Site maintenance: Ilka Miloucheva

• M.Alexa,M.Mueller,Visualizing Time-Series on Spirals.,Proc.of the IEEE InfoVis Symposium, IEEE Press, Los Alamitos CA,2001
• Becker,G.Eick,A.Wilks,Visualizing Network Data,Reading in Information Visualization:Using Vision to think,Stuart Card,Jock Mackinlay,Ben Schneiderman,editors,Morgan Kaufman Publishers,1999
• J.Brown,A.McGregor,H-W Braun,Network Perfomance Visualization Insight through Animation,PAM 2000
• Browser,A Tool for Time Series Exploration,http://www.ncea.org.au/Browser
  [DM 99] Introduction to Data Mining and Knowledge Discovery, ISBN 1-892095-02-05, Two Crows Corporation, 1999.
• L.Chittaro, C.Combi ,G. Trapasso,Visual Data Mining of Clinical Databases: An Application To the hemodialytic treatment based on 3D Interactive Bar Charts,2002
• L.Chittaro,Information Visualization and its application to medecin,Artificial Intelligence in Medicine Journal,2001
• J.Carlis,J.Konstan,Interactive Visualization of Serial Periodic Data,In Proc Uist 98,San Francisco CA,November,1998
• M.Derthick,, J.Kolojejchick,S.FRoth, An Interactive Visual Query Environment for Exploring Data. Proceedings of the ACM Symposium on User Interface Software and Technology ACM Press, October 1997
• S. K. Card, J. D. Mackinlay,B. Shneiderman. Readings in Information Visualization: Using Vision To Think,San Francisco,Calif., Morgan Kaufmann, 1999.
• U.Fayyad,A.Wierse,G.Grinstein,Information Visualization in Data Mining and Knowledge Discovery, San Francisco,Calif ,Morgan Kaufmann Series 2001
• U.Fayyad,Advances in knowledge discovery and data mining ,Menlo Park, Calif,AAAI Press, Cambridge, Mass,The MIT Pr,1996
• M.Ganesh,S.Han,V.Kumar,S.Shekhar,J.Srivastava,Visual Data Mining Framework and Algorithms Development,Technical Report 96-021,March 1996.
• M.Garofalakis,J.Gehrke,R.Rastori,Querying and Mining Data Streams,You only get one look,A Tutorial,Bell Laboratories,Cornell University,VLDB’02,2002
• N.Grady,R.Flanery,J.Donato,J.Schryver,Time Series Data Exploration,Codata Euro-American Workshop,Visualization of Information and Data,June 1997
• Haber, R. B.; Mc Nabb, D. A.: Visualization idioms: A conceptual model for scientific Visualization Systems. In: Shriver, B.; Nielson, G. M.; Rosenblum, L. (eds): Visualization in Scientific Computing. IEEE-Computer Society Press, Los Alamitos, 1990, P.74-93
• H.Hochheiser, B.Shneiderman,Visual Queries for Finding Patterns in Time Series Data,Technical Report, University of Maryland, Computer Science Dept.,2002
• H.Hochheiser,B.Shneiderman,Visual Specification of Queries for Finding Patterns in Time-Series Data,Proceedings Discovery Science 2001,Technical Report,University of Maryland, Computer Science Dept,2001.
• David Hand,Heikki Mannila,Padhraic Smith,Principles of Data Mining,
  Massachutes,MIT Press, 2001
  (Chapter 3:Visualizing and Exploring Data,Chapter 2.5 The Form of Data p 41-44)
• David Hand, Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,KDD-2002,Edmonton, Alberta, Canada ed. by David Hand ,New York,ACM,2002
• Jiawei Han,Micheline Kamber,Data Mining,Concepts and Techniques, San Francisco,Calif.,Morgan Kaufmann, 2001
  (Chapter 4.3 Designing GUI Based on a Data Mining Query Language)
• Specification of Visual Data Mining and user interface,Deliverable7,INTERMON Consortia, www.ist-intermon.org, December, 2002.
