Grid computing vrs Cloud Computing
In grid computing a large project is divided among multiple computers to make use of their resources. Cloud computing which is opposite, allows multiple smaller applications to run at the same time.
World Community Grid’s Mission
World Community Grid’s mission is to create the world’s largest public computing grid to tackle projects that benefit humanity.
Our work has developed the technical infrastructure that serves as the grid’s foundation for scientific research. Our success depends upon individuals collectively contributing their unused computer time to change the world for the better.
World Community Grid is making technology available only to public and not-for-profit organizations to use in humanitarian research that might otherwise not be completed due to the high cost of the computer infrastructure required in the absence of a public grid. As part of our commitment to advancing human welfare, all results will be in the public domain and made public to the global research community.
Rules and Check constraint
Rules are a backward-compatibility feature that perform some of the same functions as CHECK constraints. CHECK constraints are the preferred, standard way to restrict the values in a column. CHECK constraints are also more concise than rules; there can only be one rule applied to a column, but multiple CHECK constraints can be applied. CHECK constraints are specified as part of the CREATE TABLE statement, while rules are created as separate objects and then bound to the column.
This example creates a rule that performs the same function as the CHECK constraint example in the preceding topic. The CHECK constraint is the preferred method to use in Microsoft® SQL Server™ 2000.
CREATE RULE id_chk AS @id BETWEEN 0 and 10000
GO
CREATE TABLE cust_sample
(
cust_id int
PRIMARY KEY,
cust_name char(50),
cust_address char(50),
cust_credit_limit money,
)
GO
sp_bindrule id_chk, ‘cust_sample.cust_id’
GO
Sourec: Sql Server 2000 books online
Difference between Index and Primary Key
This post is on very important topic – “Difference between Index and Key” and is explained in very clear and easy terms by Michael J. Hernandez in his book Database Design for Mer Mortals on page 61. We are emphasizing in this post that most of the IT books lacks clarity or makes concepts more complicated resultantly negative impact on the productivity and probably should look at the above book how it explains an important topic.
An index is a structure RDBMS provides to improves the data processing. Your particular RDBMS program will determine how the index works and how you use it. How index has absolutely nothing to do with logical data structure and people often confuse it with term key.
Index and key are just two more terms that are widely and frequently misused throughout the database industry and in numerous database related publications – you’ll always know the difference between the two if you remember that keys are logical structures you use to identify records within a table and indexes are physical structures you use to optimize data processing.
IBM Cognos solutions for business analytics
Because midsize businesses are the engines of a Smarter Planet™, IBM has designed a business analytics solution for organizations like yours.
The IBM Cognos Express solution helps C-level executives and other business users create reports, analyze data and identify trends quickly—with no special training or IT involvement—using dashboards, gauges and other visual tools. You can get started with the IBM Cognos Express business analytics solution in about an hour and follow these steps to a more profitable bottom line:
Unlock data across your organization.
Uncover new insights.
Take action to drive business performance.
Changing a Traditional information system to adapt new technologies for effective decision making
This is about an experience of an anonymous writer of this post describing his/her hands on experience in upgrading company’s information to adapt to new technologies for effective decision making and resultantly growth of the company. This post also reveals that how the hands on experience sets the foundations of understanding new technologies, needs and seamless coordination between management and technology.
When I joined the company in 1995 most of the accounting data was recorded in Manual registers ledgers / spreadsheets consequently extensive time was spent on preparing reports and making timely decisions.
In 1996, I initiated utilization of MS Access and led the logical data modeling of the Altech, and gradual implementation of RDMB format in MS Access. The data in MS Excel flat files was imported into MS Access and transformed into RDMS format. Created tables to store Master data about all accounts including customers, suppliers, employees and entity related to Altech business. Designed tables to record financial and material transactions and coordinated with Accounts departments to create general ledger and related reports in Crystal reports 8-9.
In 2000 this data base was upgraded to SQL Server 7 utilizing MS Access as front end using Access data project. All the data entry forms and reports were upgraded accordingly. From 2000 to 2002 all the data was centralized in a SQL Server database on server. This facilitates the in-depth business analyses through Crystal reports 9-10 and fast and correct data entry using MS Access Forms.
