The 2nd International Workshop on Database Technology and Applications (DBTA2010) * ]7 G$ O( G- H% `+ Z第二届IEEE数据库技术与应用国际会议-DBTA2010(DBTA2009已全部EI Compendex检索) 4 Y6 J# F/ E' N- K11月27-28,2010,武汉,中国 * q- Q0 X2 E2 r; i' U5 }9 A# Qhttp://www.icdbta.org/! Y( n: n X I+ \5 `' M5 o. V
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论文提交日期: 2010年8月18日* W7 U7 {2 j% P1 }/ ^& a* F: X
论文录用通知日期: 2010年9月 16 日" y5 j/ N9 G1 l
论文修订版本提交日期: 2010年9月22日 , n- G2 `; A% j3 n9 Z. @1 F0 u论文注册日期: 2010年9月28日- i& ~+ d+ z5 J* i, i7 \
论文提交系统: http://www.icdbta.org/dbta2010/submission/8 c) n* M- q5 i; M t" N w- G/ Y
会议论文模版: http://www.icdbta.org/dbta2010/instruct8.5x11.doc(只接受英文稿件) " N+ P9 H+ p' jIEEE会议论文版权表: http://www.icdbta.org/dbta2010/IEEECopyrightForm.doc (录用注册后提交)- L# `' ^* {- h! C' V4 h
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第二届IEEE数据库技术与应用国际会议(DBTA2010)将于2010年11月27-28日在中国-武汉召开。第一届IEEE数据库技术与应用国际会议(DBTA2009)全部收录的论文已经被EI Compendex检索。DBTA2010将由美国IEEE出版社出版,收录的论文将全部被ISTP和EI Compendex检索。会议优秀论文将被推荐选入EI或SCI国际期刊专刊发表。 ; O3 v* }* L- ~1 G+ w5 n0 V ! Q$ J D T/ ?# _欢迎研究员、工程师、教师和学生踊跃投稿,会议论文主题由以下领域构成,但并不局限于:8 ?: \' j5 m& ` c1 F" _, m- W
2 `, l }5 l1 {- w7 G" N5 r1. Database and Related Issue * I5 v9 Y' F/ S, Z/ W3 W+ ?Temporal Data S# N* \2 r1 y- D6 \4 t: p
Scientific Databases ) V7 a$ Q6 f+ k/ D4 U% G7 ~Business database software& x1 }- K/ v- M6 @
Computer data processing ! j# Y7 C c% l* G$ K/ `- vData processing services 0 z; m' T, u/ p& r$ z/ v; xData processing supplies " D* w$ c* g- q2 f3 O( T+ a
Data processing systems ( I4 }( u1 J7 X, [5 u4 N& BMetadata Management , F! T+ ?- G2 [+ o0 P2 v# JMobile Data and Information ) p, Q2 f, w [2 D- s** Databases$ `6 A- [# S- X+ s" v
WWW and Databases ) I/ o# x+ @8 wWorkflow Management and Databases3 q8 E- j% ]' o z# {) r" S$ B7 y. Z
XML and Databases5 c+ r: d: u( g8 p
** Databases # `( y7 G8 W! t$ P" w6 TData modeling and architectures % I/ J9 o' a+ M/ DData streaming, data provenance and data quality4 y# n$ h) Z- c
Data Security, privacy, and data integrity 1 Y- d C- X; K0 b
Web Data and the Internet- [# n5 b% b( {: y, @
XML and databases, web services 0 v# ~ ]. p8 g* a# G9 XSemi-structured data, metadata $ l/ W B% {7 n. m2 {0 ie-commence 3 ]7 ^# r! L; q, ~5 z ) h3 I2 W& f( ]& P' q2. Data warehouse and Data mining' P. ]7 h9 d! n
Grid/Parallel/distributed data warehousing 4 J% z0 }- i e( G0 T* I" NWeb/** data warehouses f: U" S' W# f
Data warehousing and the semantic web 4 C9 j, h; m4 G; g2 x- Q5 [Data warehousing with unstructured data 6 e) n( d5 k9 B+ x+ v' UIntegration of Data Warehousing3 j+ u4 ~9 }& m7 q- h) u# L
Data and knowledge representation( D( R% D+ x! [% ]
Languages and inte**ces for data mining' n6 M" i, K4 q* }, ~# \( m, ^7 x
Data integration and interoperability3 M. G6 T p/ r5 }2 c# o( u3 f
Data extraction, cleansing, transforming and loading. G2 q' Q$ s V. K4 D2 S
