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  • BigData and Customer Experience: Happy Together

    - by Isabel F. Peñuelas
    The two big buzzes of the year may lay closer than it appears. Both concepts intersect at various points: BigData and Return of Investment of Marketing Campaigns On a recent post Big Data Is The Future Of Marketing Jeff Dachis explains very clearly how “Big data analytics finally allows marketers to identify, measure, and manage what is positively impacting their Brand”. Regression analysis applied to big data volumes coming from social media will substitute the failed attempts to justify marketing investments on social media in terms of followers and likes, he continues, “the measurement models applied by marketers on TV Campaigns don´t work on social”, we need to study the data with fresh eyes and maybe then we will start understanding and measuring brand engagemet. Social CRM and BigData The real value of Social CRM start by analyzing mass of big data from social media in order of applying social intelligence techniques that allow us to classify new customer niches and communities and define appropriated strategies to contact potential customers. Gartner Says that the Market for Social CRM is on pace to surpass $1 Billion in Revenue by Year-End 2012 but in words of Zach Hofer-Shall, Analyst at Forrester Research “Social customer relationship management is hard” (The Social CRM Arms Race Heats ). To succeed brands need three things: Investing in new social tools, investing in consultancy and investing in infrastructure for massive data storage and analysis. Neither CeX or BigData are easy and cheap wins. But what are the customer benefits of such investments? Big Data and Brand Engagement Time is the most valuable asset of todays consumers: tired of information overload, exhausted by the terabytes of offering, anxious because of not having the same fast multichannel experience with their services’ marketers or preferred goods providers than the one they found on their social media. Yes, I know you have read this before- me too. But is real. The motto of the Customer Experience philosophy of providing a consistent experience through multiple touchpoints that makes the relationship customer/brand easier and valuable finds it basis on understanding customer/s preferences and context for which BigData analysis is another imperative. In summary, I believe that using BigData Analysis in combination with appropriated CeX strategies and technologies is a promising direction for achieving: efficiency and marketing cost-savings; growing the customer base; and increasing customer conversion and retention. In a world: The Direction of Future Marketing.

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  • MySQL and Hadoop Integration - Unlocking New Insight

    - by Mat Keep
    “Big Data” offers the potential for organizations to revolutionize their operations. With the volume of business data doubling every 1.2 years, analysts and business users are discovering very real benefits when integrating and analyzing data from multiple sources, enabling deeper insight into their customers, partners, and business processes. As the world’s most popular open source database, and the most deployed database in the web and cloud, MySQL is a key component of many big data platforms, with Hadoop vendors estimating 80% of deployments are integrated with MySQL. The new Guide to MySQL and Hadoop presents the tools enabling integration between the two data platforms, supporting the data lifecycle from acquisition and organisation to analysis and visualisation / decision, as shown in the figure below The Guide details each of these stages and the technologies supporting them: Acquire: Through new NoSQL APIs, MySQL is able to ingest high volume, high velocity data, without sacrificing ACID guarantees, thereby ensuring data quality. Real-time analytics can also be run against newly acquired data, enabling immediate business insight, before data is loaded into Hadoop. In addition, sensitive data can be pre-processed, for example healthcare or financial services records can be anonymized, before transfer to Hadoop. Organize: Data is transferred from MySQL tables to Hadoop using Apache Sqoop. With the MySQL Binlog (Binary Log) API, users can also invoke real-time change data capture processes to stream updates to HDFS. Analyze: Multi-structured data ingested from multiple sources is consolidated and processed within the Hadoop platform. Decide: The results of the analysis are loaded back to MySQL via Apache Sqoop where they inform real-time operational processes or provide source data for BI analytics tools. So how are companies taking advantage of this today? As an example, on-line retailers can use big data from their web properties to better understand site visitors’ activities, such as paths through the site, pages viewed, and comments posted. This knowledge can be combined with user profiles and purchasing history to gain a better understanding of customers, and the delivery of highly targeted offers. Of course, it is not just in the web that big data can make a difference. Every business activity can benefit, with other common use cases including: - Sentiment analysis; - Marketing campaign analysis; - Customer churn modeling; - Fraud detection; - Research and Development; - Risk Modeling; - And more. As the guide discusses, Big Data is promising a significant transformation of the way organizations leverage data to run their businesses. MySQL can be seamlessly integrated within a Big Data lifecycle, enabling the unification of multi-structured data into common data platforms, taking advantage of all new data sources and yielding more insight than was ever previously imaginable. Download the guide to MySQL and Hadoop integration to learn more. I'd also be interested in hearing about how you are integrating MySQL with Hadoop today, and your requirements for the future, so please use the comments on this blog to share your insights.

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  • #MDX in London and speculation about future books

    - by Marco Russo (SQLBI)
    Chris Webb, who wrote the Expert Cube Development with Microsoft SQL Server 2008 Analysis Services book with me and Alberto , is preparing another Introduction to MDX course in London, this time from October 26th to 28th. It is now a three day course (previously it was two day) and you can find every other detail here . You might be wondering whether we are writing something else... well, we don't have plan to release a new edition of the Analysis Services book - after all, all the content of the...(read more)

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  • Understanding Data Science: Recent Studies

    - by Joe Lamantia
    If you need such a deeper understanding of data science than Drew Conway's popular venn diagram model, or Josh Wills' tongue in cheek characterization, "Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician." two relatively recent studies are worth reading.   'Analyzing the Analyzers,' an O'Reilly e-book by Harlan Harris, Sean Patrick Murphy, and Marck Vaisman, suggests four distinct types of data scientists -- effectively personas, in a design sense -- based on analysis of self-identified skills among practitioners.  The scenario format dramatizes the different personas, making what could be a dry statistical readout of survey data more engaging.  The survey-only nature of the data,  the restriction of scope to just skills, and the suggested models of skill-profiles makes this feel like the sort of exercise that data scientists undertake as an every day task; collecting data, analyzing it using a mix of statistical techniques, and sharing the model that emerges from the data mining exercise.  That's not an indictment, simply an observation about the consistent feel of the effort as a product of data scientists, about data science.  And the paper 'Enterprise Data Analysis and Visualization: An Interview Study' by researchers Sean Kandel, Andreas Paepcke, Joseph Hellerstein, and Jeffery Heer considers data science within the larger context of industrial data analysis, examining analytical workflows, skills, and the challenges common to enterprise analysis efforts, and identifying three archetypes of data scientist.  As an interview-based study, the data the researchers collected is richer, and there's correspondingly greater depth in the synthesis.  The scope of the study included a broader set of roles than data scientist (enterprise analysts) and involved questions of workflow and organizational context for analytical efforts in general.  I'd suggest this is useful as a primer on analytical work and workers in enterprise settings for those who need a baseline understanding; it also offers some genuinely interesting nuggets for those already familiar with discovery work. We've undertaken a considerable amount of research into discovery, analytical work/ers, and data science over the past three years -- part of our programmatic approach to laying a foundation for product strategy and highlighting innovation opportunities -- and both studies complement and confirm much of the direct research into data science that we conducted. There were a few important differences in our findings, which I'll share and discuss in upcoming posts.

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  • Using The Data Mining Query Task in SSIS

    SQL Server Integration Services (SSIS) is a Business Intelligence tool which can be used by database developers or administrators to perform Extract, Transform & Load (ETL) operations. In my previous article Using Analysis Services Processing Task & Analysis Services ... [Read Full Article]

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  • WildPackets Monitors Diverse Networks

    WildPackets offers portable network analysis products which are designed for use on enterprise networks and in test and measurement labs, plus distributed network analysis solutions for enterprise-wide applications.

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  • Parent-child hierarchies and unary operators in PowerPivot

    - by Marco Russo (SQLBI)
    Alberto wrote an excellent post describing how to implement the Unary Operator feature (which is present in Analysis Services) in PowerPivot (there was a previous post about parent-child hierarchies, too). I have to say that the solution is not so easy to implement as in Analysis Services, but it just works and, from a practical point of view, it is not so difficult to implement if you understand how it works and accept its limitations (only sum and subtractions are supported). I think that many...(read more)

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  • Search Engine Optimizing

    Search Engine Optimization is a process by which a web site is improved so that it can be more easily found by search engines, rank higher and be found by its target audience. The main components to SEO are: keyword analysis, content analysis, title and meta tags, relevant link building, search engine submission, and maintenance. Below are steps in the process.

