition one large table into two table spaces. Overhead of checking which partition will be used against the query is added 280
to execution time. Also, DBA would manage the DBMS easier. To retrieve auditing information, XQuery such as FLWER expression, in SQL/XML provides us access to elements and attributes in XML data. XML indexing is adding to commercial databases while semantic searching on XML is on horizon [14]. These issues and others will provide support to semistructured approaches. We suggest a various number of solutions of database auditing. Some solutions are suitable for databases that do not efficiently support semistructured data. While semi-structure data are emerging into some commercial databases. Database auditing is a major concern in some systems. Choosing the right design would prevent the performance deterioration, reduce data redundancy, save the storage, and simplify auditing queries. REFERENCES [1] G. Bhargava, S. K. Gadia, “Relational Database Systems with Zero Information Loss” IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No. 1, pp. 76- 87, February 1993 [2] X. Chen, I. Petrounias, “A Framework for Temporal Data Mining” Database and Expert Systems Applications, Springer Berlin / Heidelberg pp. 796-805 Vol. 1460/1998 [3] S. Bose, L. Fegaras, “Data Stream Management for Historical XML Data”, SIGMOD 2004 Paris, France, pp. 239-250, June 13-18, 2004 [4] R. F. Chong, X. Wang, M. Dang, D. R. Snow, “Understanding DB2 Learning Visually with Examples”, 2nd edition, 2007 [5] “Oracle VARRAY Examples” http://www.dba-oracle. com/tips_oracle_varray.htm [6] “Introduction to the DB2 Universal Database (DB2 UDB) audit facility” http://publib.boulder.ibm.com/ infocenter/db2luw/v8/index.jsptopic=/com.ibm.db2.ud b.doc/admin/ c0005483.htm [7] W. Lu, G. Miklau, “Auditing a Database Under Retention Restrictions”, IEEE Inter. Conf. on Data Eng., ICDE 2009, pp. 42-53 [8] D. Luebbers, U. Grimmer, M. Jarke1, “Systematic Development of Data Mining-Based Data Quality Tools”, proc. of the 29th VLDB Conference, pp. 548 - 559, Berlin, Germany, 2003 [9] “Understanding SQL Server Audit” http://msdn.micro soft.com/en-us/library/cc280386.aspx [10] “Simple Data Auditing” http://forge.mysql.com/wiki/ SimpleDataAuditing [11] “Logging, Auditing, and Monitoring the Directory” http://download.oracle.com/docs/cd/B14099_19/idmana ge.1012/b14082/logging.htm#i126963 [12] The PostgreSQL Global Development Group, “Postgre SQL 8.4.0 Documentation” http://www.postgresql.org/ files/documentation/pdf/8.4/postgresql-8.4.0-A4.pdf [13] N. H. Park, W. S. Lee, “Anomaly Detection over Clustering Multi-dimensional Transactional Audit Streams”, IEEE International Workshop on Semantic Computing and Applications IWSCE 2008, pp. 78-80 [14] U. Supasitthimethee, T. Shimizu, M. Yoshikawa, K. Porkaew, “Meaningful Interrelated Object Tree for XML Keyword Search”, 2 nd International Conference on Computer and Automation Engineering, Singapore, pp. 339-344, January 26 – 28, 2010 [15] N. Waraporn, K. Porkaew, "Null Semantics for Subqueries and Atomic Predicates" IAENG International Journal of Computer Science, Vol. 35, Issue 3, pp. 305-313, September 2008 [16] Li Yang, “Teaching Database Security and Auditing”, SIGCSE’09, pp. 241-245, March 4–7, 2009 281
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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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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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If we define element of student as
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QoS, each frame data is divided int
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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PDF document structure is a tree st
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support 10 Mb / s. But ENC28J60 onl
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condition the first byte output of
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According to maximum membership deg
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ubber according to the mass ratio o
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Ⅲ. AN IMPROVED DNA ALGORITHM FOR
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Ⅴ.CONCLUSION REMARKS DNA computer
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its first child q 1 on the left, th
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chains can greatly improve efficien
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II. RELATED WORK A. Mobile Service
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special services. SOAP is used to b
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detection methods of DDoS attacks m
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Step4 calculate the new subordinate
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Figure 1. An analysis of the partit
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The preceding three formulas can be
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And the decay speed of buffer seque
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distance of view point. Given that
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Ⅳ. EVALUATION OF BLENDED LEARNING
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symmetric with respect to the origi
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each sample belongs to each categor
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A. Data Preparing To generate our t
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oth α and β . determination of Qu
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Strong earthquake 0.1< M L
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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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In order to reduce to the number of
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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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nodes will be formed one cluster, t
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Where n is the number of data point
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Keyboard event Application User mod
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SQL Azure will eventually include a
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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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ontology, and the domain dictionary
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Eq.4 is NP-hard and can be solved b
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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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in routing table. When search resou
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