• Advanced architecture for INTER-domain quality of service MONitoring, modelling and visualisation, INTERMON-IST-2001-3412 http://www.ist-intermon.org.
• Jung,V. :Integrierte Benutzerunterstützung für die Visualisierung in Geo-Informationssystemen. Dissertationen am Fachbereich Informatik Fachbereich GRIS, TU Darmstadt, Fraunhofer IRB Verlag, Stuttgart, 1998
• E.Keogh,H.Hochheiser,B.Shneiderman,An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data, Proc. Fifth International Conference on Flexible Query Answering Systems,Copenhagen, Denmark, October 2002
• M.Kreuseler,H.Schumann,A Flexible Approach for Visual Data Mining,IEEE Transactions on visualization and Computer Graphics,Vol.8,March 2002
• J.Komorowski,Principles of data mining and knowledge discovery : first European Symposium, PKDD'97,Trondheim, Norway, June,1997,proceedings,Berlin Springer,(Lecture notes in computer science),1997
• Visual Data Mining Wokshop,KDD Woskshop Report,
  http://www.inf.uni-konstanz.de/~keim/KDD_Woskshop/KDD_Proceedings.pdf
• M.Livny,R.Ramakrishnan,J.Myllymaki, Visual exploration of large data sets, in Proc. of SPIE,Int. Soc. Opt. Eng., vol. 2657, San Jose, CA, Jan. 1996
• I. Miloucheva, A. Anzaloni, E. Müller, A practical approach for QoS forecasting considering outliers, Inter-domain performance and simulation Workshop, IPS 2003, February, 2003.
• I. Miloucheva,Spatio-Temporal QoS Analysis of multimedia traffic in large scale Internet environment,2003
• S. Michaelis, J. Seger, Concept of configurable filters for Visual Data Mining System, Inter-domain performance and simulation Workshop, IPS 2003, February, 2003.
• Moxon, Bruce: Defining Data Mining, in: DBMS Data Warehouse Supplement, August 1996
• Mackinglay, J: Automating the Design of Graphical Presentation of Relational Information. ACM Transactions on Graphics, Vol. 5, Nr. 2, April 1986, P. 110-141
• Ncsa Homepage,3D Visualization Tool for Time Series Data, http://www.ncsa.uiuc.edu,
• Robertson, P.K. ; De Ferrari, L. : Systematic approaches to visualization: is a reference model needed. In: Rosenblum u.a. (eds): Scientific Visualization. Academic Press, Los Alamitos, 1994, P. 287-305
• R. Koodli, R. Ravikant, One-way Loss Pattern Sample Metrics Author(s): Status: Informational Date: August 2002, URL: ftp://ftp.rfc-editor.org/in-notes/rfc3357.txt
• B.Schneiderman,The Eyes Have it,A Task by Data Type Taxonomy for Information Visualization,Proc IEEE Symposium on Visual Languages,Los Alarnitos,1996
• B.Schneiderman,Designing the User Interface,Addison Wesley,MA,1997
• S.Silva,T.Catarci,Visualization of Linear Time Oriented Data:A survey,in Proc of the First international conference on Web information Systems Eingineerings(wise’00),Hong Kong,June 2000
• Schuhmann, H.; Müller W.: Visualisierung -> Grundlagen und allgemeine Methoden; Springer Verlag Berlin Heidelberg 2000
• E.Tufte,The Visual Display of Quantitative Information,Graphic Press,1983
• Time-Searcher,Visual Exploration of Time Series data,http://www.cs.umd.edu/hcil/timesearcher/
• International Workshop on Visual Data Mining in conjunction with ECML/PKDD2001 – 2nd European Conference on Machine Learning and 5th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2001
• J.Wijk,E.Selow,Cluster and Calendar based Visualization of Time Series Data, IEEE Symposium on Information Visualization,San Francisco,October 25-26,1999
• Wood, J.; Broodlie, K.; Wright, H.: Visualisation Over The WorldWideWeb And Its Application to Environmental Data. Proceedings Visualization’96, IEEE Computer Society Press, Los Alamitos, 1996, P. 81-86
• W3C-Word Wide Web Consortium, http://www.w3c.org

Modelling and forecasting

• M. Castagli, S. Eubank, Nonlinear Modelling and Forecasting, Addison Wesley, 1992.