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  • Powerful Lessons in Data from the Presidential Election

    - by Christina McKeon
    Now that we’ve had a few days to recover from the U.S. presidential election, it’s a good time to take a step back from politics and look for the customer experience lessons that we can take away. The most powerful lesson is that when you know more about your base, you will have an advantage over your competition. That advantage will translate into you winning and your competition losing. Michael Scherer of TIME was given access to Obama’s data analysts two days before the election. His account is documented in Inside the Secret World of the Data Crunchers Who Helped Obama Win. What we learned from Scherer’s inside view is how well Obama’s team did in getting the right data, analyzing it, and acting on it. This data team recognized how critical it was to break down data silos within the campaign. As Scherer noted, they created “a single system that merged information from pollsters, fundraisers, field workers, consumer databases, and social-media and mobile contacts with the main Democratic voter files in the swing states.” The Obama analysis was so meticulous that they knew which celebrity and which type of celebrity event would help them maximize campaign contributions. With a single system, their data models became more precise. They determined which messages were more successful with specific demographic groups and that who made the calls mattered. Data analysis also led to many other changes in Obama’s campaign including a new ad buying strategy, using social media and applications to tap into supporters’ friends, and using new social news sites. While we did not have that same inside view into Romney’s campaign, much of the post-mortem coverage indicates that Romney’s team did not have the right analysis. As Peter Hamby of CNN wrote in Analysis: Why Romney Lost, “Romney officials had modeled an electorate that looked something like a mix of 2004 and 2008….” That historical data did not account for the changing demographics in the U.S. Does your organization approach data like the Obama or Romney team? Do you really know your base? How well can you predict what is going to happen in your business? If you haven’t already put together a strategy and plan to know more, this week’s civics lesson is a powerful reason to do it sooner rather than later. Your competitors are probably thinking the same thing that you are!

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  • Botnet Malware Sleeps Eight Months Activation, Child Concerns

    Daily Safety Check experts used a computer forensic analysis of a significant botnet that consisted of Carberp and SpyEye malware to come up with the details for their report. The analysis found that the botnet profiled the behavior of the slave computers it infected, similar to surveillance techniques used by law enforcement agencies, for an average of eight months. During the eight months, the botnet analyzed each computer's users and assigned ratings to certain activities to form a complete profile for each. Doing so allowed those behind the scheme to determine which were the most favora...

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  • Tissue Specific Electrochemical Fingerprinting on the NetBeans Platform

    - by Geertjan
    Proteomics and metalloproteomics are rapidly developing interdisciplinary fields providing enormous amounts of data to be classified, evaluated, and interpreted. Approaches offered by bioinformatics and also by biostatistical data analysis and treatment are therefore becoming increasingly relevant. A bioinformatics tool has been developed at universities in Prague and Brno, in the Czech Republic, for analysis and visualization in this domain, on the NetBeans Platform: More info:  http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0049654

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  • Feature: Lead with Intelligence

    Business efficiency depends on business decisions, and business decisions depend on current, accurate information and powerful analysis. See how Oracle data warehousing, business intelligence, and enterprise performance management solutions deliver the information, analysis, and efficiencies to propel your business ahead of the competition.

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  • What’s Your Tax Strategy? Automate the Tax Transfer Pricing Process!

    - by tobyehatch
    Does your business operate in multiple countries? Well, whether you like it or not, many local and international tax authorities inspect your tax strategy.  Legal, effective tax planning is perceived as a “moral” issue. CEOs are being asked to testify on their process of tax transfer pricing between multinational legal entities.  Marc Seewald, Senior Director of Product Management for EPM Applications specializing in all tax subjects and Product Manager for Oracle Hyperion Tax Provisioning, and Bart Stoehr, Senior Director of Product Strategy for Oracle Hyperion Profitability and Cost Management joined me for a discussion/podcast on this interesting subject.  So what exactly is “tax transfer pricing”? Marc defined it this way. “Tax transfer pricing is a profit allocation methodology required to be used by multinational corporations. Specifically, the ultimate goal of the transfer pricing is to ensure that the global multinational pays their fair share of income tax in each of their local markets. Specifically, it prevents companies from unfairly moving profit from ‘high tax’ countries to ‘low tax’ countries.” According to Marc, in today’s global economy, profitability can be significantly impacted by goods and services exchanged between the related divisions within a single multinational company.  To ensure that these cost allocations are done fairly, there are rules that govern the process. These rules ensure that intercompany allocations fairly represent the actual nature of the businesses activity- as if two divisions were unrelated - and provide a clear audit trail of how the costs have been allocated to prove that allocations fall within reasonable ranges.  What are the repercussions of improper tax transfer pricing? How important is it? Tax transfer pricing allocations can materially impact the amount of overall corporate income taxes paid by a company worldwide, in some cases by hundreds of millions of dollars!  Since so much tax revenue is at stake, revenue agencies like the IRS, and international regulatory bodies like the Organization for Economic Cooperation and Development (OECD) are pushing to reform and clarify reporting for tax transfer pricing. Most recently the OECD announced an “Action Plan for Base Erosion and Profit Shifting”. As Marc explained, the times are changing and companies need to be responsive to this issue. “It feels like every other week there is another company being accused of avoiding taxes,” said Marc. Most recently, Caterpillar was accused of avoiding billions of dollars in taxes. In the last couple of years, Apple, GE, Ikea, and Starbucks, have all been accused of tax avoidance. It’s imperative that companies like these have a clear and auditable tax transfer process that enables them to justify tax transfer pricing allocations and avoid steep penalties and bad publicity. Transparency and efficiency are what is needed when it comes to the tax transfer pricing process. Bart explained that tax transfer pricing is driving a deeper inspection of profit recognition specifically focused on the tax element of profit.  However, allocations needed to support tax profitability are nearly identical in process to allocations taking place in other parts of the finance organization. For example, the methods and processes necessary to arrive at tax profitability by legal entity are no different than those used to arrive at fully loaded profitability for a product line. In fact, there is a great opportunity for alignment across these two different functions.So it seems that tax transfer pricing should be reflected in profitability in general. Bart agreed and told us more about some of the critical sub-processes of an overall tax transfer pricing process within the Oracle solution for tax transfer pricing.  “First, there is a ton of data preparation, enrichment and pre-allocation data analysis that is managed in the Oracle Hyperion solution. This serves as the “data staging” to the next, critical sub-processes.  From here, we leverage the Oracle EPM platform’s ability to re-use dimensions and legal entity driver data and financial data with Oracle Hyperion Profitability and Cost Management (HPCM).  Within HPCM, we manage the driver data, define the legal entity to legal entity allocation rules (like cost plus), and have the option to test out multiple, simultaneous tax transfer pricing what-if scenarios.  Once processed, a tax expert can evaluate the effectiveness of any one scenario result versus another via a variance analysis configured with HPCM’s pre-packaged reporting capability known as Oracle Hyperion SmartView for Office.”   Further, Bart explained that the ability to visibly demonstrate how a cost or revenue has been allocated is really helpful and auditable.  “HPCM’s Traceability Maps are that visual representation of all allocation flows that have been executed and is the tax transfer analyst’s best friend in maintaining clear documentation for tax transfer pricing audits. Simply click and drill as you inspect the chain of allocation definitions and results. Once final, the post-allocated tax data can be compared to the GL to create invoices and journal entries for posting to your GL system of choice.  Of course, there is a framework for overall governance of the journal entries, allocation percentages, and reporting to include necessary approvals.” Lastly, Marc explained that the key value in using the Oracle Hyperion solution for tax transfer pricing is that it keeps everything in alignment in one single place. Specifically, Oracle Hyperion effectively becomes the single book of record for the GAAP, management, and the tax set of books. There are many benefits to having one source of the truth. These include EFFICIENCY, CONTROLS and TRANSPARENCY.So, what’s your tax strategy? Why not automate the tax transfer pricing process!To listen to the entire podcast, click here.To learn more about Oracle Hyperion Profitability and Cost Management (HPCM), click here.

