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1:51:19 Technische Universität Braunschweig, Institut für Informationssysteme English 2010

Build the DW, ETL (09.12.2010)

In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
  • Published: 2010
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:01:10 Technische Universität Braunschweig, Institut für Informationssysteme English 2010

Architecture, Data Modeling (Conceptual Model) (04.11.2010)

In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
  • Published: 2010
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
1:44:39 Technische Universität Braunschweig, Institut für Informationssysteme English 2010

DWs in Praxis (03.02.2011)

In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
  • Published: 2010
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
34:23 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Introduction in Audio Retrieval 1 (12.05.2011)

In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:29:23 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Audio Low level Features, Difference Limen, Pitch Recognition (19.05.2011)

In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:20:49 Technische Universität Braunschweig, Institut für Informationssysteme English 2010

Data Modeling (Logical & Physical Models) (11.11.2010)

In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
  • Published: 2010
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:40:13 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Texture Features, Low-Level Texture Features, Tamura Measure, Random Field Models, Transform Domain Features (21.04.2011)

In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:13:54 Technische Universität Braunschweig, Institut für Informationssysteme English 2010

Optimization (25.11.2010)

In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
  • Published: 2010
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:21:53 Technische Universität Braunschweig, Institut für Informationssysteme English 2010

Classification (13.01.2011)

In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
  • Published: 2010
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:32:38 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Video Similarity (30.06.2011)

In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:10:52 Technische Universität Braunschweig, Institut für Informationssysteme English 2010

Data Mining Overview, Association Rule Mining (16.12.10)

In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
  • Published: 2010
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:38:41 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Query by Humming, Melody Representation, Hidden Markov Model (26.05.11)

In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:14:55 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Fuzzy retrieval model, Coordination level matching, Vector space retrieval model (13.4.2011)

This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:19:12 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Latent Semantic Indexing (11.5.2011)

This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:13:02 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Web crawling (29.6.2011)

This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:16:44 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Relevance feedback, Classification (1.6.2011)

This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:34:43 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Miscellaneous (13.7.2011)

This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:17:21 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Introduction and fundamental notions (6.4.2011)

This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:32:31 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Introduction to Web retrieval (22.6.2011)

This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:18:36 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Document clustering (25.5.2011)

This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:21:24 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Support vector machines (8.6.2011)

This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:22:51 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Link analysis (6.7.2011)

This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:21:00 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Shot Detection (23.06.2011)

In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:12:18 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Indexing (27.4.2011)

This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:00:12 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Video Abstraction (07.07.2011)

In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:31:47 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Probabilistic retrieval models (20.4.2011)

This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:24:03 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Chain Codes, Area based Retrieva, Moment Invariants, Query by Visual example (05.05.2011)

In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
1:57:48 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Indexes (14.07.2011)

In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
1:13:54 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Introduction in Audio Retrieval 2 (12.05.2011)

In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
1:23:32 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Language models, Retrieval evaluation (18.5.2011)

This lecture provides an introduction to the fields of information retrieval and web search. We will discuss how relevant information can be found in very large and mostly unstructured data collections; this is particularly interesting in cases where users cannot provide a clear formulation of their current information need. Web search engines like Google are a typical application of the techniques covered by this course.
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
1:44:21 Technische Universität Braunschweig, Institut für Informationssysteme English 2010

Decision Support Systems (27.01.2011)

In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
  • Published: 2010
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
1:55:25 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Hidden Markov Model, Video Retrieval (09.06.11)

In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
1:47:40 Technische Universität Braunschweig, Institut für Informationssysteme English 2010

Sequence Pattern Mining & Time Series (06.01.2011)

In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
  • Published: 2010
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
1:40:05 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Basic concepts, Evaluation procedures (07.04.11)

In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
2:07:34 Technische Universität Braunschweig, Institut für Informationssysteme English 2011

Multiresolution Analysis, Form based Features, Thresholding, Edge Detection, Morphological Operators (28.04.2011)

In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees
  • Published: 2011
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
1:46:20 Technische Universität Braunschweig, Institut für Informationssysteme English 2010

Data Warehousing and Data Mining Techniques - Introduction (28.10.2010)

In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
  • Published: 2010
  • Publisher: Technische Universität Braunschweig, Institut für Informationssysteme
  • Language: English
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Version

AV-Portal 3.7.0 (943df4b4639bec127ddc6b93adb0c7d8d995f77c)