Data mining and information extraction 2 M& e7 ~0 |5 S; qKDD Process and Human Interaction : B) K, }: w: @1 ]# [OLAP and Data Mining ; A4 [) y* ~+ ]; L+ q! V, rParallel and Distributed Data Mining # @7 J, |: R0 d2 v# e0 {" pPhysical database design and performance evaluation# S6 ^% P* ^% ^: j, J/ A0 G
Query processing and optimization* R4 D& P* V5 s" W4 i) M
Reliability and Robustness Issues2 L: s' J, {. Y- F& b8 V
Semantic web and ontology$ A) F4 ?& D+ ^* [. x
Software Warehouse and Software Mining2 K8 F7 L( N1 d$ @
Social and mathematical statistics( Q, I9 [" q5 h- ^& M# H
Novel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis) " A3 A0 v i5 _ `5 W8 M0 _Developing a unifying theory of data mining ; Q3 Z7 q3 X2 ^% ?# p j8 B# W. l: nMining sequences and sequential data8 C7 P6 G2 h/ y4 Q" L8 K
Data pre-processing, data reduction, feature selection, and feature transformation 7 {* P. o2 ?" H0 I& }/ n+ HQuality assessment, interestingness analysis, and post-processing 4 X" q0 u/ T2 L7 T6 q7 H+ NMining unstructured, semi-structured, and structured data% g+ \* i5 ?5 I% j* _- }4 f
Mining temporal, spatial, spatio-temporal data A% V$ C0 Q# }4 M n8 B8 z5 vMining data streams and sensor data 8 W0 R8 t( m6 j- O& S" uMining ** data( L$ x) r/ f, g" T
Mining social network data ' w0 Q# F- y8 S/ ^, E7 YHuman-machine interaction and visual data mining " @" [( W1 O5 M+ xData mining applications (bioinformatics, E-commerce, Web, intrusion/fraud detection, finance, healthcare, marketing, telecommunications, etc) * @5 o# v3 ^; \9 u# B6 HKnowledge Acquisition & Management " u$ x: t e& @. F6 A. Y+ rKnowledge Modeling$ Z5 F+ p$ R, C
Knowledge Processing 7 k3 S3 J. W+ s+ ]3 PIntegrated KDD applications and systems ; H. e' q" Q) \% O4 IBusiness Process Intelligence # Z. q, C/ Q8 v& U2 u5 fCluster Analysis and Knowledge Base system # ]* Z' d1 i1 X" c4 O7 F2 V* x1 mInformation systems technology , j% s5 }' j* J& }' T6 T; fOther related technology about data mining 0 p$ Z6 F* V. p/ U9 v R- Q% S% M$ I, K* \4 x3. Computer Science and Related Technology 3 U/ \7 j7 l3 ]! z8 M0 oImage and signal processing 3 A' G. W+ U2 o: w3 _; ]( g6 z
Artificial Intelligence ) Q( g* d- q8 j4 ~* [; q+ D
Software engineering ]5 C; Z( B& t/ L4 P1 E1 K4 eSystems Engineering 8 @, @: M% u9 l m! OComputer Graphics 1 O1 x/ X8 p' `Computer Application - @. M2 _" i' h( e. z, C/ z1 m, s
Control Technology 5 s# r1 M) n7 ]# ]8 U0 k
Network Technology % J2 C* Y* N" O, s4 j0 bNetwork security 8 v& P$ D9 U1 _. F4 ]$ jNumerical and symbolic computation# t; e# j4 y" U# d
Computer Modeling and Simulation 9 b/ `$ l, D: f; M iCommunication Technology . d. t8 L; i4 W1 |# M$ YAlgorithms and data structures$ t: f ^% }/ l; f3 M4 H
Computer Education + s7 G( z) j9 C9 o1 {5 F5 rOther Advanced Technology 4 Z" P' k3 Q6 U6 v3 j1 L. W- d U) @; w) P
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