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  • Strange focus bug in Firefox (chrome vs content)

    - by Marius
    Here is a strange bug I'm experiencing in Firefox: I can only use either the chrome, or the content, not both at the same time! For example, I can click on tabs and the toolbar icons, focus the search bar and write in it as well as the address bar, but if I try to click on anything in the content (eg a link or a textfield to write something), then nothing happens. The mouse pointer doesn't change either, it just stays a pointer when I hover over things, and the links I hover don't react either. But if I alt-tab to another program (or click on it in the taskbar), then back to Firefox, then I can use the area that I click on. So if I click somewhere on the webpage to get focus back to Firefox, then I can click on links and write things (like this text), but I cannot click on tabs or refresh or anything else in the chrome. I can't even click on the minimize, restore and close icons! To get focus back on the chrome I have to alt-tab to another program, and then click on the chrome to get back to Firefox to be able to use the chrome again. I've tried closing and starting it again, but the bug is still there. I have experienced this before, but I don't remember what I did to fix it. This bug seems to occur sometimes when I wake up the computer from standby, but I leave by computer in standby all the time, so that is not the only factor.

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  • New Replication, Optimizer and High Availability features in MySQL 5.6.5!

    - by Rob Young
    As the Product Manager for the MySQL database it is always great to announce when the MySQL Engineering team delivers another great product release.  As a field DBA and developer it is even better when that release contains improvements and innovation that I know will help those currently using MySQL for apps that range from modest intranet sites to the most highly trafficked web sites on the web.  That said, it is my pleasure to take my hat off to MySQL Engineering for today's release of the MySQL 5.6.5 Development Milestone Release ("DMR"). The new highlighted features in MySQL 5.6.5 are discussed here: New Self-Healing Replication ClustersThe 5.6.5 DMR improves MySQL Replication by adding Global Transaction Ids and automated utilities for self-healing Replication clusters.  Prior to 5.6.5 this has been somewhat of a pain point for MySQL users with most developing custom solutions or looking to costly, complex third-party solutions for these capabilities.  With 5.6.5 these shackles are all but removed by a solution that is included with the GPL version of the database and supporting GPL tools.  You can learn all about the details of the great, problem solving Replication features in MySQL 5.6 in Mat Keep's Developer Zone article.  New Replication Administration and Failover UtilitiesAs mentioned above, the new Replication features, Global Transaction Ids specifically, are now supported by a set of automated GPL utilities that leverage the new GTIDs to provide administration and manual or auto failover to the most up to date slave (that is the default, but user configurable if needed) in the event of a master failure. The new utilities, along with links to Engineering related blogs, are discussed in detail in the DevZone Article noted above. Better Query Optimization and ThroughputThe MySQL Optimizer team continues to amaze with the latest round of improvements in 5.6.5. Along with much refactoring of the legacy code base, the Optimizer team has improved complex query optimization and throughput by adding these functional improvements: Subquery Optimizations - Subqueries are now included in the Optimizer path for runtime optimization.  Better throughput of nested queries enables application developers to simplify and consolidate multiple queries and result sets into a single unit or work. Optimizer now uses CURRENT_TIMESTAMP as default for DATETIME columns - For simplification, this eliminates the need for application developers to assign this value when a column of this type is blank by default. Optimizations for Range based queries - Optimizer now uses ready statistics vs Index based scans for queries with multiple range values. Optimizations for queries using filesort and ORDER BY.  Optimization criteria/decision on execution method is done now at optimization vs parsing stage. Print EXPLAIN in JSON format for hierarchical readability and Enterprise tool consumption. You can learn the details about these new features as well all of the Optimizer based improvements in MySQL 5.6 by following the Optimizer team blog. You can download and try the MySQL 5.6.5 DMR here. (look under "Development Releases")  Please let us know what you think!  The new HA utilities for Replication Administration and Failover are available as part of the MySQL Workbench Community Edition, which you can download here .Also New in MySQL LabsAs has become our tradition when announcing DMRs we also like to provide "Early Access" development features to the MySQL Community via the MySQL Labs.  Today is no exception as we are also releasing the following to Labs for you to download, try and let us know your thoughts on where we need to improve:InnoDB Online OperationsMySQL 5.6 now provides Online ADD Index, FK Drop and Online Column RENAME.  These operations are non-blocking and will continue to evolve in future DMRs.  You can learn the grainy details by following John Russell's blog.InnoDB data access via Memcached API ("NotOnlySQL") - Improved refresh of an earlier feature releaseSimilar to Cluster 7.2, MySQL 5.6 provides direct NotOnlySQL access to InnoDB data via the familiar Memcached API. This provides the ultimate in flexibility for developers who need fast, simple key/value access and complex query support commingled within their applications.Improved Transactional Performance, ScaleThe InnoDB Engineering team has once again under promised and over delivered in the area of improved performance and scale.  These improvements are also included in the aggregated Spring 2012 labs release:InnoDB CPU cache performance improvements for modern, multi-core/CPU systems show great promise with internal tests showing:    2x throughput improvement for read only activity 6x throughput improvement for SELECT range Read/Write benchmarks are in progress More details on the above are available here. You can download all of the above in an aggregated "InnoDB 2012 Spring Labs Release" binary from the MySQL Labs. You can also learn more about these improvements and about related fixes to mysys mutex and hash sort by checking out the InnoDB team blog.MySQL 5.6.5 is another installment in what we believe will be the best release of the MySQL database ever.  It also serves as a shining example of how the MySQL Engineering team at Oracle leads in MySQL innovation.You can get the overall Oracle message on the MySQL 5.6.5 DMR and Early Access labs features here. As always, thanks for your continued support of MySQL, the #1 open source database on the planet!

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  • MySQL Connect 9 Days Away – Optimizer Sessions

    - by Bertrand Matthelié
    72 1024x768 Normal 0 false false false EN-US X-NONE X-NONE /* 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:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Following my previous blog post focusing on InnoDB talks at MySQL Connect, let us review today the sessions focusing on the MySQL Optimizer: Saturday, 11.30 am, Room Golden Gate 6: MySQL Optimizer Overview—Olav Sanstå, Oracle The goal of MySQL optimizer is to take a SQL query as input and produce an optimal execution plan for the query. This session presents an overview of the main phases of the MySQL optimizer and the primary optimizations done to the query. These optimizations are based on a combination of logical transformations and cost-based decisions. Examples of optimization strategies the presentation covers are the main query transformations, the join optimizer, the data access selection strategies, and the range optimizer. For the cost-based optimizations, an overview of the cost model and the data used for doing the cost estimations is included. Saturday, 1.00 pm, Room Golden Gate 6: Overview of New Optimizer Features in MySQL 5.6—Manyi Lu, Oracle Many optimizer features have been added into MySQL 5.6. This session provides an introduction to these great features. Multirange read, index condition pushdown, and batched key access will yield huge performance improvements on large data volumes. Structured explain, explain for update/delete/insert, and optimizer tracing will help users analyze and speed up queries. And last but not least, the session covers subquery optimizations in Release 5.6. Saturday, 7.00 pm, Room Golden Gate 4: BoF: Query Optimizations: What Is New and What Is Coming? This BoF presents common techniques for query optimization, covers what is new in MySQL 5.6, and provides a discussion forum in which attendees can tell the MySQL optimizer team which optimizations they would like to see in the future. Sunday, 1.15 pm, Room Golden Gate 8: Query Performance Comparison of MySQL 5.5 and MySQL 5.6—Øystein Grøvlen, Oracle MySQL Release 5.6 contains several improvements in the query optimizer that create improved performance for complex queries. This presentation looks at how MySQL 5.6 improves the performance of many of the queries in the DBT-3 benchmark. Based on the observed improvements, the presentation discusses what makes the specific queries perform better in Release 5.6. It describes the relevant new optimization techniques and gives examples of the types of queries that will benefit from these techniques. Sunday, 4.15 pm, Room Golden Gate 4: Powerful EXPLAIN in MySQL 5.6—Evgeny Potemkin, Oracle The EXPLAIN command of MySQL has long been a very useful tool for understanding how MySQL will execute a query. Release 5.6 of the MySQL database offers several new additions that give more-detailed information about the query plan and make it easier to understand at the same time. This presentation gives an overview of new EXPLAIN features: structured EXPLAIN in JSON format, EXPLAIN for INSERT/UPDATE/DELETE, and optimizer tracing. Examples in the session give insights into how you can take advantage of the new features. They show how these features supplement and relate to each other and to classical EXPLAIN and how and why the MySQL server chooses a particular query plan. You can check out the full program here as well as in the September edition of the MySQL newsletter. Not registered yet? You can still save US$ 300 over the on-site fee – Register Now!