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  • HPCM 11.1.2.2.x - How to find data in an HPCM Standard Costing database

    - by Jane Story
    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:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} When working with a Hyperion Profitability and Cost Management (HPCM) Standard Costing application, there can often be a requirement to check data or allocated results using reporting tools e.g Smartview. To do this, you are retrieving data directly from the Essbase databases related to your HPCM model. For information, running reports is covered in Chapter 9 of the HPCM User documentation. The aim of this blog is to provide a quick guide to finding this data for reporting in the HPCM generated Essbase database in v11.1.2.2.x of HPCM. In order to retrieve data from an HPCM generated Essbase database, it is important to understand each of the following dimensions in the Essbase database and where data is located within them: Measures dimension – identifies Measures AllocationType dimension – identifies Direct Allocation Data or Genealogy Allocation data Point Of View (POV) dimensions – there must be at least one, maximum of four. Business dimensions: Stage Business dimensions – these will be identified by the Stage prefix. Intra-Stage dimension – these will be identified by the _Intra suffix. Essbase outlines and reporting is explained in the documentation here:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/ch09s02.html For additional details on reporting measures, please review this section of the documentation:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/apas03.html Reporting requirements in HPCM quite often start with identifying non balanced items in the Stage Balancing report. The following documentation link provides help with identifying some of the items within the Stage Balancing report:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/generatestagebalancing.html The following are some types of data upon which you may want to report: Stage Data: Direct Input Assigned Input Data Assigned Output Data Idle Cost/Revenue Unassigned Cost/Revenue Over Driven Cost/Revenue Direct Allocation Data Genealogy Allocation Data Stage Data Stage Data consists of: Direct Input i.e. input data, the starting point of your allocation e.g. in Stage 1 Assigned Input Data i.e. the cost/revenue received from a prior stage (i.e. stage 2 and higher). Assigned Output Data i.e. for each stage, the data that will be assigned forward is assigned post stage data. Reporting on this data is explained in the documentation here:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/ch09s03.html Dimension Selection Measures Direct Input: CostInput RevenueInput Assigned Input (from previous stages): CostReceivedPriorStage RevenueReceivedPriorStage Assigned Output (to subsequent stages): CostAssignedPostStage RevenueAssignedPostStage AllocationType DirectAllocation POV One member from each POV dimension Stage Business Dimensions Any members for the stage business dimensions for the stage you wish to see the Stage data for. All other Dimensions NoMember Idle/Unassigned/OverDriven To view Idle, Unassigned or Overdriven Costs/Revenue, first select which stage for which you want to view this data. If multiple Stages have unassigned/idle, resolve the earliest first and re-run the calculation as differences in early stages will create unassigned/idle in later stages. Dimension Selection Measures Idle: IdleCost IdleRevenue Unassigned: UnAssignedCost UnAssignedRevenue Overdriven: OverDrivenCost OverDrivenRevenue AllocationType DirectAllocation POV One member from each POV dimension Dimensions in the Stage with Unassigned/ Idle/OverDriven Cost All the Stage Business dimensions in the Stage with Unassigned/Idle/Overdriven. Zoom in on each dimension to find the individual members to find which members have Unassigned/Idle/OverDriven data. All other Dimensions NoMember Direct Allocation Data Direct allocation data shows the data received by a destination intersection from a source intersection where a direct assignment(s) exists. Reporting on direct allocation data is explained in the documentation here:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/ch09s04.html You would select the following to report direct allocation data Dimension Selection Measures CostReceivedPriorStage AllocationType DirectAllocation POV One member from each POV dimension Stage Business Dimensions Any members for the SOURCE stage business dimensions and the DESTINATION stage business dimensions for the direct allocations for the stage you wish to report on. All other Dimensions NoMember Genealogy Allocation Data Genealogy allocation data shows the indirect data relationships between stages. Genealogy calculations run in the HPCM Reporting database only. Reporting on genealogy data is explained in the documentation here:http://docs.oracle.com/cd/E17236_01/epm.1112/hpm_user/ch09s05.html Dimension Selection Measures CostReceivedPriorStage AllocationType GenealogyAllocation (IndirectAllocation in 11.1.2.1 and prior versions) POV One member from each POV dimension Stage Business Dimensions Any stage business dimension members from the STARTING stage in Genealogy Any stage business dimension members from the INTERMEDIATE stage(s) in Genealogy Any stage business dimension members from the ENDING stage in Genealogy All other Dimensions NoMember Notes If you still don’t see data after checking the above, please check the following Check the calculation has been run. Here are couple of indicators that might help them with that. Note the size of essbase cube before and after calculations ensure that a calculation was run against the database you are examing. Export the essbase data to a text file to confirm that some data exists. Examine the date and time on task area to see when, if any, calculations were run and what choices were used (e.g. Genealogy choices) If data does not exist in places where they are expecting, it could be that No calculations/genealogy were run No calculations were successfully run The model/data at feeder location were either absent or incompatible, resulting in no allocation e.g no driver data. Smartview Invocation from HPCM From version 11.1.2.2.350 of HPCM (this version will be GA shortly), it is possible to directly invoke Smartview from HPCM. There is guided navigation before the Smartview invocation and it is then possible to see the selected value(s) in SmartView. Click to Download HPCM 11.1.2.2.x - How to find data in an HPCM Standard Costing database (Right click or option-click the link and choose "Save As..." to download this pdf file)

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  • Developing Schema Compare for Oracle (Part 6): 9i Query Performance

    - by Simon Cooper
    All throughout the EAP and beta versions of Schema Compare for Oracle, our main request was support for Oracle 9i. After releasing version 1.0 with support for 10g and 11g, our next step was then to get version 1.1 of SCfO out with support for 9i. However, there were some significant problems that we had to overcome first. This post will concentrate on query execution time. When we first tested SCfO on a 9i server, after accounting for various changes to the data dictionary, we found that database registration was taking a long time. And I mean a looooooong time. The same database that on 10g or 11g would take a couple of minutes to register would be taking upwards of 30 mins on 9i. Obviously, this is not ideal, so a poke around the query execution plans was required. As an example, let's take the table population query - the one that reads ALL_TABLES and joins it with a few other dictionary views to get us back our list of tables. On 10g, this query takes 5.6 seconds. On 9i, it takes 89.47 seconds. The difference in execution plan is even more dramatic - here's the (edited) execution plan on 10g: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 108K| 939 || 1 | SORT ORDER BY | | 108K| 939 || 2 | NESTED LOOPS OUTER | | 108K| 938 ||* 3 | HASH JOIN RIGHT OUTER | | 103K| 762 || 4 | VIEW | ALL_EXTERNAL_LOCATIONS | 2058 | 3 ||* 20 | HASH JOIN RIGHT OUTER | | 73472 | 759 || 21 | VIEW | ALL_EXTERNAL_TABLES | 2097 | 3 ||* 34 | HASH JOIN RIGHT OUTER | | 39920 | 755 || 35 | VIEW | ALL_MVIEWS | 51 | 7 || 58 | NESTED LOOPS OUTER | | 39104 | 748 || 59 | VIEW | ALL_TABLES | 6704 | 668 || 89 | VIEW PUSHED PREDICATE | ALL_TAB_COMMENTS | 2025 | 5 || 106 | VIEW | ALL_PART_TABLES | 277 | 11 |------------------------------------------------------------------------------- And the same query on 9i: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 16P| 55G|| 1 | SORT ORDER BY | | 16P| 55G|| 2 | NESTED LOOPS OUTER | | 16P| 862M|| 3 | NESTED LOOPS OUTER | | 5251G| 992K|| 4 | NESTED LOOPS OUTER | | 4243M| 2578 || 5 | NESTED LOOPS OUTER | | 2669K| 1440 ||* 6 | HASH JOIN OUTER | | 398K| 302 || 7 | VIEW | ALL_TABLES | 342K| 276 || 29 | VIEW | ALL_MVIEWS | 51 | 20 ||* 50 | VIEW PUSHED PREDICATE | ALL_TAB_COMMENTS | 2043 | ||* 66 | VIEW PUSHED PREDICATE | ALL_EXTERNAL_TABLES | 1777K| ||* 80 | VIEW PUSHED PREDICATE | ALL_EXTERNAL_LOCATIONS | 1744K| ||* 96 | VIEW | ALL_PART_TABLES | 852K| |------------------------------------------------------------------------------- Have a look at the cost column. 10g's overall query cost is 939, and 9i is 55,000,000,000 (or more precisely, 55,496,472,769). It's also having to process far more data. What on earth could be causing this huge difference in query cost? After trawling through the '10g New Features' documentation, we found item 1.9.2.21. Before 10g, Oracle advised that you do not collect statistics on data dictionary objects. From 10g, it advised that you do collect statistics on the data dictionary; for our queries, Oracle therefore knows what sort of data is in the dictionary tables, and so can generate an efficient execution plan. On 9i, no statistics are present on the system tables, so Oracle has to use the Rule Based Optimizer, which turns most LEFT JOINs into nested loops. If we force 9i to use hash joins, like 10g, we get a much better plan: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 7587K| 3704 || 1 | SORT ORDER BY | | 7587K| 3704 ||* 2 | HASH JOIN OUTER | | 7587K| 822 ||* 3 | HASH JOIN OUTER | | 5262K| 616 ||* 4 | HASH JOIN OUTER | | 2980K| 465 ||* 5 | HASH JOIN OUTER | | 710K| 432 ||* 6 | HASH JOIN OUTER | | 398K| 302 || 7 | VIEW | ALL_TABLES | 342K| 276 || 29 | VIEW | ALL_MVIEWS | 51 | 20 || 50 | VIEW | ALL_PART_TABLES | 852K| 104 || 78 | VIEW | ALL_TAB_COMMENTS | 2043 | 14 || 93 | VIEW | ALL_EXTERNAL_LOCATIONS | 1744K| 31 || 106 | VIEW | ALL_EXTERNAL_TABLES | 1777K| 28 |------------------------------------------------------------------------------- That's much more like it. This drops the execution time down to 24 seconds. Not as good as 10g, but still an improvement. There are still several problems with this, however. 10g introduced a new join method - a right outer hash join (used in the first execution plan). The 9i query optimizer doesn't have this option available, so forcing a hash join means it has to hash the ALL_TABLES table, and furthermore re-hash it for every hash join in the execution plan; this could be thousands and thousands of rows. And although forcing hash joins somewhat alleviates this problem on our test systems, there's no guarantee that this will improve the execution time on customers' systems; it may even increase the time it takes (say, if all their tables are partitioned, or they've got a lot of materialized views). Ideally, we would want a solution that provides a speedup whatever the input. To try and get some ideas, we asked some oracle performance specialists to see if they had any ideas or tips. Their recommendation was to add a hidden hook into the product that allowed users to specify their own query hints, or even rewrite the queries entirely. However, we would prefer not to take that approach; as well as a lot of new infrastructure & a rewrite of the population code, it would have meant that any users of 9i would have to spend some time optimizing it to get it working on their system before they could use the product. Another approach was needed. All our population queries have a very specific pattern - a base table provides most of the information we need (ALL_TABLES for tables, or ALL_TAB_COLS for columns) and we do a left join to extra subsidiary tables that fill in gaps (for instance, ALL_PART_TABLES for partition information). All the left joins use the same set of columns to join on (typically the object owner & name), so we could re-use the hash information for each join, rather than re-hashing the same columns for every join. To allow us to do this, along with various other performance improvements that could be done for the specific query pattern we were using, we read all the tables individually and do a hash join on the client. Fortunately, this 'pure' algorithmic problem is the kind that can be very well optimized for expected real-world situations; as well as storing row data we're not using in the hash key on disk, we use very specific memory-efficient data structures to store all the information we need. This allows us to achieve a database population time that is as fast as on 10g, and even (in some situations) slightly faster, and a memory overhead of roughly 150 bytes per row of data in the result set (for schemas with 10,000 tables in that means an extra 1.4MB memory being used during population). Next: fun with the 9i dictionary views.

