File Name: introduction to business intelligence and data warehousing ibm phi .zip
Asha Ambhaikar 1.
- Business Intelligence Techniques
- DATA WAREHOUSING & DATA MINING. by: Prof. Asha Ambhaikar
- Data Warehousing & Data Mining.pdf
Data Warehouse Wikipedia.
CRM analyticse. Data mining C. Decision support D. Both A and B E.
Business Intelligence Techniques
Asha Ambhaikar 1. Planning And Requirements: Project planning and management, Collecting the requirements. Architecture And Infrastructure: Architectural components, Infrastructure and metadata 3. Arun K. Pujari, Data mining Techniques, Universities Press. Information A process of transforming data into information and making it available to users in a timely enough manner to make a difference Data 6. Thus making decisions that were not previous possible A decision support database maintained separately from the organization s operational database 7.
A data warehouse is a subjectoriented, integrated, timevariant, and nonvolatile collection of data in support of management s decision-making process. Inmon Data warehousing: The process of constructing and using data warehouses 8.
Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing. Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process. Ensure consistency in naming conventions, encoding structures, attribute measures, etc.
When data is moved to the warehouse, it is converted. Operational database: current value data. Data warehouse data: provide information from a historical perspective e. Operational update of data does not occur in the data warehouse environment. Does not require transaction processing, recovery, and concurrency control mechanisms Requires only two operations in data accessing: initial loading of data and access of data.
Heterogeneous DBMS Traditional heterogeneous DB integration: Build on top of heterogeneous databases Query driven approach When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set Complex information filtering, compete for resources Data warehouse: update-driven, high performance Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis Manager manage with the data they want rather than the data they get.
Less time spent gathering data from various systems and more time available to analyze and act. Ability to quickly answer a series of questions, each of which depends upon the answer to the previous question. OLAP : User and system orientation: customer vs. Data Warehouse OLTP systems are tuned for known transactions and workloads while workload is not known a priori in a data warehouse Special data organization, access methods and implementation methods are needed to support data warehouse queries typically multidimensional queries Prof.
Makes the organization s information consistent. Is an adaptive and durable source of information Is a secure support that protects the organization s information asset. Is the foundation for decision making Loading, periodic synchronization of replicas. Semantic integration. Queries based on spreadsheet-style operations and multidimensional view of data. Interactive and online queries. It has repository that is metadata data about data Which is responsible for extracting the information from DW according to the queries given by the end users Metadata is the bridge between DW and the DSS It provides logical linkage between data and application Metadata can pinpoint access to information across the entire DW.
Analysis tools and 3. Data Mining Tools It acts as an interface between the user and the server This layer takes queries from the users And then send it to the servers Receiving information records back and Gives them as output to the end users. Analysis of weather forecasting, predictions and so on. The top most 0-D cuboid, which holds the highest-level of summarization, is called the apex cuboid.
The lattice of cuboids forms a data cube. A Canada Mexico Country sum It is used to perform analysis on data and transform it into information for decision making purpose.
OLAP is a continuous iterative process. A common operation is to aggregate a measure over one or more dimensions. Find total sales. Find total sales for each city, or for each state. Find top five products ranked by total sales. When roll up is performed by dimension reduction, one or more dimensions are removed from the given cube.
Drill down roll down : reverse of roll-up from higher level summary to lower level summary or detailed data, or introducing new dimensions It navigates from less detailed data to more detailed data. This can be realized by either stepping down a concept hierarchy for a dimension or introducing additional dimensions.
Slice and dice: project and select The slice operation performs a selection on one dimension of the given cube resulting in a sub cube The dice operation defines a sub cube by performing a selection on two or more dimensions Pivot rotate : It is visualization operation that rotates the data axes in new view in order to provide an alternative presentation of the data. Other operations drill across: Executes queries involving across more than one fact table drill through: Operation uses relational SQL facilities to drill through the bottom level of the data cube to its back-end relational tables The difference between the snowflake and star schema model is that the dimension tables of the snowflake model can be kept in a normalized form to reduce redundancy.
Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation Easy to maintain and saves storage T i m e C u s t date, custno, prodno, cityname, Spain Canada Mexico city Frankfurt Toronto office L.
Wind Data marts has OLAP It is smaller than data warehouse It contains information from a single department of a business or organization It is Flexible Customized by Department Source is departmentally structured data warehouse Operational data held is first generation, hierarchical and network database. It is often structured and supplied with data in the same way as the data warehouse. But in fact it simply act as a staging area for data to be moved in to warehouse.
Load Manager Load Manager is called the backend component It performs all the operations associated with the extraction and loading of the data in to the warehouse. These operation includes simple transformation of the data to prepare the data for entry in to warehouse. Warehouse Manager Warehouse Manager performs all the operations associated with the management of the data in the warehouse. The operation performed by the component includes Analysis of the data to ensure consistency Transformation and merging of source data Creation of indexes and views Archiving and backing-up of data Query Manager Query Manager is also called front end tool It performs all the operation associated with the management of user queries.
The operation performed by this component includes directing queries to the appropriate tables and scheduling the execution of queries. Data reporting and query tools 2. Application development tools 3. Executive information System EIS tools 4.
Data Mining Tools Up flow: The process associated with adding value to the data in the warehouse through summarizing, packaging and backing up of data in the warehouse. Out Flow: The process associated with making the data available to the end-users. Meta Flow: The process associated with the management of the metadata.
In most cases, the detailed data is not stored online but aggregated to the next level of detail. On regular basis, detailed data is added to the warehouse to supplement the aggregated data. Transient as it will be subject to change on a ongoing basis in order to respond to changing query profiles.
The purpose of summary information is to. Speed up the performance of queries. Removes the requirement to continuously perform summary operations such as sort or group by in answering user queries. The summary data is updated continuously as new data is loaded in to warehouse. May be necessary to backup online summary of data, if this data is kept beyond the retention period for detailed data.
The data is transferred to storage archives such as magnetic tape or optical disk. It is used for variety of purposes. Extraction and loading process: Meta data is used to map data sources to common view of information with in the warehouse.
Warehouse management process: Meta data is used to automate the production of summary tables. Query management process: Meta data is used to direct a query to the most appropriate data source. There are five main groups of access tools. Presentation Layer 2. Application or Business logic Layer 3.
RCET Bhilai. Application logic makes the difference between an order entry system and an inventory control system. It is often called business logic layer because it contains the business rules that drive a given enterprise.
This layer provides the generalized services needed by the other layers. Such as file services, print services, communication services and most important database services. Presentation layer 2. In One tier application the presentation layer, business logic and services are tightly integrated with in the single program. In this, the presentation layer has intimate and detailed knowledge of the database structure. The application layer is often interwoven with both the presentation and services layer All three layers, including the database engine, almost always run on the same computer.
That means database services are separated from the application in two tier design.
DATA WAREHOUSING & DATA MINING. by: Prof. Asha Ambhaikar
Modern businesses generate huge volumes of accounting data on a daily basis. The recent advancements in information technology have given organizations the ability to capture and store these data in an efficient and effective manner. However, there is a widening gap between this data storage and usage of the data. Business intelligence techniques can help an organization obtain and process relevant accounting data quickly and cost efficiently. Such techniques include, query and reporting tools, online analytical processing OLAP , statistical analysis, text mining, data mining, and visualization. While these chapters stand of their own, taken together they provide a comprehensive overview of how to exploit accounting data in the business environment.
Data Warehousing & Data Mining.pdf
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Bhedi 1, Shrinivas P. Deshpande 2, Ujwal A. The proposed data warehouse architecture for financial institute will be well-built to execute a position to augment the present financial core system with BUID. The major advantage of this proposed architecture is that, the architecture will be identify customer various transactions and different accounts detail in different branches of different banks and financial institutions.
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