• Michael S. Borella, Gregory B. Brewster: Measurement and Analysis of Long-Range Dependent Behavior of Internet Packet Delay. INFOCOM 1998.
• F. Baumgartner, M. Scheidegger, T. Braun, Enhancing Discrete Event Network Simulatord with Analytical Network Cloud Models, Inter-domain performance and simulation Workshop, IPS 2003, February, 2003.
• Chen-Mou Cheng, H.T.Kung, Koan-Sin Tan, Use of Spectral Analysis in Defense Against DoS Attacks, GLOBECOM 2002
• M.J.Coates, A.O.Nero, R. Nowak, B.Yu, Internet tomography, review article, IEEE Signal Processing Magazine, May, 2002
• D. Donjerkovic, Y. Ioannidis, and R. Ramakrishnan.Dynamic histograms: Capturing evolving data sets. Technical report, Department of Computer Science. University of Wisconsin-Madison, 1999.
• A. Erramilli, 0. Narayan, and W. Willinger, “Experimental queueing analysis with long-range dependent packet traffic ,“ IEEE/ACM Transactions on Networking, vol. 4, pp. 209-223, Apr. 1996.
• G. Cristallo & C. Jacquenet; An Approach to Inter-domain Traffic Engineering; Proceedings of XVIII World Telecommunications Congress (WTC2002), Paris, France, September 2002.
• S.Guha, N.Koudas,K.Shim.Data Streams and Histograms. ACM Symposium on Theory of Computing,2001
• Heribert Kirschfink, Claire Chadenas, Traffic Situation Prediction Applying Pattern Matching and Fuzzy Classification, European Symposium on Intelligent Techniques, June 3-4, 1999 Orthodox Academy of Crete, Greece
• P. Haber, G.Bergholz, U.Hofmann, I.Miloucheva, Multi-class signal flow model for inter-domain traffic flow simulation, Inter-domain performance and simulation Workshop, IPS 2003, February, 2003.
• Hassan Hajji, Behrouz H. Far, Jingde Cheng "Detection of Network Faults and Performance Problems" , Proceedings of the Internet Conference 2001, (IC' 2001), Osaka, Japan., November, 200.
• Polly Huang, Anja Feldmann, and Walter Willinger. A non-intrusive, wavelet-based approach to detecting network performance problems. In Proceeding of ACM SIGCOMM Internet Measurement Workshop 2001, San Francisco Bay Area, November, 2001.
• D. Hetzer, I. Miloucheva, U. Hofmann, J. Quittek, F. Saluta, Integrated Information System for Inter-Domain QoS Monitoring, Modelling and Verification, EURESCOM Summit Conference, Heidelberg, 2002.
• U. Hofmann, I. Miloucheva, F. Strohmeier, T. Pfeiffenberger, Evaluation of architectures for QoS analysis of applications in Internet environment, The 10th International Conference on Telecommunication Systems, Modeling and Analysis Monterey, CA, USA, October 3-6, 2002.
• U. Hofmann, Trace based traffic modelling, Inter-domain Performance and Simulation Workshop, IPS 2003, Salzburg, 2003.
• J. Ilow, ``Forecasting Network Traffic Using FARIMA Models with Heavy Tailed Innovations'', ICASSP 2000, Istanbul, Turkey, June 2000.
• J. Jung, B. Krishnamurthy, and M. Rabinovich, “Flash crowds and denial of service attacks: Characterisation and implications for CDNs and web sites,” in WWW-02, Hawaii, May, 2002.
• H.V. Jagadish, V.Poosala, N.Koudas, K.Sevcik, S. Muthukrishnan, T.Suel.Optimal Histograms with Quality Guarantees.Proc. 24th Int. Conf. Very Large Data Bases, VLDB,1998
• Heribert Kirschfink, Claire Chadenas, Traffic Situation Prediction Applying Pattern Matching and Fuzzy Classification, European Symposium on Intelligent Techniques, June 3-4, 1999.
