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Data & Information Integration Framework for Highway Projects Mid-Continent Transportation Symposium

Asregedew Woldesenbet David H. Jeong (Ph.D.) Michael P. Lewis (Ph.D., P.E.). Data & Information Integration Framework for Highway Projects Mid-Continent Transportation Symposium. August 15, 2013. Research Question Lessons Learned Methodology Evolution Integration Framework Case Study

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Data & Information Integration Framework for Highway Projects Mid-Continent Transportation Symposium

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  1. AsregedewWoldesenbet David H. Jeong (Ph.D.) Michael P. Lewis (Ph.D., P.E.) Data & Information Integration Framework for Highway ProjectsMid-Continent Transportation Symposium August 15, 2013

  2. Research Question • Lessons Learned • Methodology • Evolution • Integration Framework • Case Study • Gap Analysis • Conclusion/Future Work Outline

  3. Is data currently being collected provides the information needed for decision-making? • Minimal recognition or interest in using these data • Lack of in-house resources and capabilities to analyze data • Insufficient data for any meaningful analysis • Nonstandard /non-digital data format • Poorly defined procedures/mechanism Research Question

  4. Strategic decisions supported by statistically reliable information • Credit card industry • Retail industry • Healthcare industry • Big Data • System/Tools • KM tools and KDD approaches • DM, AI, DSS, ML, BI • Management philosophies • BPR, TQM, SCM, CE, LC • Database System • Ontology frameworks, cloud computing Lessons Learned

  5. Generations of Data &Information Management: Transportation Industry

  6. Evolution of Data and Information Integration for Highway Agencies

  7. Context Graph Data & Information Integration Input/Output Matrix

  8. Three-Tiered Hierarchical Framework Data & Information Integration Framework

  9. Daily Work Reports (DWR) • Preconstruction Cost Data • Pavement Condition Data Case Study

  10. Case Study

  11. Current Data Utilization

  12. Ideal Data, Information & Decision-Making Framework

  13. Three-Tiered Framework

  14. Gap Analysis

  15. Gap Analysis

  16. Summary • DWR are often utilized in reporting and preparation of legal disputes. • Reported quantity and work item are the primary data that are utilized in contractor payments and tracking project progress. • More than 35% of the DWR data are linguistic in nature. • Conclusion • Lack of skilled data analysts and experts to analyze data • Lack of well-developed requirement analysis and performance measures. • Focus of specific divisions or business processes to promote own division’s need rather than develop integrated system Conclusion

  17. Data, Information & Decision-making Guideline Conclusion

  18. Contribution • Ability to show types of data that should be collected and potential information & knowledge generation • A general guide to highway agencies in the development of active utilization of currently existing databases. • Help develop new data collection, information & knowledge generation plan to support key decisions Future Study • Emphasize in developing an enterprise wide ontology-based framework • Application of big data analytics to justify the return on investment for the data collection efforts and effectively utilize the increasing amount of data. Conclusion

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