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  • NGINX MIME TYPE

    - by justanotherprogrammer
    I have my nginx conf file so that when ever a mobile device visits my site the url gets rewritten to m.mysite.com I did it by adding the following set $mobile_rewrite do_not_perform; if ($http_user_agent ~* "android.+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino") { set $mobile_rewrite perform; } if ($http_user_agent ~* "^(1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|e\-|e\/|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(di|rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|xda(\-|2|g)|yas\-|your|zeto|zte\-)") { set $mobile_rewrite perform; } if ($mobile_rewrite = perform) { rewrite ^ http://m.mywebsite.com redirect; break; } I got it from http://detectmobilebrowsers.com/ IT WORKS.But none of my images/js/css files load only the HTML. And I know its the chunk of code I mentioned above because when I remove it and visit m.mywebsite.com from my mobile device everything loads up.So this bit of code does SOMETHING to my css/img/js MIME TYPES. I found this out through the the console error messages from safari with the user agent set to iphone. text.cssResource interpreted as stylesheet but transferred with MIME type text/html. 960_16_col.cssResource interpreted as stylesheet but transferred with MIME type text/html. design.cssResource interpreted as stylesheet but transferred with MIME type text/html. navigation_menu.cssResource interpreted as stylesheet but transferred with MIME type text/html. reset.cssResource interpreted as stylesheet but transferred with MIME type text/html. slide_down_panel.cssResource interpreted as stylesheet but transferred with MIME type text/html. myrealtorpage_view.cssResource interpreted as stylesheet but transferred with MIME type text/html. head.jsResource interpreted as script but transferred with MIME type text/html. head.js:1SyntaxError: Parse error isaac:208ReferenceError: Can't find variable: head mrp_home_icon.pngResource interpreted as image but transferred with MIME type text/html. M_1_L_289_I_499_default_thumb.jpgResource interpreted as image but transferred with MIME type text/html. M_1_L_290_I_500_default_thumb.jpgResource interpreted as image but transferred with MIME type text/html. M_1_default.jpgResource interpreted as image but transferred with MIME type text/html. default_listing_image.pngResource interpreted as image but transferred with MIME type text/html. here is my whole nginx conf file just incase... worker_processes 1; events { worker_connections 1024; } http { include mime.types; include /etc/nginx/conf/fastcgi.conf; default_type application/octet-stream; sendfile on; keepalive_timeout 65; #server1 server { listen 80; server_name mywebsite.com www.mywebsite.com ; index index.html index.htm index.php; root /srv/http/mywebsite.com/public; access_log /srv/http/mywebsite.com/logs/access.log; error_log /srv/http/mywebsite.com/logs/error.log; #---------------- For CodeIgniter ----------------# # canonicalize codeigniter url end points # if your default controller is something other than "welcome" you should change the following if ($request_uri ~* ^(/main(/index)?|/index(.php)?)/?$) { rewrite ^(.*)$ / permanent; } # removes trailing "index" from all controllers if ($request_uri ~* index/?$) { rewrite ^/(.*)/index/?$ /$1 permanent; } # removes trailing slashes (prevents SEO duplicate content issues) if (!-d $request_filename) { rewrite ^/(.+)/$ /$1 permanent; } # unless the request is for a valid file (image, js, css, etc.), send to bootstrap if (!-e $request_filename) { rewrite ^/(.*)$ /index.php?/$1 last; break; } #---------------------------------------------------# #--------------- For Mobile Devices ----------------# set $mobile_rewrite do_not_perform; if ($http_user_agent ~* "android.+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino") { set $mobile_rewrite perform; } if ($http_user_agent ~* "^(1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|e\-|e\/|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(di|rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|xda(\-|2|g)|yas\-|your|zeto|zte\-)") { set $mobile_rewrite perform; } if ($mobile_rewrite = perform) { rewrite ^ http://m.mywebsite.com redirect; #rewrite ^(.*)$ $scheme://mywebsite.com/mobile/$1; #return 301 http://m.mywebsite.com; #break; } #---------------------------------------------------# location / { index index.html index.htm index.php; } error_page 500 502 503 504 /50x.html; location = /50x.html { root html; } location ~ \.php$ { try_files $uri =404; fastcgi_pass 127.0.0.1:9000; fastcgi_index index.php; fastcgi_param SCRIPT_FILENAME $document_root$fastcgi_script_name; fastcgi_param PATH_INFO $fastcgi_script_name; include /etc/nginx/conf/fastcgi_params; } }#sever1 #server 2 server { listen 80; server_name m.mywebsite.com; index index.html index.htm index.php; root /srv/http/mywebsite.com/public; access_log /srv/http/mywebsite.com/logs/access.log; error_log /srv/http/mywebsite.com/logs/error.log; #---------------- For CodeIgniter ----------------# # canonicalize codeigniter url end points # if your default controller is something other than "welcome" you should change the following if ($request_uri ~* ^(/main(/index)?|/index(.php)?)/?$) { rewrite ^(.*)$ / permanent; } # removes trailing "index" from all controllers if ($request_uri ~* index/?$) { rewrite ^/(.*)/index/?$ /$1 permanent; } # removes trailing slashes (prevents SEO duplicate content issues) if (!-d $request_filename) { rewrite ^/(.+)/$ /$1 permanent; } # unless the request is for a valid file (image, js, css, etc.), send to bootstrap if (!-e $request_filename) { rewrite ^/(.*)$ /index.php?/$1 last; break; } #---------------------------------------------------# location / { index index.html index.htm index.php; } error_page 500 502 503 504 /50x.html; location = /50x.html { root html; } location ~ \.php$ { try_files $uri =404; fastcgi_pass 127.0.0.1:9000; fastcgi_index index.php; fastcgi_param SCRIPT_FILENAME $document_root$fastcgi_script_name; fastcgi_param PATH_INFO $fastcgi_script_name; include /etc/nginx/conf/fastcgi_params; } }#sever2 }#http I could just detect the mobile browsers with php or javascript but i need to make the detection at the server level so that i can use the 'm' in m.mywebsite.com as a flag in my controllers (codeigniter) to serve up the right view. I hope someone can help me! Thank you!

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  • How does jQuery stores data with .data()?

    - by TK
    I am a little confused how jQuery stores data with .data() functions. Is this something called expando? Or is this using HTML5 Web Storage although I think this is very unlikely? The documentation says: The .data() method allows us to attach data of any type to DOM elements in a way that is safe from circular references and therefore from memory leaks. As I read about expando, it seems to have a rick of memory leak. Unfortunately my skills are not enough to read and understand jQuery code itself, but I want to know how jQuery stores such data by using data(). http://api.jquery.com/data/

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  • ASP.Net Layered app - Share Entity Data Model amongst layers

    - by Chris Klepeis
    How can I share the auto-generated entity data model (generated object classes) amongst all layers of my C# web app whilst only granting query access in the data layer? This uses the typical 3 layer approach: data, business, presentation. My data layer returns an IEnumerable<T> to my business layer, but I cannot return type T to the presentation layer because I do not want the presentation layer to know of the existence of the data layer - which is where the entity framework auto-generated my classes. It was recommended to have a seperate layer with just the data model, but I'm unsure how to seperate the data model from the query functionality the entity framework provides.

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  • How does jQuery store data with .data()?

