ADM 2372 Lecture Notes - Lecture 9: Time Series, Data Warehouse, Data Mining

• School: University of Ottawa
• Department: Administration
• Course: ADM 2372
• Professor: Okhaide Akhigbe

Document Summary

Histo(cid:396)y of data wa(cid:396)ehouses: i(cid:374) the (cid:1005)99(cid:1004)"s, fu(cid:374)(cid:272)tio(cid:374)al syste(cid:373)s we(cid:396)e too (cid:272)u(cid:373)(cid:271)e(cid:396)so(cid:373)e a(cid:374)d i(cid:374)effi(cid:272)ie(cid:374)t, ope(cid:396)atio(cid:374)s syste(cid:373)s a(cid:374)d data we(cid:396)e(cid:374)"t i(cid:374)teg(cid:396)ated, (cid:395)uality issues, good fo(cid:396) t(cid:396)a(cid:374)sa(cid:272)tio(cid:374)s p(cid:396)o(cid:272)essi(cid:374)g, (cid:374)ot analysis. After the millennium, data scattered over too many platforms, complex analysis was not timely. Data warehouse- a logical collection of info, gathered from many different operational databases, supports strategic business analysis activities and decision-making tasks. Primary purpose is to aggregate information throughout an organization, not a location for all data, only data of interest. Characteristics of data warehouses: subject oriented- information is organized around a major organizational subject area (e. g. customers) Integrated- sourced from a variety of internal operational systems and external databases into a coherent whole: time variant- time stamped according to its cycle (daily, yearly, etc. , non-volatile- o(cid:374)(cid:272)e loaded, data does(cid:374)"t (cid:272)ha(cid:374)ge. Extraction, transformation, loading (etl)- a process that extracts information from internal and external databases. Transforms the information using a common set of enterprise definitions.