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2009, ISBN: 9783642037306

Data warehousing and knowledge discovery are increasingly becoming mission-critical technologies for most organizations, both commercial and public, as it becomes incre- ingly important t… Mehr…

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ISBN: 9783642037306

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Data Warehousing and Knowledge Discovery : 11th International Conference, DaWaK 2009 Linz, Austria, August 31-September 2, 2009 Proceedings - Rainer Stegmann
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Details zum Buch

Detailangaben zum Buch - Data Warehousing and Knowledge Discovery


EAN (ISBN-13): 9783642037306
Erscheinungsjahr: 2009
Herausgeber: Mukesh K. Mohania#Min A. Tjoa

Buch in der Datenbank seit 2016-05-06T18:19:13+02:00 (Zurich)
Detailseite zuletzt geändert am 2024-03-02T06:19:45+01:00 (Zurich)
ISBN/EAN: 9783642037306

ISBN - alternative Schreibweisen:
978-3-642-03730-6
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: stegmann
Titel des Buches: austria international, knowledge discovery, linz 2009, linz 1860 1930


Daten vom Verlag:

Autor/in: Mukesh K. Mohania; A Min Tjoa
Titel: Information Systems and Applications, incl. Internet/Web, and HCI; Lecture Notes in Computer Science; Data Warehousing and Knowledge Discovery - 11th International Conference, DaWaK 2009 Linz, Austria, August 31-September 2, 2009 Proceedings
Verlag: Springer; Springer Berlin
480 Seiten
Erscheinungsjahr: 2009-08-28
Berlin; Heidelberg; DE
Sprache: Englisch
53,49 € (DE)
55,00 € (AT)
59,00 CHF (CH)
Available
XIV, 480 p.

EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Datenbanken; Verstehen; Markov model; OLAP; cluster analysis; clustering; data mining; data warehouse; database; database management; hidden markov model; knowledge discovery; pattern matching; performance; relational database; visualization; web mining; C; Database Management; Data Mining and Knowledge Discovery; Information Storage and Retrieval; Computer and Information Systems Applications; Computer Communication Networks; Automated Pattern Recognition; Computer Science; Data Mining; Wissensbasierte Systeme, Expertensysteme; Informationsrückgewinnung, Information Retrieval; Data Warehousing; Angewandte Informatik; Netzwerk-Hardware; Mustererkennung; BC

Invited Talk.- New Challenges in Information Integration.- Data Warehouse Modeling.- What Is Spatio-Temporal Data Warehousing?.- Towards a Modernization Process for Secure Data Warehouses.- Visual Modelling of Data Warehousing Flows with UML Profiles.- Data Streams.- CAMS: OLAPing Multidimensional Data Streams Efficiently.- Data Stream Prediction Using Incremental Hidden Markov Models.- History Guided Low-Cost Change Detection in Streams.- Physical Design.- HOBI: Hierarchically Organized Bitmap Index for Indexing Dimensional Data.- A Joint Design Approach of Partitioning and Allocation in Parallel Data Warehouses.- Fast Loads and Fast Queries.- Pattern Mining.- TidFP: Mining Frequent Patterns in Different Databases with Transaction ID.- Non-Derivable Item Set and Non-Derivable Literal Set Representations of Patterns Admitting Negation.- Which Is Better for Frequent Pattern Mining: Approximate Counting or Sampling?.- A Fast Feature-Based Method to Detect Unusual Patterns in Multidimensional Datasets.- Data Cubes.- Efficient Online Aggregates in Dense-Region-Based Data Cube Representations.- BitCube: A Bottom-Up Cubing Engineering.- Exact and Approximate Sizes of Convex Datacubes.- Data Mining Applications.- Finding Clothing That Fit through Cluster Analysis and Objective Interestingness Measures.- Customer Churn Prediction for Broadband Internet Services.- Mining High-Correlation Association Rules for Inferring Gene Regulation Networks.- Analytics.- Extend UDF Technology for Integrated Analytics.- High Performance Analytics with the R3-Cache.- Open Source BI Platforms: A Functional and Architectural Comparison.- Ontology-Based Exchange and Immediate Application of Business Calculation Definitions for Online Analytical Processing.- Data Mining.- Skyline View: Efficient Distributed Subspace Skyline Computation.- HDB-Subdue: A Scalable Approach to Graph Mining.- Mining Violations to Relax Relational Database Constraints.- Arguing from Experience to Classifying Noisy Data.- Clustering.- Dynamic Clustering-Based Estimation of Missing Values in Mixed Type Data.- The PDG-Mixture Model for Clustering.- Clustering for Video Retrieval.- Spatio-Temporal Mining.- Trends Analysis of Topics Based on Temporal Segmentation.- Finding N-Most Prevalent Colocated Event Sets.- Rule Mining.- Rule Learning with Probabilistic Smoothing.- Missing Values: Proposition of a Typology and Characterization with an Association Rule-Based Model.- Olap Recommendation.- Recommending Multidimensional Queries.- Preference-Based Recommendations for OLAP Analysis.

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