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  • ROracle support for TimesTen In-Memory Database

    - by Sam Drake
    Today's guest post comes from Jason Feldhaus, a Consulting Member of Technical Staff in the TimesTen Database organization at Oracle.  He shares with us a sample session using ROracle with the TimesTen In-Memory database.  Beginning in version 1.1-4, ROracle includes support for the Oracle Times Ten In-Memory Database, version 11.2.2. TimesTen is a relational database providing very fast and high throughput through its memory-centric architecture.  TimesTen is designed for low latency, high-volume data, and event and transaction management. A TimesTen database resides entirely in memory, so no disk I/O is required for transactions and query operations. TimesTen is used in applications requiring very fast and predictable response time, such as real-time financial services trading applications and large web applications. TimesTen can be used as the database of record or as a relational cache database to Oracle Database. ROracle provides an interface between R and the database, providing the rich functionality of the R statistical programming environment using the SQL query language. ROracle uses the OCI libraries to handle database connections, providing much better performance than standard ODBC.The latest ROracle enhancements include: Support for Oracle TimesTen In-Memory Database Support for Date-Time using R's POSIXct/POSIXlt data types RAW, BLOB and BFILE data type support Option to specify number of rows per fetch operation Option to prefetch LOB data Break support using Ctrl-C Statement caching support Times Ten 11.2.2 contains enhanced support for analytics workloads and complex queries: Analytic functions: AVG, SUM, COUNT, MAX, MIN, DENSE_RANK, RANK, ROW_NUMBER, FIRST_VALUE and LAST_VALUE Analytic clauses: OVER PARTITION BY and OVER ORDER BY Multidimensional grouping operators: Grouping clauses: GROUP BY CUBE, GROUP BY ROLLUP, GROUP BY GROUPING SETS Grouping functions: GROUP, GROUPING_ID, GROUP_ID WITH clause, which allows repeated references to a named subquery block Aggregate expressions over DISTINCT expressions General expressions that return a character string in the source or a pattern within the LIKE predicate Ability to order nulls first or last in a sort result (NULLS FIRST or NULLS LAST in the ORDER BY clause) Note: Some functionality is only available with Oracle Exalytics, refer to the TimesTen product licensing document for details. Connecting to TimesTen is easy with ROracle. Simply install and load the ROracle package and load the driver. > install.packages("ROracle") > library(ROracle) Loading required package: DBI > drv <- dbDriver("Oracle") Once the ROracle package is installed, create a database connection object and connect to a TimesTen direct driver DSN as the OS user. > conn <- dbConnect(drv, username ="", password="", dbname = "localhost/SampleDb_1122:timesten_direct") You have the option to report the server type - Oracle or TimesTen? > print (paste ("Server type =", dbGetInfo (conn)$serverType)) [1] "Server type = TimesTen IMDB" To create tables in the database using R data frame objects, use the function dbWriteTable. In the following example we write the built-in iris data frame to TimesTen. The iris data set is a small example data set containing 150 rows and 5 columns. We include it here not to highlight performance, but so users can easily run this example in their R session. > dbWriteTable (conn, "IRIS", iris, overwrite=TRUE, ora.number=FALSE) [1] TRUE Verify that the newly created IRIS table is available in the database. To list the available tables and table columns in the database, use dbListTables and dbListFields, respectively. > dbListTables (conn) [1] "IRIS" > dbListFields (conn, "IRIS") [1] "SEPAL.LENGTH" "SEPAL.WIDTH" "PETAL.LENGTH" "PETAL.WIDTH" "SPECIES" To retrieve a summary of the data from the database we need to save the results to a local object. The following call saves the results of the query as a local R object, iris.summary. The ROracle function dbGetQuery is used to execute an arbitrary SQL statement against the database. When connected to TimesTen, the SQL statement is processed completely within main memory for the fastest response time. > iris.summary <- dbGetQuery(conn, 'SELECT SPECIES, AVG ("SEPAL.LENGTH") AS AVG_SLENGTH, AVG ("SEPAL.WIDTH") AS AVG_SWIDTH, AVG ("PETAL.LENGTH") AS AVG_PLENGTH, AVG ("PETAL.WIDTH") AS AVG_PWIDTH FROM IRIS GROUP BY ROLLUP (SPECIES)') > iris.summary SPECIES AVG_SLENGTH AVG_SWIDTH AVG_PLENGTH AVG_PWIDTH 1 setosa 5.006000 3.428000 1.462 0.246000 2 versicolor 5.936000 2.770000 4.260 1.326000 3 virginica 6.588000 2.974000 5.552 2.026000 4 <NA> 5.843333 3.057333 3.758 1.199333 Finally, disconnect from the TimesTen Database. > dbCommit (conn) [1] TRUE > dbDisconnect (conn) [1] TRUE We encourage you download Oracle software for evaluation from the Oracle Technology Network. See these links for our software: Times Ten In-Memory Database,  ROracle.  As always, we welcome comments and questions on the TimesTen and  Oracle R technical forums.

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  • ROracle support for TimesTen In-Memory Database

    - by Sherry LaMonica
    Today's guest post comes from Jason Feldhaus, a Consulting Member of Technical Staff in the TimesTen Database organization at Oracle.  He shares with us a sample session using ROracle with the TimesTen In-Memory database.  Beginning in version 1.1-4, ROracle includes support for the Oracle Times Ten In-Memory Database, version 11.2.2. TimesTen is a relational database providing very fast and high throughput through its memory-centric architecture.  TimesTen is designed for low latency, high-volume data, and event and transaction management. A TimesTen database resides entirely in memory, so no disk I/O is required for transactions and query operations. TimesTen is used in applications requiring very fast and predictable response time, such as real-time financial services trading applications and large web applications. TimesTen can be used as the database of record or as a relational cache database to Oracle Database. ROracle provides an interface between R and the database, providing the rich functionality of the R statistical programming environment using the SQL query language. ROracle uses the OCI libraries to handle database connections, providing much better performance than standard ODBC.The latest ROracle enhancements include: Support for Oracle TimesTen In-Memory Database Support for Date-Time using R's POSIXct/POSIXlt data types RAW, BLOB and BFILE data type support Option to specify number of rows per fetch operation Option to prefetch LOB data Break support using Ctrl-C Statement caching support Times Ten 11.2.2 contains enhanced support for analytics workloads and complex queries: Analytic functions: AVG, SUM, COUNT, MAX, MIN, DENSE_RANK, RANK, ROW_NUMBER, FIRST_VALUE and LAST_VALUE Analytic clauses: OVER PARTITION BY and OVER ORDER BY Multidimensional grouping operators: Grouping clauses: GROUP BY CUBE, GROUP BY ROLLUP, GROUP BY GROUPING SETS Grouping functions: GROUP, GROUPING_ID, GROUP_ID WITH clause, which allows repeated references to a named subquery block Aggregate expressions over DISTINCT expressions General expressions that return a character string in the source or a pattern within the LIKE predicate Ability to order nulls first or last in a sort result (NULLS FIRST or NULLS LAST in the ORDER BY clause) Note: Some functionality is only available with Oracle Exalytics, refer to the TimesTen product licensing document for details. Connecting to TimesTen is easy with ROracle. Simply install and load the ROracle package and load the driver. > install.packages("ROracle") > library(ROracle) Loading required package: DBI > drv <- dbDriver("Oracle") Once the ROracle package is installed, create a database connection object and connect to a TimesTen direct driver DSN as the OS user. > conn <- dbConnect(drv, username ="", password="", dbname = "localhost/SampleDb_1122:timesten_direct") You have the option to report the server type - Oracle or TimesTen? > print (paste ("Server type =", dbGetInfo (conn)$serverType)) [1] "Server type = TimesTen IMDB" To create tables in the database using R data frame objects, use the function dbWriteTable. In the following example we write the built-in iris data frame to TimesTen. The iris data set is a small example data set containing 150 rows and 5 columns. We include it here not to highlight performance, but so users can easily run this example in their R session. > dbWriteTable (conn, "IRIS", iris, overwrite=TRUE, ora.number=FALSE) [1] TRUE Verify that the newly created IRIS table is available in the database. To list the available tables and table columns in the database, use dbListTables and dbListFields, respectively. > dbListTables (conn) [1] "IRIS" > dbListFields (conn, "IRIS") [1] "SEPAL.LENGTH" "SEPAL.WIDTH" "PETAL.LENGTH" "PETAL.WIDTH" "SPECIES" To retrieve a summary of the data from the database we need to save the results to a local object. The following call saves the results of the query as a local R object, iris.summary. The ROracle function dbGetQuery is used to execute an arbitrary SQL statement against the database. When connected to TimesTen, the SQL statement is processed completely within main memory for the fastest response time. > iris.summary <- dbGetQuery(conn, 'SELECT SPECIES, AVG ("SEPAL.LENGTH") AS AVG_SLENGTH, AVG ("SEPAL.WIDTH") AS AVG_SWIDTH, AVG ("PETAL.LENGTH") AS AVG_PLENGTH, AVG ("PETAL.WIDTH") AS AVG_PWIDTH FROM IRIS GROUP BY ROLLUP (SPECIES)') > iris.summary SPECIES AVG_SLENGTH AVG_SWIDTH AVG_PLENGTH AVG_PWIDTH 1 setosa 5.006000 3.428000 1.462 0.246000 2 versicolor 5.936000 2.770000 4.260 1.326000 3 virginica 6.588000 2.974000 5.552 2.026000 4 <NA> 5.843333 3.057333 3.758 1.199333 Finally, disconnect from the TimesTen Database. > dbCommit (conn) [1] TRUE > dbDisconnect (conn) [1] TRUE We encourage you download Oracle software for evaluation from the Oracle Technology Network. See these links for our software: Times Ten In-Memory Database,  ROracle.  As always, we welcome comments and questions on the TimesTen and  Oracle R technical forums.