    - by TK
    I am a little confused how jQuery stores data with .data() functions. Is this something called expando? Or is this using HTML5 Web Storage although I think this is very unlikely? The documentation says: The .data() method allows us to attach data of any type to DOM elements in a way that is safe from circular references and therefore from memory leaks. As I read about expando, it seems to have a rick of memory leak. Unfortunately my skills are not enough to read and understand jQuery code itself, but I want to know how jQuery stores such data by using data(). http://api.jquery.com/data/

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  • Extracting ID from data packet GPS

    - by user604134
    Hi , I am trying to configure a GPS device to my systems. The GPS device send the data packet to my IP in the following format : $$?W??¬ÿÿÿÿ™U042903.000,A,2839.6408,N,07717.0905,E,0.00,,230111,,,A*7C|1.2|203|0000÷ I am able to extract the latitude, longitude and other information but I am not able to extract the Tracker ID out of the string. According to the manual the ID is in hex format.And the format of the packet is $$\r\n I dont know what to do with it, I have tried converting this to hex..but it didnt work. Any help will be greatly appreciate. Thanks

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  • T-SQL (SCD) Slowly Changing Dimension Type 2 using a merge statement

    - by AtulThakor
    Working on stored procedure recently which loads records into a data warehouse I found that the existing record was being expired using an update statement followed by an insert to add the new active record. Playing around with the merge statement you can actually expire the current record and insert a new record within one clean statement. This is how the statement works, we do the normal merge statement to insert a record when there is no match, if we match the record we update the existing record by expiring it and deactivating. At the end of the merge statement we use the output statement to output the staging values for the update,  we wrap the whole merge statement within an insert statement and add new rows for the records which we inserted. I’ve added the full script at the bottom so you can paste it and play around.   1: INSERT INTO ExampleFactUpdate 2: (PolicyID, 3: Status) 4: SELECT -- these columns are returned from the output statement 5: PolicyID, 6: Status 7: FROM 8: ( 9: -- merge statement on unique id in this case Policy_ID 10: MERGE dbo.ExampleFactUpdate dp 11: USING dbo.ExampleStag s 12: ON dp.PolicyID = s.PolicyID 13: WHEN NOT MATCHED THEN -- when we cant match the record we insert a new record record and this is all that happens 14: INSERT (PolicyID,Status) 15: VALUES (s.PolicyID, s.Status) 16: WHEN MATCHED --if it already exists 17: AND ExpiryDate IS NULL -- and the Expiry Date is null 18: THEN 19: UPDATE 20: SET 21: dp.ExpiryDate = getdate(), --we set the expiry on the existing record 22: dp.Active = 0 -- and deactivate the existing record 23: OUTPUT $Action MergeAction, s.PolicyID, s.Status -- the output statement returns a merge action which can 24: ) MergeOutput -- be insert/update/delete, on our example where a record has been updated (or expired in our case 25: WHERE -- we'll filter using a where clause 26: MergeAction = 'Update'; -- here   Complete source for example 1: if OBJECT_ID('ExampleFactUpdate') > 0 2: drop table ExampleFactUpdate 3:  4: Create Table ExampleFactUpdate( 5: ID int identity(1,1), 3: go 6: PolicyID varchar(100), 7: Status varchar(100), 8: EffectiveDate datetime default getdate(), 9: ExpiryDate datetime, 10: Active bit default 1 11: ) 12:  13:  14: insert into ExampleFactUpdate( 15: PolicyID, 16: Status) 17: select 18: 1, 19: 'Live' 20:  21: /*Create Staging Table*/ 22: if OBJECT_ID('ExampleStag') > 0 23: drop table ExampleStag 24: go 25:  26: /*Create example fact table */ 27: Create Table ExampleStag( 28: PolicyID varchar(100), 29: Status varchar(100)) 30:  31: --add some data 32: insert into ExampleStag( 33: PolicyID, 34: Status) 35: select 36: 1, 37: 'Lapsed' 38: union all 39: select 40: 2, 41: 'Quote' 42:  43: select * 44: from ExampleFactUpdate 45:  46: select * 47: from ExampleStag 48:  49:  50: INSERT INTO ExampleFactUpdate 51: (PolicyID, 52: Status) 53: SELECT -- these columns are returned from the output statement 54: PolicyID, 55: Status 56: FROM 57: ( 58: -- merge statement on unique id in this case Policy_ID 59: MERGE dbo.ExampleFactUpdate dp 60: USING dbo.ExampleStag s 61: ON dp.PolicyID = s.PolicyID 62: WHEN NOT MATCHED THEN -- when we cant match the record we insert a new record record and this is all that happens 63: INSERT (PolicyID,Status) 64: VALUES (s.PolicyID, s.Status) 65: WHEN MATCHED --if it already exists 66: AND ExpiryDate IS NULL -- and the Expiry Date is null 67: THEN 68: UPDATE 69: SET 70: dp.ExpiryDate = getdate(), --we set the expiry on the existing record 71: dp.Active = 0 -- and deactivate the existing record 72: OUTPUT $Action MergeAction, s.PolicyID, s.Status -- the output statement returns a merge action which can 73: ) MergeOutput -- be insert/update/delete, on our example where a record has been updated (or expired in our case 74: WHERE -- we'll filter using a where clause 75: MergeAction = 'Update'; -- here 76:  77:  78: select * 79: from ExampleFactUpdate 80: 

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  • Ubuntu and Windows 8 shared partition gets corrupted

