Software architecture is about translating business strategies into technical strategies [HP Openview].

The TEC in Architect indicates that software architecture is rooted in technical details. A software architect must be expert in the related technologies like the software design technologies, software development technologies, testing and deployment technologies. Even the software architect may be directly involved in coding. As he/she has to take technical decisions on software development, he needs to know development well. He should be able to solve technical development problems, provide implementation strategies, and high level algorithms to solve problems.

The ARCHI in the Architect indicates he should be able to design/understand/analyze the big picture of the total system. He/she needs to understand the interactions among different system components and what are the must required components and why?. Identify the most important components and how to design/develop what.

Remember, design is also architecture, when you use UML/Visio to create component diagram, class diagram, to show what components/classes the software should implement, and to show interations among components and classes these are also architecture but one step lower. All architecture is design but not all design is architecture.

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Post Data:2010-06-26 02:03:07

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• Most BI projects usually fail. It is not due to the errors in construction steps but due to the inappropriate design steps and methodologies.
• Waterfall method is not appropriate for BI. A method like developmental spiral may be more appropriate. Ref: [W. A. Giovinazzo ]
• Steps in developmental spiral: Definition, Analysis, Design, Development, Implementation/Deployment, Maintenance, Evaluation
• Developmental spiral method is effective for BI only when object oriented approach is taken
• In developmental spiral, object modeling is used to represent objects in the related business areas. In object modeling, objects are represented as rectangles, the rectangles can be treated as classes. Classes/objects can have sub-classes/sub-objects. Objects have attributes. Objects also have some special properties like: accelerators, stops.
• Definition – first step of developmental spiral: Define a short description what you want to build – like the problem statement in a research proposal. Example:I want to build a data mart that will provide data to support the analysis of customer demographics such as age, gender, and income for all product lines
• Analysis Phase: In this step, analyze the requirements of the BI system, the nature of the BI system, the expectations from the BI system. Interviewing different parties will help a lot:
• Interview the sponsor to get a detail understanding of the mission. The questions will include: Data-related(which objects are related to the business needs?), user-related: who are the users, system evaluation: what parameters will make the project a success?
• Interviewing management: To understand management’s expectation of the data warehouse. Questions may include: User profile related (what types of systems you use/will use to access BI?), System vision: what do you see as the objective of the data ware house, System Evaluation: what parameters will make the project a success?
• Interviewing Users : Interviewing users who will use the system
• Interviewing information technology staffs: To know the current state of the organization’s information systems
• Design phase: output – object model, implementation model
• Start with the analysis phase information. The design phase is complete when all the objects related to the mission are well defined. An object diagram needs to be drawn showing all objects, relationships, attributes.
• Another chart listing all the attributes and their data types will be useful. Objects will have relations like super classes or sub-classes.
• Cardinality relationships: exactly one, one to many, zero or one, zero to many
• aggregation may be required in some cases. Aggregation: an object is composed of other objects.
• Implementation Model: Data warehouse databases are multi dimensional databases. Objects are represented in more than two dimensions. Common practice is: three dimensional databases. Such as an object/table represented with three dimensions (called cube) like: product, dealer, and time. RDBMS’s are two dimensional.
• Data cubes: have six possible different views [permutation of three dimensions].
• Why not always multidimensional databases: because of space requirements.
• Star schema: Provides a multidimensional flavor in two dimensional relational databases.
• Star schema uses a concept called fact tables to bind dimensions to create a multidimensional space.
• Denormalization of the tables are utilized in star schema to create multi dimension.
• Dimension table: Think about a three dimensional cube. Each wall represents a dimension table. Or think it as a mathematical combination of the dimensions.
• All objects represented in the dimensions can slowly change over time – slowly changing dimensions. There are many approaches to address this issue.
• Snowflakes: normalizing dimensions – not always a great thing
• Implementation considerations: Parallel processing, Bitmapped Indexing, Star Query Optimization, Summation Tables