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  • Need help with Xpath methods in javascript (selectSingleNode, selectNodes)

    - by Andrija
    I want to optimize my javascript but I ran into a bit of trouble. I'm using XSLT transformation and the basic idea is to get a part of the XML and subquery it so the calls are faster and less expensive. This is a part of the XML: <suite> <table id="spis" runat="client"> <rows> <row id="spis_1"> <dispatch>'2008', '288627'</dispatch> <data col="urGod"> <title>2008</title> <description>Ur. god.</description> </data> <data col="rbr"> <title>288627</title> <description>Rbr.</description> </data> ... </rows> </table> </suite> In the page, this is the javascript that works with this: // this is my global variable for getting the elements so I just get the most // I can in one call elemCollection = iDom3.Table.all["spis"].XML.DOM.selectNodes("/suite/table/rows/row").context; //then I have the method that uses this by getting the subresults from elemCollection //rest of the method isn't interesting, only the selectNodes call _buildResults = function (){ var _RowList = elemCollection.selectNodes("/data[@col = 'urGod']/title"); var tmpResult = ['']; var substringResult=""; for (i=0; i<_RowList.length; i++) { tmpResult.push(_RowList[i].text,iDom3.Global.Delimiter); } ... //this variant works elemCollection = iDom3.Table.all["spis"].XML.DOM _buildResults = function (){ var _RowList = elemCollection.selectNodes("/suite/table/rows/row/data[@col = 'urGod']/title"); var tmpResult = ['']; var substringResult=""; for (i=0; i<_RowList.length; i++) { tmpResult.push(_RowList[i].text,iDom3.Global.Delimiter); } ... The problem is, I can't find a way to use the subresults to get what I need.

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  • SQLiteException and SQLite error near "(": syntax error with Subsonic ActiveRecord

    - by nvuono
    I ran into an interesting error with the following LiNQ query using LiNQPad and when using Subsonic 3.0.x w/ActiveRecord within my project and wanted to share the error and resolution for anyone else who runs into it. The linq statement below is meant to group entries in the tblSystemsValues collection into their appropriate system and then extract the system with the highest ID. from ksf in KeySafetyFunction where ksf.Unit == 2 && ksf.Condition_ID == 1 join sys in tblSystems on ksf.ID equals sys.KeySafetyFunction join xval in (from t in tblSystemsValues group t by t.tblSystems_ID into groupedT select new { sysId = groupedT.Key, MaxID = groupedT.Max(g=>g.ID), MaxText = groupedT.First(gt2 => gt2.ID == groupedT.Max(g=>g.ID)).TextValue, MaxChecked = groupedT.First(gt2 => gt2.ID == groupedT.Max(g=>g.ID)).Checked }) on sys.ID equals xval.sysId select new {KSFDesc=ksf.Description, sys.Description, xval.MaxText, xval.MaxChecked} On its own, the subquery for grouping into groupedT works perfectly and the query to match up KeySafetyFunctions with their System in tblSystems also works perfectly on its own. However, when trying to run the completed query in linqpad or within my project I kept running into a SQLiteException SQLite Error Near "(" First I tried splitting the queries up within my project because I knew that I could just run a foreach loop over the results if necessary. However, I continued to receive the same exception! I eventually separated the query into three separate parts before I realized that it was the lazy execution of the queries that was killing me. It then became clear that adding the .ToList() specifier after the myProtectedSystem query below was the key to avoiding the lazy execution after combining and optimizing the query and being able to get my results despite the problems I encountered with the SQLite driver. // determine the max Text/Checked values for each system in tblSystemsValue var myProtectedValue = from t in tblSystemsValue.All() group t by t.tblSystems_ID into groupedT select new { sysId = groupedT.Key, MaxID = groupedT.Max(g => g.ID), MaxText = groupedT.First(gt2 => gt2.ID ==groupedT.Max(g => g.ID)).TextValue, MaxChecked = groupedT.First(gt2 => gt2.ID ==groupedT.Max(g => g.ID)).Checked}; // get the system description information and filter by Unit/Condition ID var myProtectedSystem = (from ksf in KeySafetyFunction.All() where ksf.Unit == 2 && ksf.Condition_ID == 1 join sys in tblSystem.All() on ksf.ID equals sys.KeySafetyFunction select new {KSFDesc = ksf.Description, sys.Description, sys.ID}).ToList(); // finally join everything together AFTER forcing execution with .ToList() var joined = from protectedSys in myProtectedSystem join protectedVal in myProtectedValue on protectedSys.ID equals protectedVal.sysId select new {protectedSys.KSFDesc, protectedSys.Description, protectedVal.MaxChecked, protectedVal.MaxText}; // print the gratifying debug results foreach(var protectedItem in joined) { System.Diagnostics.Debug.WriteLine(protectedItem.Description + ", " + protectedItem.KSFDesc + ", " + protectedItem.MaxText + ", " + protectedItem.MaxChecked); }

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  • PostgreSQL - fetch the row which has the Max value for a column

    - by Joshua Berry
    I'm dealing with a Postgres table (called "lives") that contains records with columns for time_stamp, usr_id, transaction_id, and lives_remaining. I need a query that will give me the most recent lives_remaining total for each usr_id There are multiple users (distinct usr_id's) time_stamp is not a unique identifier: sometimes user events (one by row in the table) will occur with the same time_stamp. trans_id is unique only for very small time ranges: over time it repeats remaining_lives (for a given user) can both increase and decrease over time example: time_stamp|lives_remaining|usr_id|trans_id ----------------------------------------- 07:00 | 1 | 1 | 1 09:00 | 4 | 2 | 2 10:00 | 2 | 3 | 3 10:00 | 1 | 2 | 4 11:00 | 4 | 1 | 5 11:00 | 3 | 1 | 6 13:00 | 3 | 3 | 1 As I will need to access other columns of the row with the latest data for each given usr_id, I need a query that gives a result like this: time_stamp|lives_remaining|usr_id|trans_id ----------------------------------------- 11:00 | 3 | 1 | 6 10:00 | 1 | 2 | 4 13:00 | 3 | 3 | 1 As mentioned, each usr_id can gain or lose lives, and sometimes these timestamped events occur so close together that they have the same timestamp! Therefore this query won't work: SELECT b.time_stamp,b.lives_remaining,b.usr_id,b.trans_id FROM (SELECT usr_id, max(time_stamp) AS max_timestamp FROM lives GROUP BY usr_id ORDER BY usr_id) a JOIN lives b ON a.max_timestamp = b.time_stamp Instead, I need to use both time_stamp (first) and trans_id (second) to identify the correct row. I also then need to pass that information from the subquery to the main query that will provide the data for the other columns of the appropriate rows. This is the hacked up query that I've gotten to work: SELECT b.time_stamp,b.lives_remaining,b.usr_id,b.trans_id FROM (SELECT usr_id, max(time_stamp || '*' || trans_id) AS max_timestamp_transid FROM lives GROUP BY usr_id ORDER BY usr_id) a JOIN lives b ON a.max_timestamp_transid = b.time_stamp || '*' || b.trans_id ORDER BY b.usr_id Okay, so this works, but I don't like it. It requires a query within a query, a self join, and it seems to me that it could be much simpler by grabbing the row that MAX found to have the largest timestamp and trans_id. The table "lives" has tens of millions of rows to parse, so I'd like this query to be as fast and efficient as possible. I'm new to RDBM and Postgres in particular, so I know that I need to make effective use of the proper indexes. I'm a bit lost on how to optimize. I found a similar discussion here. Can I perform some type of Postgres equivalent to an Oracle analytic function? Any advice on accessing related column information used by an aggregate function (like MAX), creating indexes, and creating better queries would be much appreciated! P.S. You can use the following to create my example case: create TABLE lives (time_stamp timestamp, lives_remaining integer, usr_id integer, trans_id integer); insert into lives values ('2000-01-01 07:00', 1, 1, 1); insert into lives values ('2000-01-01 09:00', 4, 2, 2); insert into lives values ('2000-01-01 10:00', 2, 3, 3); insert into lives values ('2000-01-01 10:00', 1, 2, 4); insert into lives values ('2000-01-01 11:00', 4, 1, 5); insert into lives values ('2000-01-01 11:00', 3, 1, 6); insert into lives values ('2000-01-01 13:00', 3, 3, 1);

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  • Select latest group by in nhibernate

    - by Kendrick
    I have Canine and CanineHandler objects in my application. The CanineHandler object has a PersonID (which references a completely different database), an EffectiveDate (which specifies when a handler started with the canine), and a FK reference to the Canine (CanineID). Given a specific PersonID, I want to find all canines they're currently responsible for. The (simplified) query I'd use in SQL would be: Select Canine.* from Canine inner join CanineHandler on(CanineHandler.CanineID=Canine.CanineID) inner join (select CanineID,Max(EffectiveDate) MaxEffectiveDate from caninehandler group by CanineID) as CurrentHandler on(CurrentHandler.CanineID=CanineHandler.CanineID and CurrentHandler.MaxEffectiveDate=CanineHandler.EffectiveDate) where CanineHandler.HandlerPersonID=@PersonID Edit: Added mapping files below: <class name="CanineHandler" table="CanineHandler" schema="dbo"> <id name="CanineHandlerID" type="Int32"> <generator class="identity" /> </id> <property name="EffectiveDate" type="DateTime" precision="16" not-null="true" /> <property name="HandlerPersonID" type="Int64" precision="19" not-null="true" /> <many-to-one name="Canine" class="Canine" column="CanineID" not-null="true" access="field.camelcase-underscore" /> </class> <class name="Canine" table="Canine"> <id name="CanineID" type="Int32"> <generator class="identity" /> </id> <property name="Name" type="String" length="64" not-null="true" /> ... <set name="CanineHandlers" table="CanineHandler" inverse="true" order-by="EffectiveDate desc" cascade="save-update" access="field.camelcase-underscore"> <key column="CanineID" /> <one-to-many class="CanineHandler" /> </set> <property name="IsDeleted" type="Boolean" not-null="true" /> </class> I haven't tried yet, but I'm guessing I could do this in HQL. I haven't had to write anything in HQL yet, so I'll have to tackle that eventually anyway, but my question is whether/how I can do this sub-query with the criterion/subqueries objects. I got as far as creating the following detached criteria: DetachedCriteria effectiveHandlers = DetachedCriteria.For<Canine>() .SetProjection(Projections.ProjectionList() .Add(Projections.Max("EffectiveDate"),"MaxEffectiveDate") .Add(Projections.GroupProperty("CanineID"),"handledCanineID") ); but I can't figure out how to do the inner join. If I do this: Session.CreateCriteria<Canine>() .CreateCriteria("CanineHandler", "handler", NHibernate.SqlCommand.JoinType.InnerJoin) .List<Canine>(); I get an error "could not resolve property: CanineHandler of: OPS.CanineApp.Model.Canine". Obviously I'm missing something(s) but from the documentation I got the impression that should return a list of Canines that have handlers (possibly with duplicates). Until I can make this work, adding the subquery isn't going to work... I've found similar questions, such as http://stackoverflow.com/questions/747382/only-get-latest-results-using-nhibernate but none of the answers really seem to apply with the kind of direct result I'm looking for. Any help or suggestion is greatly appreciated.

