1 / 28

Cube Presentation Model for Multi-dimensional Information from Databases

Explore the Cube Presentation Model (CPM) for visualizing multi-dimensional data from databases. Separate presentation from data retrieval, map CPM to advanced visualization techniques, and discuss its applications in Desktop and Mobile OLAP environments.

tstevenson
Download Presentation

Cube Presentation Model for Multi-dimensional Information from Databases

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Μοντέλα Απεικόνισης και Παρουσίασης Πολυδιάστατων Πληροφοριών από Αποθήκες Δεδομένων Ανδρέας Μανιάτης • Εθνικό Μετσόβιο Πολυτεχνείο • Dept. of Electrical & Computer Engineering • Knowledge and Database Systems Laboratory Συνάντηση DBLABΤρίτη, 13 Ιανουαρίου 2004

  2. Outline • Motivation • Contribution: The Cube Presentation Model (CPM) • Logical Layer • Presentation Layer • Modeling with UML and XML • Mapping CPM to Visualization Techniques • Discussion • Conclusions and Future Work

  3. Motivation • Models & languages customized for one query and one answer at a time • Relational model, SQL, OLAP models, … • Modern applications involve sessions of screens, each screen comprising multiple queries and answers • Generally, the presentation model is rarely – if ever – tampered with by the DB community. • Data Visualization is not adequately investigated, particularly in the contents of OLAP

  4. Βάση Δεδομένων Αποθήκη Δεδομένων Metadata Data Mart Data Mart Data Mart DW Architecture Εφαρμογές Ανάλυσης ΠηγέςΔεδομένων Εξωτερικά Δεδομένα Άλλες Πηγές

  5. Contribution • To capture this, we separate presentation from data retrieval into CPM • CPM can be modeled using UML & XML • CPM can be mapped to advanced visualization techniques from HCI (namely, the Table Lens) • CPM is suitable for Desktop & Mobile OLAP Applications

  6. Outline • Motivation • CPM • Logical Layer • Presentation Layer • Mapping CPM to Visualization Techniques • Discussion • Conclusions and Future Work

  7. CPM – Logical Layer • Extension of Vassiliadis & Skiadopoulos @CAiSE’00. It comprises: • dimensions defined as lattices of dimension levels • ancestor functions mapping values between related levels of a dimension • detailed data sets, practically modeling fact tables at the lowest granule of information for all their dimensions and • primary cubes, for simple aggregations over detailed data sets • secondary cubes, adding ORDER BY and HAVING functionality • www.dblab.ece.ntua.gr/~andreas/publications/CPM_dawak03.pdf

  8. CPM – 2 layers (models) • Combination of two previous paradigms: • MS MDX • TAPE • In CPM, we separate presentation from data retrieval • Presentation: we provide a model for the presentation of multi-query reports • Data Retrieval: we mask the complexity of the retrieval/computation of the various queries in a different modeling layer

  9. CPM – Presentation Layer • Intuitively based on the geometrical representation of a cube • Entities of CPM • Points • Axes • Multicubes • 2D – Slice • Tape • Cross-join • Contents function

  10. Microsoft MDX

  11. CPM – Presentation Layer Points

  12. CPM – Presentation Layer Axis: finite sets of points Multicube: sets of axes

  13. CPM – Presentation Layer 2D – Slice

  14. CPM – Presentation Layer Tape 2 Tape Axis 2 Point Tape 1 Cross Join Axis 1 Tapes & Cross Joins

  15. CPM – Presentation Layer • Cross-join: • Defined over two tapes on a 2D – Slice • The tapes are not parallel to the same axis • Typical OLAP operations can be mapped to operations over 2D – Slices and Tapes

  16. Cube <<derives>> * Cube 1..* 2D Slice Schema <<derives>> * 1..* Tape 1..* 1..* Axis 1..* 1..* Selection Condition * * 1 1 Axis Schema Point 1..* 1..* 1..* Dimension Equality Selection Group Condition 1..* 1..* 1..* 1..* Attribute Group (AG) 1..* 1..* Dimension * * Attributes UML Metamodel of CPM

  17. Outline • Motivation • CPM • Presentation Layer • Logical Layer • Mapping CPM to Visualization Techniques • Discussion • Conclusions and Future Work

  18. Table Lens • A Focus + Context Visualization Technique • Suitable for Tabular, Multivariate and Multidimensional Data (actually large 2D Tables) • Based on the Cross-Tabular paradigm (popular for OLAP screens)

  19. Mapping CPM to Table Lens Axes Transfer Function 2D Space DOI

  20. Motivating Example in Table Lens

  21. Algorithms • Straightforward Algorithm: • Summarize all cells per cross-join • Sort cross-joins and compute the average cross-join value • Pinpoint the three regions of interest • Assumptions: • Cross-joins constitute homogeneous pieces of information • We are allowed to perform certain aggregate functions over the data • Guided Greedy Generic Algorithm: GenericFocusWindow • www.dblab.ece.ntua.gr/~andreas/publications/VIS_OLAP_DOLAP03.pdf

  22. Outline • Motivation • CPM • Presentation Layer • Logical Layer • Mapping CPM to Visualization Techniques • Discussion • Conclusions and Future Work

  23. Discussion • Visualization is one of the big issues of database research (Lowell Report) – OLAP and DSS to be the areas mostly affected • Is a new model necessary? • Visualization for Mobile OLAP (on small, wireless devices, PDAs, phones etc) • No squeezing of the screen contents • No report rewriting • Proactive User Decision Support for large datasets

  24. Mobile OLAP • Suitable visualization techniques from the HCI domain for Mobile, Wireless OLAP • Implement a Prototype (CubeView for Mobile OLAP) • Investigate New Visualization Techniques

  25. Outline • Motivation • CPM • Presentation Layer • Logical Layer • Mapping CPM to Visualization Techniques • Discussion • Conclusions and Future Work

  26. Conclusions • CPM: A novel presentation model for OLAP • Table Lens: An Advanced Visualization technique from the HCI domain • Natural mapping of CPM into Table Lens • Suitable algorithms for proactive automated support of the user • Mobile OLAP: A different approach for OLAP Visualization on Mobile Devices • Discussion on the usefulness and applicability of these techniques

  27. Thank you!

  28. References • M. Gebhardt, M. Jarke, S. Jacobs: A Toolkit for Negotiation Support Interfaces to Multi-Dimensional Data. ACM SIGMOD 1997, pp. 348 – 356. • Various Authors: The Lowell Database Research Self Assessment. Lowell, Massachusetts USA, May 4-6, 2003. Available at: http://research.microsoft.com/~Gray/lowell/. • Microsoft Corp. OLEDB for OLAP February 1998. Available at: http://www.microsoft.com/data/oledb/olap/. • Andreas Maniatis, Panos Vassiliadis, Spiros Skiadopoulos, Yannis Vassiliou: CPM: A Cube Presentation Model for OLAP. DaWaK 2003, Prague, Czech Republic, September 3 – 5 2003. • Ramana Rao, Stuart K. Card: The Table Lens: Merging Graphical and Symbolic Representations in an effective Focus + Context Visualization for Tabular Information. Proceedings of the ACM SIGCHI (CHI ’94), Boston, Massachusetts USA, April 24-28, 1994. • Panos Vassiliadis, Spiros Skiadopoulos: Modeling and Optimization Issues for Multidimensional Databases. Proc. of CAiSE’00, Stockholm, Sweden, 2000.

More Related