Data Warehousing and Data mining December‚ 9 2013 Data Mining and Data Warehousing Companies and organizations all over the world are blasting on the scene with data mining and data warehousing trying to keep an extreme competitive leg up on the competition. Always trying to improve the competiveness and the improvement of the business process is a key factor in expanding and strategically maintaining a higher standard for the most cost effective means in any
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Outline Introduction Distributed DBMS Architecture Distributed Database Design Distributed Query Processing Distributed Transaction Management Data Replication Consistency criteria Update propagation protocols Parallel Database Systems Data Integration Systems Web Search/Querying Peer-to-Peer Data Management Data Stream Management Distributed & Parallel DBMS M. Tamer Özsu Page 6.1 Acknowledgements Many of these slides are from notes prepared by Prof. Gustavo
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Case Study 2 1.) Compare and contrast Inmon and Kimball’s definition of Data Warehousing. Bill Inmon advocates a top-down development approach that adapts traditional relational database tools to the development needs of an enterprise wide data warehouse. From this enterprise wide data store‚ individual departmental databases are developed to serve most decision support needs. Ralph Kimball‚ on the other hand‚ suggests a bottom-up approach that uses dimensional modeling‚ a data modeling approach
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1. Data mart definition A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse that is usually oriented to a specific business line or team. Data marts are small slices of the data warehouse. Whereas data warehouses have an enterprise-wide depth‚ the information in data marts pertains to a single department. In some deployments‚ each department or business unit is considered the owner of its data
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Recognition is a process which involves us using basic sensory descriptions of an object and turning it into a 3D description‚ this description must then match stored representations of what we have seen before‚ irrespective of the angle its seen from. The process has 3 stages‚ Converting‚ Comparing and Identifying. An object model of recognition we will look at is Marr and Nisihara’s special process used to generate an object centered 3D description‚ and a facial recognition model we will compare this
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victims and offenders have high levels of satisfaction with the process and the outcomes. Studies also suggest that offenders are more likely to follow through with restitution or community service‚ and that there is some reduction in repeat offending (2. Frank D. Hill‚ 2011).There are some many different ways to handle in restorative justice here are some of the steps that are taken to make it effectively. First is the understanding that the victim and the surrounding community have both been affected
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Data Preprocessing 3 Today’s real-world databases are highly susceptible to noisy‚ missing‚ and inconsistent data due to their typically huge size (often several gigabytes or more) and their likely origin from multiple‚ heterogenous sources. Low-quality data will lead to low-quality mining results. “How can the data be preprocessed in order to help improve the quality of the data and‚ consequently‚ of the mining results? How can the data be preprocessed so as to improve the efficiency and ease
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Recruitment and selection processes Index Executive summary 3 1. Analysis 3 1.1 Vacant position analysis 3 1.2 Position description 4 1.3 Advertisement 4 1.4 Shortlisting 6 1.5 Interview preparation 6 1.6 Interview applicant 8 1.7 Reference check 9 1.8 Job offer 9 2. Evidence and referenced sources 10 3. Conclusion 10 4. Recommendations 11 Reference list 11 Executive summary This report is based on a
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Components of DSS (Decision Support System) Data Store – The DSS Database Data Extraction and Filtering End-User Query Tool End User Presentation Tools Operational Stored in Normalized Relational Database Support transactions that represent daily operations (Not Query Friendly) Differences with DSS 3 Main Differences Time Span Granularity Dimensionality Operational DSS Time span Real time Historic Current transaction Short time frame Long time frame Specific Data facts Patterns Granularity Specific
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_____110000_____ m = ____110000000________ mm 3.7 hectometers =____370_____ m =_____37000_______ cm 451‚000‚000 μm = ____.000451_____ m = ____.0000451_______ dam 2) Imagine a field is about 100 meters long. If you run a 5K race how many meters is it? Approximately how many “fields” does this equate to? 50 football fields 3) Measure the following objects. A) Your computer screen (in meters) Length______________ Width ______________ Area _______________ Volume ____________ B) A 100 mL beaker: (in millimeters)
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