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  • Answers to Conference Revenue Tweet Questions

    - by D'Arcy Lussier
    Originally posted on: http://geekswithblogs.net/dlussier/archive/2014/05/27/156612.aspxI tweeted this the other day… …and I had some people tweet back questioning/asking about the profit number. So here’s how I came to that figure. Total Revenue Let’s talk total revenue first. This conference has a huge list of companies/organizations paying some amount for sponsorship. Platinum ($1500) x 5 = $7500 Gold ($1000) x 3 = $3000 Silver ($500) x 9 = $4500 Bronze ($250) x 13 = $3250 There’s also a title sponsor level but there’s no mention of how much that is…more than $1500 though, so let’s just say $2500. Total Sponsorship Revenue: $20750.00 For registrations, this conference is claiming over 300 attendees. We’ll just calculate at 300 and the discounted “member rate” – $249. Total Registration Revenue: $74700.00 Booth space is also sold for a vendor area, but let’s just leave that out of the calculation. Total Event Revenue: $95450.00 Now that we know how much money we’re playing with, let’s knock out the costs for the event. Total Costs Hard Costs Audio/Visual Services $2000 Conference Rooms (4 Breakouts + Plenary) $2500 Insurance $700 Printing/Signage $1500 Travel/Hotel Rooms $2000 Keynotes $2000 So let’s talk about these hard costs first. First you may be asking about the Audio Visual. Yes those services can be that high, actually higher. But since there’s an A/V company touted as the official A/V provider, I gotta think there’s some discount for being branded as such. Conference rooms are actually an inflated amount of $500 per. Venues make money on the food they sell at events, not on room rentals. The more food, the cheaper the rooms tend to be offered at. Still, for the sake of argument, let’s set the rooms at $500 each knowing that they could be lower. For travel and hotel rooms…it appears that most of the speakers at this conference are local, meaning there’s no travel or hotel cost. But a few of them I wasn’t too sure…so let’s factor in enough to cover two outside speakers (airfare and hotel). There are two keynotes for this event and depending on the event those may be paid gigs. I’m not sure if they are or not, but considering the closing one is a comedian I’m going to add some funds here for that just in case. Total Hard Costs: $10700 Now that the hard costs are out of the way, let’s talk about the food costs. Food Costs The conference is providing a continental breakfast (YEEEESH!), some level of luncheon, and I have to assume coffee breaks in between. Let’s look at those costs. Continental Breakfast $12 per person Lunch Buffet $18 per person Coffee Breaks (2) $6 per person (or $3 a cup) Snacks (2) $10 per person (or $5 each) Note that the lunch buffet assumes a *good* lunch buffet – two entrees, starch, vegetable, salads, and bread. Not sure if there’ll be snacks during coffee breaks but let’s assume so. Total Food Cost Per Person: $46 Food Cost: $14950 Gratuity: $2691 Total Food Cost: $17641 Total food cost is based on the $46 per person cost x 325. 300 for attendance, 12 for speakers, extra 13 for volunteers/organizers. Gratuity is 18%. Grand Totals So let’s sum things up here. Total Costs Hard Costs: $10700.00 Food Costs: $17641.00 Total:          $28341.00 Taxes:         $3685.00 Grand Total  $32026.00 Total Revenue Sponsorship  $20750 Registration   $74700 Grand Total   $95450.00 Total Profit $63424.00 Now what if the registration numbers were lower and they only got 100 people to show up. In that scenario there’d still be a profit of just under $26000. Closing Comments A couple of things to note: - I haven’t factored in anything for prizes. Not sure if any will be given out - We didn’t add in the booth space revenue - We’re assuming speakers aren’t getting paid, but even if they were at the high end its $12000 ($1000 per session), which is probably an inflated number for local speakers. - Note that all registrations were set to the “member” discounted price. The non-member registration price is higher. There is also an option for those that just want to show up for the opening keynote. There you have it! Let me know if you have any questions. D

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  • Need a CDN with SSL

    - by Till
    We currently use Edgecast through Speedyrails. Back when I did my research they were both fast and very cost-effective. I haven't looked in a while, but now we need SSL on our assets as well. I reached out to our current provider and they want a setup fee and something like 260 USD per host per month (we use multiple hosts currently). I looked at AWS Cloudfront and it seems the most cost affective way to get SSL, but it's not a custom domain then (e.g. cdn.example.org), which I could live with. Has any else researched this lately and has any providers to get in touch with - can be resellers or direct buys. I'm not looking for a bargain, I just want to get an idea what these things cost. Edit, 2012-08-23: Must have is custom origin. E.g. I don't want to manually upload files somewhere else. Edgecast and Cloudfront both support this.

