The 2nd International Workshop on Database Technology and Applications (DBTA2010)' v `4 K9 Y; w3 z V' P: k
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; h; [( j: B) w6 G11月27-28,2010,武汉,中国 + _; |0 t- o" x: ^$ I( y- d! Ahttp://www.icdbta.org/' W8 R1 `- M$ W% l4 v( I
; l* {- ?3 L- r+ D/ P! Q P论文提交日期: 2010年8月18日 # x- r+ G. p% C( a; L论文录用通知日期: 2010年9月 16 日 4 u! Y# i- i; G0 p7 q论文修订版本提交日期: 2010年9月22日! {+ s' v, U, I$ m9 i/ l5 t3 F
论文注册日期: 2010年9月28日, e' E! V% T4 U" a
论文提交系统: http://www.icdbta.org/dbta2010/submission/ . O( \7 P" D) w) \* a9 U会议论文模版: http://www.icdbta.org/dbta2010/instruct8.5x11.doc(只接受英文稿件)/ H/ \2 e! D. h- ~; }. Q
IEEE会议论文版权表: http://www.icdbta.org/dbta2010/IEEECopyrightForm.doc (录用注册后提交) $ I* ^3 n9 H' R9 g3 t+ b+ Y' a: ~* S
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。2 T3 f- X/ e1 t7 e1 r
. g, {. L. G: j" I; m$ b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: 9 g6 e* O T' U , {7 j; @4 E+ f1. Database and Related Issue . X1 L( h5 ]" D. G$ u; ]Temporal Data & X* l/ F: ?: Z* e4 O8 n' oScientific Databases4 W& {7 o1 I' a- B
Business database software - t: G2 x1 Q5 U h0 U* BComputer data processing * Y! n% D( ]; T4 s1 s o" F0 |
Data processing services 0 G8 Z+ Y* l+ ? m4 ?& fData processing supplies 0 e) `; E% D1 ~6 \$ M: LData processing systems ! y* s7 c0 \ v+ ~Metadata Management , q+ h; K. ?" OMobile Data and Information: b9 O. J- r* N$ g, ~2 i$ A
** Databases 5 k+ J% F/ _( [& [% HWWW and Databases 8 V' F' g8 F0 j. G+ k3 s: Q; U+ dWorkflow Management and Databases* v. v) _, Z' b$ [: k
XML and Databases 2 o" S, }) B) M a** Databases ; g& E. i" F1 n# o* MData modeling and architectures ! m1 X, s/ ^- J& ^$ n8 @% G5 vData streaming, data provenance and data quality 5 g$ B" B7 S/ a2 j+ dData Security, privacy, and data integrity & `& `( `, c1 @/ A# R |& |2 i, `
Web Data and the Internet9 I3 r8 h- q8 a# S. i
XML and databases, web services , w# J* t: q+ Y8 F) j* D. hSemi-structured data, metadata( m9 Y9 b% _: `
e-commence " ?0 f. P6 J) F6 H9 r* Y3 [! n7 L) ~4 Z % a" {% @/ {) ?2 g2 O% y2. Data warehouse and Data mining% M0 u1 A7 W, }4 t" A
Grid/Parallel/distributed data warehousing 5 @6 D8 n5 P0 t, | K' uWeb/** data warehouses . J' o8 w; |2 v y2 @+ OData warehousing and the semantic web . Z( e8 o, J) [# K0 FData warehousing with unstructured data , [0 G' q) G4 f' \Integration of Data Warehousing4 N8 n- V, I# x5 X. ]# L% H0 O1 Z
Data and knowledge representation ' |. M; p/ p0 N. i( qLanguages and inte**ces for data mining8 `: N$ S, U" u3 D$ S1 `
Data integration and interoperability+ ^; ]/ @8 _2 u
Data extraction, cleansing, transforming and loading! p" P9 R: }- d# }% V0 Q; R
Data mining and information extraction* y& T+ R. v K" a/ r e" _
KDD Process and Human Interaction 5 z2 Z5 \" \6 q# sOLAP and Data Mining5 v: C9 F' f1 v0 T* @( F
Parallel and Distributed Data Mining $ ^% Q" S. o. T; m. f% v9 gPhysical database design and performance evaluation 0 T: x N9 z) f% I; Z% UQuery processing and optimization , K) Y5 ]& c) _3 K$ l0 ?0 BReliability and Robustness Issues- ^6 A/ p' D0 s' W& Z0 G6 l
Semantic web and ontology J: X% x) A- v4 `$ wSoftware Warehouse and Software Mining 2 Y3 X' w/ G9 I! r5 I7 XSocial and mathematical statistics / H" d7 t! {' \ v! sNovel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis) 7 \' v6 r& G! ]" u: `Developing a unifying theory of data mining ; G1 W Q; a" q' A! G( Q* F& IMining sequences and sequential data, X4 F6 |; c2 v# B y! X; D7 w
Data pre-processing, data reduction, feature selection, and feature transformation $ |' @% s: N" |; n& ^- @
Quality assessment, interestingness analysis, and post-processing 5 X2 A! ~) d8 v, ^% _1 }9 W- b
Mining unstructured, semi-structured, and structured data : N& y. l7 T1 F( @, e- a0 b, RMining temporal, spatial, spatio-temporal data # v! g- H+ ^: v( oMining data streams and sensor data7 ]6 ?5 E7 a! j
Mining ** data 9 j) a# t7 o4 E1 h4 W r5 D& HMining social network data ' f% E8 N' U4 T- ]Human-machine interaction and visual data mining ) S- I/ J; D# V5 T% Q
Data mining applications (bioinformatics, E-commerce, Web, intrusion/fraud detection, finance, healthcare, marketing, telecommunications, etc)" }8 c0 [! N1 p( n
Knowledge Acquisition & Management : z% g6 i) n* u' C# Q4 X( tKnowledge Modeling, E7 P# L Y3 m8 C9 n
Knowledge Processing ( U3 W' _/ R6 z! MIntegrated KDD applications and systems ) S8 h# o7 q+ K' x- M' w
Business Process Intelligence3 S, Q' e1 S9 H% j0 r) i
Cluster Analysis and Knowledge Base system 0 u9 p/ P4 {/ tInformation systems technology* k* Q, t. K4 w$ P
Other related technology about data mining + w [" S% u" T( Y/ A+ G, J4 Z/ U& n9 j2 i) k7 I9 A
3. Computer Science and Related Technology % |* x! }4 Q1 @ F g6 @* OImage and signal processing ( }1 n D R" U; E" b6 f
Artificial Intelligence ! a5 J0 X+ P( j* V
Software engineering v$ d5 L1 S$ B- w$ {. V- xSystems Engineering 3 I/ A% I: a' F2 y( E/ F
Computer Graphics _/ j5 v. {5 J! m) h, n5 X
Computer Application 2 h. @. ^- k+ j* c' y
Control Technology # Z4 h" H% t% G) z7 P" b8 RNetwork Technology ; [2 @) \9 a0 |; A) u: [/ r* { U9 q
Network security 0 U5 X/ C. N6 O6 D( o mNumerical and symbolic computation( h6 E" K* {) T) b O; n0 r
Computer Modeling and Simulation % y% ?9 Y- @ K3 yCommunication Technology 5 g2 R' h# b, i: ~
Algorithms and data structures % p: O+ C6 F! Y9 c# @) v) cComputer Education3 `3 ~* x% G! N V# T
Other Advanced Technology) g$ `2 ?7 S. H. C! T" W