German English

Seminar Data Warehousing und Data Mining SS98 Literatur

Literatur zum Seminar
"Data Warehousing und Data Mining" (SS 98)


Folgende URLs stellen für nahezu alle Seminarthemen Literaturlisten bereit (zur weiteren Recherche):

Data Warehousing:

Data Mining:

Desweiteren bieten die u.g. Bücher aus den Überblicksvorträgen zu Data Warehousing (Thema 1) bzw. Data Mining (Thema 8) zusätzlich Material für nahezu alle Seminarthemen.

Themenkomplex I: Data Warehousing


Thema 1: Einführung: Begriffe, Architekturen, ...
* Inmon, W.H.: Building the Data Warehouse, Wiley Computer Publishing, 1996 (2. Ed.)

* Anahory, S.; Murray, D.: Data Warehouse - Planung, Implementierung und Administration, Addison-Wesley, 1997

* Chaudhuri, S.; Dayal, U.: An Overview of Data Warehousing and OLAP-Technology, SIGMOD Record 26 (1), März 1997

* Tresch, M.; Rys, M.: Data Warehousing Architektur für Online Analytical Processing, Theorie u. Praxis der Wirtschaftsinformatik, Nr. 195(34), Hüthig Verlag 1997

* Wu, M.-C.; Buchmann, A.P.: Research Issues in Data Warehousing, Proc. BTW, pp. 61-82, 1997

* Widom, J.: Research Problems in Data Warehousing. Proc. CIKM 1995, pp. 25-30, Baltimore, Maryland, 1995

Thema 2: Datenextraktion und -bereinigung

* Squire, C.: Data Extraction and Transformation for the Data Warehouse, Proc. SIGMOD Conf., 1995

* Jagadish, H. V. et al.: Incremental Organization for Data Recording and Warehousing, Proc. VLDB, 1997

* Weiss, S. M.; Indurkhya, N.: Predictive Data Mining, Morgan Kaufmann, 1998: Kap. 3 (Preparing the Data)

* Labio, W. J.; Garcia-Molina, H.: Comparing Very Large Database Snapshots, TechReport CS-TN-95-27, Stanford Univ., 1995

* Labio, W. J.; Garcia-Molina, H.: Efficient Snapshot Differential Algorithms for Data Warehousing, Proc. VLDB, 1996

* Hurwicz, M.: Take your Data to the Cleaners, Byte Magazine 1, 1997

* div. White Papers von Tool-Anbietern

Thema 3: Schemaintegration und Metadaten

* Conrad, S.: Föderierte Datenbanksysteme, Springer-Verlag, 1997

* Anahory (vgl. Thema 1): Kap. 9: Metadaten

* Zhou, G. et al.: Data Integration and Warehousing Using H20, DataEng.Bull 18(2), 1995

* Brackett, Michael H.: The Data Warehouse Challenge, Kap. 18, Wiley Computer Publishing, 1996

* Musick, R., Miller Ch.: Report on the 2. IEEE Metadata Conference (Metadata '97)
http://computer.org/conferen/proceed/meta97/
dort finden sich auch die HTML-Versionen der dortigen Papers: .../list_papers.html

* Satya Sachdeva: Metadata for Data Warehouse (SYBASE):
http://www.sybase.com/services/dwpractice/meta.html

* Literaturliste (Höfling, FORWISS):
http://www.forwiss.tu-muenchen.de/~system42/public/Line42/Literatur/REPOSIT.html
(z.B. 'What is Metadata?', 'Standardizing Metadata', 'Guiding Users through disparate data layers' ...)