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  • Announcing StorageTek VSM 6

    - by uwes
    On 23rd of October Oracle announced the 6th generation StorageTek Virtual Storage Manager system (StorageTek VSM 6). StorageTek VSM 6 provides customers simple, flexible and mainframe class reliability all while reducing a customer’s total cost of ownership: Simple – Efficiently manages data and storage resources according to customer-defined rules, while streamlining overall tape operations Flexible – Engineered with flexibility in mind, can be deployed to meet each enterprise’s unique business requirements  Reliable – Reduces a customer’s exposure by providing superior data protection, end-to-end high availability architecture and closed loop data integrity checking Low Total Cost of Ownership and Investment Protection – Low asset acquisition cost, high-density data center footprint and physical tape energy efficiency keeps customers storage spending within budget For More Information Go To: Oracle.com Tape PageOracle Technology Network Tape Page

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  • mod perl in apache 2.2 not parsing perl scripts

    - by futureelite7
    Hi, I've set up a fresh Apache 2.2.15 server on windows server 2008 R2 with mod_perl (mod perl v2.0.4 / perl v5.10.1). Mod_perl and Perl 5.10 has been installed and loaded without problems. However, despite my configuration, the mod_perl module is failing to recognize and execute my .pl file, instead opting to print out the perl source instead. What did I do wrong, and how do I make perl process my pl script instead of sending it to the client? My configuration: <VirtualHost *:80> ServerAdmin [email protected] DocumentRoot "C:\Program Files (x86)\AWStats\wwwroot" ServerName analysis.example.com ServerAlias analysis.example.com ErrorLog "logs/analysis.example.com-error.log" CustomLog "logs/analysis.example.com-access.log" common DirectoryIndex index.php index.htm index.html PerlSwitches -T <Directory "C:\Program Files (x86)\AWStats\wwwroot"> Options Indexes FollowSymLinks AllowOverride None Order allow,deny Allow from all </Directory> <Directory "C:\Program Files (x86)\AWStats\wwwroot\cgi-bin"> AllowOverride None Options None Order allow,deny Allow from all <FilesMatch "\.pl$"> SetHandler perl-script # #PerlResponseHandler ModPerl::Registry PerlOptions +ParseHeaders Options +ExecCGI </FilesMatch> </directory> </VirtualHost> Many many thanks for the help!

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  • What is the best free or low-cost Java reporting library (e.g. BIRT, JasperReports, etc.) for making

    - by Max3000
    I want to print, email and write to PDF very simple reports. The reports are basically a list of items, divided in various sections/columns. The sections are not necessarily identical. Think newspaper. I just wasted a solid 2 days of work trying to make this kind of reports using JasperReports. I find that Jasper is great for outputing "normalized" data. The kind that would come out of a database for instance, each row neatly describing an item and each item printed on a line. I'm simplifying a bit but that's the idea. However, given what I want to do I always ended up completely lost. Data not being displayed for no apparent reason, columns of texts never the correct size, column positioning always ending up incorrect, pagination not sanely possible (I was never able to figure it out; the FAQ gives an obscure workaround), etc. I came to the conclusion that Jasper is really not built to make the kind of reports I want. Am I missing something? I'm ready to pay for a tool, as long as the price is reasonable. By reasonable I mean a few $100s. Thanks. EDIT: To answer cetus, here is more information about the report I made in Jasper. What I want is something like this: text text text text ------------------- text | text text |---------- text | text text | text --------| text text |---------- text | text What I made in jasper is this: (detail band) subreport | subreport ------------------------------------ subreport | subreport ------------------------------------ subreport | subreport The subreports are all the same actual report. This report has one field (called "field") and basically just prints this field in a detail band. Hence, running a single subreport simply lists all items from the datasource. The datasource itself is a simple custom JRDatasource containing a collection of strings in the field "field". The datasource iterates over the collection until there are no more strings. Each subreport has its own datasource. I tried many different variations of the above, with all sorts of different properties for the report, subreports, etc. IMO, this is fairly simple stuff. However, the problems I encounter are as follows: Subreports starting from the 3rd don't show up when their position type is 'float'. They do show up when they have 'fix relative to top'. However, I don't want to do this because the first two subreports can be of any length. I can't make each subreport to stretch according to its own length. Instead, they either don't stretch at all (which is not desirable because they have different lenghts) or they stretch according to the longest subreport. This makes a weird layout for sure. Pagination doesn't happen. If some subreports fall outside the page, they simple don't show. One alternative is to increase the 'page height' considerably and the 'detail band height' accordingly. However, in this case it is not really possibly to know the total height in advance. So I'm stuck with calculating/guessing it myself, before the report is even generated. More importantly, long reports end up on one page and this is not acceptable (the printout text is too small, it's ugly/non-professional to have different reports with different PDF page lengths, etc.). BTW, I used iReport so it's possibly limitations of iReport I'm listing here and not of Jasper itself. That's one of the things I'm trying to find out asking this question here. One alternative would be to generate the jrxml myself with just static text but I'm afraid I'll encounter the very same limitations. Anyway, I just generally wasted so much time getting anything done with Jasper that I can't help thinking its not the right tool for the job. (Not to say that Jasper doesn't excel in what it's good at).

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  • Understanding the value of Customer Experience & Loyalty for the Telecommunications Industry