    - by Bruno-P
    I have a dual boot (Ubuntu 12.04 and Windows 8) system. Both systems have access to an NTFS "DATA" partition which contains all my images, documents, music and some application data like Chrome and Thunderbird Profiles which used by both OS. Everything was working fine in my Dual boot Ubuntu/Windows 7, but after updating to Windows 8 I am having a lot of troubles. First, sometimes, I add some files from Ubuntu into my DATA partition but they don't show up in Windows. Sometimes, I can't even use the DATA partition from Windows. When I try to save a file it gives an error "The directory or file is corrupted or unreadable". I need to run checkdisk to fix it but after some time, same error appears. Before upgrading to Windows 8 I also installed a new hard drive and copied the old data using clonezilla (full disk clone). Here is the log of my last chkdisk: Chkdsk was executed in read/write mode. Checking file system on D: Volume dismounted. All opened handles to this volume are now invalid. Volume label is DATA. CHKDSK is verifying files (stage 1 of 3)... Deleted corrupt attribute list entry with type code 128 in file 67963. Unable to find child frs 0x12a3f with sequence number 0x15. The attribute of type 0x80 and instance tag 0x2 in file 0x1097b has allocated length of 0x560000 instead of 0x427000. Deleted corrupt attribute list entry with type code 128 in file 67963. Unable to locate attribute with instance tag 0x2 and segment reference 0x1e00000001097b. The expected attribute type is 0x80. Deleting corrupt attribute record (128, "") from file record segment 67963. Attribute record of type 0x80 and instance tag 0x3 is cross linked starting at 0x2431b2 for possibly 0x20 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x3 in file 0x1791e is already in use. Deleting corrupt attribute record (128, "") from file record segment 96542. Attribute record of type 0x80 and instance tag 0x4 is cross linked starting at 0x6bc7 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x4 in file 0x17e83 is already in use. Deleting corrupt attribute record (128, "") from file record segment 97923. Attribute record of type 0x80 and instance tag 0x4 is cross linked starting at 0x1f7cec for possibly 0x5 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x4 in file 0x17eaf is already in use. Deleting corrupt attribute record (128, "") from file record segment 97967. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x441bd7f for possibly 0x9 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x32085 is already in use. Deleting corrupt attribute record (128, "") from file record segment 204933. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4457850 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x320be is already in use. Deleting corrupt attribute record (128, "") from file record segment 204990. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4859249 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x3726b is already in use. Deleting corrupt attribute record (128, "") from file record segment 225899. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x485d309 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x3726c is already in use. Deleting corrupt attribute record (128, "") from file record segment 225900. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x48a47de for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37286 is already in use. Deleting corrupt attribute record (128, "") from file record segment 225926. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x48ac80b for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37287 is already in use. Deleting corrupt attribute record (128, "") from file record segment 225927. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x48ae7ef for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37288 is already in use. Deleting corrupt attribute record (128, "") from file record segment 225928. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x48af7f8 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x3728a is already in use. Deleting corrupt attribute record (128, "") from file record segment 225930. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x48c39b6 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37292 is already in use. Deleting corrupt attribute record (128, "") from file record segment 225938. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x495d37a for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x372d7 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226007. Attribute record of type 0xa0 and instance tag 0x5 is cross linked starting at 0x4d0bd38 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0xa0 and instance tag 0x5 in file 0x372dc is already in use. Deleting corrupt attribute record (160, $I30) from file record segment 226012. Attribute record of type 0xa0 and instance tag 0x5 is cross linked starting at 0x4c2d9bc for possibly 0x1 clusters. Some clusters occupied by attribute of type 0xa0 and instance tag 0x5 in file 0x372ed is already in use. Deleting corrupt attribute record (160, $I30) from file record segment 226029. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4a4c1c3 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37354 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226132. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4a8e639 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37376 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226166. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4a8f6eb for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37379 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226169. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4ae1aa8 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37391 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226193. Attribute record of type 0xa0 and instance tag 0x5 is cross linked starting at 0x4b00d45 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0xa0 and instance tag 0x5 in file 0x37396 is already in use. Deleting corrupt attribute record (160, $I30) from file record segment 226198. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4b02d50 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x3739c is already in use. Deleting corrupt attribute record (128, "") from file record segment 226204. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4b3407a for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x373a8 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226216. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4bd8a1b for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x373db is already in use. Deleting corrupt attribute record (128, "") from file record segment 226267. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4bd9a28 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x373dd is already in use. Deleting corrupt attribute record (128, "") from file record segment 226269. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4c2fb24 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x373f3 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226291. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cb67e9 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37424 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226340. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cba829 for possibly 0x2 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37425 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226341. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cbe868 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37427 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226343. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cbf878 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37428 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226344. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cc58d8 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x3742a is already in use. Deleting corrupt attribute record (128, "") from file record segment 226346. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4ccc943 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x3742b is already in use. Deleting corrupt attribute record (128, "") from file record segment 226347. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cd199b for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x3742d is already in use. Deleting corrupt attribute record (128, "") from file record segment 226349. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cd29a8 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x3742f is already in use. Deleting corrupt attribute record (128, "") from file record segment 226351. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cd39b8 for possibly 0x2 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37430 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226352. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cd49c8 for possibly 0x2 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37432 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226354. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cd9a16 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37435 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226357. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cdca46 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37436 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226358. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4ce0a78 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37437 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226359. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4ce6ad9 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x3743a is already in use. Deleting corrupt attribute record (128, "") from file record segment 226362. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cebb28 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x3743b is already in use. Deleting corrupt attribute record (128, "") from file record segment 226363. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4ceeb67 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x3743d is already in use. Deleting corrupt attribute record (128, "") from file record segment 226365. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cf4bc6 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x3743e is already in use. Deleting corrupt attribute record (128, "") from file record segment 226366. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cfbc3a for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37440 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226368. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4cfcc48 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37442 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226370. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4d02ca9 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37443 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226371. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4d06ce8 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37444 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226372. Attribute record of type 0xa0 and instance tag 0x5 is cross linked starting at 0x4d9a608 for possibly 0x2 clusters. Some clusters occupied by attribute of type 0xa0 and instance tag 0x5 in file 0x37449 is already in use. Deleting corrupt attribute record (160, $I30) from file record segment 226377. Attribute record of type 0xa0 and instance tag 0x5 is cross linked starting at 0x4d844ab for possibly 0x1 clusters. Some clusters occupied by attribute of type 0xa0 and instance tag 0x5 in file 0x3744b is already in use. Deleting corrupt attribute record (160, $I30) from file record segment 226379. Attribute record of type 0xa0 and instance tag 0x5 is cross linked starting at 0x4d6c32b for possibly 0x1 clusters. Some clusters occupied by attribute of type 0xa0 and instance tag 0x5 in file 0x3744c is already in use. Deleting corrupt attribute record (160, $I30) from file record segment 226380. Attribute record of type 0xa0 and instance tag 0x5 is cross linked starting at 0x4d2af25 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0xa0 and instance tag 0x5 in file 0x3744e is already in use. Deleting corrupt attribute record (160, $I30) from file record segment 226382. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4d0fd78 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x80 and instance tag 0x2 in file 0x37451 is already in use. Deleting corrupt attribute record (128, "") from file record segment 226385. Attribute record of type 0x80 and instance tag 0x2 is cross linked starting at 0x4d16ef8 for possibly 0x1 clusters. Some clusters occupied by attribute of type 0x8 Can anyone help? Thank you

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  • Accessing and Updating Data in ASP.NET: Filtering Data Using a CheckBoxList

    Filtering Database Data with Parameters, an earlier installment in this article series, showed how to filter the data returned by ASP.NET's data source controls. In a nutshell, the data source controls can include parameterized queries whose parameter values are defined via parameter controls. For example, the SqlDataSource can include a parameterized SelectCommand, such as: SELECT * FROM Books WHERE Price > @Price. Here, @Price is a parameter; the value for a parameter can be defined declaratively using a parameter control. ASP.NET offers a variety of parameter controls, including ones that use hard-coded values, ones that retrieve values from the querystring, and ones that retrieve values from session, and others. Perhaps the most useful parameter control is the ControlParameter, which retrieves its value from a Web control on the page. Using the ControlParameter we can filter the data returned by the data source control based on the end user's input. While the ControlParameter works well with most types of Web controls, it does not work as expected with the CheckBoxList control. The ControlParameter is designed to retrieve a single property value from the specified Web control, but the CheckBoxList control does not have a property that returns all of the values of its selected items in a form that the CheckBoxList control can use. Moreover, if you are using the selected CheckBoxList items to query a database you'll quickly find that SQL does not offer out of the box functionality for filtering results based on a user-supplied list of filter criteria. The good news is that with a little bit of effort it is possible to filter data based on the end user's selections in a CheckBoxList control. This article starts with a look at how to get SQL to filter data based on a user-supplied, comma-delimited list of values. Next, it shows how to programmatically construct a comma-delimited list that represents the selected CheckBoxList values and pass that list into the SQL query. Finally, we'll explore creating a custom parameter control to handle this logic declaratively. Read on to learn more! Read More >

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  • SQLAuthority News – Fast Track Data Warehouse 3.0 Reference Guide

