A New OLAP Aggregation Based on the AHC Technique

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DOLAP 2004
A New OLAP Aggregation Based on the AHC Technique
R. Ben Messaoud, O. Boussaid, S. Rabaséda
Laboratoire ERIC Université de Lyon 2 5, avenue
Pierre-MendèsFrance 69676, Bron Cedex
France http//eric.univ-lyon2.fr
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Complex data

• Definition
• Data are considered complex if they are
• Multi-formats information can be supported by
  different kind of data (numeric, symbolic, texts,
  images, sounds, videos )
• Multi-structures structured, unstructured or
  semi-structured (relational databases, XML
  documents )
• Multi-sources data come from different sources
  (distributed databases, web )
• Multi-modals the same information can be
  described differently (data in different
  languages )
• Multi-versions data are updated through time
  (temporal databases, periodical inventory )

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General context

• Complex data
• Huge volumes of complex data
• Warehousing complex data
• OLAP facts as complex objects
• Analyze complex data
• Current OLAP tools arent suited to process
  complex data
• Data mining is able to process complex data like
  images, texts, videos
• Coupling OLAP and data mining
• Analyze complex data on-line
• New operator OpAC Operator of Aggregation by
  Clustering (AHC)

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Outline
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• Complex data and general context
• Related work Coupling OLAP and data mining
• Objectives of the proposed operator
• Formalization of the operator
• Implementation and demonstration
• Conclusion and future works

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Related work

• Three approaches for coupling OLAP and data
  mining
• First approach Extending the query languages of
  decision support systems
• Second approach Adapting multidimensional
  environment to classical data mining techniques
• Third approach Adapting data mining methods for
  multidimensional data

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Related work

• These works proved that
• Associating data mining to OLAP is a promising
  way to involve rich analysis tasks
• Data mining is able to extend the analysis power
  of OLAP

• Use data mining to enhance OLAP tools in order to
  process complex data
• OpAC A new OLAP operator based on a data mining
  technique

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Objectives

• Classic OLAP aggregation Vs OpAC aggregation
• Classic OLAP
• Summarizes numerical data in a fewer number of
  values
• Computes additive measures (Sum, Average, Max,
  Min )

Example Sales cube
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Objectives

• Classic OLAP aggregation Vs OpAC aggregation
• OpAC aggregation
• What about aggregating complex objects?
• How to aggregate images, texts or videos with
  classic OLAP tools?
• Complex objects are not additive OLAP measures

Example Images cube
?
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Objectives

• How to aggregate complex objects?
• Using a data mining technique AHC (Agglomerative
  Hierarchical Clustering)
• The AHC aggregates data
• The hierarchical aspect of the AHC

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Objectives
Images
Very high High Medium Low Very low
Very high High Medium Low Very low
Entropy
Homogeneity
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Formalization

• Di the ith dimension of a data cube C
• hij the jth hirarchical level of the
  dimension Di
• gijt the tth modality of hij

• The set of individuals

W Ì gijt / gijt Î hij

• The set of variables
• Dimension retained for individuals cant generate
  variables
• Only one hierarchical level of a dimension is
  allowed to generate variables

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Formalization

• Evaluation tools
• Minimize the intra-cluster distances
• Maximize the inter-cluster distances
• Inter and intra-cluster inertia
• A1 , A2 , , Ak is a partition of W
• P(Ai) is the weight of Ai
• G(Ai) is the gravity center of Ai

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Formalization

• Individuals
• Modalities from the dimension of images
• Variables
• L1Normalized values of images for all possible
  modalities of the entropy dimension
• L1Normalized values of images for all possible
  modalities of the homogeneity dimension

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Formalization

• Results
• Exploits the cubes facts describing images to
  construct groups of similar complex objects
• Highlights significant groups of objects by a
  clustering technique
• Clusters aggregates- are defined both from
  dimensions and measures of a data cube
• Implementation of a prototype

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Implementation

• Prototype
• Data loading module
• Connects to a data cube on Analysis Services of
  MS SQL Server
• Uses MDX queries to import information about the
  cubes structure
• Extract data selected by the user
• Parameter setting interface
• Assists the user to extract individuals and
  variables from the cube
• Selects modalities and measures
• Defines the clustering problem
• Clustering module
• Allows the definition of the clustering
  parameters like dissimilarity metric and
  aggregation criterion
• Constructs the AHC
• Plots the results of the AHC on a dendrogram

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Implementation

• Images dataset
• 3000 images collected from the web
• Semantic annotation Description, subject and
  theme
• Descriptors of texture like
• ENT Entropy
• CON Contrast
• L1Normalized Medium Color Characteristic
• Three color channels RGB

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Implementation
Demonstration
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Conclusion

• OpAC is a possible way to realize on-line
  analysis over complex data
• OpAC aggregates complex objects
• Aggregates clusters- are defined from both
  dimensions and measures of a data cube
• Prototype available at
• http//bdd.univ-lyon2.fr/?pagelogicielid5

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Future works

• The current evaluation tool may present some
  limits
• Use other evaluation indicators to evaluate the
  quality of partitions
• Assist user to find the best number of clusters
• Exploit the aggregates generated by OpAC in order
  to reorganize the cubes dimensions
• Get a new cube with remarkable regions
• Use other data mining technique to enhance the
  OLAP power with explanation and prediction
  capabilities

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The End