    - by raul.goycoolea
    Worried by economic woes and market forces, especially in mature markets, communications service providers (CSPs) increasingly focus on improving customer experience. In fact, it seems difficult to find a major message by a C-level executive in the developed world that does not include something on "meeting and exceeding customers' needs". Frequently in customer satisfaction studies by prominent firms, CSPs fall short of the leadership demonstrated by other industries that take customer-centric approaches to their bottom-line strategies. Consider the following:Despite the continued impact of global economic crisis, in July 2010, Apple Computer posted record revenue and net quarterly profit. Those who attribute the results primarily to the iPhone 4 launch should note that Apple also shipped around 30% more Macintosh computers than the same period the previous year. Even sales of the iPod line increased by 8% in a highly commoditized, shrinking media player market. Finally, Apple began selling iPads during the quarter, with total sales of more than 3 million units. What does Apple have that the others lack? Well, some great products (and services) to be sure, but it also excels at customer service and support, marketing, and distribution, and has one of the strongest brands globally. Its products are useful, simple to use, easy to acquire and augment, high quality, and considered very cool. They also evoke such an emotional response from many of Apple's customers, which they turn up their noses at competitive products.In other words, Apple appears to have mastered virtually every aspect of customer experience and the resultant loyalty of its customer base - even in difficult financial times. Through that unwavering customer focus, Apple continues to drive its revenues and profits to new heights. Other customer loyalty leaders like Wal-Mart, Google, Toyota and Honda are also doing well by focusing on customer experience as an essential driver of profitability. Service providers should note this performance and ask themselves how they might leverage the same principles to increase their own profitability. After all, that is what customer experience and loyalty are all about: profitability.To successfully manage all the critical touch points of customer experience, CSPs must shun the one-size-fits-all approach. They can no longer afford to view customer service fundamentally as an act of altruism - which mentality dates back to the industry's civil service days, when CSPs were typically government organizations that were critical to economic development and public safety.As regulators and public officials have pushed, and continue to push, service providers to new heights of reliability - using incentives and punishments - most CSPs already have some of the fundamental building blocks of customer service in place. Yet despite that history and experience, service providers still lag other industries in providing what is seen as good customer service.As we observed in the TMF's 2009 Insights Research report, Customer Experience Management: Driving Loyalty & Profitability there has been resurgence in interest by CSPs. More and more of them have stated ambitions to catch up other industries, and they are realizing that good customer service is a powerful strategy for increasing business performance and profitability, not an act of good will.CSPs are recognizing the connection between customer experience and profitability, as demonstrated in many studies. For example, according to research by Bain & Company, a 5 percent improvement in customer retention rates can yield as much as a 75 percent increase in profits for companies across a range of industries.After decades of customer experience strategy formulation, Bain partner and business author, Frederick Reichheld, considers "would you recommend us to a friend?" as the ultimate question for a customer. How many times have you or your friends recommended an iPod, iPhone or a Mac? What do your children recommend to their peers? Their peers to them?There are certain steps service providers have to take to create more personalized relationships with their customers, as well as reduce churn and increase profitability, all while becoming leaner and more agile. First, they have to define customer experience, we define it as the result of the sum of observations, perceptions, thoughts and feelings arising from interactions and relationships between customers and their service provider(s). Virtually every customer touch point - whether directly or indirectly linked to service providers and their partners - contributes to customer perception, satisfaction, loyalty, and ultimately profitability. Gaining leadership in customer experience and satisfaction will not be a simple task, as it is affected by virtually every customer-facing aspect of the service provider, and in turn impacts the service provider deeply - especially on the all-important bottom line. The scope of issues affecting customer experience is complex and dynamic.With new services, devices and applications extending the basis of customer experience to domains beyond the direct control of the service provider, it is likely to increase in complexity and dynamism.Customer loyalty = increased profitsAs stated earlier, customer experience programs are not fundamentally altruistic exercises, but a strategic means of improving competitiveness and profitability in the short and long term. Loyalty is essential to deriving long term profits from customers.Some of the earliest loyalty programs date back to the 1930s, when packaged goods companies offered embedded coupons for rewards to buyers, and eventually retail chains began offering reward programs to frequent shoppers. These programs continued for decades but were leapfrogged in the 1980s by more aggressive programs from the airlines.This movement was led by American Airlines, which launched the first full-scale loyalty marketing program of the modern era with the AAdvantage frequent flyer scheme. It was the first to reward frequent fliers with notional air miles that could be accumulated and later redeemed for free travel. Figure 1: Opportunities example of Customer loyalty driven profitOther airlines and travel providers were quick to grasp the incredible value of providing customers with an incentive to use their company exclusively. Within a few years, dozens of travel industry companies launched similar initiatives and now loyalty programs are achieving near-ubiquity in many service industries, especially those in which it is difficult to differentiate offerings by product attributes.The belief is that increased profitability will result from customer retention efforts because:•    The cost of acquisition occurs only at the beginning of a relationship: the longer the relationship, the lower the amortized cost;•    Account maintenance costs decline as a percentage of total costs, or as a percentage of revenue, over the lifetime of the relationship;•    Long term customers tend to be less inclined to switch and less price sensitive which can result in stable unit sales volume and increases in dollar-sales volume;•    Long term customers may initiate word-of-mouth promotions and referrals, which cost the company nothing and arguably are the most effective form of advertising;•    Long-term customers are more likely to buy ancillary products and higher margin supplemental products;•    Long term customers tend to be satisfied with their relationship with the company and are less likely to switch to competitors, making market entry or competitors gaining market share difficult;•    Regular customers tend to be less expensive to service, as they are familiar with the processes involved, require less 'education', and are consistent in their order placement;•    Increased customer retention and loyalty makes the employees' jobs easier and more satisfying. In turn, happy employees feed back into higher customer satisfaction in a virtuous circle. Figure 2: The virtuous circle of customer loyaltyFigure 2 represents a high-level example of a virtuous cycle driven by customer satisfaction and loyalty, depicting how superiority in product and service offerings, as well as strong customer support by competent employees, lead to higher sales and ultimately profitability. As stated above, this is not a new concept, but succeeding with it is difficult. It has eluded many a company driven to achieve profitability goals. Of course, for this circle to be virtuous, the customer relationship(s) must be profitable.Trying to maintain the loyalty of unprofitable customers is not a viable business strategy. It is, therefore, important that marketers can assess the profitability of each customer (or customer segment), and either improve or terminate relationships that are not profitable. This means each customer's 'relationship costs' must be understood and compared to their 'relationship revenue'. Customer lifetime value (CLV) is the most commonly used metric here, as it is generally accepted as a representation of exactly how much each customer is worth in monetary terms, and therefore a determinant of exactly how much a service provider should be willing to spend to acquire or retain that customer.CLV models make several simplifying assumptions and often involve the following inputs:•    Churn rate represents the percentage of customers who end their relationship with a company in a given period;•    Retention rate is calculated by subtracting the churn rate percentage from 100;•    Period/horizon equates to the units of time into which a customer relationship can be divided for analysis. A year is the most commonly used period for this purpose. Customer lifetime value is a multi-period calculation, often projecting three to seven years into the future. In practice, analysis beyond this point is viewed as too speculative to be reliable. The model horizon is the number of periods used in the calculation;•    Periodic revenue is the amount of revenue collected from a customer in a given period (though this is often extended across multiple periods into the future to understand lifetime value), such as usage revenue, revenues anticipated from cross and upselling, and often some weighting for referrals by a loyal customer to others; •    Retention cost describes the amount of money the service provider must spend, in a given period, to retain an existing customer. Again, this is often forecast across multiple periods. Retention costs include customer support, billing, promotional incentives and so on;•    Discount rate means the cost of capital used to discount future revenue from a customer. Discounting is an advanced method used in more sophisticated CLV calculations;•    Profit margin is the projected profit as a percentage of revenue for the period. This may be reflected as a percentage of gross or net profit. Again, this is generally projected across the model horizon to understand lifetime value.A strong focus on managing these inputs can help service providers realize stronger customer relationships and profits, but there are some obstacles to overcome in achieving accurate calculations of CLV, such as the complexity of allocating costs across the customer base. There are many costs that serve all customers which must be properly allocated across the base, and often a simple proportional allocation across the whole base or a segment may not accurately reflect the true cost of serving that customer;  This is made worse by the fragmentation of customer information, which is likely to be across a variety of product or operations groups, and may be difficult to aggregate due to different representations.In addition, there is the complexity of account relationships and structures to take into consideration. Complex account structures may not be understood or properly represented. For example, a profitable customer may have a separate account for a second home or another family member, which may appear to be unprofitable. If the service provider cannot relate the two accounts, CLV is not properly represented and any resultant cancellation of the apparently unprofitable account may result in the customer churning from the profitable one.In summary, if service providers are to realize strong customer relationships and their attendant profits, there must be a very strong focus on data management. This needs to be coupled with analytics that help business managers and those who work in customer-facing functions offer highly personalized solutions to customers, while maintaining profitability for the service provider. It's clear that acquiring new customers is expensive. Advertising costs, campaign management expenses, promotional service pricing and discounting, and equipment subsidies make a serious dent in a new customer's profitability. That is especially true given the rising subsidies for Smartphone users, which service providers hope will result in greater profits from profits from data services profitability in future.  The situation is made worse by falling prices and greater competition in mature markets.Customer acquisition through industry consolidation isn't cheap either. A North American service provider spent about $2,000 per subscriber in its acquisition of a smaller company earlier this year. While this has allowed it to leapfrog to become the largest mobile service provider in the country, it required a total investment of more than $28 billion (including assumption of the acquiree's debt).While many operating cost synergies clearly made this deal more attractive to the acquiring company, this is certainly an expensive way to acquire customers: the cost per subscriber in this case is not out of line with the prices others have paid for acquisitions.While growth by acquisition certainly increases overall revenues, it often creates tremendous challenges for profitability. Organic growth through increased customer loyalty and retention is a more effective driver of profit, as well as a stronger predictor of future profitability. Service providers, especially those in mature markets, are increasingly recognizing this and taking steps toward a creating a more personalized, flexible and satisfying experience for their customers.In summary, the clearest path to profitability for companies in virtually all industries is through customer retention and maximization of lifetime value. Service providers would do well to recognize this and focus attention on profitable customer relationships.

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  • CBO????????

