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Travelled to:
1 × Brazil
1 × Canada
1 × China
1 × Egypt
1 × Germany
1 × India
1 × Italy
1 × Switzerland
1 × United Kingdom
2 × Portugal
8 × USA
Collaborated with:
A.Hinneburg S.Berchtold H.Kriegel S.Kisilevich L.Rokach T.Schreck C.Böhm F.Fischer F.Mansmann D.W.Fellner B.Bustos M.Wawryniuk C.C.Aggarwal A.Stoffel D.Spretke H.Kinnemann R.Byshko M.Tsibelman A.Lasry L.Bam B.Braunmüller M.L.Kersten G.Weikum M.J.Franklin A.P.Buchmann S.Chaudhuri N.Meuschke C.Gondek D.Seebacher C.Breitinger B.Gipp
Talks about:
databas (9) dimension (7) cluster (6) high (6) data (6) visual (5) structur (4) larg (4) base (4) multimedia (3)

Person: Daniel A. Keim

DBLP DBLP: Keim:Daniel_A=

Contributed to:

SAC 20122012
ICEIS v2 20112011
ICEIS HCI 20102010
Post-ICEIS 20102010
SAC 20102010
SAC 20082008
SAC 20052005
VLDB 20032003
SIGMOD 20022002
VLDB 20002000
KDD-T 19991999
SIGMOD 19991999
VLDB 19991999
KDD 19981998
SIGMOD 19981998
PODS 19971997
SIGMOD 19971997
SIGMOD 19961996
VLDB 19961996
SIGMOD 19951995
JCDL 20182018

Wrote 22 papers:

SAC-2012-FischerMK #data type #realtime #visual notation
Real-time visual analytics for event data streams (FF, FM, DAK), pp. 801–806.
ICEIS-v2-2011-KisilevichKBTR #open source #tool support #using
Developing a Price Management Decision Support System for Hotel Brokers using Free and Open Source Tools (SK, DAK, RB, MT, LR), pp. 147–156.
ICEIS-HCI-2010-KisilevichKR #framework #named
GEO-SPADE — A Generic Google Earth-based Framework for Analyzing and Exploring Spatio-temporal Data (SK, DAK, LR), pp. 13–20.
ICEIS-J-2010-KisilevichKLBR #case study
Developing Analytical GIS Applications with GEO-SPADE: Three Success Case Studies (SK, DAK, AL, LB, LR), pp. 495–511.
SAC-2010-StoffelSKK #analysis #documentation #using #visual notation
Enhancing document structure analysis using visual analytics (AS, DS, HK, DAK), pp. 8–12.
SAC-2008-SchreckFK #automation #multi #optimisation #towards
Towards automatic feature vector optimization for multimedia applications (TS, DWF, DAK), pp. 1197–1201.
SAC-2005-BustosKS
A pivot-based index structure for combination of feature vectors (BB, DAK, TS), pp. 1180–1184.
VLDB-2003-KerstenWFKBC #database #how
A Database Striptease or How to Manage Your Personal Databases (MLK, GW, MJF, DAK, APB, SC), pp. 1043–1044.
SIGMOD-2002-HinneburgKW #clustering #named #visual notation
HD-Eye: visual clustering of high dimensional data (AH, DAK, MW), p. 629.
VLDB-2000-HinneburgAK #nearest neighbour #question #what
What Is the Nearest Neighbor in High Dimensional Spaces? (AH, CCA, DAK), pp. 506–515.
KDD-T-1999-KeimH #clustering #scalability #set
Clustering Techniques for Large Data Sets — from the Past to the Future (DAK, AH), pp. 141–181.
SIGMOD-1999-HinneburgK #clustering #database #scalability
Clustering Methods for Large Databases: From the Past to the Future (AH, DAK), p. 509.
SIGMOD-1999-Keim #3d #database #geometry #performance #similarity
Efficient Geometry-based Similarity Search of 3D Spatial Databases (DAK), pp. 419–430.
VLDB-1999-KeimH #clustering #towards
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering (AH, DAK), pp. 506–517.
KDD-1998-HinneburgK #approach #clustering #database #multi #performance #scalability
An Efficient Approach to Clustering in Large Multimedia Databases with Noise (AH, DAK), pp. 58–65.
SIGMOD-1998-BerchtoldK #database
High-Dimensional Index Structures, Database Support for Next Decade’s Applications (SB, DAK), p. 501.
PODS-1997-BerchtoldBKK #cost analysis #nearest neighbour
A Cost Model For Nearest Neighbor Search in High-Dimensional Data Space (SB, CB, DAK, HPK), pp. 78–86.
SIGMOD-1997-BerchtoldBBKK #database #parallel #performance #similarity
Fast Parallel Similarity Search in Multimedia Databases (SB, CB, BB, DAK, HPK), pp. 1–12.
SIGMOD-1996-Keim #database #visualisation
Databases and Visualization (DAK), p. 543.
VLDB-1996-BerchtoldKK
The X-tree : An Index Structure for High-Dimensional Data (SB, DAK, HPK), pp. 28–39.
SIGMOD-1995-KeimK #database #named #scalability #visualisation
VisDB: A System for Visualizing Large Databases (DAK, HPK), p. 482.
JCDL-2018-MeuschkeGSBKG #adaptation #approach #detection
An Adaptive Image-based Plagiarism Detection Approach (NM, CG, DS, CB, DAK, BG), pp. 131–140.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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