• F.A. Kuipers, T. Korkmaz, M. Krunz and P. Van Mieghem, .Overview of Constraint-Based Path Selection Algorithms for QoS Routing., IEEE Communications Magazine, vol. 40, no. 12, December 2002.
• A.Kock, Flexibke Traffic Matrix Analyser for Inter-domain Network Operation and Planning, Inter-domain performance and simulation Workshop, IPS 2003, February, 2003.
• S. McCreary and K. Claffy, "Trends in Wide Area IP Traffic Patterns --- A View from Ames Internet Exchange", Proceedings of ITC'2000.
• I. Miloucheva (ed.), Traffic Measurement Roadmap, NGNI Deliverable, October 2002
• I. Matta and A. Bestavros. QoS Controllers for the Internet. NSF Workshop on Information Technology, March 12-14, 2000.
• T. Mahr, T. Dreillinger, A. Vidacs, Time Series Based Simulation Architecture, Inter-domain performance and simulation Workshop, IPS 2003, February, 2003.
• I. Miloucheva, U. Hofmann, Distributed Measurement and Monitoring in IP Networks (CMToolset for AQUILA DiffServ), SCI/ISAS, Orlando 2001.
• Van Mieghem, P. and F. A. Kuipers, 2003, On the Complexity of QoS Routing, Computer Communications, vol. 26, No. 4, March 2003, pp. 376-387.
• Vinay A. Mahadik¤, Xiaoyong Wuy and Douglas S. Reeves, Detection of Denial-of-QoS Attacks Based On Â2 Statistic And EWMA Control Charts, January 31, 2002.
• Network Modelling and Simulation Project http://www.caida.org/projects/nms/
• http://dast.nlanr.net/Articles /measurements/surveyor.html
• K. Papagiannali, S. Moon, C. Fraleigh, P. Thiran, F. Tobagi, C. Diod, Analysis of Measured Single-Hop Delay from an Operational Backbone Network, in Proceedings of INFOCOM, June 2002.
• T. Pfeiffenberger, I. Miloucheva, U. Hofmann, A. Nassri, Inferencing of inter-domain path characteristic based on active end-to-end QoS monitoring - scenario studies in emulation environment, Inter-domain Performance and Simulation Workshop, IPS 2003, Salzburg, 2003.
• S. Salza, M. Draoli, C. Gaibisso, A. Laureti Palma, R. Puccinelli, Methods and Tools for the Objective Evaluation of Voice-over-IP Communications , INET Proceedings, 2000.
• F. Strohmeier, H. Dörken, B. Hechenleitner. AQUILA Distributed QoS Measurement. Proc. of COMOCON8 Conference, Crete, Greece, pages 177-185, June 2001.
• SELFIS: A Tool For Self-Similarity and Long-Range Dependence Analysis.
  ThomasKaragiannis,MichalisFaloutsos
  1st Workshop on Fractals and Self-Similarity in Data Mining: Issues and Approaches (in KDD) Edmonton, Canada, July 23, 2002.
• M. Steinder and A. S. Sethi. The present and future of event correlation: A need for end-to-end service fault localisation. In Proc. World Multi-Conf. Systemics, Cybernetics, and Informatics (SCI), Orlando, FL, vol. XII, pp. 124-129, 2001.
• M. Tsuru, T.Takine, Y.Oie, Inferring arrival rate statistics of each flow from aggregated flow rate measurements, 2002 http://www.genesis.tao.go.jp/pub/TR/measure.html
• N. Ye, “A markov chain model of temporal behaviour for anomaly detection,” in Workshop on Information Assurance and Security, West Point, NY, June 2000.
• S. Vazhkudai, J.Schorf, I.Foster, Predicting the performance of wide area data transfers. In 16th International Parallel and distributed Processing symposium (IPDPS), 2002.