    - by pinaldave
    http://msdn.microsoft.com/en-us/library/gg605238.aspx I am very excited that Fast Track Data Warehouse 3.0 reference guide has been announced. As a consultant I have always enjoyed working with Fast Track Data Warehouse project as it truly expresses the potential of the SQL Server Engine. Here is few details of the enhancement of the Fast Track Data Warehouse 3.0 reference architecture. The SQL Server Fast Track Data Warehouse initiative provides a basic methodology and concrete examples for the deployment of balanced hardware and database configuration for a data warehousing workload. Balance is measured across the key components of a SQL Server installation; storage, server, application settings, and configuration settings for each component are evaluated. Description Note FTDW 3.0 Architecture Basic component architecture for FT 3.0 based systems. New Memory Guidelines Minimum and maximum tested memory configurations by server socket count. Additional Startup Options Notes for T-834 and setting for Lock Pages in Memory. Storage Configuration RAID1+0 now standard (RAID1 was used in FT 2.0). Evaluating Fragmentation Query provided for evaluating logical fragmentation. Loading Data Additional options for CI table loads. MCR Additional detail and explanation of FTDW MCR Rating. Read white paper on fast track data warehousing. Reference: Pinal Dave (http://blog.SQLAuthority.com)   Filed under: Business Intelligence, Data Warehousing, PostADay, SQL, SQL Authority, SQL Documentation, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, SQL White Papers, SQLAuthority News, T SQL, Technology

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  • Accessing and Updating Data in ASP.NET: Filtering Data Using a CheckBoxList

    Filtering Database Data with Parameters, an earlier installment in this article series, showed how to filter the data returned by ASP.NET's data source controls. In a nutshell, the data source controls can include parameterized queries whose parameter values are defined via parameter controls. For example, the SqlDataSource can include a parameterized SelectCommand, such as: SELECT * FROM Books WHERE Price > @Price. Here, @Price is a parameter; the value for a parameter can be defined declaratively using a parameter control. ASP.NET offers a variety of parameter controls, including ones that use hard-coded values, ones that retrieve values from the querystring, and ones that retrieve values from session, and others. Perhaps the most useful parameter control is the ControlParameter, which retrieves its value from a Web control on the page. Using the ControlParameter we can filter the data returned by the data source control based on the end user's input. While the ControlParameter works well with most types of Web controls, it does not work as expected with the CheckBoxList control. The ControlParameter is designed to retrieve a single property value from the specified Web control, but the CheckBoxList control does not have a property that returns all of the values of its selected items in a form that the CheckBoxList control can use. Moreover, if you are using the selected CheckBoxList items to query a database you'll quickly find that SQL does not offer out of the box functionality for filtering results based on a user-supplied list of filter criteria. The good news is that with a little bit of effort it is possible to filter data based on the end user's selections in a CheckBoxList control. This article starts with a look at how to get SQL to filter data based on a user-supplied, comma-delimited list of values. Next, it shows how to programmatically construct a comma-delimited list that represents the selected CheckBoxList values and pass that list into the SQL query. Finally, we'll explore creating a custom parameter control to handle this logic declaratively. Read on to learn more! Read More >

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  • Magento Onepage Success Conversion Tracking Design Pattern

    - by user1734954
    My intent is to track conversions through multiple channels by inserting third party javascript (for example google analytics, optimizely, pricegrabber etc.) into the footer of onepage success . I've accomplished this by adding a block to the footer reference inside of the checkout success node within local.xml and everything works appropriately. My questions are more about efficiency and extensibility. It occurred to me that it would be better to combine all of the blocks into a single block reference and then use a various methods acting on a single call to the various related models to provide the data needed for insertion into the javascript for each of the conversion tracking scripts. Some examples of the common data that conversion tracking may rely on(pseudo): Order ID , Order Total, Order.LineItem.Name(foreach) and so on Currently for each of the scripts I've made a call to the appropriate model passing the customers last order id as the load value and the calling a get() assigning the return value to a variable and then iterating through the data to match the values with the expectations of the given third party service. All of the data should be pulled once when checkout is complete each third party services may expect different data in different formats Here is an example of one of the conversion tracking template files which loads at the footer of checkout success. $order = Mage::getModel('sales/order')->loadByIncrementId(Mage::getSingleton('checkout/session')->getLastRealOrderId()); $amount = number_format($order->getGrandTotal(),2); $customer = Mage::helper('customer')->getCustomer()->getData(); ?> <script type="text/javascript"> popup_email = '<?php echo($customer['email']);?>'; popup_order_number = '<?php echo $this->getOrderId() ?>'; </script> <!-- PriceGrabber Merchant Evaluation Code --> <script type="text/javascript" charset="UTF-8" src="https://www.pricegrabber.com/rating_merchrevpopjs.php?retid=<something>"></script> <noscript><a href="http://www.pricegrabber.com/rating_merchrev.php?retid=<something>" target=_blank> <img src="https://images.pricegrabber.com/images/mr_noprize.jpg" border="0" width="272" height="238" alt="Merchant Evaluation"></a></noscript> <!-- End PriceGrabber Code --> Having just a single piece of code like this is not that big of a deal, but we are doing similar things with a number of different third party services. Pricegrabber is one of the simpler examples. A more sophisticated tracking service expects a comma separated list of all of the product names, ids, prices, categories , order id etc. I would like to make it all more manageable so my idea to do the following: combine all of the template files into a single file Develop a helper class or library to deliver the data to the conversion template Goals Include Extensibility Minimal Model Calls Minimal Method Calls The Questions 1. Is a Mage helper the best route to take? 2. Is there any design pattern you may recommend for the "helper" class? 3. Why would this the design pattern you've chosen be best for this instance?

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  • extjs data store load data on fly

    - by CKeven
    I'm trying to create a data store that will load the data schema and records on fly. Here is the current code i have and I'm not sure how to setup the array reader properly since i don't have the schema before query returns. ds = new Ext.data.Store({ url: 'http://10.10.97.83/cgi-bin/cgiip.exe/WService=wsdev/majax/jsbrdgx.p', baseParams: { cr: Ext.util.JSON.encode(omgtobxParms) }, reader: new Ext.data.ArrayReader({ //root:data.value.records }, col_names) }); {"name": "tmp_buy_book", "schema": [ { "name": "a", "type": "C"}, { "name": "b", "type": "C"} "records": [["1", ""], ["1",""]]}

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  • Big Data – Buzz Words: What is MapReduce – Day 7 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned what is Hadoop. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – MapReduce. What is MapReduce? MapReduce was designed by Google as a programming model for processing large data sets with a parallel, distributed algorithm on a cluster. Though, MapReduce was originally Google proprietary technology, it has been quite a generalized term in the recent time. MapReduce comprises a Map() and Reduce() procedures. Procedure Map() performance filtering and sorting operation on data where as procedure Reduce() performs a summary operation of the data. This model is based on modified concepts of the map and reduce functions commonly available in functional programing. The library where procedure Map() and Reduce() belongs is written in many different languages. The most popular free implementation of MapReduce is Apache Hadoop which we will explore tomorrow. Advantages of MapReduce Procedures The MapReduce Framework usually contains distributed servers and it runs various tasks in parallel to each other. There are various components which manages the communications between various nodes of the data and provides the high availability and fault tolerance. Programs written in MapReduce functional styles are automatically parallelized and executed on commodity machines. The MapReduce Framework takes care of the details of partitioning the data and executing the processes on distributed server on run time. During this process if there is any disaster the framework provides high availability and other available modes take care of the responsibility of the failed node. As you can clearly see more this entire MapReduce Frameworks provides much more than just Map() and Reduce() procedures; it provides scalability and fault tolerance as well. A typical implementation of the MapReduce Framework processes many petabytes of data and thousands of the processing machines. How do MapReduce Framework Works? A typical MapReduce Framework contains petabytes of the data and thousands of the nodes. Here is the basic explanation of the MapReduce Procedures which uses this massive commodity of the servers. Map() Procedure There is always a master node in this infrastructure which takes an input. Right after taking input master node divides it into smaller sub-inputs or sub-problems. These sub-problems are distributed to worker nodes. A worker node later processes them and does necessary analysis. Once the worker node completes the process with this sub-problem it returns it back to master node. Reduce() Procedure All the worker nodes return the answer to the sub-problem assigned to them to master node. The master node collects the answer and once again aggregate that in the form of the answer to the original big problem which was assigned master node. The MapReduce Framework does the above Map () and Reduce () procedure in the parallel and independent to each other. All the Map() procedures can run parallel to each other and once each worker node had completed their task they can send it back to master code to compile it with a single answer. This particular procedure can be very effective when it is implemented on a very large amount of data (Big Data). The MapReduce Framework has five different steps: Preparing Map() Input Executing User Provided Map() Code Shuffle Map Output to Reduce Processor Executing User Provided Reduce Code Producing the Final Output Here is the Dataflow of MapReduce Framework: Input Reader Map Function Partition Function Compare Function Reduce Function Output Writer In a future blog post of this 31 day series we will explore various components of MapReduce in Detail. MapReduce in a Single Statement MapReduce is equivalent to SELECT and GROUP BY of a relational database for a very large database. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – HDFS. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • Currency Conversion in Oracle BI applications