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  • What is the best way to reduce code and loop through a hierarchial commission script?

    - by JM4
    I have a script which currently "works" but is nearly 3600 lines of code and makes well over 50 database calls within a single script. From my experience, there is no way to really "loop" the script and minimize it because each call to the database is a subquery of the ones before based on referral ids. Perhaps I can give a very simple example of what I am trying to accomplish and see if anybody has experience with something similar. In my example, there are three tables: Table 1 - Sellers ID | Comm_level | Parent ----------------------------------- 1 | 4 | NULL 2 | 3 | 1 3 | 2 | 1 4 | 2 | 2 5 | 2 | 2 6 | 1 | 3 Where ID is the id of one of our sales agents, comm_level will determine what his commission percentage is for each product he sells, parent indicates the ID for whom recruited that particular agent. In the example above, 1 is the top agent, he recruited two agents, 2 and 3. 2 recruited two agents, 4 and 5. 3 recruited one agent, 6. NOTE: An agent can NEVER recruit anybody equal to or higher than their own level. Table 2 - Commissions Level | Item 1 | Item 2 | Item 3 ----------------------------------------------------- 4 | .5 | .4 | .3 3 | .45 | .35 | .25 2 | .4 | .3 | .2 1 | .35 | .25 | .15 This table lays out the commission percentages for each agent based on their actual comm_level (if an agent is at a level 4, he will receive 50% on every item 1 sold, 40% on every item 2, 30% on every item 3 and so on. Table 3 - Items Sold ID | Item --------------------- 4 | item_1 4 | item_2 1 | item_1 2 | item_3 6 | item_2 1 | item_3 This table pairs the actual item sold with the seller who sold the item. When generating the commission report, calculating individual values is very simple. Calculating their commission based on their sub_sellers however is very difficult. In this example, Seller ID 1 gets a piece of every single item sold. The commission percentages indicate individual sales or the height of their commission. For example: When seller ID 6 sold one of item_2 above, the tree for commissions will look like the following: -ID 6 - 25% of cost(item_1) -ID 3 - 5% of cost(item_1) - (30% is his comm - 25% comm of seller id 6) -ID 1 - 10% of cost(item_1) - (40% is his comm - 30% of seller id 3) This must be calculated for every agent in the system from the top down (hence the DB calls within while loops throughout my enormous script). Anybody have a good suggestion or samples they may have used in the past?

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  • 'Make' command compiling errors

    - by G_T
    Im trying to locally install a program which is written in C++. I have downloaded the program and am attempting to use the "make" command to compile the program as the programs instructions dictate. However when I do I get this error: /usr/include/stdc-predef.h:30:26: fatal error: bits/predefs.h: No such file or directory compilation terminated. Looking around on the internet some people seem to address this problem by sudo apt-get install libc6-dev-i386 I checked to see if this package was installed and it was not. When I try to install it I get E: Unable to locate package libc6-dev-i386 I have already run sudo apt get update Im sure this is a rookie question but any help is appreciated, I'm running 13.10 32-bit. UPDATE: I've tried other suggestions I've found on similar error. All I have managed is a different but similar error. Here is what I get. Geoffrey@Geoffrey-Latitude-E6400:/usr/local/src/trinityrnaseq_r2013_08_14$ make Using gnu compiler for Inchworm and Chrysalis cd Inchworm && (test -e configure || autoreconf) \ && ./configure --prefix=`pwd` && make install checking for a BSD-compatible install... /usr/bin/install -c checking whether build environment is sane... yes checking for gawk... no checking for mawk... mawk checking whether make sets $(MAKE)... yes checking for g++... g++ checking for C++ compiler default output file name... a.out checking whether the C++ compiler works... yes checking whether we are cross compiling... no checking for suffix of executables... checking for suffix of object files... o checking whether we are using the GNU C++ compiler... yes checking whether g++ accepts -g... yes checking for style of include used by make... GNU checking dependency style of g++... gcc3 checking for library containing cos... none required configure: creating ./config.status config.status: creating Makefile config.status: creating src/Makefile config.status: creating config.h config.status: config.h is unchanged config.status: executing depfiles commands make[1]: Entering directory `/usr/local/src/trinityrnaseq_r2013_08_14/Inchworm' Making install in src make[2]: Entering directory `/usr/local/src/trinityrnaseq_r2013_08_14/Inchworm/src' if g++ -DHAVE_CONFIG_H -I. -I. -I.. -pedantic -fopenmp -Wall -Wextra -Wno-long-long -Wno-deprecated -m64 -g -O2 -MT Fasta_entry.o -MD -MP -MF ".deps/Fasta_entry.Tpo" -c -o Fasta_entry.o Fasta_entry.cpp; \ then mv -f ".deps/Fasta_entry.Tpo" ".deps/Fasta_entry.Po"; else rm -f ".deps/Fasta_entry.Tpo"; exit 1; fi In file included from Fasta_entry.hpp:4:0, from Fasta_entry.cpp:1: /usr/include/c++/4.8/string:38:28: fatal error: bits/c++config.h: No such file or directory #include <bits/c++config.h> ^ compilation terminated. make[2]: *** [Fasta_entry.o] Error 1 make[2]: Leaving directory `/usr/local/src/trinityrnaseq_r2013_08_14/Inchworm/src' make[1]: *** [install-recursive] Error 1 make[1]: Leaving directory `/usr/local/src/trinityrnaseq_r2013_08_14/Inchworm' make: *** [inchworm] Error 2

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  • Markus Zirn, "Big Data with CEP and SOA" @ SOA, Cloud &amp; Service Technology Symposium 2012

    - by JuergenKress
    ORACLE PROMOTIONAL DISCOUNT FOR EXCLUSIVE ORACLE DISCOUNT, ENTER PROMO CODE: DJMXZ370 Early-Bird Registration is Now Open with Special Pricing! Register before July 1, 2012 to qualify for discounts. Visit the Registration page for details. The International SOA, Cloud + Service Technology Symposium is a yearly event that features the top experts and authors from around the world, providing a series of keynotes, talks, demonstrations, and panels, as well as training and certification workshops - all dedicated to empowering IT professionals to realize modern service technologies and practices in the real world. Click here for a two-page printable conference overview (PDF). Big Data with CEP and SOA - September 25, 2012 - 14:15 Speaker: Markus Zirn, Oracle and Baz Kuthi, Avocent The "Big Data" trend is driving new kinds of IT projects that process machine-generated data. Such projects store and mine using Hadoop/ Map Reduce, but they also analyze streaming data via event-driven patterns, which can be called "Fast Data" complementary to "Big Data". This session highlights how "Big Data" and "Fast Data" design patterns can be combined with SOA design principles into modern, event-driven architectures. We will describe specific architectures that combines CEP, Distributed Caching, Event-driven Network, SOA Composites, Application Development Framework, as well as Hadoop. Architecture patterns include pre-processing and filtering event streams as close as possible to the event source, in memory master data for event pattern matching, event-driven user interfaces as well as distributed event processing. Focus is on how "Fast Data" requirements are elegantly integrated into a traditional SOA architecture. Markus Zirn is Vice President of Product Management covering Oracle SOA Suite, SOA Governance, Application Integration Architecture, BPM, BPM Solutions, Complex Event Processing and UPK, an end user learning solution. He is the author of “The BPEL Cookbook” (rated best book on Services Oriented Architecture in 2007) as well as “Fusion Middleware Patterns”. Previously, he was a management consultant with Booz Allen & Hamilton’s High Tech practice in Duesseldorf as well as San Francisco and Vice President of Product Marketing at QUIQ. Mr. Zirn holds a Masters of Electrical Engineering from the University of Karlsruhe and is an alumnus of the Tripartite program, a joint European degree from the University of Karlsruhe, Germany, the University of Southampton, UK, and ESIEE, France. KEYNOTES & SPEAKERS More than 80 international subject matter experts will be speaking at the Symposium. Below are confirmed keynotes and speakers so far. Over 50% of the agenda has not yet been finalized. Many more speakers to come. View the partial program calendars on the Conference Agenda page. CONFERENCE THEMES & TRACKS Cloud Computing Architecture & Patterns New SOA & Service-Orientation Practices & Models Emerging Service Technology Innovation Service Modeling & Analysis Techniques Service Infrastructure & Virtualization Cloud-based Enterprise Architecture Business Planning for Cloud Computing Projects Real World Case Studies Semantic Web Technologies (with & without the Cloud) Governance Frameworks for SOA and/or Cloud Computing Projects Service Engineering & Service Programming Techniques Interactive Services & the Human Factor New REST & Web Services Tools & Techniques Oracle Specialized SOA & BPM Partners Oracle Specialized partners have proven their skills by certifications and customer references. To find a local Specialized partner please visit http://solutions.oracle.com SOA & BPM Partner Community For regular information on Oracle SOA Suite become a member in the SOA & BPM Partner Community for registration please visit  www.oracle.com/goto/emea/soa (OPN account required) If you need support with your account please contact the Oracle Partner Business Center. Blog Twitter LinkedIn Mix Forum Technorati Tags: Markus Zirn,SOA Symposium,Thomas Erl,SOA Community,Oracle SOA,Oracle BPM,BPM Community,OPN,Jürgen Kress

