The 2nd International Workshop on Database Technology and Applications (DBTA2010)1 _& x8 @0 j$ @9 s$ E
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)% C: L4 a: o, w( B+ E! O
11月27-28,2010,武汉,中国 & `9 ?$ W7 } D% m/ l2 _2 ]' h9 c' z http://www.icdbta.org/ & _ m$ R5 T2 Z$ e5 j) v, w5 w6 U$ ` B; D3 x" G, Z/ d' d. k9 @
论文提交日期: 2010年8月18日 7 m6 s% l" b6 M& `0 R( F论文录用通知日期: 2010年9月 16 日 3 ]- a% r, n& e: K4 G7 _( N论文修订版本提交日期: 2010年9月22日- T' ~) I( L/ {) y
论文注册日期: 2010年9月28日 0 c- Q* y( |' D6 Q. F% s2 o论文提交系统: http://www.icdbta.org/dbta2010/submission/( N; j& ` ]9 K$ g4 Z9 d1 L7 B+ ], {
会议论文模版: http://www.icdbta.org/dbta2010/instruct8.5x11.doc(只接受英文稿件) 5 M: G+ [4 v7 UIEEE会议论文版权表: http://www.icdbta.org/dbta2010/IEEECopyrightForm.doc (录用注册后提交); J- Q! r; u3 H6 J% z6 o
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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。 6 R" i+ d' u: C3 R" |; O/ m' C$ X1 g u# O# I8 ?8 e( m) L) B5 @
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: 2 H6 ]; \* E9 G. Y# V3 N8 v0 _" k% b* Z% r/ g# _
1. Database and Related Issue / P+ U; C" ]) Y, VTemporal Data + ?' y$ T0 T/ D, q' i* w# jScientific Databases " w$ X, h. b: m# q: K8 V8 sBusiness database software , e$ I) U" a9 d* T0 F& PComputer data processing : U3 t) {# i' P9 A5 c4 c( M0 X" AData processing services % `9 L( I3 K6 y: {% Q! K4 q% UData processing supplies * }7 h9 W5 ^) r6 @$ b) I/ o
Data processing systems8 N4 U" Z7 x" U" Y" U! M
Metadata Management( i+ ?4 b; S3 G# q# V+ X7 c7 G, @
Mobile Data and Information 4 E7 A0 H5 n+ ?, m$ [% H- k( j** Databases: i$ ?) B* h7 q- j; J
WWW and Databases% |$ D- Q& a8 k, l6 ]+ V5 r
Workflow Management and Databases1 t/ o# C! X" |6 M! G1 r+ ]
XML and Databases$ M+ s; h2 _- v9 }$ j. E
** Databases $ L+ }& Z0 t1 h2 eData modeling and architectures8 \# g+ c+ E; {( l6 g7 v2 I
Data streaming, data provenance and data quality3 C9 a( m1 H" m$ b
Data Security, privacy, and data integrity 2 b1 f3 c3 f, `. y5 J
Web Data and the Internet$ f5 W4 i# _5 ?