    - by Saurabh Verma
    Authored by Vijay Aggarwal and Hichem Sellami A typical data warehouse contains Star and/or Snowflake schema, made up of Dimensions and Facts. The facts store various numerical information including amounts. Example; Order Amount, Invoice Amount etc. With the true global nature of business now-a-days, the end-users want to view the reports in their own currency or in global/common currency as defined by their business. This presents a unique opportunity in BI to provide the amounts in converted rates either by pre-storing or by doing on-the-fly conversions while displaying the reports to the users. Source Systems OBIA caters to various source systems like EBS, PSFT, Sebl, JDE, Fusion etc. Each source has its own unique and intricate ways of defining and storing currency data, doing currency conversions and presenting to the OLTP users. For example; EBS stores conversion rates between currencies which can be classified by conversion rates, like Corporate rate, Spot rate, Period rate etc. Siebel stores exchange rates by conversion rates like Daily. EBS/Fusion stores the conversion rates for each day, where as PSFT/Siebel store for a range of days. PSFT has Rate Multiplication Factor and Rate Division Factor and we need to calculate the Rate based on them, where as other Source systems store the Currency Exchange Rate directly. OBIA Design The data consolidation from various disparate source systems, poses the challenge to conform various currencies, rate types, exchange rates etc., and designing the best way to present the amounts to the users without affecting the performance. When consolidating the data for reporting in OBIA, we have designed the mechanisms in the Common Dimension, to allow users to report based on their required currencies. OBIA Facts store amounts in various currencies: Document Currency: This is the currency of the actual transaction. For a multinational company, this can be in various currencies. Local Currency: This is the base currency in which the accounting entries are recorded by the business. This is generally defined in the Ledger of the company. Global Currencies: OBIA provides five Global Currencies. Three are used across all modules. The last two are for CRM only. A Global currency is very useful when creating reports where the data is viewed enterprise-wide. Example; a US based multinational would want to see the reports in USD. The company will choose USD as one of the global currencies. OBIA allows users to define up-to five global currencies during the initial implementation. The term Currency Preference is used to designate the set of values: Document Currency, Local Currency, Global Currency 1, Global Currency 2, Global Currency 3; which are shared among all modules. There are four more currency preferences, specific to certain modules: Global Currency 4 (aka CRM Currency) and Global Currency 5 which are used in CRM; and Project Currency and Contract Currency, used in Project Analytics. When choosing Local Currency for Currency preference, the data will show in the currency of the Ledger (or Business Unit) in the prompt. So it is important to select one Ledger or Business Unit when viewing data in Local Currency. More on this can be found in the section: Toggling Currency Preferences in the Dashboard. Design Logic When extracting the fact data, the OOTB mappings extract and load the document amount, and the local amount in target tables. It also loads the exchange rates required to convert the document amount into the corresponding global amounts. If the source system only provides the document amount in the transaction, the extract mapping does a lookup to get the Local currency code, and the Local exchange rate. The Load mapping then uses the local currency code and rate to derive the local amount. The load mapping also fetches the Global Currencies and looks up the corresponding exchange rates. The lookup of exchange rates is done via the Exchange Rate Dimension provided as a Common/Conforming Dimension in OBIA. The Exchange Rate Dimension stores the exchange rates between various currencies for a date range and Rate Type. Two physical tables W_EXCH_RATE_G and W_GLOBAL_EXCH_RATE_G are used to provide the lookups and conversions between currencies. The data is loaded from the source system’s Ledger tables. W_EXCH_RATE_G stores the exchange rates between currencies with a date range. On the other hand, W_GLOBAL_EXCH_RATE_G stores the currency conversions between the document currency and the pre-defined five Global Currencies for each day. Based on the requirements, the fact mappings can decide and use one or both tables to do the conversion. Currency design in OBIA also taps into the MLS and Domain architecture, thus allowing the users to map the currencies to a universal Domain during the implementation time. This is especially important for companies deploying and using OBIA with multiple source adapters. Some Gotchas to Look for It is necessary to think through the currencies during the initial implementation. 1) Identify various types of currencies that are used by your business. Understand what will be your Local (or Base) and Documentation currency. Identify various global currencies that your users will want to look at the reports. This will be based on the global nature of your business. Changes to these currencies later in the project, while permitted, but may cause Full data loads and hence lost time. 2) If the user has a multi source system make sure that the Global Currencies and Global Rate Types chosen in Configuration Manager do have the corresponding source specific counterparts. In other words, make sure for every DW specific value chosen for Currency Code or Rate Type, there is a source Domain mapping already done. Technical Section This section will briefly mention the technical scenarios employed in the OBIA adaptors to extract data from each source system. In OBIA, we have two main tables which store the Currency Rate information as explained in previous sections. W_EXCH_RATE_G and W_GLOBAL_EXCH_RATE_G are the two tables. W_EXCH_RATE_G stores all the Currency Conversions present in the source system. It captures data for a Date Range. W_GLOBAL_EXCH_RATE_G has Global Currency Conversions stored at a Daily level. However the challenge here is to store all the 5 Global Currency Exchange Rates in a single record for each From Currency. Let’s voyage further into the Source System Extraction logic for each of these tables and understand the flow briefly. EBS: In EBS, we have Currency Data stored in GL_DAILY_RATES table. As the name indicates GL_DAILY_RATES EBS table has data at a daily level. However in our warehouse we store the data with a Date Range and insert a new range record only when the Exchange Rate changes for a particular From Currency, To Currency and Rate Type. Below are the main logical steps that we employ in this process. (Incremental Flow only) – Cleanup the data in W_EXCH_RATE_G. Delete the records which have Start Date > minimum conversion date Update the End Date of the existing records. Compress the daily data from GL_DAILY_RATES table into Range Records. Incremental map uses $$XRATE_UPD_NUM_DAY as an extra parameter. Generate Previous Rate, Previous Date and Next Date for each of the Daily record from the OLTP. Filter out the records which have Conversion Rate same as Previous Rates or if the Conversion Date lies within a single day range. Mark the records as ‘Keep’ and ‘Filter’ and also get the final End Date for the single Range record (Unique Combination of From Date, To Date, Rate and Conversion Date). Filter the records marked as ‘Filter’ in the INFA map. The above steps will load W_EXCH_RATE_GS. Step 0 updates/deletes W_EXCH_RATE_G directly. SIL map will then insert/update the GS data into W_EXCH_RATE_G. These steps convert the daily records in GL_DAILY_RATES to Range records in W_EXCH_RATE_G. We do not need such special logic for loading W_GLOBAL_EXCH_RATE_G. This is a table where we store data at a Daily Granular Level. However we need to pivot the data because the data present in multiple rows in source tables needs to be stored in different columns of the same row in DW. We use GROUP BY and CASE