General Datawarehousing

The Secure Sockets Layer (SSL) is a commonly-used protocol for managing the security of a message transmission on the Internet. SSL has recently been succeeded by Transport Layer Security (TLS), which is based on SSL. SSL uses a program layer located between the Internet's Hypertext Transfer Protocol (HTTP) and Transport Control Protocol (TCP) layers. SSL is included as part of both the Microsoft and Netscape browsers and most Web server products. Developed by Netscape, SSL also gained the support of Microsoft and other Internet client/server developers as well and became the de facto standard until evolving into Transport Layer Security. The "sockets" part of the term refers to the sockets method of passing data back and forth between a client and a server program in a network or between program layers in the same computer. SSL uses the public-and-private key encryption system from RSA, which also includes the use of a digital certificate.

TLS and SSL are an integral part of most Web browsers (clients) and Web servers. If a Web site is on a server that supports SSL, SSL can be enabled and specific Web pages can be identified as requiring SSL access. Any Web server can be enabled by using Netscape's SSLRef program library which can be downloaded for noncommercial use or licensed for commercial use.

TLS and SSL are not interoperable. However, a message sent with TLS can be handled by a client that handles SSL but not TLS.

Main Differences between OLTP and OLAP are:-

1. User and System Orientation

OLTP: customer-oriented, used for data analysis and querying by clerks, clients and IT professionals.

OLAP: market-oriented, used for data analysis by knowledge workers( managers, executives, analysis).

2. Data Contents

OLTP: manages current data, very detail-oriented.

OLAP: manages large amounts of historical data, provides facilities for summarization and aggregation, stores information at different levels of granularity to support decision making process.

3. Database Design

OLTP: adopts an entity relationship(ER) model and an application-oriented database design.

OLAP: adopts star, snowflake or fact constellation model and a subject-oriented database design.

4. View

OLTP: focuses on the current data within an enterprise or department.

OLAP: spans multiple versions of a database schema due to the evolutionary process of an organization; integrates information from many organizational locations and data stores

Real-time data warehousing is a combination of two things: 1) real-time activity and 2) data warehousing. Real-time activity is activity that is happening right now. The activity could be anything such as the sale of widgets. Once the activity is complete, there is data about it.

Data warehousing captures business activity data. Real-time data warehousing captures business activity data as it occurs. As soon as the business activity is complete and there is data about it, the completed activity data flows into the data warehouse and becomes available instantly. In other words, real-time data warehousing is a framework for deriving information from data as the data becomes available.

The original Piconet was a USB-style expansion port on RM Nimbus computers.

These days, a piconet is an ad-hoc computer network linking a user group of devices using Bluetooth technology protocols to allow one master device to interconnect with up to seven active slave devices (because a three-bit MAC address is used). Up to 255 further slave devices can be inactive, or parked, which the master device can bring into active status at any time.

A piconet typically has a range of about 10 m and a transfer rate between about 400 and 700 kbit/s, depending on whether synchronous or asynchronous connection is used.

All Parked Slaves have 8 bit parked member address (PMA) and all the active slaves have 3 bit active member address (AMA). The AMA is used by the master to send packets to a specific slave and to identify that the slave has sent a response packet.

1. ODS means Operational Data Store.

Submitted by Francis C. ( xxchen74 @ hotmail . com )

2. A collection of operation or bases data that is extracted from operation databases and standardized, cleansed, consolidated, transformed, and loaded into an enterprise data architecture. An ODS is used to support data mining of operational data, or as the store for base data that is summarized for a data warehouse. The ODS may also be used to audit the data warehouse to assure summarized and derived data is calculated properly. The ODS may further become the enterprise shared operational database, allowing operational systems that are being reengineered to use the ODS as there operation databases.

1.Normalization is process for assigning attributes to entities?Reducesdata redundancies?Helps eliminate data anomalies?Produces controlledredundancies to link tables

2.Normalization is the analysis offunctional dependency between attributes / data items of userviews􀁺It reduces a complex user view to a set of small andstable subgroups of fields / relations

1NF:Repeating groups must beeliminated, Dependencies can be identified, All key attributesdefined,No repeating groups in table

2NF: The Table is already in1NF,Includes no partial dependencies?No attribute dependent on a portionof primary key, Still possible to exhibit transitivedependency,Attributes may be functionally dependent on non-keyattributes

3NF: The Table is already in 2NF, Contains no transitivedependencies