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  • Grouping data in LINQ with the help of group keyword

    - by vik20000in
    While working with any kind of advanced query grouping is a very important factor. Grouping helps in executing special function like sum, max average etc to be performed on certain groups of data inside the date result set. Grouping is done with the help of the Group method. Below is an example of the basic group functionality.     int[] numbers = { 5, 4, 1, 3, 9, 8, 6, 7, 2, 0 };         var numberGroups =         from num in numbers         group num by num % 5 into numGroup         select new { Remainder = numGroup.Key, Numbers = numGroup };  In the above example we have grouped the values based on the reminder left over when divided by 5. First we are grouping the values based on the reminder when divided by 5 into the numgroup variable.  numGroup.Key gives the value of the key on which the grouping has been applied. And the numGroup itself contains all the records that are contained in that group. Below is another example to explain the same. string[] words = { "blueberry", "abacus", "banana", "apple", "cheese" };         var wordGroups =         from num in words         group num by num[0] into grp         select new { FirstLetter = grp.Key, Words = grp }; In the above example we are grouping the value with the first character of the string (num[0]). Just like the order operator the group by clause also allows us to write our own logic for the Equal comparison (That means we can group Item by ignoring case also by writing out own implementation). For this we need to pass an object that implements the IEqualityComparer<string> interface. Below is an example. public class AnagramEqualityComparer : IEqualityComparer<string> {     public bool Equals(string x, string y) {         return getCanonicalString(x) == getCanonicalString(y);     }      public int GetHashCode(string obj) {         return getCanonicalString(obj).GetHashCode();     }         private string getCanonicalString(string word) {         char[] wordChars = word.ToCharArray();         Array.Sort<char>(wordChars);         return new string(wordChars);     } }  string[] anagrams = {"from   ", " salt", " earn", "  last   ", " near "}; var orderGroups = anagrams.GroupBy(w => w.Trim(), new AnagramEqualityComparer()); Vikram  

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  • Beyond Chatting: What ‘Social’ Means for CRM

    - by Divya Malik
    A guest post by Steve Diamond, Senior Director, Outbound Product Management, Oracle In a recent post on the Oracle Applications blog, my colleague Steve Boese asked three questions related to the widespread popularity and incredibly rapid growth of Facebook, Pinterest, and LinkedIn. Steve then addressed the many applications for collaborative solutions in the area of Human Capital Management. So, in turning to a conversation about Customer Relationship Management (CRM) and Sales Force Automation (SFA), let me ask you one simple question. How many sales people, particularly at business-to-business companies, consistently meet or beat their quotas in their roles by working alone, with no collaboration among fellow sales people, sales executives, employees in product groups, in service, in Legal, third-party partners, etc.? Hello? Is anybody out there? What’s that cricket noise I hear? That’s correct. Nobody! When it comes to Sales, introverts arguably have a distinct disadvantage. While it’s certainly a truism that “success” in most professional endeavors requires working with people, it’s a mandatory success factor in Sales. This fact became abundantly clear to me one early morning in the late 1990s when I joined the former Hyperion Solutions (now part of Oracle) and attended a Sales Award Ceremony. The Head of Sales at that time gave out dozens of awards – none of them to individuals and all of them to TEAMS of individuals. That’s how it works in Sales. Your colleagues help provide you with product intelligence and competitive intelligence. They help you build the best presentations, pitches, and proposals. They help you develop the most killer RFPs. They align you with the best product people to ensure you’re matching the best products for the opportunity and join you in critical meetings. They help knock the socks of your prospects in “bake off” demo’s. They bring in the best partners to either add complementary products to your opportunity or help you implement a solution. They work with you as a collective team. And so how is all this collaboration STILL typically done today? Through email. And yet we all silently or not so silently grimace about email. It’s relatively siloed. It’s painful to search. It’s difficult to align by topic. And it’s nearly impossible to re-trace meaningful and helpful conversations that occurred among a group or a team at some point in history. This is where social networking for Sales comes into play. It’s about PURPOSEFUL social networking versus chattering. What is purposeful social networking? It’s collaboration that’s built around opportunities, accounts, and contacts. It’s collaboration that delivers valuable context – on the target company, and on key competitors – just to name two examples. It’s collaboration that can scale to provide coaching for larger numbers of sales representatives, both for general purposes, and as we’ve largely discussed here, for specific ‘deals.’ And it’s collaboration that allows a team of people to collectively edit and iterate on a document like an RFP or a soon-to-be killer presentation that is maintained in a central repository, with no time wasted searching for it or worrying about version control. But lest we get carried away, let’s remember that collaboration “happens” among sales people whether there is specialized software to support it or not. The human practice of sales has not changed much in the last 80 to 90 years. Collaboration has been a mainstay during this entire time. But what social networking in general, and Oracle Social Networking in particular delivers, is the opportunity for sales teams to dramatically increase their effectiveness and efficiency – to identify and close more high quality and lucrative opportunities more quickly. For most sales organizations, this is how the game is won. To learn more please visit Oracle Social Network and Oracle Fusion Customer Relationship Management on oracle.com

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  • BYOD is not a fashion statement; it’s an architectural shift - by Indus Khaitan

    - by Greg Jensen
    Ten years ago, if you asked a CIO, “how mobile is your enterprise?”. The answer would be, “100%, we give Blackberry to all our employees.”Few things have changed since then: 1.    Smartphone form-factors have matured, especially after the launch of iPhone. 2.    Rapid growth of productivity applications and services that enable creation and consumption of digital content 3.    Pervasive mobile data connectivityThere are two threads emerging from the change. Users are rapidly mingling their personas of an individual as well as an employee. In the first second, posting a picture of a fancy dinner on Facebook, to creating an expense report for the same meal on the mobile device. Irrespective of the dual persona, a user’s personal and corporate lives intermingle freely on a single hardware and more often than not, it’s an employees personal smartphone being used for everything. A BYOD program enables IT to “control” an employee owned device, while enabling productivity. More often than not the objective of BYOD programs are financial; instead of the organization, an employee pays for it.  More than a fancy device, BYOD initiatives have become sort of fashion statement, of corporate productivity, of letting employees be in-charge and a show of corporate empathy to not force an archaic form-factor in a world of new device launches every month. BYOD is no longer a means of effectively moving expense dollars and support costs. It does not matter who owns the device, it has to be protected.  BYOD brings an architectural shift.  BYOD is an architecture, which assumes that every device is vulnerable, not just what your employees have brought but what organizations have purchased for their employees. It's an architecture, which forces us to rethink how to provide productivity without comprising security.Why assume that every device is vulnerable? Mobile operating systems are rapidly evolving with leading upgrade announcement every other month. It is impossible for IT to catch-up. More than that, user’s are savvier than earlier.  While IT could install locks at the doors to prevent intruders, it may degrade productivity—which incentivizes user’s to bypass restrictions. A rapidly evolving mobile ecosystem have moving parts which are vulnerable. Hence, creating a mobile security platform, which uses the fundamental blocks of BYOD architecture such as identity defragmentation, IT control and data isolation, ensures that the sprawl of corporate data is contained. In the next post, we’ll dig deeper into the BYOD architecture. Normal 0 false false false EN-US JA X-NONE /* 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-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:12.0pt; font-family:"Cambria","serif"; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin;}

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  • Beyond Chatting: What ‘Social’ Means for CRM