logic to achieve this. Fusion: Fusion has extraction logic very similar to EBS. The only difference is that the Cleanup logic that was mentioned in step 0 above does not use $$XRATE_UPD_NUM_DAY parameter. In Fusion we bring all the Exchange Rates in Incremental as well and do the cleanup. The SIL then takes care of Insert/Updates accordingly. PeopleSoft:PeopleSoft does not have From Date and To Date explicitly in the Source tables. Let’s look at an example. Please note that this is achieved from PS1 onwards only. 1 Jan 2010 – USD to INR – 45 31 Jan 2010 – USD to INR – 46 PSFT stores records in above fashion. This means that Exchange Rate of 45 for USD to INR is applicable for 1 Jan 2010 to 30 Jan 2010. We need to store data in this fashion in DW. Also PSFT has Exchange Rate stored as RATE_MULT and RATE_DIV. We need to do a RATE_MULT/RATE_DIV to get the correct Exchange Rate. We generate From Date and To Date while extracting data from source and this has certain assumptions: If a record gets updated/inserted in the source, it will be extracted in incremental. Also if this updated/inserted record is between other dates, then we also extract the preceding and succeeding records (based on dates) of this record. This is required because we need to generate a range record and we have 3 records whose ranges have changed. Taking the same example as above, if there is a new record which gets inserted on 15 Jan 2010; the new ranges are 1 Jan to 14 Jan, 15 Jan to 30 Jan and 31 Jan to Next available date. Even though 1 Jan record and 31 Jan have not changed, we will still extract them because the range is affected. Similar logic is used for Global Exchange Rate Extraction. We create the Range records and get it into a Temporary table. Then we join to Day Dimension, create individual records and pivot the data to get the 5 Global Exchange Rates for each From Currency, Date and Rate Type. Siebel: Siebel Facts are dependent on Global Exchange Rates heavily and almost none of them really use individual Exchange Rates. In other words, W_GLOBAL_EXCH_RATE_G is the main table used in Siebel from PS1 release onwards. As of January 2002, the Euro Triangulation method for converting between currencies belonging to EMU members is not needed for present and future currency exchanges. However, the method is still available in Siebel applications, as are the old currencies, so that historical data can be maintained accurately. The following description applies only to historical data needing conversion prior to the 2002 switch to the Euro for the EMU member countries. If a country is a member of the European Monetary Union (EMU), you should convert its currency to other currencies through the Euro. This is called triangulation, and it is used whenever either currency being converted has EMU Triangulation checked. Due to this, there are multiple extraction flows in SEBL ie. EUR to EMU, EUR to NonEMU, EUR to DMC and so on. We load W_EXCH_RATE_G through multiple flows with these data. This has been kept same as previous versions of OBIA. W_GLOBAL_EXCH_RATE_G being a new table does not have such needs. However SEBL does not have From Date and To Date columns in the Source tables similar to PSFT. We use similar extraction logic as explained in PSFT section for SEBL as well. What if all 5 Global Currencies configured are same? As mentioned in previous sections, from PS1 onwards we store Global Exchange Rates in W_GLOBAL_EXCH_RATE_G table. The extraction logic for this table involves Pivoting data from multiple rows into a single row with 5 Global Exchange Rates in 5 columns. As mentioned in previous sections, we use CASE and GROUP BY functions to achieve this. This approach poses a unique problem when all the 5 Global Currencies Chosen are same. For example – If the user configures all 5 Global Currencies as ‘USD’ then the extract logic will not be able to generate a record for From Currency=USD. This is because, not all Source Systems will have a USD->USD conversion record. We have _Generated mappings to take care of this case. We generate a record with Conversion Rate=1 for such cases. Reusable Lookups Before PS1, we had a Mapplet for Currency Conversions. In PS1, we only have reusable Lookups- LKP_W_EXCH_RATE_G and LKP_W_GLOBAL_EXCH_RATE_G. These lookups have another layer of logic so that all the lookup conditions are met when they are used in various Fact Mappings. Any user who would want to do a LKP on W_EXCH_RATE_G or W_GLOBAL_EXCH_RATE_G should and must use these Lookups. A direct join or Lookup on the tables might lead to wrong data being returned. Changing Currency preferences in the Dashboard: In the 796x series, all amount metrics in OBIA were showing the Global1 amount. The customer needed to change the metric definitions to show them in another Currency preference. Project Analytics started supporting currency preferences since 7.9.6 release though, and it published a Tech note for other module customers to add toggling between currency preferences to the solution. List of Currency Preferences Starting from 11.1.1.x release, the BI Platform added a new feature to support multiple currencies. The new session variable (PREFERRED_CURRENCY) is populated through a newly introduced currency prompt. This prompt can take its values from the xml file: userpref_currencies_OBIA.xml, which is hosted in the BI Server installation folder, under :< home>\instances\instance1\config\OracleBIPresentationServicesComponent\coreapplication_obips1\userpref_currencies.xml This file contains the list of currency preferences, like“Local Currency”, “Global Currency 1”,…which customers can also rename to give them more meaningful business names. There are two options for showing the list of currency preferences to the user in the dashboard: Static and Dynamic. In Static mode, all users will see the full list as in the user preference currencies file. In the Dynamic mode, the list shown in the currency prompt drop down is a result of a dynamic query specified in the same file. Customers can build some security into the rpd, so the list of currency preferences will be based on the user roles…BI Applications built a subject area: “Dynamic Currency Preference” to run this query, and give every user only the list of currency preferences required by his application roles. Adding Currency to an Amount Field When the user selects one of the items from the currency prompt, all the amounts in that page will show in the Currency corresponding to that preference. For example, if the user selects “Global Currency1” from the prompt, all data will be showing in Global Currency 1 as specified in the Configuration Manager. If the user select “Local Currency”, all amount fields will show in the Currency of the Business Unit selected in the BU filter of the same page. If there is no particular Business Unit selected in that filter, and the data selected by the query contains amounts in more than one currency (for example one BU has USD as a functional currency, the other has EUR as functional currency), then subtotals will not be available (cannot add USD and EUR amounts in one field), and depending on the set up (see next paragraph), the user may receive an error. There are two ways to add the Currency field to an amount metric: In the form of currency code, like USD, EUR…For this the user needs to add the field “Apps Common Currency Code” to the report. This field is in every subject area, usually under the table “Currency Tag” or “Currency Code”… In the form of currency symbol ($ for USD, € for EUR,…) For this, the user needs to format the amount metrics in the report as a currency column, by specifying the currency tag column in the Column Properties option in Column Actions drop down list. Typically this column should be the “BI Common Currency Code” available in every subject area. Select Column Properties option in the Edit list of a metric. In the Data Format tab, select Custom as Treat Number As. Enter the following syntax under Custom Number Format: [$:currencyTagColumn=Subjectarea.table.column] Where Column is the “BI Common Currency Code” defined to take the currency code value based on the currency preference chosen by the user in the Currency preference prompt.