XML and databases, web services ! l+ Z/ E- t9 W) r1 NSemi-structured data, metadata# _+ j5 O( J) |+ i; |1 a
e-commence K% Q* Z2 {, v1 O& \# f u
, \! `! @4 K: s- |9 E2. Data warehouse and Data mining2 d$ z; n& k7 l# m6 d3 i' s/ n
Grid/Parallel/distributed data warehousing 4 x8 K$ ~1 C fWeb/** data warehouses ) t) t2 p. A2 X; k/ @7 `Data warehousing and the semantic web# C" H2 [( f- n8 c; z% T
Data warehousing with unstructured data 1 c! h6 S: q6 T, F+ N/ }Integration of Data Warehousing- `9 i% R" D0 f; A
Data and knowledge representation" j, v/ f% {; J7 B$ e
Languages and inte**ces for data mining7 ~* r3 W! C- g: k% d
Data integration and interoperability - T1 w' V* g7 d a9 u2 tData extraction, cleansing, transforming and loading0 t* {4 z5 n3 b l0 ]& E5 ^( |8 ^
Data mining and information extraction" o3 Z6 r, r. ?/ b* U8 n
KDD Process and Human Interaction% g. d0 J( }* n2 D
OLAP and Data Mining6 M% T0 k$ u N
Parallel and Distributed Data Mining # P7 g) g9 I: m/ o, `7 WPhysical database design and performance evaluation * v- w( [& `7 D1 b8 }# Z" @/ xQuery processing and optimization- d5 c1 o' w# P7 f. P- X
Reliability and Robustness Issues # f& M2 @ X( e# V7 hSemantic web and ontology8 k. k8 ^+ t# n: r/ S
Software Warehouse and Software Mining% |, A n- l' j3 E& @# A
Social and mathematical statistics3 q g8 ~* O9 @3 [: M/ q! q
Novel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis) & q7 K1 W% G: i0 P8 F# l( T
Developing a unifying theory of data mining & W$ L7 w6 r+ m( A1 H+ t* lMining sequences and sequential data 3 U9 q4 Z @. ?5 Y1 J& yData pre-processing, data reduction, feature selection, and feature transformation 8 n; T4 u# T7 k5 WQuality assessment, interestingness analysis, and post-processing a1 u( G A' Z
Mining unstructured, semi-structured, and structured data8 r6 X; c. \. P
Mining temporal, spatial, spatio-temporal data 2 ^9 ]7 O$ O- g- p6 F0 O; TMining data streams and sensor data 2 I3 o. A2 s* h" GMining ** data 1 V" x: A. E' |& d; g: ]Mining social network data `4 q+ b$ i# C0 e8 I" s8 r( kHuman-machine interaction and visual data mining & E- H. |5 m/ A k. L4 ^Data mining applications (bioinformatics, E-commerce, Web, intrusion/fraud detection, finance, healthcare, marketing, telecommunications, etc) : e2 e4 N. z5 E( Y( K( m' u! xKnowledge Acquisition & Management3 Z* ~0 P, r: m$ _! D% J
Knowledge Modeling9 V& }( f: q2 Y; b9 V: D9 T! Z
Knowledge Processing / N: ^* {6 X4 l& s! m4 S# uIntegrated KDD applications and systems 8 P O" D5 g; wBusiness Process Intelligence + R8 b! M# C N: J7 x( bCluster Analysis and Knowledge Base system- L/ d9 B6 N0 `5 ]8 I9 k- |9 n4 T) q
Information systems technology, B5 `( B' N$ Q- V: M
Other related technology about data mining 2 L/ ^$ v& `) W- C; e) |8 H2 _, w+ Y; S* y x3 Q
3. Computer Science and Related Technology 6 u" I* H5 Z' {" F. ]" m) a m5 ]
Image and signal processing 6 f' p1 ?) z. s# p, B2 NArtificial Intelligence * ^" Y6 N! a' a" c6 g, V- C# ESoftware engineering 1 X% b6 b2 S+ C2 c+ u
Systems Engineering `; G% c+ a) _6 y
Computer Graphics ( O7 G b4 j& H+ k# r" y) ?
Computer Application ( f: C) Q2 P+ U
Control Technology : c3 O7 K& B( d4 P* y
Network Technology % I/ @# \% R" c0 f! G' Z* gNetwork security 9 `# j3 l$ b( l3 {) \
Numerical and symbolic computation) d3 N' h' o) J
Computer Modeling and Simulation 8 Z) A r4 _( fCommunication Technology 9 B/ z- y6 V& ]4 iAlgorithms and data structures* f! |& v6 \# e# S4 Y5 }: K
Computer Education ) }( J) u2 Y d8 F/ s' {5 ~Other Advanced Technology- Q' h/ e0 V: @( r1 C" Y