    - by Divya Malik
    A guest post by Steve Diamond, Senior Director, Outbound Product Management, Oracle In a recent post on the Oracle Applications blog, my colleague Steve Boese asked three questions related to the widespread popularity and incredibly rapid growth of Facebook, Pinterest, and LinkedIn. Steve then addressed the many applications for collaborative solutions in the area of Human Capital Management. So, in turning to a conversation about Customer Relationship Management (CRM) and Sales Force Automation (SFA), let me ask you one simple question. How many sales people, particularly at business-to-business companies, consistently meet or beat their quotas in their roles by working alone, with no collaboration among fellow sales people, sales executives, employees in product groups, in service, in Legal, third-party partners, etc.? Hello? Is anybody out there? What’s that cricket noise I hear? That’s correct. Nobody! When it comes to Sales, introverts arguably have a distinct disadvantage. While it’s certainly a truism that “success” in most professional endeavors requires working with people, it’s a mandatory success factor in Sales. This fact became abundantly clear to me one early morning in the late 1990s when I joined the former Hyperion Solutions (now part of Oracle) and attended a Sales Award Ceremony. The Head of Sales at that time gave out dozens of awards – none of them to individuals and all of them to TEAMS of individuals. That’s how it works in Sales. Your colleagues help provide you with product intelligence and competitive intelligence. They help you build the best presentations, pitches, and proposals. They help you develop the most killer RFPs. They align you with the best product people to ensure you’re matching the best products for the opportunity and join you in critical meetings. They help knock the socks of your prospects in “bake off” demo’s. They bring in the best partners to either add complementary products to your opportunity or help you implement a solution. They work with you as a collective team. And so how is all this collaboration STILL typically done today? Through email. And yet we all silently or not so silently grimace about email. It’s relatively siloed. It’s painful to search. It’s difficult to align by topic. And it’s nearly impossible to re-trace meaningful and helpful conversations that occurred among a group or a team at some point in history. This is where social networking for Sales comes into play. It’s about PURPOSEFUL social networking versus chattering. What is purposeful social networking? It’s collaboration that’s built around opportunities, accounts, and contacts. It’s collaboration that delivers valuable context – on the target company, and on key competitors – just to name two examples. It’s collaboration that can scale to provide coaching for larger numbers of sales representatives, both for general purposes, and as we’ve largely discussed here, for specific ‘deals.’ And it’s collaboration that allows a team of people to collectively edit and iterate on a document like an RFP or a soon-to-be killer presentation that is maintained in a central repository, with no time wasted searching for it or worrying about version control. But lest we get carried away, let’s remember that collaboration “happens” among sales people whether there is specialized software to support it or not. The human practice of sales has not changed much in the last 80 to 90 years. Collaboration has been a mainstay during this entire time. But what social networking in general, and Oracle Social Networking in particular delivers, is the opportunity for sales teams to dramatically increase their effectiveness and efficiency – to identify and close more high quality and lucrative opportunities more quickly. For most sales organizations, this is how the game is won. To learn more please visit Oracle Social Network and Oracle Fusion Customer Relationship Management on oracle.com

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  • SQL SERVER – CXPACKET – Parallelism – Advanced Solution – Wait Type – Day 7 of 28

    - by pinaldave
    Earlier we discussed about the what is the common solution to solve the issue with CXPACKET wait time. Today I am going to talk about few of the other suggestions which can help to reduce the CXPACKET wait. If you are going to suggest that I should focus on MAXDOP and COST THRESHOLD – I totally agree. I have covered them in details in yesterday’s blog post. Today we are going to discuss few other way CXPACKET can be reduced. Potential Reasons: If data is heavily skewed, there are chances that query optimizer may estimate the correct amount of the data leading to assign fewer thread to query. This can easily lead to uneven workload on threads and may create CXPAKCET wait. While retrieving the data one of the thread face IO, Memory or CPU bottleneck and have to wait to get those resources to execute its tasks, may create CXPACKET wait as well. Data which is retrieved is on different speed IO Subsystem. (This is not common and hardly possible but there are chances). Higher fragmentations in some area of the table can lead less data per page. This may lead to CXPACKET wait. As I said the reasons here mentioned are not the major cause of the CXPACKET wait but any kind of scenario can create the probable wait time. Best Practices to Reduce CXPACKET wait: Refer earlier article regarding MAXDOP and Cost Threshold. De-fragmentation of Index can help as more data can be obtained per page. (Assuming close to 100 fill-factor) If data is on multiple files which are on multiple similar speed physical drive, the CXPACKET wait may reduce. Keep the statistics updated, as this will give better estimate to query optimizer when assigning threads and dividing the data among available threads. Updating statistics can significantly improve the strength of the query optimizer to render proper execution plan. This may overall affect the parallelism process in positive way. Bad Practice: In one of the recent consultancy project, when I was called in I noticed that one of the ‘experienced’ DBA noticed higher CXPACKET wait and to reduce them, he has increased the worker threads. The reality was increasing worker thread has lead to many other issues. With more number of the threads, more amount of memory was used leading memory pressure. As there were more threads CPU scheduler faced higher ‘Context Switching’ leading further degrading performance. When I explained all these to ‘experienced’ DBA he suggested that now we should reduce the number of threads. Not really! Lower number of the threads may create heavy stalling for parallel queries. I suggest NOT to touch the setting of number of the threads when dealing with CXPACKET wait. Read all the post in the Wait Types and Queue series. Note: The information presented here is from my experience and I no way claim it to be accurate. I suggest reading book on-line for further clarification. All the discussion of Wait Stats over here is generic and it varies by system to system. You are recommended to test this on development server before implementing to production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: DMV, Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • It’s the thought that counts…

    - by Tony Davis
    I recently finished editing a book called Tribal SQL, and it was a fantastic experience. It’s a community-sourced book written by first-timers. Fifteen previously unpublished authors contributed one chapter each, with the seemingly simple remit to write about “what makes them passionate about working with SQL Server, something that all SQL Server DBAs and developers really need to know”. Sure, some of the writing skills were a bit rusty as one would expect from busy people, but the ideas and energy were sheer nectar. Any seasoned editor can deal easily with the problem of fixing the output of untrained writers. We can handle with the occasional technical error too, which is why we have technical reviewers. The editor’s real job is to hone the clarity and flow of ideas, making the author’s knowledge and experience accessible to as many others as possible. What the writer needs to bring, on the other hand, is enthusiasm, attention to detail, common sense, and a sense of the person behind the writing. If any of these are missing, no editor can fix it. We can see these essential characteristics in many of the more seasoned and widely-published writers about SQL. To illustrate what I mean by enthusiasm, or passion, take a look at the work of Laerte Junior or Fabiano Amorim. Both authors have English as a second language, but their energy, enthusiasm, sheer immersion in a technology and thirst to know more, drives them, with a little editorial help, to produce articles of far more practical value than one can find in the “manuals”. There’s the attention to detail of the likes of Jonathan Kehayias, or Paul Randal. Read their work and one begins to understand the knowledge coupled with incredible rigor, the willingness to bend and test every piece of advice offered to make sure it’s correct, that marks out the very best technical writing. There’s the common sense of someone like Louis Davidson. All writers, including Louis, like to stretch the grey matter of their readers, but some of the most valuable writing is that which takes a complicated idea, or distils years of experience, and expresses it in a way that sounds like simple common sense. There’s personality and humor. Contrary to what you may have been told, they can and do mix well with technical writing, as long as they don’t become a distraction. Read someone like Rodney Landrum, or Phil Factor, for numerous examples of articles that teach hard technical lessons but also make you smile at least twice along the way. Writing well is not easy and it takes a certain bravery to expose your ideas and knowledge for dissection by others, but it doesn’t mean that writing should be the preserve only of those trained in the art, or best left to the MVPs. I believe that Tribal SQL is testament to the fact that if you have passion for what you do, and really know your topic then, with a little editorial help, you can write, and people will learn from what you have to say. You can read a sample chapter, by Mark Rasmussen, in this issue of Simple-Talk and I hope you’ll consider checking out the book (if you needed any further encouragement, it’s also for a good cause, Computers4Africa). Cheers, Tony  

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  • A temporary disagreement

    - by Tony Davis
    Last month, Phil Factor caused a furore amongst some MVPs with an article that attempted to offer simple advice to developers regarding the use of table variables, versus local and global temporary tables, in their code. Phil makes clear that the table variables do come with some fairly major limitations.no distribution statistics, no parallel query plans for queries that modify table variables.but goes on to suggest that for reasonably small-scale strategic uses, and with a bit of due care and testing, table variables are a "good thing". Not everyone shares his opinion; in fact, I imagine he was rather aghast to learn that there were those felt his article was akin to pulling the pin out of a grenade and tossing it into the database; table variables should be avoided in almost all cases, according to their advice, in favour of temp tables. In other words, a fairly major feature of SQL Server should be more-or-less 'off limits' to developers. The problem with temp tables is that, because they are scoped either in the procedure or the connection, it is easy to allow them to hang around for too long, eating up precious memory and bulking up the shared tempdb database. Unless they are explicitly dropped, global temporary tables, and local temporary tables created within a connection rather than within a stored procedure, will persist until the connection is closed or, with connection pooling, until the connection is reused. It's also quite common with ASP.NET applications to have connection leaks, as Bill Vaughn explains in his chapter in the "SQL Server Deep Dives" book, meaning that the web page exits without closing the connection object, maybe due to an error condition. This will then hang around in the heap for what might be hours before picked up by the garbage collector. Table variables are much safer in this regard, since they are batch-scoped and so are cleaned up automatically once the batch is complete, which also means that they are intuitive to use for the developer because they conform to scoping rules that are closer to those in procedural code. On the surface then, an ideal way to deal with issues related to tempdb memory hogging. So why did Phil qualify his recommendation to use Table Variables? This is another of those cases where, like scalar UDFs and table-valued multi-statement UDFs, developers can sometimes get into trouble with a relatively benign-looking feature, due to way it's been implemented in SQL Server. Once again the biggest problem is how they are handled internally, by the SQL Server query optimizer, which can make very poor choices for JOIN orders and so on, in the absence of statistics, especially when joining to tables with highly-skewed data. The resulting execution plans can be horrible, as will be the resulting performance. If the JOIN is to a large table, that will hurt. Ideally, Microsoft would simply fix this issue so that developers can't get burned in this way; they've been around since SQL Server 2000, so Microsoft has had a bit of time to get it right. As I commented in regard to UDFs, when developers discover issues like with such standard features, the database becomes an alien planet to them, where death lurks around each corner, and they continue to avoid these "killer" features years after the problems have been eventually resolved. In the meantime, what is the right approach? Is it to say "hammers can kill, don't ever use hammers", or is it to try to explain, as Phil's article and follow-up blog post have tried to do, what the feature was intended for, why care must be applied in its use, and so enable developers to make properly-informed decisions, without requiring them to delve deep into the inner workings of SQL Server? Cheers, Tony.