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  • SQL Developer Data Modeler v3.3 Early Adopter: Search

    - by thatjeffsmith
    photo: Stuck in Customs via photopin cc The next version of Oracle SQL Developer Data Modeler is now available as an Early Adopter (read, beta) release. There are many new major feature enhancements to talk about, but today’s focus will be on the brand new Search mechanism. Data, data, data – SO MUCH data Google has made countless billions of dollars around a very efficient and intelligent search business. People have become accustomed to having their data accessible AND searchable. Data models can have thousands of entities or tables, each having dozens of attributes or columns. Imagine how hard it could be to find what you’re looking for here. This is the challenge we have tackled head-on in v3.3. Same location as the Search toolbar in Oracle SQL Developer (and most web browsers) Here’s how it works: Search as you type – wicked fast as the entire model is loaded into memory Supports regular expressions (regex) Results loaded to a new panel below Search across designs, models Search EVERYTHING, or filter by type Save your frequent searches Save your search results as a report Open common properties of object in search results and edit basic properties on-the-fly Want to just watch the video? We have a new Oracle Learning Library resource available now which introduces the new and improved Search mechanism in SQL Developer Data Modeler. Go watch the video and then come back. Some Screenshots This will be a pretty easy feature to pick up. Search is intuitive – we’ve already learned how to do search. Now we just have a better interface for it in SQL Developer Data Modeler. But just in case you need a couple of pointers… The SYS data dictionary in model form with Search Results If I type ‘translation’ in the search dialog, then the results will come up as hits are ‘resolved.’ By default, everything is searched, although I can filter the results after-the-fact. You can see where the search finds a match in the ‘Content’ column Save the Results as a Report If you limit the search results to a category and a model, then you can save the results as a report. All of the usual suspects You can optionally include the search string, which displays in the top of of the report as ‘PATTERN.’ You can save you common reporting setups as a template and reuse those as well. Here’s a sample HTML report: Yes, I like to search my search results report! Two More Ways to Search You can search ‘in context’ by opening the ‘Find’ dialog from an active design. You can do this using the ‘Search’ toolbar button or from a model context menu. Searching a specific model Instead of bringing up the old modal Find dialog, you now get to use the new and improved Search panel. Notice there’s no ‘Model’ drop-down to select and that the active Search form is now in the Search panel versus the search toolbar up top. What else is new in SQL Developer Data Modeler version 3.3? All kinds of goodies. You can send your model to Excel for quick edits/reviews and suck the changes back into your model, you can share objects between models, and much much more. You’ll find new videos and blog posts on the subject in the new few days and weeks. Enjoy! If you have any feedback or want to report bugs, please visit our forums.

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  • Big Data – Operational Databases Supporting Big Data – Key-Value Pair Databases and Document Databases – Day 13 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the Relational Database and NoSQL database in the Big Data Story. In this article we will understand the role of Key-Value Pair Databases and Document Databases Supporting Big Data Story. Now we will see a few of the examples of the operational databases. Relational Databases (Yesterday’s post) NoSQL Databases (Yesterday’s post) Key-Value Pair Databases (This post) Document Databases (This post) Columnar Databases (Tomorrow’s post) Graph Databases (Tomorrow’s post) Spatial Databases (Tomorrow’s post) Key Value Pair Databases Key Value Pair Databases are also known as KVP databases. A key is a field name and attribute, an identifier. The content of that field is its value, the data that is being identified and stored. They have a very simple implementation of NoSQL database concepts. They do not have schema hence they are very flexible as well as scalable. The disadvantages of Key Value Pair (KVP) database are that they do not follow ACID (Atomicity, Consistency, Isolation, Durability) properties. Additionally, it will require data architects to plan for data placement, replication as well as high availability. In KVP databases the data is stored as strings. Here is a simple example of how Key Value Database will look like: Key Value Name Pinal Dave Color Blue Twitter @pinaldave Name Nupur Dave Movie The Hero As the number of users grow in Key Value Pair databases it starts getting difficult to manage the entire database. As there is no specific schema or rules associated with the database, there are chances that database grows exponentially as well. It is very crucial to select the right Key Value Pair Database which offers an additional set of tools to manage the data and provides finer control over various business aspects of the same. Riak Rick is one of the most popular Key Value Database. It is known for its scalability and performance in high volume and velocity database. Additionally, it implements a mechanism for collection key and values which further helps to build manageable system. We will further discuss Riak in future blog posts. Key Value Databases are a good choice for social media, communities, caching layers for connecting other databases. In simpler words, whenever we required flexibility of the data storage keeping scalability in mind – KVP databases are good options to consider. Document Database There are two different kinds of document databases. 1) Full document Content (web pages, word docs etc) and 2) Storing Document Components for storage. The second types of the document database we are talking about over here. They use Javascript Object Notation (JSON) and Binary JSON for the structure of the documents. JSON is very easy to understand language and it is very easy to write for applications. There are two major structures of JSON used for Document Database – 1) Name Value Pairs and 2) Ordered List. MongoDB and CouchDB are two of the most popular Open Source NonRelational Document Database. MongoDB MongoDB databases are called collections. Each collection is build of documents and each document is composed of fields. MongoDB collections can be indexed for optimal performance. MongoDB ecosystem is highly available, supports query services as well as MapReduce. It is often used in high volume content management system. CouchDB CouchDB databases are composed of documents which consists fields and attachments (known as description). It supports ACID properties. The main attraction points of CouchDB are that it will continue to operate even though network connectivity is sketchy. Due to this nature CouchDB prefers local data storage. Document Database is a good choice of the database when users have to generate dynamic reports from elements which are changing very frequently. A good example of document usages is in real time analytics in social networking or content management system. Tomorrow In tomorrow’s blog post we will discuss about various other Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • The data reader returned by the store data provider does not have enough columns

    - by molgan
    Hello I get the following error when I try to execute a stored procedure: "The data reader returned by the store data provider does not have enough columns" When I in the sql-manager execute it like this: DECLARE @return_value int, @EndDate datetime EXEC @return_value = [dbo].[GetSomeDate] @SomeID = 91, @EndDate = @EndDate OUTPUT SELECT @EndDate as N'@EndDate' SELECT 'Return Value' = @return_value GO It returns the value properly.... @SomeDate = '2010-03-24 09:00' And in my app I have: if (_entities.Connection.State == System.Data.ConnectionState.Closed) _entities.Connection.Open(); using (EntityCommand c = new EntityCommand("MyAppEntities.GetSomeDate", (EntityConnection)this._entities.Connection)) { c.CommandType = System.Data.CommandType.StoredProcedure; EntityParameter paramSomeID = new EntityParameter("SomeID", System.Data.DbType.Int32); paramSomeID.Direction = System.Data.ParameterDirection.Input; paramSomeID.Value = someID; c.Parameters.Add(paramSomeID); EntityParameter paramSomeDate = new EntityParameter("SomeDate", System.Data.DbType.DateTime); SomeDate.Direction = System.Data.ParameterDirection.Output; c.Parameters.Add(paramSomeDate); int retval = c.ExecuteNonQuery(); return (DateTime?)c.Parameters["SomeDate"].Value; Why does it complain about columns? I googled on error and someone said something about removing RETURN in sp, but I dont have any RETURN there. last like is like SELECT @SomeDate = D.SomeDate FROM .... /M