    - by Liu Maclean(???)
    ???Itpub????????CBO??????????, ????????: SQL> create table maclean1 as select * from dba_objects; Table created. SQL> update maclean1 set status='INVALID' where owner='MACLEAN'; 2 rows updated. SQL> commit; Commit complete. SQL> create index ind_maclean1 on maclean1(status); Index created. SQL> exec dbms_stats.gather_table_stats('SYS','MACLEAN1',cascade=>true); PL/SQL procedure successfully completed. SQL> explain plan for select * from maclean1 where status='INVALID'; Explained. SQL> set linesize 140 pagesize 1400 SQL> select * from table(dbms_xplan.display()); PLAN_TABLE_OUTPUT --------------------------------------------------------------------------- Plan hash value: 987568083 ------------------------------------------------------------------------------ | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------ | 0 | SELECT STATEMENT | | 11320 | 1028K| 85 (0)| 00:00:02 | |* 1 | TABLE ACCESS FULL| MACLEAN1 | 11320 | 1028K| 85 (0)| 00:00:02 | ------------------------------------------------------------------------------ Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter("STATUS"='INVALID') 13 rows selected. 10053 trace Access path analysis for MACLEAN1 *************************************** SINGLE TABLE ACCESS PATH   Single Table Cardinality Estimation for MACLEAN1[MACLEAN1]   Column (#10): STATUS(     AvgLen: 7 NDV: 2 Nulls: 0 Density: 0.500000   Table: MACLEAN1  Alias: MACLEAN1     Card: Original: 22639.000000  Rounded: 11320  Computed: 11319.50  Non Adjusted: 11319.50   Access Path: TableScan     Cost:  85.33  Resp: 85.33  Degree: 0       Cost_io: 85.00  Cost_cpu: 11935345       Resp_io: 85.00  Resp_cpu: 11935345   Access Path: index (AllEqRange)     Index: IND_MACLEAN1     resc_io: 185.00  resc_cpu: 8449916     ix_sel: 0.500000  ix_sel_with_filters: 0.500000     Cost: 185.24  Resp: 185.24  Degree: 1   Best:: AccessPath: TableScan          Cost: 85.33  Degree: 1  Resp: 85.33  Card: 11319.50  Bytes: 0 ?????10053????????????,?????Density = 0.5 ?? 1/ NDV ??? ??????????????STATUS='INVALID"???????????, ????????????????? ????”STATUS”=’INVALID’ condition???2?,?status??????,??????dbms_stats?????????????,???CBO????INDEX Range ind_maclean1,???????,??????opitimizer?????? ?????????????????????????,????????,??????????status=’INVALID’???????card??,????????: [oracle@vrh4 ~]$ sqlplus / as sysdba SQL*Plus: Release 11.2.0.2.0 Production on Mon Oct 17 19:15:45 2011 Copyright (c) 1982, 2010, Oracle. All rights reserved. Connected to: Oracle Database 11g Enterprise Edition Release 11.2.0.2.0 - 64bit Production With the Partitioning, OLAP, Data Mining and Real Application Testing options SQL> select * from v$version; BANNER -------------------------------------------------------------------------------- Oracle Database 11g Enterprise Edition Release 11.2.0.2.0 - 64bit Production PL/SQL Release 11.2.0.2.0 - Production CORE 11.2.0.2.0 Production TNS for Linux: Version 11.2.0.2.0 - Production NLSRTL Version 11.2.0.2.0 - Production SQL> show parameter optimizer_fea NAME TYPE VALUE ------------------------------------ ----------- ------------------------------ optimizer_features_enable string 11.2.0.2 SQL> select * from global_name; GLOBAL_NAME -------------------------------------------------------------------------------- www.oracledatabase12g.com & www.askmaclean.com SQL> drop table maclean; Table dropped. SQL> create table maclean as select * from dba_objects; Table created. SQL> update maclean set status='INVALID' where owner='MACLEAN'; 2 rows updated. SQL> commit; Commit complete. SQL> create index ind_maclean on maclean(status); Index created. SQL> exec dbms_stats.gather_table_stats('SYS','MACLEAN',cascade=>true, method_opt=>'FOR ALL COLUMNS SIZE 2'); PL/SQL procedure successfully completed. ???????2?bucket????, ??????????????? ???Quest???Guy Harrison???????FREQUENCY????????,??????: rem rem Generate a histogram of data distribution in a column as recorded rem in dba_tab_histograms rem rem Guy Harrison Jan 2010 : www.guyharrison.net rem rem hexstr function is from From http://asktom.oracle.com/pls/asktom/f?p=100:11:0::::P11_QUESTION_ID:707586567563 set pagesize 10000 set lines 120 set verify off col char_value format a10 heading "Endpoint|value" col bucket_count format 99,999,999 heading "bucket|count" col pct format 999.99 heading "Pct" col pct_of_max format a62 heading "Pct of|Max value" rem col endpoint_value format 9999999999999 heading "endpoint|value" CREATE OR REPLACE FUNCTION hexstr (p_number IN NUMBER) RETURN VARCHAR2 AS l_str LONG := TO_CHAR (p_number, 'fm' || RPAD ('x', 50, 'x')); l_return VARCHAR2 (4000); BEGIN WHILE (l_str IS NOT NULL) LOOP l_return := l_return || CHR (TO_NUMBER (SUBSTR (l_str, 1, 2), 'xx')); l_str := SUBSTR (l_str, 3); END LOOP; RETURN (SUBSTR (l_return, 1, 6)); END; / WITH hist_data AS ( SELECT endpoint_value,endpoint_actual_value, NVL(LAG (endpoint_value) OVER (ORDER BY endpoint_value),' ') prev_value, endpoint_number, endpoint_number, endpoint_number - NVL (LAG (endpoint_number) OVER (ORDER BY endpoint_value), 0) bucket_count FROM dba_tab_histograms JOIN dba_tab_col_statistics USING (owner, table_name,column_name) WHERE owner = '&owner' AND table_name = '&table' AND column_name = '&column' AND histogram='FREQUENCY') SELECT nvl(endpoint_actual_value,endpoint_value) endpoint_value , bucket_count, ROUND(bucket_count*100/SUM(bucket_count) OVER(),2) PCT, RPAD(' ',ROUND(bucket_count*50/MAX(bucket_count) OVER()),'*') pct_of_max FROM hist_data; WITH hist_data AS ( SELECT endpoint_value,endpoint_actual_value, NVL(LAG (endpoint_value) OVER (ORDER BY endpoint_value),' ') prev_value, endpoint_number, endpoint_number, endpoint_number - NVL (LAG (endpoint_number) OVER (ORDER BY endpoint_value), 0) bucket_count FROM dba_tab_histograms JOIN dba_tab_col_statistics USING (owner, table_name,column_name) WHERE owner = '&owner' AND table_name = '&table' AND column_name = '&column' AND histogram='FREQUENCY') SELECT hexstr(endpoint_value) char_value, bucket_count, ROUND(bucket_count*100/SUM(bucket_count) OVER(),2) PCT, RPAD(' ',ROUND(bucket_count*50/MAX(bucket_count) OVER()),'*') pct_of_max FROM hist_data ORDER BY endpoint_value; ?????,??????????FREQUENCY?????: ??dbms_stats ?????STATUS=’INVALID’ bucket count=9 percent = 0.04 ,??????10053 trace????????: SQL> explain plan for select * from maclean where status='INVALID'; Explained. SQL>  select * from table(dbms_xplan.display()); PLAN_TABLE_OUTPUT ------------------------------------- Plan hash value: 3087014066 ------------------------------------------------------------------------------------------- | Id  | Operation                   | Name        | Rows  | Bytes | Cost (%CPU)| Time     | ------------------------------------------------------------------------------------------- |   0 | SELECT STATEMENT            |             |     9 |   837 |     2   (0)| 00:00:01 | |   1 |  TABLE ACCESS BY INDEX ROWID| MACLEAN     |     9 |   837 |     2   (0)| 00:00:01 | |*  2 |   INDEX RANGE SCAN          | IND_MACLEAN |     9 |       |     1   (0)| 00:00:01 | ------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): ---------------------------------------------------    2 - access("STATUS"='INVALID') ??????????????CBO???????STATUS=’INVALID’?cardnality?? , ??????????? ,??index range scan??Full table scan? ????????????????10053 trace: SQL> alter system flush shared_pool; System altered. SQL> oradebug setmypid; Statement processed. SQL> oradebug event 10053 trace name context forever ,level 1; Statement processed. SQL> explain plan for select * from maclean where status='INVALID'; Explained. SINGLE TABLE ACCESS PATH Single Table Cardinality Estimation for MACLEAN[MACLEAN] Column (#10): NewDensity:0.000199, OldDensity:0.000022 BktCnt:22640, PopBktCnt:22640, PopValCnt:2, NDV:2 ???NewDensity= bucket_count / SUM(bucket_count) /2 Column (#10): STATUS( AvgLen: 7 NDV: 2 Nulls: 0 Density: 0.000199 Histogram: Freq #Bkts: 2 UncompBkts: 22640 EndPtVals: 2 Table: MACLEAN Alias: MACLEAN Card: Original: 22640.000000 Rounded: 9 Computed: 9.00 Non Adjusted: 9.00 Access Path: TableScan Cost: 85.30 Resp: 85.30 Degree: 0 Cost_io: 85.00 Cost_cpu: 10804625 Resp_io: 85.00 Resp_cpu: 10804625 Access Path: index (AllEqRange) Index: IND_MACLEAN resc_io: 2.00 resc_cpu: 20763 ix_sel: 0.000398 ix_sel_with_filters: 0.000398 Cost: 2.00 Resp: 2.00 Degree: 1 Best:: AccessPath: IndexRange Index: IND_MACLEAN Cost: 2.00 Degree: 1 Resp: 2.00 Card: 9.00 Bytes: 0 ???????????2 bucket?????CBO????????????,???????????????????,???dbms_stats.DEFAULT_METHOD_OPT????????????????????? ???dbms_stats?????????????????????col_usage$??????predicate???????,??col_usage$??<????????SMON??(?):??col_usage$????>? ??????????dbms_stats????????,col_usage$????????????predicate???,??dbms_stats??????????????????, ?: SQL> drop table maclean; Table dropped. SQL> create table maclean as select * from dba_objects; Table created. SQL> update maclean set status='INVALID' where owner='MACLEAN'; 2 rows updated. SQL> commit; Commit complete. SQL> create index ind_maclean on maclean(status); Index created. ??dbms_stats??method_opt??maclean? SQL> exec dbms_stats.gather_table_stats('SYS','MACLEAN'); PL/SQL procedure successfully completed. @histogram.sql Enter value for owner: SYS old  12:    WHERE owner = '&owner' new  12:    WHERE owner = 'SYS' Enter value for table: MACLEAN old  13:      AND table_name = '&table' new  13:      AND table_name = 'MACLEAN' Enter value for column: STATUS old  14:      AND column_name = '&column' new  14:      AND column_name = 'STATUS' no rows selected ????col_usage$?????,????????status????? declare begin for i in 1..500 loop execute immediate ' alter system flush shared_pool'; DBMS_STATS.FLUSH_DATABASE_MONITORING_INFO; execute immediate 'select count(*) from maclean where status=''INVALID'' ' ; end loop; end; / PL/SQL procedure successfully completed. SQL> select obj# from obj$ where name='MACLEAN';       OBJ# ----------      97215 SQL> select * from  col_usage$ where  OBJ#=97215;       OBJ#    INTCOL# EQUALITY_PREDS EQUIJOIN_PREDS NONEQUIJOIN_PREDS RANGE_PREDS LIKE_PREDS NULL_PREDS TIMESTAMP ---------- ---------- -------------- -------------- ----------------- ----------- ---------- ---------- ---------      97215          1              1              0                 0           0          0          0 17-OCT-11      97215         10            499              0                 0           0          0          0 17-OCT-11 SQL> exec dbms_stats.gather_table_stats('SYS','MACLEAN'); PL/SQL procedure successfully completed. @histogram.sql Enter value for owner: SYS Enter value for table: MACLEAN Enter value for column: STATUS Endpoint        bucket         Pct of value            count     Pct Max value ---------- ----------- ------- -------------------------------------------------------------- INVALI               2     .04 VALIC3           5,453   99.96  *************************************************

