A Business Intelligence Process to support Information Retrieval in an Ontology-Based Environment Tommaso Federici Tuscia ...
Introduction <ul><li>The competitiveness of a company depends on how fast the company manages to take decisions regarding ...
<ul><li>We propose to use the BI techniques to manage and reuse all data stored in an ontology-based Data Warehouse (DW): ...
Summary <ul><li>Semantic indexing process and system overview </li></ul><ul><li>Dynamic dimension definition process </li><...
The semantic indexing process unstructured docs ontologies terms set
The overall system dm1 presentation layer structured data index CUSTOM ETL PROCESS data warehouse and datamarts Addictiona...
Summary <ul><li>Semantic indexing process and system overview </li></ul><ul><li>Dynamic dimension definition process </li><...
Dynamic dimension definition <ul><li>Business Intelligence can be successfully applied to ontology-based retrieval systems...
Dynamic dimension definition dimension table bridge table fact table Unbalanced ontology tree dynamic computation concept_...
<ul><li>We suggest the definition of some ad-hoc ontologies describing the scope of our analysis, with the aim to dynamical...
Summary <ul><li>Semantic indexing process and system overview </li></ul><ul><li>Dynamic dimension definition process </li><...
Integration of the indexed data SELECT * FROM index JOIN op_fact_table  ON index.op_fact_id = op_fact_table.id; SELECT * F...
Summary <ul><li>Semantic indexing process and system overview </li></ul><ul><li>Dynamic dimension definition process </li><...
The Star Schema <ul><li>The resulting schema, we obtained by our system, is composed by one dimension and one fact </li></...
Pivot Table <ul><li>By this solution, the system supports a Human Resource manager to analyze his enterprise CV repository...
Conclusions and Future Works <ul><li>Our process allows for the reuse of ontologies defined in semantic search engines dict...
Thanks to all for your attention Questions?
Upcoming SlideShare
Loading in …5
×

International Conference on Intelligent Systems Design and Applications 2009

415 views
379 views

Published on

A Business Intelligence process to support Information Retrieval in an Ontology-based Environment

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
415
On SlideShare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
6
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • Today the power of a company depends on how fast it take decisions about its business. Currently BI processes, to support companies business, rely only on traditional keywords searching. We think that is a good thing to extend this way to do with a semantic approach
  • Presentation breaks down as follow: Firstly i will introduce the semantic indexing process over this one we have developed our system. Secondly i will illustrate the dynamic dimension definition process, ranging from the working hypotheses, to the treatment of Bridge Tables and the automatic generation of OLAP dimensions. After i will explain the processes lying behind the integration of the indexed data, pointing out the most significant SQL code parts. Last of all I will present a simple case study to summarize all the presented concepts.
  • Presentation breaks down as follow: Firstly i will introduce the semantic indexing process over this one we have developed our system. Secondly i will illustrate the dynamic dimension definition process, ranging from the working hypotheses, to the treatment of Bridge Tables and the automatic generation of OLAP dimensions. After i will explain the processes lying behind the integration of the indexed data, pointing out the most significant SQL code parts. Last of all I will present a simple case study to summarize all the presented concepts.
  • Presentation breaks down as follow: Firstly i will introduce the semantic indexing process over this one we have developed our system. Secondly i will illustrate the dynamic dimension definition process, ranging from the working hypotheses, to the treatment of Bridge Tables and the automatic generation of OLAP dimensions. After i will explain the processes lying behind the integration of the indexed data, pointing out the most significant SQL code parts. Last of all I will present a simple case study to summarize all the presented concepts.
  • Presentation breaks down as follow: Firstly i will introduce the semantic indexing process over this one we have developed our system. Secondly i will illustrate the dynamic dimension definition process, ranging from the working hypotheses, to the treatment of Bridge Tables and the automatic generation of OLAP dimensions. After i will explain the processes lying behind the integration of the indexed data, pointing out the most significant SQL code parts. Last of all I will present a simple case study to summarize all the presented concepts.
  • This aspect is now left to the user’s capacity of developing consistent schemas, but it is our intention to introduce a management system based on weighted trees
  • International Conference on Intelligent Systems Design and Applications 2009