Thema 4: Materialisierte Sichten

Auswahl und Erzeugung:

* Labio, W. J.; Quass, D.; Adelberg, B.: Physical Database Design for Data Warehouses, Proc. ICDE, 1997

* Baralis, E.; Paraboschi, S.; Teniente, E.: Materialized View Selection in a Multidimensional Database, Proc. VLDB, 1997

* Theodoratos, D.; Sellis, T.: Data Warehouse Configuration, Proc. VLDB, 1997

* Yang, J.; Karlapalem, K.; Li, Q.: Algorithm for Materialized View Design in Data Warehousing Environment, Proc. VLDB, 1997

Pflege:
* Gupta, A.; Mumick, I. S.: Maintenance of Materialized Views: Problems, Techniques, and Applications, IEEE Data Engineering 6, 1995

* Baekgaard, L.; Roussopoulos, N.: Efficient Refreshment of Data Warehouse Views, TechReport CS-TR-3642, Univ. Maryland, 1996

* Huyn, N.: Multiple-View Self-Maintenance in Data Warehousing Environments, Proc. VLDB, 1997

* Zhuge, Y. et al.: View Maintenance in a Warehousing Environment, Proc. SIGMOD Conf., 1995

* Quass, D.; Widom, J.: On-Line Warehouse View Maintenance, Proc. SIGMOD Conf., 1997

Thema 5: Entwurf des Data Warehouse

* Anahory (vgl. Thema 1): aus Teil III (Der Entwurf)
Modellierung:
* Agrawal, Gupta et al: Modeling Multidimensional Databases, IBM Research Report, Almaden, San José, 1995

* Raden, N.: Modeling the Data Warehouse:
http://user.aol.com/nraden/iw0196_1.htm

* div. Übersichtliteratur zu DW bzgl. Star-/Snowflake-Schema, ...

* DW-Architektur für das Web, Datenbank Focus, Jan. 1998 (ROLAP vs. MOLAP)

Indexierungstechniken:
* Sarawagi, S.: Indexing OLAP data, Data Eng. Bulletin 20(1), 3/97

* Leslie, H. et al.: Efficient Search of Multidimensional B-Trees, Proc. VLDB, Zürich 1995

* Literaturliste M.-C. Wu, TH Darmstadt (Index-Techniken, z.B. 'Encoded Bitmap Indexing for Data Warehouses', ..):
http://www.informatik.th-darmstadt.de/DVS1/staff/wu.german.html

* Johnson, Th.; Shasha, D.: Some Approaches to Index Design for Cube Forests, Data Eng. Bulletin 20 (1), 3/97

* Sybase IQ - Optimizing Interactive Performance for the Data Warehouse,
(zu finden unter den Web-Seiten zu Sybase, http://www.sybase.com/)

Thema 6: Anfrageverarbeitung

OLAP:

* Pilot-Software OLAP White Paper (OLAP-'Kurs'):
http://www.pilotsw.com/olap/olap.htm

* OLAP Benckmark Study, OLAP Council:
http://www.olapcouncil.org/bmark.html

* existierende OLAP- bzw. DSS-Tools div. Hersteller

Aggregation, Cube-Operator:
* Gray, J. et al.: Data Cube: A Relational Aggregation Operator Generalizing Group-By, ..., Data Mining and Knowledge Discovery 1, Kluwer Academic Publishers, 1997

* Agarwal, S. et al.: On the Computation of Multidimensional Aggregates, Proc. VLDB, Mumbai, India, 1996

* Harinarayan, A.; Rajaraman, V.; Ullman, J. D.: Implementing Data Cubes Efficiently, Proc. SIGMOD Conf., 1996

* Deshpande, P.M. et al: Cubing Algorithms, Storage Estimation, and Storage and Processing Alternatives for OLAP, Data Eng. Bulletin 20 (1), März 97

Speziell bzw. technisch
* Gupta, A. et al.: Aggregate-Query Processing in Data Warehousing Environments, Proc. VLDB, Zürich 1995

* Ross, Srivastava: Fast Computation of Sparse Datacubes, Proc. VLDB, 1997

* Zhao, Y. et al.: An Array-Based Algorithm for Simultaneous Multidimensional Aggregates, Proc. SIGMOD Conf, Tucson, Arizona, 1997 (SIGMOD Record 26 (2))

