The 2nd International Workshop on Database Technology and Applications (DBTA2010)4 R+ h- j+ m9 ^3 b$ ?3 p
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) @# }+ \- O# u; o- N7 `5 C
11月27-28，2010，武汉，中国 / D# P0 ^3 ~0 r# `2 x) qhttp://www.icdbta.org/ 3 Q1 x5 J# U) e" _1 t( k P0 ^1 v0 j, B- V( |
论文提交日期: 2010年8月18日 " @- J& u5 _+ V/ d# s+ K4 f$ _论文录用通知日期: 2010年9月 16 日2 r3 Q4 v1 I: ~3 g. _3 _
论文修订版本提交日期: 2010年9月22日" }: I! h* g( O2 o8 ]( D
论文注册日期: 2010年9月28日3 r. t1 \5 t& J5 P# {
论文提交系统: http://www.icdbta.org/dbta2010/submission/ + h* H) g: t: \, Y6 X. m) U4 X会议论文模版: http://www.icdbta.org/dbta2010/instruct8.5x11.doc（只接受英文稿件） 7 I1 }: w" l' g# Z% t; \IEEE会议论文版权表: http://www.icdbta.org/dbta2010/IEEECopyrightForm.doc （录用注册后提交）8 k6 G. u3 a8 t* K/ f
7 C2 S. Y& H% s+ {" I; L9 J7 R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。 e0 }0 }6 ?% ^0 E/ n y / Y: U! K; `; E- Q; ^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：& o! e) D0 l! s# a
5 @" n$ C# ]2 v% t' K' v" h1. Database and Related Issue D6 |7 M2 F; E9 c) TTemporal Data1 [( q) b+ ^% K6 F; j
Scientific Databases ; O# A1 C9 H+ J6 A2 q( jBusiness database software; W% T; j% v4 P3 V$ d8 ]4 ^1 q$ t" f
Computer data processing 5 ^$ c/ O8 l- H S' ^) Y
Data processing services ( t ?: K4 m! \; D3 XData processing supplies ' M" D! d. b4 z' W0 P, T
Data processing systems 3 ^1 l6 H4 O; E VMetadata Management : S( z7 o1 F! BMobile Data and Information 9 Y) d& m5 e) f0 R; g ]7 h** Databases & X& V! O( b0 O# w9 yWWW and Databases 0 B0 ?" @: U7 k0 S' FWorkflow Management and Databases% Z# V8 V( \# y6 E
XML and Databases ' `4 G8 D4 G# g! J$ M** Databases) V# }8 V* T1 q* z0 f
Data modeling and architectures& n" ]6 J5 j; G ]& N H/ e
Data streaming, data provenance and data quality $ g& N% P. |' Z9 W' RData Security, privacy, and data integrity 5 L& c" k' M1 c6 r' l) }
Web Data and the Internet2 I: b* U, ?; q8 x6 ~- C4 E7 J2 d( w
XML and databases, web services( l& U5 _# O% P1 |
Semi-structured data, metadata5 }/ |6 @, D: [# n
e-commence ) W) j( k. o( f' b H8 o+ H* C / ^+ A+ W0 s/ }6 d, J2 n% S2. Data warehouse and Data mining6 J5 T. [9 Y' t' X7 Q
Grid/Parallel/distributed data warehousing S7 G* g2 [ J% s( Q+ }8 [
Web/** data warehouses& h. U$ R# J! t3 p$ Z }
Data warehousing and the semantic web 5 y+ b6 R! @6 L! S- xData warehousing with unstructured data 5 x, M- x" V! S9 [' F" X, }Integration of Data Warehousing2 V& A4 [! n- x+ o
Data and knowledge representation % E; {* Y$ D$ Z8 x6 q% FLanguages and inte**ces for data mining + ^% ~6 ~4 @) e+ H; e7 BData integration and interoperability% R x6 u- @: o. P5 Z4 t
Data extraction, cleansing, transforming and loading : T# u0 T9 |" t6 |" BData mining and information extraction 8 m/ E; M. ~: A( [/ U0 c. H vKDD Process and Human Interaction 4 K0 l# b0 s' `OLAP and Data Mining ( p: M, [% g: H3 ]Parallel and Distributed Data Mining" d8 }, W. d) j% E" F- e