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  • How to Set Up Your Enterprise Social Organization

    - by Mike Stiles
    The rush for business organizations to establish, grow, and adopt social was driven out of necessity and inevitability. The result, however, was a sudden, booming social presence creating touch points with customers, partners and influencers, but without any corporate social organization or structure in place to effectively manage it. Even today, many business leaders remain uncertain as to how to corral this social media thing so that it makes sense for their enterprise. Imagine their panic when they hear one of the most beneficial approaches to corporate use of social involves giving up at least some hierarchical control and empowering employees to publicly engage customers. And beyond that, they should also be empowered, regardless of their corporate status, to engage and collaborate internally, spurring “off the grid” innovation. An HBR blog points out that traditionally, enterprise organizations function from the top down, and employees work end-to-end, structured around business processes. But the social enterprise opens up structures that up to now have not exactly been embraced by turf-protecting executives and managers. The blog asks, “What if leaders could create a future where customers, associates and suppliers are no longer seen as objects in the system but as valued sources of innovation, ideas and energy?” What if indeed? The social enterprise activates internal resources without the usual obsession with position. It is the dawn of mass collaboration. That does not, however, mean this mass collaboration has to lead to uncontrolled chaos. In an extended interview with Oracle, Altimeter Group analyst Jeremiah Owyang and Oracle SVP Reggie Bradford paint a complete picture of today’s social enterprise, including internal organizational structures Altimeter Group has seen emerge. One sign of a mature social enterprise is the establishing of a social Center of Excellence (CoE), which serves as a hub for high-level social strategy, training and education, research, measurement and accountability, and vendor selection. This CoE is led by a corporate Social Strategist, most likely from a Marketing or Corporate Communications background. Reporting to them are the Community Managers, the front lines of customer interaction and engagement; business unit liaisons that coordinate the enterprise; and social media campaign/product managers, social analysts, and developers. With content rising as the defining factor for social success, Altimeter also sees a Content Strategist position emerging. Across the enterprise, Altimeter has seen 5 organizational patterns. Watching the video will give you the pros and cons of each. Decentralized - Anyone can do anything at any time on any social channel. Centralized – One central groups controls all social communication for the company. Hub and Spoke – A centralized group, but business units can operate their own social under the hub’s guidance and execution. Most enterprises are using this model. Dandelion – Each business unit develops their own social strategy & staff, has its own ability to deploy, and its own ability to engage under the central policies of the CoE. Honeycomb – Every employee can do social, but as opposed to the decentralized model, it’s coordinated and monitored on one platform. The average enterprise has a whopping 178 social accounts, nearly ¼ of which are usually semi-idle and need to be scrapped. The last thing any C-suite needs is to cope with fragmented technologies, solutions and platforms. It’s neither scalable nor strategic. The prepared, effective social enterprise has a technology partner that can quickly and holistically integrate emerging platforms and technologies, such that whatever internal social command structure you’ve set up can continue efficiently executing strategy without skipping a beat. @mikestiles

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  • SQL SERVER – ASYNC_IO_COMPLETION – Wait Type – Day 11 of 28

    - by pinaldave
    For any good system three things are vital: CPU, Memory and IO (disk). Among these three, IO is the most crucial factor of SQL Server. Looking at real-world cases, I do not see IT people upgrading CPU and Memory frequently. However, the disk is often upgraded for either improving the space, speed or throughput. Today we will look at another IO-related wait type. From Book On-Line: Occurs when a task is waiting for I/Os to finish. ASYNC_IO_COMPLETION Explanation: Any tasks are waiting for I/O to finish. If by any means your application that’s connected to SQL Server is processing the data very slowly, this type of wait can occur. Several long-running database operations like BACKUP, CREATE DATABASE, ALTER DATABASE or other operations can also create this wait type. Reducing ASYNC_IO_COMPLETION wait: When it is an issue related to IO, one should check for the following things associated to IO subsystem: Look at the programming and see if there is any application code which processes the data slowly (like inefficient loop, etc.). Note that it should be re-written to avoid this  wait type. Proper placing of the files is very important. We should check the file system for proper placement of the files – LDF and MDF on separate drive, TempDB on another separate drive, hot spot tables on separate filegroup (and on separate disk), etc. Check the File Statistics and see if there is a higher IO Read and IO Write Stall SQL SERVER – Get File Statistics Using fn_virtualfilestats. Check event log and error log for any errors or warnings related to IO. If you are using SAN (Storage Area Network), check the throughput of the SAN system as well as configuration of the HBA Queue Depth. In one of my recent projects, the SAN was performing really badly and so the SAN administrator did not accept it. After some investigations, he agreed to change the HBA Queue Depth on the development setup (test environment). As soon as we changed the HBA Queue Depth to quite a higher value, there was a sudden big improvement in the performance. It is very likely to happen that there are no proper indexes on the system and yet there are lots of table scans and heap scans. Creating proper index can reduce the IO bandwidth considerably. If SQL Server can use appropriate cover index instead of clustered index, it can effectively reduce lots of CPU, Memory and IO (considering cover index has lesser columns than cluster table and all other; it depends upon the situation). You can refer to the following two articles I wrote that talk about how to optimize indexes: Create Missing Indexes Drop Unused Indexes Checking Memory Related Perfmon Counters SQLServer: Memory Manager\Memory Grants Pending (Consistent higher value than 0-2) SQLServer: Memory Manager\Memory Grants Outstanding (Consistent higher value, Benchmark) SQLServer: Buffer Manager\Buffer Hit Cache Ratio (Higher is better, greater than 90% for usually smooth running system) SQLServer: Buffer Manager\Page Life Expectancy (Consistent lower value than 300 seconds) Memory: Available Mbytes (Information only) Memory: Page Faults/sec (Benchmark only) Memory: Pages/sec (Benchmark only) Checking Disk Related Perfmon Counters Average Disk sec/Read (Consistent higher value than 4-8 millisecond is not good) Average Disk sec/Write (Consistent higher value than 4-8 millisecond is not good) Average Disk Read/Write Queue Length (Consistent higher value than benchmark is not good) Read all the post in the Wait Types and Queue series. Note: The information presented here is from my experience and there is no way that I claim it to be accurate. I suggest reading Book OnLine for further clarification. All the discussions of Wait Stats in this blog are generic and vary from system to system. It is recommended that you test this on a development server before implementing it to a production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • SQL SERVER – IO_COMPLETION – Wait Type – Day 10 of 28

    - by pinaldave
    For any good system three things are vital: CPU, Memory and IO (disk). Among these three, IO is the most crucial factor of SQL Server. Looking at real-world cases, I do not see IT people upgrading CPU and Memory frequently. However, the disk is often upgraded for either improving the space, speed or throughput. Today we will look at an IO-related wait types. From Book On-Line: Occurs while waiting for I/O operations to complete. This wait type generally represents non-data page I/Os. Data page I/O completion waits appear as PAGEIOLATCH_* waits. IO_COMPLETION Explanation: Any tasks are waiting for I/O to finish. This is a good indication that IO needs to be looked over here. Reducing IO_COMPLETION wait: When it is an issue concerning the IO, one should look at the following things related to IO subsystem: Proper placing of the files is very important. We should check the file system for proper placement of files – LDF and MDF on a separate drive, TempDB on another separate drive, hot spot tables on separate filegroup (and on separate disk),etc. Check the File Statistics and see if there is higher IO Read and IO Write Stall SQL SERVER – Get File Statistics Using fn_virtualfilestats. Check event log and error log for any errors or warnings related to IO. If you are using SAN (Storage Area Network), check the throughput of the SAN system as well as the configuration of the HBA Queue Depth. In one of my recent projects, the SAN was performing really badly so the SAN administrator did not accept it. After some investigations, he agreed to change the HBA Queue Depth on development (test environment) set up and as soon as we changed the HBA Queue Depth to quite a higher value, there was a sudden big improvement in the performance. It is very possible that there are no proper indexes in the system and there are lots of table scans and heap scans. Creating proper index can reduce the IO bandwidth considerably. If SQL Server can use appropriate cover index instead of clustered index, it can effectively reduce lots of CPU, Memory and IO (considering cover index has lesser columns than cluster table and all other; it depends upon the situation). You can refer to the two articles that I wrote; they are about how to optimize indexes: Create Missing Indexes Drop Unused Indexes Checking Memory Related Perfmon Counters SQLServer: Memory Manager\Memory Grants Pending (Consistent higher value than 0-2) SQLServer: Memory Manager\Memory Grants Outstanding (Consistent higher value, Benchmark) SQLServer: Buffer Manager\Buffer Hit Cache Ratio (Higher is better, greater than 90% for usually smooth running system) SQLServer: Buffer Manager\Page Life Expectancy (Consistent lower value than 300 seconds) Memory: Available Mbytes (Information only) Memory: Page Faults/sec (Benchmark only) Memory: Pages/sec (Benchmark only) Checking Disk Related Perfmon Counters Average Disk sec/Read (Consistent higher value than 4-8 millisecond is not good) Average Disk sec/Write (Consistent higher value than 4-8 millisecond is not good) Average Disk Read/Write Queue Length (Consistent higher value than benchmark is not good) Note: The information presented here is from my experience and there is no way that I claim it to be accurate. I suggest reading Book OnLine for further clarification. All the discussions of Wait Stats in this blog are generic and vary from system to system. It is recommended that you test this on a development server before implementing it to a production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Types, SQL White Papers, T SQL, Technology

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  • Algorithm to Find the Aggregate Mass of "Granola Bar"-Like Structures?

