Jochen Hipp - researchr alias

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Viewing Publication 1 - 28 from 28

2022

Identifying Scenarios in Field Data to Enable Validation of Highly Automated Driving SystemsChristian Reichenbächer, Maximilian Rasch, Zafer Kayatas, Florian Wirthmüller, Jochen Hipp, Thao Dang 0002, Oliver Bringmann 0001. vehits 2022: 134-142 [doi]

2021

2020

A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External ConditionsFlorian Wirthmüller, Marvin Klimke, Julian Schlechtriemen, Jochen Hipp, Manfred Reichert. ssci 2020: 2739-2745 [doi]

Towards Incorporating Contextual Knowledge into the Prediction of Driving BehaviorFlorian Wirthmüller, Julian Schlechtriemen, Jochen Hipp, Manfred Reichert. itsc 2020: 1-7 [doi]

2019

CPD: Crowd-based Pothole DetectionFlorian Wirthmueller, Jochen Hipp, Kai-Uwe Sattler, Manfred Reichert. vehits 2019: 33-42 [doi]

2016

Deriving HD maps for highly automated driving from vehicular probe dataKay Massow, B. Kwella, N. Pfeifer, Florian Hausler, Jens Pontow, Ilja Radusch, Jochen Hipp, Frank Dölitzscher, Martin Haueis. itsc 2016: 1745-1752 [doi]

2014

2010

Interactivity Closes the Gap - Lessons Learned in an Automotive Industry ApplicationAxel Blumenstock, Markus Mueller, Carsten Lanquillon, Steffen Kempe, Jochen Hipp, Rüdiger Wirth. In Carlos Soares, Rayid Ghani, editors, Data Mining for Business Applications. Volume 218 of Frontiers in Artificial Intelligence and Applications, pages 17-34, IOS Press, 2010. [doi]

2008

Mining Frequent Temporal Patterns in Interval SequencesSteffen Kempe, Jochen Hipp, Carsten Lanquillon, Rudolf Kruse. ijufks, 16(5):645-661, 2008. [doi]

2007

Rule-Based Measurement Of Data Quality In Nominal DataJochen Hipp, Markus Müller, Johannes Hohendorff, Felix Naumann. iq 2007: 364-378 [doi]

FSMTree: An Efficient Algorithm for Mining Frequent Temporal PatternsSteffen Kempe, Jochen Hipp, Rudolf Kruse. gfkl 2008: 253-260 [doi]

2006

Mining Sequences of Temporal IntervalsSteffen Kempe, Jochen Hipp. pkdd 2006: 569-576 [doi]

2005

Regelbasierte Ausreißersuche zur DatenqualitätsanalyseJoachim Kübart, Udo Grimmer, Jochen Hipp. dbsk, 14:22-28, 2005.

2004

2002

Is pushing constraints deeply into the mining algorithms really what we want?: an alternative approach for association rule miningJochen Hipp, Ulrich Güntzer. sigkdd, 4(1):50-55, 2002. [doi]

Data Mining of Association Rules and the Process of Knowledge Discovery in DatabasesJochen Hipp, Ulrich Güntzer, Gholamreza Nakhaeizadeh. incdm 2002: 15-36 [doi]

Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule MiningJochen Hipp, Christoph Mangold, Ulrich Güntzer, Gholamreza Nakhaeizadeh. pakdd 2002: 52-65 [doi]

2001

Data Quality Mining - Making a Virute of NecessityJochen Hipp, Ulrich Güntzer, Udo Grimmer. dmkd 2001: [doi]

Integrating Association Rule Mining Algorithms with Relational Database SystemsJochen Hipp, Ulrich Güntzer, Udo Grimmer. ICEIS 2001: 130-137

2000

Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today s ApproachesJochen Hipp, Ulrich Güntzer, Gholamreza Nakhaeizadeh. pkdd 2000: 159-168 [doi]

Algorithms for Association Rule Mining - A General Survey and ComparisonJochen Hipp, Ulrich Güntzer, Gholamreza Nakhaeizadeh. sigkdd, 2(1):58-64, 2000. [doi]

1999

Analysing Warranty Claims of Automobiles; An Application Description Following the CRISP-DM Data Mining ProcessJochen Hipp, Guido Lindner. semco 1999: 31-40

1998

A New Algorithm for Faster Mining of Generalized Association RulesJochen Hipp, Andreas Myka, Rüdiger Wirth, Ulrich Güntzer. pkdd 1998: 74-82

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