The 2nd International Workshop on Database Technology and Applications (DBTA2010) 9 J* S, @5 o% k! O/ C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) : W0 q" [- r' o# N9 w. U" E0 N; @11月27-28,2010,武汉,中国 7 l" `4 d" a6 p5 h http://www.icdbta.org/6 c$ ]- d& A% k, |+ ]+ r
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论文提交日期: 2010年8月18日 : G) @1 i- P9 V2 y6 x+ h论文录用通知日期: 2010年9月 16 日 + u p. v0 \5 r$ l$ k论文修订版本提交日期: 2010年9月22日 ; k8 c5 ~$ f# w4 o论文注册日期: 2010年9月28日 9 v1 w! r3 ^( r, f9 o0 n2 j论文提交系统: http://www.icdbta.org/dbta2010/submission/ ( @ Y( B- n1 i$ o U* [5 I9 H' h会议论文模版: http://www.icdbta.org/dbta2010/instruct8.5x11.doc(只接受英文稿件)9 @, M9 k$ P% S/ U4 u. ]
IEEE会议论文版权表: http://www.icdbta.org/dbta2010/IEEECopyrightForm.doc (录用注册后提交)# G$ j$ y7 w6 F) V# E8 ?" H& y
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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。. C$ Q1 ~0 R2 H: _
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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:; l* o- L0 D) W
* b* C/ h' \. F! P8 E1. Database and Related Issue / i( ]3 r/ c$ M' z' ?5 STemporal Data1 z6 J9 v4 _+ v" }2 w8 u2 a
Scientific Databases ! L6 s1 E) z3 u0 r# e( y! pBusiness database software ( p! R( H# N2 \5 O/ }- gComputer data processing 6 y% @ a( ?. l1 aData processing services 4 ]9 {) J1 e6 ]# v( {Data processing supplies # i3 X& ^* W$ ?Data processing systems 9 C/ T! O) i; I( Z) bMetadata Management) ]* d- y5 E2 K# V4 W, V0 s
Mobile Data and Information9 i8 j. L X- j& B2 D
** Databases/ W T4 j) b. h9 v* q( U
WWW and Databases. |( L# q" `; C& E Z4 G- @
Workflow Management and Databases 2 n5 H+ `; {) O) y! ^+ C* _- {XML and Databases4 a) o3 s, b* d" [
** Databases $ T7 ^; L2 D% k( ^Data modeling and architectures' k! {. x5 R% i5 Y x6 s$ J
Data streaming, data provenance and data quality ) r4 }6 ]8 s8 _8 Q# x# f% ~( XData Security, privacy, and data integrity 4 k9 T& |* p3 t( V3 J3 A+ s& eWeb Data and the Internet 4 Y- d/ s6 }: T5 V/ b7 tXML and databases, web services7 q& k0 O9 |8 i: C2 Y$ s
Semi-structured data, metadata# a9 Y3 V1 u! z* e& e# e( i' Y
e-commence) K3 I1 }# x( a2 a
1 Z {# A- o; X, I7 f$ r1 P2. Data warehouse and Data mining9 r2 a2 Q5 C. @: _
Grid/Parallel/distributed data warehousing& E% @6 b( _* M5 P. b
Web/** data warehouses # A1 w7 S0 l; VData warehousing and the semantic web 0 |9 N( O; p+ X5 D0 h6 N; IData warehousing with unstructured data / G. D6 q8 N( K: h( pIntegration of Data Warehousing J1 B Q# Y. u9 o0 m) H* A' c+ fData and knowledge representation s& y# h- {3 E1 ?$ DLanguages and inte**ces for data mining$ @7 I& E+ N$ y
Data integration and interoperability - o- q5 W, e0 H- o1 h8 ?Data extraction, cleansing, transforming and loading ) k5 c! ^; h1 m# XData mining and information extraction0 b% c7 H0 h" B' a# R
KDD Process and Human Interaction$ i ~- [( v5 v) j) V( w
OLAP and Data Mining6 y6 _* E/ j; Q5 C4 Y
Parallel and Distributed Data Mining7 x8 {1 n, a4 \7 j- ]; x* W
Physical database design and performance evaluation ' w2 {& b9 @8 B- O0 u; nQuery processing and optimization 0 W) E# E( o- m% KReliability and Robustness Issues; O9 l) \ y9 g2 d0 ~9 L
Semantic web and ontology* Z6 A' {( _" v
Software Warehouse and Software Mining ) E8 A2 b' v0 F$ \, qSocial and mathematical statistics3 a. F" t0 h5 z$ X7 b
Novel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis) " g3 }; o9 S0 A
Developing a unifying theory of data mining : Y- ?; X( w) W7 l: O7 T
Mining sequences and sequential data! L6 a: p, o! Z" ^9 v" n
Data pre-processing, data reduction, feature selection, and feature transformation - `/ G) W' @( j: K5 q% E5 i; BQuality assessment, interestingness analysis, and post-processing 0 G7 a( @# ? O& g6 g) D! OMining unstructured, semi-structured, and structured data 4 ]3 K( u, n# |8 ^Mining temporal, spatial, spatio-temporal data2 C2 M- z8 u7 q; y! Q
Mining data streams and sensor data! u6 z7 w E$ v# \: v9 |4 c
Mining ** data : h2 `- z8 F# R. K! i j- ]2 d; m2 WMining social network data6 Q L$ q8 v0 e3 `" r, ]# S
Human-machine interaction and visual data mining j7 n( ?6 m: T, d
Data mining applications (bioinformatics, E-commerce, Web, intrusion/fraud detection, finance, healthcare, marketing, telecommunications, etc)2 U+ G5 ~0 f4 W( w3 n& n3 u
Knowledge Acquisition & Management$ F% ~7 w i8 c( X3 V
Knowledge Modeling 6 K% o$ n& e$ |# W$ ~ S3 mKnowledge Processing / m1 Y9 P) ]8 b; |2 S* V. FIntegrated KDD applications and systems 6 j2 E% ~# c8 Z, V% P# } T1 ^Business Process Intelligence # `9 A! y5 o8 y% K: r; kCluster Analysis and Knowledge Base system$ |! O2 g, H# B- m7 [ B. d4 }
Information systems technology + V9 \$ @4 s* e; lOther related technology about data mining3 v+ W6 ?1 l/ j7 n# G1 ?; U8 }
9 F( F* F/ y; o/ Z. s3. Computer Science and Related Technology + @6 v' h7 w3 g) C2 r s
Image and signal processing + }. a7 ]. t9 U2 |& ]$ y0 z
Artificial Intelligence 6 o% f# H8 {0 h" V4 A' u
Software engineering 6 g! p7 _; ]0 y$ b" ZSystems Engineering 5 c+ \% i9 r9 ~; \+ m W2 _
Computer Graphics . e) _' q$ f/ D( y Z2 lComputer Application + t) W. m4 u& C
Control Technology # S: Q3 P; Z; SNetwork Technology , @1 `& k' e( }9 e
Network security - J7 \2 I# R/ RNumerical and symbolic computation6 g4 H" u9 d& U
Computer Modeling and Simulation. R7 _2 G6 p5 t" {
Communication Technology * K/ X+ u$ H& U1 X! s* ?$ Y S
Algorithms and data structures & j) `7 i2 X7 W7 NComputer Education( ~ a# F* z% |/ M+ b
Other Advanced Technology; ^5 x9 g2 Y" E' R8 g9 o
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DBTA2010会议联系秘书处: X5 L- N3 @/ E$ J. t3 {