Physical database design and performance evaluation- E# ?( k' h* ~4 m) t& e+ j( h0 Q
Query processing and optimization2 L! r+ K+ A# L& g1 E. t( N
Reliability and Robustness Issues - Y, a" M8 X3 I0 T2 B/ kSemantic web and ontology - t& f% e( Y$ x `( {Software Warehouse and Software Mining ( w' [+ z9 j3 u0 A) T3 Q5 e# ]4 ~Social and mathematical statistics- y. R) e4 C- s! n7 O
Novel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis) 0 O. U$ ~ c9 l9 i. A& _
Developing a unifying theory of data mining 7 C; i [/ t, {' }7 ?+ j" Q) d
Mining sequences and sequential data# J$ J) H; h. d' R, k' q. x# p
Data pre-processing, data reduction, feature selection, and feature transformation ' L7 a7 R; Z5 p) W
Quality assessment, interestingness analysis, and post-processing . r: d. p1 ]$ n3 A) Z R
Mining unstructured, semi-structured, and structured data0 q* v' h8 e; |1 a1 ]4 n
Mining temporal, spatial, spatio-temporal data# K5 `- o1 w8 s- B- k4 b# L
Mining data streams and sensor data3 _$ @0 m' R. J* u
Mining ** data0 o* Q4 y, E7 H- Y% P. m/ X1 W- b
Mining social network data 7 m7 N/ e) n/ fHuman-machine interaction and visual data mining $ ?/ A2 d9 C4 m l, r6 S
Data mining applications (bioinformatics, E-commerce, Web, intrusion/fraud detection, finance, healthcare, marketing, telecommunications, etc) % S8 r/ J" V$ t" i4 @* k9 g' A. l. k. jKnowledge Acquisition & Management ) p: `+ a# w5 }8 |, E2 u8 VKnowledge Modeling' o' F/ O* |9 }- E! A3 f+ b
Knowledge Processing( K3 b: F. Z" ]7 b' f
Integrated KDD applications and systems $ F" I: M" p- e1 }, F
Business Process Intelligence* v S! {0 E" ^, M: v1 G Q
Cluster Analysis and Knowledge Base system , \% D+ r& a b4 ]7 k& gInformation systems technology" ?" t0 m/ \, t+ ^8 H0 C: `
Other related technology about data mining ! A: H7 u' p4 u+ K3 L- U8 ^0 T V; `6 S$ ?$ [' C
3. Computer Science and Related Technology * g4 C8 @2 l& D% IImage and signal processing . c; Y* N8 ~9 l, NArtificial Intelligence 7 R4 o3 {, f7 w S2 r
Software engineering 2 U+ `( d+ g+ Y! H+ s9 V% }Systems Engineering 0 {5 F+ a; S6 V% I* o/ T
Computer Graphics : i) {& Z. v ?. Y( y8 q. i4 \
Computer Application : @: I+ a9 ^9 E" y/ k, fControl Technology 4 N4 K+ K# e! T4 |$ i" {
Network Technology + I% v' ^2 v. I. H' dNetwork security \2 p$ s# m* l; L! G) ~Numerical and symbolic computation . e( m, Y" m5 \( eComputer Modeling and Simulation ! u; y" e# H3 s' Y, H1 }Communication Technology ; F- Z: n& f0 ~2 @1 IAlgorithms and data structures . l6 _7 x! N) _ ?* x IComputer Education % t8 K( r3 k; ?( m! S7 J: f) q7 x1 m) uOther Advanced Technology) ~" K" e6 R8 T- N9 M7 v3 ?
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