Thema 7: Forschungsprojekte, Realisierungen

Forschungsprojekte:

* Whips: Data Warehousing at Stanford University
http://www-db.stanford.edu/warehousing/warehouse.html

* The Maryland ADMS Project

* Supporting Data Integration and Warehousing Using H2O
... alle beschrieben im DataEng.Bull. 18(2), 1995

kommerzielle DW-Lösungen:

* div. White Papers von Anbietern

* French, C. D.: "One Size Fits All" Database Architectures Do Not Work For DSS, Proc. SIGMOD Conf., 1995

Themenkomplex II: Data Mining


Thema 8: Überblick
* Holsheimer, Siebes: Data Mining: the search for knowledge in databases, TechReport CS-R9406, CWI Amsterdam, 1994

* Decker, Focardi: Technology Overview: A Report on Data Mining, TechReport TR-95-02, CSCS-ETH, 1995

* Chen, Han, Yu: Data Mining: An Overview from Database Perspective, IEEE TKDE 8 (6), 1996

* Fayyad, U. M. et al.: Advances in Knowledge and Data Mining, AAAI/MIT Press, 1996: Kap. I (Foundations) und Kap. VII (KDD Applications)

* Weiss, S. M.; Indurkhya, N.: Predictive Data Mining, Morgan Kaufmann, 1998

Thema 9: Assoziationsregeln, räumlich-zeitliche Muster

Assoziationsregeln:

* Fayyad et al. (vgl. Thema 8): Kap. IV (Dependency Derivation)

* Srikant, R.; Agrawal, R.: Mining Generalized Association Rules, Proc. VLDB, 1995

* Cheung, D. W. et al.: Maintenance of discovered association rules in large databases, Proc. ICDE, 1996

* Han, J.; Kamber, M.; Chiang, J.: Mining Multi-Dimensional Association Rules Using Data Cubes, TechReport CMPT-TR-97-06, Fraser Univ. Burnaby, 1997

* Klemettinen, M. et al.: Finding Interesting Rules from Large Sets of Discovered Association Rules, Proc. CIKM, 1994

* Mueller, A.: Fast Sequential and Parallel Algorithms for Association Rule Mining: A Comparison, TechReport CS-TR-3515, Maryland Univ., 1995

Mustererkennung und Trendanalyse:
* Fayyad et al. (vgl. Thema 8): Kap. III (Trend and Deviation Analysis)

* Faloutsos, C.; Ranganathan, M.; Manolopoulos, Y.: Fast Subsequence Matching in Time-Sereis Databases, Proc. SIGMOD Conf., 1994

* Agrawal, R. et al.: Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases, Proc. VLDB, 1995

* Li, C.-S.; Yu, P. S.; Castelli, V.: HierarchyScan: A Hierarchical Similarity Search Algorithm for Databases of Long Sequences, Proc. ICDE, 1996

Thema 10: Klassifikation, Clustering

* Fayyad et al. (vgl. Thema 8): Kap. II (Classification and Clustering)
Klassifikation:
* Agrawal, R. et al.: An Interval Classifier for Database Mining Applications, Proc. VLDB, 1992

* Lu, H.; Setiono, R.; Liu, H.: NeuroRule: A Connectionist Approach to Data Mining, Proc. VLDB, 1995

Clustering:
* Ng, R.; Han, J.: Efficient and effective clustering method for spatial data mining, Proc. VLDB, 1994

* Zhang, T.; Ramakrishnan, R.; Livny, M.: BIRCH: an efficient data clustering method for very large databases, Proc. SIGMOD Conf., 1996

* Fisher, D.: Optimization and simplification of hierarchical clusterings, Proc. KDD, 1995

* Ester, M.; Kriegel, H.-P.; Xu, X.: Knowledge discovery in large spatial databases: Focusing techniques for efficient class identification, Proc. SSD, 1995

04.03.98

» Druckversion