BibSLEIGH — Daniel_A

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: Keim:Daniel_A=

Contributed to:

2012

2011

2010

2008

2005

2003

2002

2000

1999

1998

1997

1996

1995

2018

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.