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Post Data:2013-06-10 08:41:13

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• Crime Detection using BI
• Credit Card fraud detection using BI
• A presentation on Oracle Data Mining
• Oracle by Example – Business Intelligence
• Oracle Data Mining Components
• Business Intelligence with Microsoft SQL Server Reporting Services – Part 1
• SQL Server 2005 Analysis Services Tutorial
• SQL Server 2005 Data Mining
• BI for MySQL
• MySQL in Data Warehousing & Business Intelligence
• Business Intelligence in Mysql

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Post Data:2008-02-04 02:39:41

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• Real internet money/business lies in B2B not in B2C.
• Decision Support System (DSS): Full spectrum of systems that allow/help management to take decisions, such as, reporting, OLAP, data mining.
• Java provides support for web enabled DSS
• JDM is Java’s Data mining/BI API.
• BI Applications: Balanced Score-card, Activity Based Costing.
• Data warehouse elements
  • Operational Environment: Maintains day to day activities. Raw Data
  • Independent data mart: A small data ware house specific to one topic/area/subject
  • Extraction: Extraction of data from operational systems
  • Extraction Store: Data storage after extraction
  • Transformation and cleansing: Before putting data into the central/main warehouse data needs to be cleaned up
  • Extraction log: Create a log in the transformation and cleansing process
  • External source: Data originated outside the organization is also incorporated into the data warehouse.
  • Data administrator: The person who ensures the quality of data
  • Central Repository: Stores all data and meta data
  • Meta data: Data about data. Example: the extraction log.
  • Dependent Data Mart: Relies on the data warehouse for the sources of its data
• Distributed Data Warehouse: Warehouse data is physically distributed across a company’s information infrastructure.
• Data mining types: classification, and estimation.
• Classification: categorize customers, Estimation: predict or estimate some numerical value
• Decision trees: modeling technique helps in deciding. Different alternatives are drawn as paths where nodes represent the selected alternative. edges/paths may carry some probability values as selection/influencing criteria.
• Neural networks: Try to mimic the work nature of human brain to take a decision.
• General modeling: Well-suited for categorizing.
• Balanced Scorecard application: views the health and well-being of the organization by more than its financial data. We need to define: Vision, Strategy, Strategic themes, Strategic Objectives, Key performance indicators.
• Activity based costing application:
• Three types of organizations: Engineer driven: They know how to make a product. Than sales it through sales force. Sales Driven: They know how to sale a particular product, then make the product and sell it. Customer driven: They Know what customers want, then make the products and sale them. Customer driven organizations are the most successful.

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Post Data:2011-04-24 09:17:20

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Data Warehouse is a storage of an organization’s historical data. Usually, data warehouse is built to provide a decision support system to the management. Data warehouse can be mined (using data mining) and queried to collect overall and summarized business information that significantly helps in decision making. It can provide trends over many years.

Operational databases are optimized for day to day operation access. Here, the relationships are normalized and further divided into more relations/tables to make accessing faster that also reduces data traffic among the systems. But data warehouse does the reverse, where relationships/tables are denormalized as much as possible to provide the required information to the management. As data warehouse provides summarized information de-normalization helps, otherwise the data would be required to collect from many tables and the queries may be running over data for 10 years (millions/billions of records). Usually, data warehouse is created separately from the operational databases.

Microsoft SQL Server and Oracle both have supports and tools to create Data warehouse Systems. There are also free tools for the purpose. From: http://sitestree.com/?p=4816
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Post Data:2006-10-13 06:30:41

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Canada has critical professional shortages in software industry. You can check the skills that are in high demand in software industry.

• http://www.cic.gc.ca/english/work/special-tech.asp

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Post Data:2009-09-27 23:47:11

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Follow this link: NSERC Funding for Research Based Masters, PHD, and Post Doctorate Programs

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Post Data:2009-04-04 23:44:09

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The following tools may be useful
-Advanced SystemCare 3 Professional
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