During the approximately 10 year of my first job, I learned and mastered several concepts of data management such as changing dimensions, notably how to keep track of historical data. For example every month company’s 300 production employees were rotated into different departments and issue was to produce employee historical reports. Database was extended to keep the monthly production activities of employees. New reports were developed in Crystal Reports in Crystal Reports 10 for example complete individual employee performance for any time span, departmental performance. Labor cost by production activities.
Automated the posting of inventory data to financial ledgers for example as soon as new raw material received it was stored in inventory table and updates vendor accounts and material accounts.
My first job set the foundation of database technologies, logical data modeling techniques like entity relationship diagram and translating these into physical model. Understanding of transact SQL Statements, Data input strategies and mastering Crystal reports software for preparing meaningful information, data cleansing/scrubbing and when to use to text files for data and spread sheets.
SAP jumps aboard the cloud computing bandwagon
By Geoff Nairn – FT.com
Published: March 2 2010 15:13 | Last updated: March 2 2010 15:13
BusinessObjects BI OnDemand is SAP’s most ambitious attempt yet to jump aboard the cloud computing bandwagon. Unlike its big brother, the on-premises BusinessObjects business intelligence platform, SAP’s hosted BI offering is aimed at those with little BI expertise. It walks ”casual” users through the process of accessing and visualising on-premises or cloud-based data, as well as creating snazzy dashboards and reports to impress partners, customers or, ahem, colleagues
Comparing SOUNDEX and DIFFERENCE
The SOUNDEX function converts a character string to a four-digit code for use in a comparison. Vowels are ignored in the comparison. Nonalphabetic characters are used to terminate the comparison. This function always returns some value.
This example displays the results of the SOUNDEX function for the similar character strings of “chappel” and “chapelle”. When character strings are similar, both strings have the same SOUNDEX codes.
select SOUNDEX (‘chappel’), SOUNDEX (‘chapelle’)
Here is the result set:
—– —–
C140 c140
(1 row(s) affected)
The DIFFERENCE function compares the SOUNDEX values of two strings and evaluates the similarity between them, returning a value from 0 through 4, where 4 is the best match. This example returns a DIFFERENCE of 4 for the first SELECT because “chappel” and “chappelle” differ by only 2 characters i.e extra l and extra e and the 4 no of characters are similar.
select difference (‘chappel’, ‘chapelle’) Here is the result set:
————
4
(1 row(s) affected)
Traditional Approaches Of Data Cleansing
This post is about “traditional approaches” a title we found in MSDN for data cleansing. We are including this in our website because we use various methods for data cleansing. For experienced professionals there is hard and fast rule because of task on hand requirement. But a traditional approach title is included in datamart.org to identify which approach was used in data cleansing, enjoy that post.
Any number of techniques and tools can be employed to handle these kinds of situations. Specialized structured query language constructs such as the T-SQL LIKE and CONTAINS clauses can be used for basic wildcard searches. But LIKE queries are limited in their ability to handle misspellings, and CONTAINS queries are used in conjunction with SQL Server Full Text indexing.
Fuzzy search databases can be amassed that compile common misspellings (or variants) of specific words which can then be substituted during the cleansing process. This technique works better for applications that check one word at a time, like Microsoft® Word, which employs a similar technique for making spelling corrections on the fly.
Phonetic matching algorithms, implemented in SQL Server as SOUNDEX queries, also detect similarities between single words by matching prominent phonetic characteristics that are then scored numerically for comparison. Key drawbacks to SOUNDEX are that the input string must be contiguous with no spaces, and if the first character of the input string is not correct, the probability of a match being made drops down to zero.
what are Data mashups ?
We were reading new features of Crystal reports 2008, and found a word powerful datamashups, which lead us to research that word.
Basically mashups combine similar types of media and information from multiple sources into a single representation. An example is AlertMap, which combines data from over 200 sources related to severe weather conditions, biohazard threats, and seismic information, and displays them on a map of the world; another example of a data mashup is Chicago Crime Map, which indicates the crime rate and location of crime in Chicago.
Source: Wikipedia.org
