Research cluster UMIC

UMIC (Ultra High-Speed Mobile Information and Communication) is a research cluster established under the excellence initiative of the German government. The goal of this cluster is the interdisciplinary design of communication systems providing an order of magnitude improvement of the perceived quality of service for the next-decade mobile Internet. We are participating in two of the four research areas: Mobile Applications and Services (B) and Cross Disciplinary Methods and Tools (D).

Research areas:

• (B) Mobile Applications & Services:

Project: Mobile Stream Data Mining

This project investigates exchange and analysis of continuous data streams. Health-net applications for example monitor vital functions of patients, such as blood pressure or pulse by means of various mobile sensors. Continuously measuring and collecting of these sensor values leads to huge volumes of data which are impossible to store or even transmit using mobile devices. In this context we focus our research on mobile stream data mining and develop new techniques for the aggregation of measurements and the detection of anomalies in order to enable fast reactions, e.g. emergency situations in the above mentioned Health-net scenarios.

• (D) Cross Disciplinary Methods and Tool:

Project: Energy Awareness of Application

While bandwidth of mobile networks and processing power of mobile devices are enhanced continuously, the energy capacity of mobile clients remains a bottleneck of mobile applications. To overcome the limitation, energy efficiency has to be considered through all layers of mobile communication. We focus our research on the energy awareness of applications especially for transmitting large volumes of data through broadcast channels.

Homepage

http://www.umic.rwth-aachen.de/

Beteiligte Mitarbeiter

Seidl T., Kranen P., Müller E., Hassani M.

Publikationen

1. Kranen P., Seidl T.: (Oct 2009)
   Harnessing the Strengths of Anytime Algorithms for Constant Data Streams
   Data Mining and Knowledge Discovery Journal (DMKD), Special Issue on Selected Papers from ECML PKDD 2009, Vol. 19, No. 2, 245-260 [DOI 10.1007/s10618-009-0139-0]
   [DMKD Journal]
   
   
2. Müller E., Günnemann S., Assent I., Seidl T.: (2009)
   Evaluating Clustering in Subspace Projections of High Dimensional Data
   Proc. 35th International Conference on Very Large Data Bases (VLDB 2009), Lyon, France, PVLDB Journal, Vol. 2, No. 1, 1270-1281 (Experiments and Analyses track, acceptance rate 23.1%)
   [VLDB 2009] [Kaufen]
   
   
3. Kranen P., Assent I., Baldauf C., Seidl T.: (2009)
   Self-Adaptive Anytime Stream Clustering
   Proc. IEEE International Conference on Data Mining (ICDM 2009), Miami, USA (full paper acceptance rate 8.9%)
   [ICDM 2009]
   
   
4. Müller E., Assent I., Günnemann S., Krieger R., Seidl T.: (2009)
   Relevant Subspace Clustering: Mining the Most Interesting Non-Redundant Concepts in High Dimensional Data
   Proc. IEEE International Conference on Data Mining (ICDM 2009), Miami, USA (full paper acceptance rate 8.9%)
   [ICDM 2009]
   
   
5. Kranen P., Seidl T.: (2009)
   Harnessing the Strengths of Anytime Algorithms for Constant Data Streams
   Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2009), Bled, Slowenia. The Paper has additionally been chosen for publication in the Data Mining and Knowledge Discovery Journal, ECML PKDD Special Issue (acceptance rate 3.3%).
   [ECML PKDD 2009]
   
   
6. Müller E., Assent I., Krieger R., Günnemann S., Seidl T.: (2009)
   DensEst: Density Estimation for Data Mining in High Dimensional Spaces
   Proc. SIAM International Conference on Data Mining (SDM 2009), Sparks, Nevada, USA. 173-184 (full paper acceptance rate 15.6%)
   [SDM 2009]
   
   
7. Günnemann S., Müller E., Färber I., Seidl T.: (2009)
   Detection of Orthogonal Concepts in Subspaces of High Dimensional Data
   Proc. 18th ACM Conference on Information and Knowledge Management (CIKM 2009), Hong Kong, China (full paper acceptance rate 14.5%)
   [CIKM 2009]
   
   
8. Seidl T., Assent I., Kranen P., Krieger R., Herrmann J.: (2009)
   Indexing Density Models for Incremental Learning and Anytime Classification on Data Streams
   Proc. 12th International Conference on Extending Database Technology (EDBT/ICDT 2009), Saint-Petersburg, Russia. 311-322
   [EDBT/ICDT 2009]
   