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  • Explicit Type Conversion and Multiple Simple Type Specifiers

    - by James McNellis
    To value initialize an object of type T, one would do something along the lines of one of the following: T x = T(); T x((T())); My question concerns types specified by a combination of simple type specifiers, e.g., unsigned int: unsigned int x = unsigned int(); unsigned int x((unsigned int())); Visual C++ 2008 and Intel C++ Compiler 11.1 accept both of these without warnings; Comeau 4.3.10.1b2 and g++ 3.4.5 (which is, admittedly, not particularly recent) do not. According to the C++ standard (C++03 5.2.3/2, expr.type.conv): The expression T(), where T is a simple-type-specifier (7.1.5.2) for a non-array complete object type or the (possibly cv-qualified) void type, creates an rvalue of the specified type, which is value-initialized 7.1.5.2 says, "the simple type specifiers are," and follows with a list that includes unsigned and int. Therefore, given that in 5.2.3/2, "simple-type-specifier" is singular, and unsigned and int are two type specifiers, are the examples above that use unsigned int invalid? (and, if so, the followup is, is it incorrect for Microsoft and Intel to support said expressions?) This question is more out of curiosity than anything else; for all of the types specified by a combination of multiple simple type specifiers, value initialization is equivalent to zero initialization. (This question was prompted by comments in response to this answer to a question about initialization).

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  • ASP.NET server data persistence

    - by Wayne Werner
    Hi, I'm not really sure exactly how the question should be phrased, so please be patient if I ask the wrong thing. I'm writing an ASP.NET application using VB as the code behind language. I have a data access class that connects to the DB to run the query (parameterized, of course), and another class to perform the validation tasks - I access this class from my aspx page. What I would like is to be able to store the data server side and wait for the user to choose from a few options based on the validity of the data. But unless my understanding is completely off, having persistent data objects on the server will give problems when multiple users connect? My ultimate goal is that once the data has been validated the end user can't modify it. Currently I'm validating the data, but I still have to retrieve it from the web form AFTER the user says OK, which obviously leaves open the possibility of injecting bad data either accidentally (unlikely) or on purpose (also unlikely for the use, but I'd prefer not to take the chance). So am I completely off in my understanding? If so, can someone point me to a resource that provides some instructions on keeping persistent data on the server, or provide instruction? Thanks!

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  • SQLAuthority News – Best Practices for Data Warehousing with SQL Server 2008 R2

    - by pinaldave
    An integral part of any BI system is the data warehouse—a central repository of data that is regularly refreshed from the source systems. The new data is transferred at regular intervals  by extract, transform, and load (ETL) processes. This whitepaper talks about what are best practices for Data Warehousing. This whitepaper discusses ETL, Analysis, Reporting as well relational database. The main focus of this whitepaper is on mainly ‘architecture’ and ‘performance’. Download Best Practices for Data Warehousing with SQL Server 2008 R2 Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Best Practices, Data Warehousing, PostADay, SQL, SQL Authority, SQL Documentation, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Nagy dobás készül az Oracle adatányászati felületen, Oracle Data Mining

    - by Fekete Zoltán
    Ahogyan már a tavaly oszi Oracle OpenWorld hírekben és eloadásokban is láthattuk a beharangozót, az Oracle nagy dobásra készül az adatbányászati fronton (Oracle Data Mining), mégpedig a remekül használható adatbányászati motor grafikus felületének a kiterjesztésével. Ha jól megfigyeljük ezt az utóbbi linket, az eddigi grafikus felület már Oracle Data Miner Classic néven fut. Hogyan is lehet használni az Oracle Data Mining-ot? - Oracle Data Miner (ingyenesen letöltheto GUI az OTN-rol) - Java-ból és PL/SQL-bol, Oracle Data Mining JDeveloper and SQL Developer Extensions - Excel felületrol, Oracle Spreadsheet Add-In for Predictive Analytics - ODM Connector for mySAP BW Oracle Data Mining technikai információ.

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  • Google I/O 2012 - Big Data: Turning Your Data Problem Into a Competitive Advantage

    Google I/O 2012 - Big Data: Turning Your Data Problem Into a Competitive Advantage Ju-kay Kwek, Navneet Joneja Can businesses get practical value from web-scale data without building proprietary web-scale infrastructure? This session will explore how new Google data services can be used to solve key data storage, transformation and analysis challenges. We will look at concrete case studies demonstrating how real life businesses have successfully used these solutions to turn data into a competitive business asset. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 1 0 ratings Time: 52:39 More in Science & Technology

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  • Data-Driven SOA with Oracle Data Integrator

    - by Irem Radzik
    v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Cambria","serif"; mso-fareast-font-family:"MS Mincho";} By Mike Eisterer, Data integration is more than simply moving data in bulk or in real-time, it is also about unifying information for improved business agility and integrating it in today’s service-oriented architectures. SOA enables organizations to easily define services which may then be discovered and leveraged by varying consumers. These consumers may be applications, customer facing portals, or complex business rules which are assembling services to automate process. Data as a foundational service provider is a key component of today’s successful SOA implementations. Oracle offers the broadest and most integrated portfolio of products to help you define, organize, orchestrate and consume data services. If you are attending Oracle OpenWorld next week, you will have ample opportunity to see the latest Oracle Data Integrator live in action and work with it yourself in two offered Hands-on Labs. Visit the hands-on lab to gain experience firsthand: Oracle Data Integrator and Oracle SOA Suite: Hands-on- Lab (HOL10480) Wed Oct 3rd 11:45AM Marriott Marquis- Salon 1/2 To learn more about Oracle Data Integrator, please visit our Introduction Hands-on LAB: Introduction to Oracle Data Integrator (HOL10481) Mon Oct 1st 3:15PM, Marriott Marquis- Salon 1/2 If you are not able to attend OpenWorld, please check out our latest resources for Data Integration.

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  • Middel East XML Currency Conversion

    - by Tim
    Hi, Using the following script to do currency conversion which relies on an xml feed. http://www.white-hat-web-design.co.uk/articles/php-currency-conversion.php It grabs the data from the following feed... var $xml_file = "www.ecb.int/stats/eurofxref/eurofxref-daily.xml"; However this xml feed has limited currencies and i require currencies for the middle east. Does anyone know where i can find an xml file with middle east currencies or have any better suggestions? Any help would be appreciated.

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  • Subsetting a data frame in a function using another data frame as parameter

    - by lecodesportif
    I would like to submit a data frame to a function and use it to subset another data frame. This is the basic data frame: foo <- data.frame(var1= c('1', '1', '1', '2', '2', '3'), var2=c('A', 'A', 'B', 'B', 'C', 'C')) I use the following function to find out the frequencies of var2 for specified values of var1. foobar <- function(x, y, z){ a <- subset(x, (x$var1 == y)) b <- subset(a, (a$var2 == z)) n=nrow(b) return(n) } Examples: foobar(foo, 1, "A") # returns 2 foobar(foo, 1, "B") # returns 1 foobar(foo, 3, "C") # returns 1 This works. But now I want to submit a data frame of values to foobar. Instead of the above examples, I would like to submit df to foobar and get the same results as above (2, 1, 1) df <- data.frame(var1=c('1','1','3'), var2=c("A", "B", "C")) When I change foobar to accept two arguments like foobar(foo, df) and use y[, c(var1)] and y[, c(var2)] instead of the two parameters x and y it still doesn't work. Which way is there to do this?

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