CS 157B: Database Management Systems II March 13 Class Meeting Department of Computer Science San Jose State University Spring 2013 Instructor: Ron Mak

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 2 Midterm Question 1.a <xs:schema xmlns:xsi=" xmlns:xs=" <xs:element name="investor" type="investor_type" minOccurs="0" maxOccurs="unbounded"/> experience.xsd

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 3 Midterm Question 1.b <xs:schema xmlns:xsi=" xmlns:xs=" <xs:element name="stock" type="stock_type" minOccurs="0" maxOccurs="unbounded"/> prices.xsd

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 4 Midterm Question 2.a { for $stk in doc("prices.xml") //stock for $inv in doc("investors.xml") //investor let $change := $stk/change where ($inv/portfolio/symbol = $stk/symbol) and ($stk/change > 0) order by $stk/symbol ascending return { ($stk/symbol, $stk/price, $stk/change, $inv/name) } } winners.xql

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 5 Midterm Question 2.b { for $inv in doc("investors.xml") //investor for $exp in doc("experience.xml") //investor for $stk in doc("prices.xml") //stock let $change := $stk/change where ($inv/name = $exp/name) and ($inv/portfolio/symbol = $stk/symbol) and ($exp/level != "beginner") and ($stk/change < 0) return { ($inv/name, $exp/level, $stk/symbol, $stk/price, $stk/change) } } losers.xql

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 6 Midterm Question 3  An application that combines information from the various data sources. an online mash-up a new web service  Operation: Schedule air travel between cities. Web service client code that connects to the airline schedules web service and the weather forecasts web service. Need to do data unmarshalling if the web services provide results in XML. Use XQuery to query the flight distance and flight duration XML documents. Unmarshal the XQuery results and combine with data from the weather forecasts web service to estimate flight duration.

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 7 Midterm Question 3  Operation: Allow airline seat selection. Use XSLT to convert the airline seating charts from XML to a more display-friendly form, such as an HTML-based web page.  Operation: Book lodging. Use Hibernate object-relational mapping to access the lodging database. Map hierarchical data:  Campsite and hotel are subclasses of lodging.  Fancy, Midpriced, and Budget are subclasses of hotel. Use the Criteria API to do the queries. _

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 8 Project #3 Presentations Next Monday  Present and demo your web services mashups in 15 minutes.  Section 1 Team INVIKO Team Lasers + 2 other teams  Section 2 Team C Team Unlimited Data + 2 other teams

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 9 Data Warehouse  A data warehouse is where a company keeps its important data assets.  Three categories of a company’s data. Operational data generated by ongoing business activities.  Examples: customer orders, shipping and receiving, financial transactions, etc. Integrated data from different parts of the business.  Combine data from disparate corporate applications that weren’t meant to work together, in order to better leverage the data and to improve synchronicity among departments. Monitoring data to keep track of how the business is doing and to understand and evaluate business processes.  Examples: reports, online “dashboards”

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 10 Data Warehouse  Data assets come from transforming operational data into integrated data or monitoring data. The data warehouse should contain only high quality data.  The data is periodically extracted from their original data sources, transformed into a higher quality, more useful form, and then loaded into the data warehouse. ETL: extract, transform, and load  Data warehousing is a well-architected information management solution... that enables analytical and information processing... while overcoming application, organizational, and other barriers.

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 11 Purpose of a Data Warehouse  Businesses today are deluged by data. A company that can make use of its key data assets has a better chance to succeed.  A data warehouse provides the platform, tools, and processes to manage and deliver the key data assets.  Decision-makers can rely on the analysis of high-quality data and not just on hunches. Predecessors to data warehousing include “decision-support systems” (DSS) from the mid 1970s. DSS evolved into “executive information systems” (EIS) in the mid 1980s.

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 12 Data Warehousing Thought Leaders  Bill Inmon The “Father of Data Warehousing” Wrote the book Building the Data Warehouse in Advocates an approach to data warehousing called the “Corporate Information Factory”.  Ralph Kimball Wrote the first edition of The Data Warehouse Toolkit in Promotes dimensional modeling.  developed the star schema  dimension tables and fact tables  denormalized tables

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 13 Star Schema Example  Facts: sales data  Dimensions: date, store, product

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 14 Star Schema Example  How many TV sets have been sold, for each brand and country, in 1997? SELECT P.Brand, S.Country, SUM(F.Units_Sold) FROM Fact_Sales F INNER JOIN Dim_Date D ON F.Date_Id = D.Id INNER JOIN Dim_Store S ON F.Store_Id = S.Id INNER JOIN Dim_Product P ON F.Product_Id = P.Id WHERE D.YEAR = 1997 AND P.Product_Category = 'tv' GROUP BY P.Brand, S.Country

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 15 Why Denormalization?  Why do we normalize tables in a standard database? Support data updates. Prevent update anomalies. Disadvantages  many tables, many joins  slow queries  A data warehouse, on the other hand... Requires rapid data access Updates occur infrequently  Example: nightly ETL  Therefore, data warehouses can contain denormalized tables. _

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 16 Related to Data Warehousing  Data mart A small data warehouse. A subset or (or a view into) a data warehouse for a particular set of users, such as financial analysts.  the preferred definition  Operational data store (ODS) A database specifically for integrating data from disparate sources, especially a company’s operational data (e.g., transactional data). Perform some operations on the data, such as cleansing and conforming. Feed the data into the data warehouse. _

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 17 Data Warehousing and Business Intelligence (DW/BI)  Business intelligence is a primary reason to have a data warehouse!  Several levels of BI: querying and reporting dashboards and scorecards business analytics data mining _

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 18 Business Intelligence  Querying and reporting “Canned” queries that generate formatted results. Generate reports on demand, or automatically and periodically. “What happened recently in my company?”  Dashboards and scorecards Graphical dashboards contain charts and gauges that monitor a company’s key performance indicators (KPI).  Examples: transactions/second, number of web hits, etc. Scorecards show performance measured against a plan or set of objectives. “What is happening right now and how are we doing?” _

Department of Computer Science Spring 2013: March 13 CS 157B: Database Management Systems II © R. Mak 19 Business Intelligence  Business analysis Online analytical processing (OLAP)  Do not confuse with an older term, online transaction processing (OLTP). Visualize data in a multidimensional manner.  Analytical processes that involve manipulating data along different dimensions. “What happened recently in my company, and why?”  Data mining Use statistics to do predictive analysis. Discover patterns and relationships in the data. Use artificial intelligence (AI) techniques to find answers even if you don’t know the questions. “What can happen? What’s interesting?”