   
9. Müller E., Assent I., Seidl T.: (2009)
   HSM: Heterogeneous Subspace Mining in High Dimensional Data
   Proc. 21st International Conference on Scientific and Statistical Database Management (SSDBM 2009), New Orleans, Louisiana, USA 497-516
   [SSDBM 2009]
   
   
10. Hassani M., Müller E., Seidl T.: (2009)
    EDISKCO: Energy Efficient Distributed In-Sensor-Network K-center Clustering with Outliers
    Proc. 3rd International Workshop on Knowledge Discovery from Sensor Data (SensorKDD 2009) in conjunction with 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2009), Paris, France
    [SensorKDD 2009]
    
    
11. Kranen P.: (2009)
    Using Index Structures for Anytime Stream Mining
    PhD Workshop of the International Conference on Very Large Data Bases (VLDB 2009), Lyon, France
    [VLDB 2009]
    
    
12. Müller E., Assent I., Günnemann S., Jansen T., Seidl T.: (2009)
    OpenSubspace: An Open Source Framework for Evaluation and Exploration of Subspace Clustering Algorithms in WEKA
    Proc. 1st Open Source in Data Mining Workshop (OSDM 2009) in conjunction with 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009), Bangkok, Thailand 2-13
    [OpenSubspace Project]
    
    
13. Schiffer M., Müller E., Seidl T.: (2009)
    SubRank: Ranking Local Outliers in Projections of High-Dimensional Spaces
    Datenbank-Spektrum Vol. 9 Issue 29 53-55 (BTW-Studierendenprogramm)
    [DB Spektrum]
    
    
14. Matthias Schiffer: (2009)
    SubRank: Ranking local outliers in projections of high-dimensional spaces
    Studierendenprogramm at the 13th GI-conference on Databases, Technology and Web (BTW 2009), Münster, Germany
    [BTW 2009 Studierendenprogramm]
    
    
15. Sergej Fries: (2009)
    Bestimmung des optimalen Verzweigungsgrades hierarchischer Anytime-Klassifikatoren
    Studierendenprogramm at the 13th GI-conference on Databases, Technology and Web (BTW 2009), Münster, Germany (ausgezeichnet als bester Beitrag im Studierendenprogramm der BTW 2009)
    [BTW 2009 Studierendenprogramm]
    
    
16. Wichterich M., Assent I., Kranen P., Seidl T.: (2008)
    Efficient EMD-based Similarity Search in Multimedia Databases via Flexible Dimensionality Reduction
    Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD 2008), Vancouver, BC, Canada. 199-212 (full paper acceptance rate 17.9%)
    © ACM, 2008. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in the proceedings of the ACM SIGMOD International Conference on Management of Data, 2008. http://doi.acm.org/10.1145/1376616.1376639
    [SIGMOD 2008] [PPT Presentation]
    
    
17. Assent I., Krieger R., Müller E., Seidl T.: (2008)
    INSCY: Indexing Subspace Clusters with In-Process-Removal of Redundancy
    Proc. IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy 719-724 (acceptance rate 20%)
    [ICDM 2008]
    
    
18. Assent I., Krieger R., Müller E., Seidl T.: (2008)
    EDSC: Efficient Density-Based Subspace Clustering
    Proc. ACM 17th Conference on Information and Knowledge Management (CIKM 2008), Napa Valley, USA 1093-1102 (full paper acceptance rate 17%)
    [CIKM 2008]
    
    
19. Ines Färber: (2009)
    Mining orthogonaler Konzepte in hochdimensionalen Datenbanken
    GI Informatiktage 27./28. März 2009 in Bonn
    [Informatiktage]
    
    
20. Müller E., Assent I., Steinhausen U., Seidl T.: (2008)
    OutRank: ranking outliers in high dimensional data
    Proc. 2nd International Workshop on Ranking in Databases (DBRank 2008) in conjunction with IEEE 24th International Conference on Data Engineering (ICDE 2008), Cancun, Mexico 600-603
    [ICDE 2008 Workshops]
    