    1. 1. A Business Intelligence Process to support Information Retrieval in an Ontology-Based Environment Tommaso Federici Tuscia University 01010 Viterbo, Italy [email_address] Filippo Sciarrone, Paolo Starace Open Informatica srl – BI Division Via dei Castelli Romani, 12/A 00040 Pomezia, Italy {f.sciarrone, p.starace}@openinformatica.org
    2. 2. Introduction <ul><li>The competitiveness of a company depends on how fast the company manages to take decisions regarding its business mission </li></ul><ul><ul><li>Today’s market requires that data processing be also for semantics, so as to achieve an extra competitive advantage in the business </li></ul></ul><ul><ul><li>In Business Intelligence (BI) applications, Information Retrieval (IR) can be a critical function because most BI applications today rely on traditional keyword searching for their primary retrieval mechanism </li></ul></ul>
    3. 3. <ul><li>We propose to use the BI techniques to manage and reuse all data stored in an ontology-based Data Warehouse (DW): </li></ul><ul><ul><li>Data level: data involved in the DW aggregation process, which are the result of an IR indexing process </li></ul></ul><ul><ul><li>OLAP Structures: multidimensional OLAP structures are automatically carried out by the ontology structures involved in the IR process </li></ul></ul>Research Question How Business Intelligence and Information Retrieval techniques can be merged to improve the decision support process?
    4. 4. Summary <ul><li>Semantic indexing process and system overview </li></ul><ul><li>Dynamic dimension definition process </li></ul><ul><li>Integration of the indexed data process </li></ul><ul><li>Case study </li></ul><ul><li>Semantic indexing process and system overview </li></ul><ul><li>Dynamic dimension definition process </li></ul><ul><li>Integration of the indexed data process </li></ul><ul><li>Case study </li></ul>
    5. 5. The semantic indexing process unstructured docs ontologies terms set
    6. 6. The overall system dm1 presentation layer structured data index CUSTOM ETL PROCESS data warehouse and datamarts Addictional integrable data dm3 dm2 semantic dictionary
    7. 7. Summary <ul><li>Semantic indexing process and system overview </li></ul><ul><li>Dynamic dimension definition process </li></ul><ul><li>Integration of the indexed data process </li></ul><ul><li>Case study </li></ul>
    8. 8. Dynamic dimension definition <ul><li>Business Intelligence can be successfully applied to ontology-based retrieval systems, where the retrieval process is addressed by means of concepts instead of single terms </li></ul><ul><li>We used the BI techniques to dynamically build OLAP schemas, starting from ontologies stored in a experimental LO repository, now at its early stage of development </li></ul>The key idea is to define ad-hoc ontologies to support analysis on a semantic indexed information system, and the subsequent automatic building of multidimensional schemas.
    9. 9. Dynamic dimension definition dimension table bridge table fact table Unbalanced ontology tree dynamic computation concept_id description parent 1 one 2 two 1 parent subsidiary distance 1 1 0 1 2 1 concept_id other_dim_id measure 1 … … … … …
    10. 10. <ul><li>We suggest the definition of some ad-hoc ontologies describing the scope of our analysis, with the aim to dynamically build those dimensional structures to join them to the ones previously obtained </li></ul><ul><ul><li>Through the Computer Expert Ontology , the analyst can perform an analysis of all the information content stored inside the unstructured documents </li></ul></ul>Dynamic dimension definition
    11. 11. Summary <ul><li>Semantic indexing process and system overview </li></ul><ul><li>Dynamic dimension definition process </li></ul><ul><li>Integration of the indexed data </li></ul><ul><li>Case study </li></ul>
    12. 12. Integration of the indexed data SELECT * FROM index JOIN op_fact_table ON index.op_fact_id = op_fact_table.id; SELECT * FROM ((index JOIN op_fact_table ON index.op_fact_id = op_fact_table.id) JOIN std_dimension_dt ON op_fact_table.column = std_dimension_dt.column); SELECT * FROM ((index JOIN op_fact_table ON index.op_fact_id = op_fact_table.id) JOIN std_dimension_dt ON op_fact_table.column = std_dimension_dt.column) JOIN ontology_dt ON index.concept = ontology_dt.concept; SELECT ontology_dt.ontology_id, std_dimension_dt.std_dimension_id, sum(op_fact_table.measure) FROM ((index JOIN op_fact_table ON index.op_fact_id = op_fact_table.id) JOIN std_dimension_dt ON op_fact_table.column = std_dimension_dt.column) JOIN ontology_dt ON index.concept = ontology_dt.concept GROUP BY std_dimension_dt.std_dimension_id, ontology_dt.ontology_id;
    13. 13. Summary <ul><li>Semantic indexing process and system overview </li></ul><ul><li>Dynamic dimension definition process </li></ul><ul><li>Integration of the indexed data process </li></ul><ul><li>Case study </li></ul>
    14. 14. The Star Schema <ul><li>The resulting schema, we obtained by our system, is composed by one dimension and one fact </li></ul><ul><li>The dimension computerexpert_dt represents the ontology tree </li></ul><ul><li>The fact experience_ft represents the number of CVs that contain the concept </li></ul>
    15. 15. Pivot Table <ul><li>By this solution, the system supports a Human Resource manager to analyze his enterprise CV repository </li></ul>
    16. 16. Conclusions and Future Works <ul><li>Our process allows for the reuse of ontologies defined in semantic search engines dictionaries as OLAP dimensions thus providing for a stable solution to integrate indexed data in a semantic context </li></ul><ul><li>To this aim, a two-steps process was implemented in such a way to let the overall system independent from the complexity of the ontologies </li></ul><ul><li>Our future work will focus on the resolution of problems related to the management of many-to- many relationships </li></ul><ul><li>A first evaluation was carried out in an industrial context yielding positive results on our proposal’s viability </li></ul>
    17. 17. Thanks to all for your attention Questions?

    ×