query about qlikview

[#1]

"In fact, the logical inference engine is what enables the user to explore data freely without predefined search paths, which is the core of Business Discovery."
please could anyone give me real example to understand what qlikview is meant by that  ?
and what is the associative query logic , please could you give me example to understand it ?
are there other advanced technique which distinguish qlikview from other BI providers ?
thanks

[Jason_Michaelides]

This is well explained on the qlik.com website.  Start with the product tour - http://www.qlik.com/us/explore/experience/product-tour?ga-link=hero

[Jason_Michaelides]

Did that help you?

[#4]

Hi,

You can also find many videos on Youtube for Qlikview explaining this logic.

Hope this help,

Anosh

[#5]

thanks for your prompt reply

in fact i have read about business discovery but my problem is the difference between it and the traditional BI isnt clear to me

and i have another problem I want more in-depth explanation of the sentence following

"In fact, the logical inference engine is what enables the user to explore data freely ""without predefined search paths"", which is the core of Business Discovery."

in traditional BI i should take requirments from the BI user and design the schema wich include the mesures and dimensions wich reply the user queries

my question is how can i explore data "without predefind serach paths " ?

i have read many qlikview white papers but i think there is something still fuzzy for me

best regards

[Jason_Michaelides]

You should have a good read of the available white papers - they explain this pretty well. In a nutshell, however, with traditional BI tools you need to predefine aggregated data based on the user's requirements e.g. "Monthly sales by product and region." This is primarily because these aggregations can't be built on the fly due to data volumes, disk speeds etc. Firstly, QV is in-memory so doesn't have this disk-based constraint. Therefore you can generally just load in the detail transactions and have the quarterly and regional aggregations built on user demand.

Secondly, QV automatically associates every field with all the others in the data model, even when separated by several tables. Let's say you have a simple star-schema data model with your detailed fact table in the middle and several dimensions including product name and customers surrounding it. You also have a full calendar table linked to the fact table. In QV you could simply create 3 listboxes - Customer name, Product Name and Month Name.  Now, if a user does nothing more than click on a product name, he will immediately see which customers have bought that product (white background) and which customers HAVEN'T bought that product (grey background). That's without defining any queries at all - all you have done is load in some basic tables, QV has joined on the common fields and associated everything together. If you now selected (clicked on) one of the customers in the white list and then deselected (clicked on) the product you select earlier, you can see which other products that customer has bought. Without doing anything else at all you can also see which months that customer purchased goods in. All these questions would have had to have been predefined with traditional tools, or at least the users would have had to have written some queries themselves.

Do you see?

Jason

[IAMDV]

Thanks Jason. Very useful explanation.

Adding my two cents :

The data records are read into the memory, so that all the processing of data may be done through memory. I am sure you know this bit. QlikView treats all the data as Data Element Type (Columns / Fields) and Data Element Values (Values / Records). So each different data element value of each data element type is assigned a binary code and the data records are stored in binary-coded form and they are also sorted. By using the binary coding, very quick searches can be done on the tables. Also, QlikView removes the redundant information information and reduces the amount of data. However, the redundant information in stored as seperately with the frequencies for each unique data element value and across each data element type. When user makes a selection on data element values then the implied selection (possible values) are kept track seperately to present them to the user. By this process QlikView can perform rapid linear searches.

I understand this is not in detail but just a quick overview.

Cheers - DV

[Jason_Michaelides]

highmountaine,

Please close this thread if you have what you need.

Cheers,

Jason

[#9]

thanks for your prompt replies

it was very helpful

i want to read more about qlikview

best regards

[Jason_Michaelides]

Please mark a correct answer (and any helpful ones) so the thread looks closed to other users (and to stop me from checking back forgetting if I've answered it already!)

Cheers,

Jason