    
21. Müller E., Assent I., Krieger R., Jansen T., Seidl T.: (2008)
    Morpheus: Interactive Exploration of Subspace Clustering
    Proc. 14th ACM SIGKDD International Conference on Knowledge Discovery in Databases (KDD 2008), Las Vegas, USA 1089-1092 (Demo)
    [KDD 2008]
    
    
22. Assent I., Müller E., Krieger R., Jansen T., Seidl T.: (2008)
    Pleiades: Subspace Clustering and Evaluation
    Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008), Antwerp, Belgium, Springer LNCS 5212. 666-671 (Demo)
    [ECML PKKD 2008]
    
    
23. Kranen P., Kensche D., Kim S., Zimmermann N., Müller E., Quix C., Li X., Gries T., Seidl T., Jarke M., Leonhardt S.: (2008)
    Mobile Mining and Information Management in HealthNet Scenarios
    Proceedings of the 9th IEEE MDM International Conference on Mobile Data Management (MDM 2008), Beijing, China 215-216 (Demo)
    [MDM 2008]
    
    
24. Müller E., Kranen P., Nett M., Reidl F., Seidl T.: (2008)
    A General Framework for Data Dissemination Simulation for Real World Scenarios
    Proceedings of the 14th ACM SIGMOBILE International Conference on Mobile Computing and Networking (MobiCom 2008), San Francisco, USA (Demo)
    [ACM MobiCom 2008]
    
    
25. Kim S., Leonhardt S., Zimmermann N., Kranen P., Kensche D., Müller E., Quix C.: (Jun 2008)
    Influence of contact pressure and moisture on the signal quality of a newly developed textile ECG sensor shirt
    5th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2008), Hong Kong, China
    [BSN 2008]
    
    
26. Assent I., Krieger R., Müller E., Seidl T.: (2007)
    DUSC: Dimensionality Unbiased Subspace Clustering
    Proc. IEEE International Conference on Data Mining (ICDM 2007), Omaha, Nebraska, USA 409-414 (acceptance rate 19%)
    [ICDM 2007] [Full Text PDF]
    
    
27. Aleksandrowicz A., Kensche D., Kim S., Kranen P., Müller E., Quix C.: (Dec 2007)
    Mobile and Wearable P2P Information Management in HEALTHNET Applications
    IEEE Benelux Chapter on Engineering in Medicine and Biology (EMB)
    
    

Diplom-/Master-arbeiten

	Self-Organizing and Energy Efficient Clustering in Sensor Networks
	mit University of Trento (Prof. Themis Palpanas)
	Student: Adriola Faqolli	Betreuer: Hassani M., Müller E.
	Mining orthogonaler Konzepte in hochdimensionalen Datenbanken
	mit Exzellenzcluster UMIC
	Studentin: Ines Färber	Betreuer: Müller E., Günnemann S.
	Mehrklassenbäume für Anytime Bayes-Klassifikation
	mit Exzellenzcluster UMIC
	Student: Sergej Fries	Betreuer: Kranen P., Günnemann S.
	Outlier Mining mittels lokaler Dichteschätzung in statistisch relevanten Projektionen
	mit Exzellenzcluster UMIC
	Student: Matthias Schiffer	Betreuer: Müller E.
	Flexible Probabilistic Clustering for Concept Drift Tracking in Data Streams
	mit Exzellenzcluster UMIC
	Student: Corinna Baldauf	Betreuer: Kranen P., Assent I.
	Bulk loading Index Structures for Anytime Classification based on Hierarchical Mixture Models
	mit Exzellenzcluster UMIC
	Student: Stefan Denker	Betreuer: Kranen P., Krieger R.
	Approximations for efficient subspace clustering in high-dimensional databases
	mit Exzellenzcluster UMIC
	Student: Stephan Günnemann	Betreuer: Müller E., Assent I., Krieger R.
	Index Support for Energy Efficient Wireless Data Dissemination
	mit Exzellenzcluster UMIC
	Student: Thorsten Wessling	Betreuer: Müller E., Kranen P.
	Outlier Detection in heterogenen Sensordaten
	mit National Instruments (Stefan Romainczyk)
	Student: Uwe Steinhausen	Betreuer: Müller E., Assent I., Krieger R.
	Effiziente Bayes Klassifikation mit Hilfe hierarchischer Indexstrukturen
	mit Exzellenzcluster UMIC
	Student: Jennifer Herrmann	Betreuer: Krieger R., Assent I.