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  • The way I think about Diagnostic tools

    - by Daniel Moth
    Every software has issues, or as we like to call them "bugs". That is not a discussion point, just a mere fact. It follows that an important skill for developers is to be able to diagnose issues in their code. Of course we need to advance our tools and techniques so we can prevent bugs getting into the code (e.g. unit testing), but beyond designing great software, diagnosing bugs is an equally important skill. To diagnose issues, the most important assets are good techniques, skill, experience, and maybe talent. What also helps is having good diagnostic tools and what helps further is knowing all the features that they offer and how to use them. The following classification is how I like to think of diagnostics. Note that like with any attempt to bucketize anything, you run into overlapping areas and blurry lines. Nevertheless, I will continue sharing my generalizations ;-) It is important to identify at the outset if you are dealing with a performance or a correctness issue. If you have a performance issue, use a profiler. I hear people saying "I am using the debugger to debug a performance issue", and that is fine, but do know that a dedicated profiler is the tool for that job. Just because you don't need them all the time and typically they cost more plus you are not as familiar with them as you are with the debugger, doesn't mean you shouldn't invest in one and instead try to exclusively use the wrong tool for the job. Visual Studio has a profiler and a concurrency visualizer (for profiling multi-threaded apps). If you have a correctness issue, then you have several options - that's next :-) This is how I think of identifying a correctness issue Do you want a tool to find the issue for you at design time? The compiler is such a tool - it gives you an exact list of errors. Compilers now also offer warnings, which is their way of saying "this may be an error, but I am not smart enough to know for sure". There are also static analysis tools, which go a step further than the compiler in identifying issues in your code, sometimes with the aid of code annotations and other times just by pointing them at your raw source. An example is FxCop and much more in Visual Studio 11 Code Analysis. Do you want a tool to find the issue for you with code execution? Just like static tools, there are also dynamic analysis tools that instead of statically analyzing your code, they analyze what your code does dynamically at runtime. Whether you have to setup some unit tests to invoke your code at runtime, or have to manually run your app (and interact with it) under the tool, or have to use a script to execute your binary under the tool… that varies. The result is still a list of issues for you to address after the analysis is complete or a pause of the execution when the first issue is encountered. If a code path was not taken, no analysis for it will exist, obviously. An example is the GPU Race detection tool that I'll be talking about on the C++ AMP team blog. Another example is the MSR concurrency CHESS tool. Do you want you to find the issue at design time using a tool? Perform a code walkthrough on your own or with colleagues. There are code review tools that go beyond just diffing sources, and they help you with that aspect too. For example, there is a new one in Visual Studio 11 and searching with my favorite search engine yielded this article based on the Developer Preview. Do you want you to find the issue with code execution? Use a debugger - let’s break this down further next. This is how I think of debugging: There is post mortem debugging. That means your code has executed and you did something in order to examine what happened during its execution. This can vary from manual printf and other tracing statements to trace events (e.g. ETW) to taking dumps. In all cases, you are left with some artifact that you examine after the fact (after code execution) to discern what took place hoping it will help you find the bug. Learn how to debug dump files in Visual Studio. There is live debugging. I will elaborate on this in a separate post, but this is where you inspect the state of your program during its execution, and try to find what the problem is. More from me in a separate post on live debugging. There is a hybrid of live plus post-mortem debugging. This is for example what tools like IntelliTrace offer. If you are a tools vendor interested in the diagnostics space, it helps to understand where in the above classification your tool excels, where its primary strength is, so you can market it as such. Then it helps to see which of the other areas above your tool touches on, and how you can make it even better there. Finally, see what areas your tool doesn't help at all with, and evaluate whether it should or continue to stay clear. Even though the classification helps us think about this space, the reality is that the best tools are either extremely excellent in only one of this areas, or more often very good across a number of them. Another approach is to offer a toolset covering all areas, with appropriate integration and hand off points from one to the other. Anyway, with that brain dump out of the way, in follow-up posts I will dive into live debugging, and specifically live debugging in Visual Studio - stay tuned if that interests you. Comments about this post by Daniel Moth welcome at the original blog.

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  • Merck Serono Gains Deep Understanding of Product Portfolio Value-Drivers, Risks, and Sales Expectations Through Forecasting Solution

    - by Melissa Centurio Lopes
    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-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; 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-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Merck Serono S.A. is the biopharmaceutical division of Merck KGaA. It offers leading brands in 150 countries to help patients with cancer, multiple sclerosis, infertility, endocrine and metabolic disorders, as well as cardiovascular diseases. Challenges: Establish a better decision-making framework for its complex, development portfolio of pharmaceutical products, where single-point estimates or expected averages of portfolio values, portfolio risks, and sales forecasts are insufficient and can be misleading Enable the company to be aware at all times of the range of possible outcomes of technical and market risks and uncertainties, such as the technical uncertainty of whether a product will produce the desired clinical outcomes, or the market-related uncertainty of whether a product will be outperformed by its competitors Solutions to Overcome the Challenges: Used Oracle Crystal Ball to devise a Monte-Carlo-based approach to better analyze and define the values and risks of the company’s development portfolio, laying the groundwork for optimized decision-making Enabled a better understanding of the range of potential values and risks to improve portfolio planning Enabled detailed analysis of the likelihood of favorable or unfavorable outcomes, such as the likelihood of whether Merck Serono can meet its sales targets planned for the next ten years with its existing product portfolio Gained the ability to take into account correlative risks, synergies and project interactions, enabling Merck Serono to better forecast what the company may achieve—for example, that there is a 70% probability of a particular sales target being met Established Monte-Carlo-based analysis using Oracle Crystal Ball as a useful element in decision-making at the board level, as the approach provides a better analysis of values and risks associated with the company’s product portfolio “Oracle Crystal Ball enables us to make Monte Carlo simulations of the potential value and sales of our development portfolio. It is a very powerful tool for gaining a thorough understanding and improved awareness of value drivers, uncertainties, and risks, along with associated probabilities.” – Riccardo Lampariello, Associate Director, Merck Serono S.A Why Oracle “We chose Oracle Crystal Ball to enable us to perform Monte Carlo analysis, which gives us a deeper understanding and improved awareness of the value drivers, uncertainties and risks of our portfolio of development projects,” said Kimber Hardy, head of valuation and analysis, Merck Serono S.A. 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-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; 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-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Click here to read the full version of the customer success story 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-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; 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-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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