    - by Stuart Robbins
    I'm a planetary science researcher and one project I'm working on is N-body simulations of Saturn's rings. The goal of this particular study is to watch as particles clump together under their own self-gravity and measure the aggregate mass of the clumps versus the mean velocity of all particles in the cell. We're trying to figure out if this can explain some observations made by the Cassini spacecraft during the Saturnian summer solstice when large structures were seen casting shadows on the nearly edge-on rings. Below is a screenshot of what any given timestep looks like. (Each particle is 2 m in diameter and the simulation cell itself is around 700 m across.) The code I'm using already spits out the mean velocity at every timestep. What I need to do is figure out a way to determine the mass of particles in the clumps and NOT the stray particles between them. I know every particle's position, mass, size, etc., but I don't know easily that, say, particles 30,000-40,000 along with 102,000-105,000 make up one strand that to the human eye is obvious. So, the algorithm I need to write would need to be a code with as few user-entered parameters as possible (for replicability and objectivity) that would go through all the particle positions, figure out what particles belong to clumps, and then calculate the mass. It would be great if it could do it for "each" clump/strand as opposed to everything over the cell, but I don't think I actually need it to separate them out. The only thing I was thinking of was doing some sort of N2 distance calculation where I'd calculate the distance between every particle and if, say, the closest 100 particles were within a certain distance, then that particle would be considered part of a cluster. But that seems pretty sloppy and I was hoping that you CS folks and programmers might know of a more elegant solution? Edited with My Solution: What I did was to take a sort of nearest-neighbor / cluster approach and do the quick-n-dirty N2 implementation first. So, take every particle, calculate distance to all other particles, and the threshold for in a cluster or not was whether there were N particles within d distance (two parameters that have to be set a priori, unfortunately, but as was said by some responses/comments, I wasn't going to get away with not having some of those). I then sped it up by not sorting distances but simply doing an order N search and increment a counter for the particles within d, and that sped stuff up by a factor of 6. Then I added a "stupid programmer's tree" (because I know next to nothing about tree codes). I divide up the simulation cell into a set number of grids (best results when grid size ˜7 d) where the main grid lines up with the cell, one grid is offset by half in x and y, and the other two are offset by 1/4 in ±x and ±y. The code then divides particles into the grids, then each particle N only has to have distances calculated to the other particles in that cell. Theoretically, if this were a real tree, I should get order N*log(N) as opposed to N2 speeds. I got somewhere between the two, where for a 50,000-particle sub-set I got a 17x increase in speed, and for a 150,000-particle cell, I got a 38x increase in speed. 12 seconds for the first, 53 seconds for the second, 460 seconds for a 500,000-particle cell. Those are comparable speeds to how long the code takes to run the simulation 1 timestep forward, so that's reasonable at this point. Oh -- and it's fully threaded, so it'll take as many processors as I can throw at it.

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  • How to Set Up Your Enterprise Social Organization?

    - by Richard Lefebvre
    By Mike Stiles on Dec 04, 2012 The rush for business organizations to establish, grow, and adopt social was driven out of necessity and inevitability. The result, however, was a sudden, booming social presence creating touch points with customers, partners and influencers, but without any corporate social organization or structure in place to effectively manage it. Even today, many business leaders remain uncertain as to how to corral this social media thing so that it makes sense for their enterprise. Imagine their panic when they hear one of the most beneficial approaches to corporate use of social involves giving up at least some hierarchical control and empowering employees to publicly engage customers. And beyond that, they should also be empowered, regardless of their corporate status, to engage and collaborate internally, spurring “off the grid” innovation. An HBR blog points out that traditionally, enterprise organizations function from the top down, and employees work end-to-end, structured around business processes. But the social enterprise opens up structures that up to now have not exactly been embraced by turf-protecting executives and managers. The blog asks, “What if leaders could create a future where customers, associates and suppliers are no longer seen as objects in the system but as valued sources of innovation, ideas and energy?” What if indeed? The social enterprise activates internal resources without the usual obsession with position. It is the dawn of mass collaboration. That does not, however, mean this mass collaboration has to lead to uncontrolled chaos. In an extended interview with Oracle, Altimeter Group analyst Jeremiah Owyang and Oracle SVP Reggie Bradford paint a complete picture of today’s social enterprise, including internal organizational structures Altimeter Group has seen emerge. One sign of a mature social enterprise is the establishing of a social Center of Excellence (CoE), which serves as a hub for high-level social strategy, training and education, research, measurement and accountability, and vendor selection. This CoE is led by a corporate Social Strategist, most likely from a Marketing or Corporate Communications background. Reporting to them are the Community Managers, the front lines of customer interaction and engagement; business unit liaisons that coordinate the enterprise; and social media campaign/product managers, social analysts, and developers. With content rising as the defining factor for social success, Altimeter also sees a Content Strategist position emerging. Across the enterprise, Altimeter has seen 5 organizational patterns. Watching the video will give you the pros and cons of each. Decentralized - Anyone can do anything at any time on any social channel. Centralized – One central groups controls all social communication for the company. Hub and Spoke – A centralized group, but business units can operate their own social under the hub’s guidance and execution. Most enterprises are using this model. Dandelion – Each business unit develops their own social strategy & staff, has its own ability to deploy, and its own ability to engage under the central policies of the CoE. Honeycomb – Every employee can do social, but as opposed to the decentralized model, it’s coordinated and monitored on one platform. The average enterprise has a whopping 178 social accounts, nearly ¼ of which are usually semi-idle and need to be scrapped. The last thing any C-suite needs is to cope with fragmented technologies, solutions and platforms. It’s neither scalable nor strategic. The prepared, effective social enterprise has a technology partner that can quickly and holistically integrate emerging platforms and technologies, such that whatever internal social command structure you’ve set up can continue efficiently executing strategy without skipping a beat. @mikestiles

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  • Developing an Implementation Plan with Iterations by Russ Pitts

    - by user535886
    Developing an Implementation Plan with Iterations by Russ Pitts  Ok, so you have come to grips with understanding that applying the iterative concept, as defined by OUM is simply breaking up the project effort you have estimated for each phase into one or more six week calendar duration blocks of work. Idea being the business user(s) or key recipient(s) of work product(s) being developed never go longer than six weeks without having some sort of review or prototyping of the work results for an iteration…”think-a-little”, “do-a-little”, and “show-a-little” in a six week or less timeframe…ideally the business user(s) or key recipients(s) are involved throughout. You also understand the OUM concept that you only plan for that which you have knowledge of. The concept further defined, a project plan initially is developed at a high-level, and becomes more detailed as project knowledge grows. Agreeing to this concept means you also have to admit to the fallacy that one can plan with precision beyond six weeks into a project…Anything beyond six weeks is a best guess in most cases when dealing with software implementation projects. Project planning, as defined by OUM begins with the Implementation Plan view, which is a very high-level perspective of the effort estimated for each of the five OUM phases, as well as the number of iterations within each phase. You might wonder how can you predict the number of iterations for each phase at this early point in the project. Remember project planning is not an exact science, and initially is high-level and abstract in nature, and then becomes more detailed and precise as the project proceeds. So where do you start in defining iterations for each phase for a project? The following are three easy steps to initially define the number of iterations for each phase: Step 1 => Start with identifying the known factors… …Prior to starting a project you should know: · The agreed upon time-period for an iteration (e.g 6 weeks, or 4 weeks, or…) within a phase (recommend keeping iteration time-period consistent within a phase, if not for the entire project) · The number of resources available for the project · The number of total number of man-day (effort) you have estimated for each of the five OUM phases of the project · The number of work days for a week Step 2 => Calculate the man-days of effort required for an iteration within a phase… Lets assume for the sake of this example there are 10 project resources, and you have estimated 2,536 man-days of work effort which will need to occur for the elaboration phase of the project. Let’s also assume a week for this project is defined as 5 business days, and that each iteration in the elaboration phase will last a calendar duration of 6 weeks. A simple calculation is performed to calculate the daily burn rate for a single iteration, which produces a result of… ((Number of resources * days per week) * duration of iteration) = Number of days required per iteration ((10 resources * 5 days/week) * 6 weeks) = 300 man days of effort required per iteration Step 3 => Calculate the number of iterations that can occur within a phase Next calculate the number of iterations that can occur for the amount of man-days of effort estimated for the phase being considered… (number of man-days of effort estimated / number of man-days required per iteration) = # of iterations for phase (2,536 man-days of estimated effort for phase / 300 man days of effort required per iteration) = 8.45 iterations, which should be rounded to a whole number such as 9 iterations* *Note - It is important to note this is an approximate calculation, not an exact science. This particular example is a simple one, which assumes all resources are utilized throughout the phase, including tech resources, etc. (rounding down or up to a whole number based on project factor considerations). It is also best in many cases to round up to higher number, as this provides some calendar scheduling contingency.

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