Travelled to:1 × Finland
1 × France
1 × Greece
1 × Italy
1 × Korea
2 × China
5 × USA
Collaborated with:J.Gehrke A.Okcan B.Panda A.J.Demers A.Das D.Fink D.Agrawal A.E.Abbadi W.M.White A.Lachmann D.Sorokina R.Caruana M.Hong I.Stanoi F.Weigel M.Calimlim S.B.Pope L.P.Chew C.Koch N.Gerner F.Yang J.Shanmugasundaram M.F.Elhawary A.Munson W.M.Hochachka S.Kelling L.Brenna J.Ossher M.Thatte
Talks about:data (6) process (5) approxim (3) join (3) analysi (2) reduc (2) event (2) web (2) map (2) exploratori (1)
Person: Mirek Riedewald
DBLP: Riedewald:Mirek
Contributed to:
2014
20132011
20082008
20072007
2006200620062004200320022001Wrote 16 papers:
- SIGMOD-2014-OkcanR #pipes and filters
- Anti-combining for MapReduce (AO, MR), pp. 839–850.
- VLDB-2013-OkcanRPF #analysis #named
- Scolopax: Exploratory Analysis of Scientific Data (AO, MR, BP, DF), pp. 1298–1301.
- SIGMOD-2011-OkcanR #pipes and filters #using
- Processing theta-joins using MapReduce (AO, MR), pp. 949–960.
- ICML-2008-SorokinaCRF #detection #interactive #statistics
- Detecting statistical interactions with additive groves of trees (DS, RC, MR, DF), pp. 1000–1007.
- VLDB-2008-LachmannR #sequence
- Finding relevant patterns in bursty sequences (AL, MR), pp. 78–89.
- VLDB-2008-WeigelPRGC #analysis #collaboration #scalability #web
- Large-scale collaborative analysis and extraction of web data (FW, BP, MR, JG, MC), pp. 1476–1479.
- PODS-2007-WhiteRGD #question #what
- What is “next” in event processing? (WMW, MR, JG, AJD), pp. 263–272.
- SIGMOD-2007-BrennaDGHOPRTW #named
- Cayuga: a high-performance event processing engine (LB, AJD, JG, MH, JO, BP, MR, MT, WMW), pp. 1100–1102.
- SIGMOD-2007-HongDGKRW #multi
- Massively multi-query join processing in publish/subscribe systems (MH, AJD, JG, CK, MR, WMW), pp. 761–772.
- KDD-2006-CaruanaEMRSFHK #mining #predict
- Mining citizen science data to predict orevalence of wild bird species (RC, MFE, AM, MR, DS, DF, WMH, SK), pp. 909–915.
- SIGMOD-2006-GernerYDGRS #automation #clustering #data-driven #web
- Automatic client-server partitioning of data-driven web applications (NG, FY, AJD, JG, MR, JS), pp. 760–762.
- VLDB-2006-PandaRPGC #approximate
- Indexing for Function Approximation (BP, MR, SBP, JG, LPC), pp. 523–534.
- SIGMOD-2004-DasGR #approximate
- Approximation Techniques for Spatial Data (AD, JG, MR), pp. 695–706.
- SIGMOD-2003-DasGR #approximate #data type
- Approximate Join Processing Over Data Streams (AD, JG, MR), pp. 40–51.
- SIGMOD-2002-RiedewaldAA #integration #performance
- Efficient integration and aggregation of historical information (MR, DA, AEA), pp. 13–24.
- VLDB-2001-StanoiRAA #database #set
- Discovery of Influence Sets in Frequently Updated Databases (IS, MR, DA, AEA), pp. 99–108.