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  • Announcing Two Papers Addressing the RPAS Fusion Client

    - by Oracle Retail Documentation Team
    Oracle Retail has published two documents to My Oracle Support addressing the Retail Predictive Application Server (RPAS) Fusion Client, a web-based rich client developed using the latest Oracle Application Development Framework (ADF). The Fusion Client provides an enhanced user experience for communicating with the RPAS server. Oracle Retail Predictive Application Server Fusion Client Getting Started Guide Doc ID 1492759.1The Retail Predictive Application Server (RPAS) is a configurable platform that provides capabilities such as a multidimensional database structure, batch and online processing, a configurable user interface, a configurable calculation engine, user security, and utility functions such as importing and exporting, all on a highly scalable technical environment that can be deployed on a variety of hardware. This paper addresses typical questions that arise during setting up and deploying the Fusion Client, provides performance recommendations, and highlights the differences between the Classic Client and the Fusion Client. Oracle Retail RPAS Fusion Client Performance Issue Report Doc ID 1493747.1Performance issues can be frustrating for customers, and Oracle Retail will strive to assist you as you attempt to enhance the performance of your systems. To ensure the timeliest processing of your issue, retailers and partners are encouraged to respond as thoroughly as possible to each question in this document, which can be sent back for analysis by logging a Service Request and following typical Customer Support processes. The sections of the document solicit information about the following: Performance Issue Description Performance Issue Details System Configuration Data Application Configuration Data Performance Log Files

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  • How can I utilize or mimic Application OnStart in an HttpModule?

    - by Sailing Judo
    We are trying to remove the global.asax from our many web applications in favor of HttpModules that are in a common code base. This works really well for many application events such as BeginRequest and PostAuthentication, but there is no Application Start event exposed in the HttpModule. I can think of a couple of smelly ways to overcome this deficit. For example, I can probably do this: protected virtual void BeginRequest(object sender, EventArgs e) { Log.Debug("Entered BeginRequest..."); var app = HttpContext.Current.Application; var hasBeenSet app["HasBeenExecuted"] == null ? false : true; if(!hasBeenSet) { app.Lock(); // ... do app level code app.Add("HasBeenSet", true); app.Unlock(); } // do regular begin request stuff ... } But this just doesn't smell well to me. What is the best way to invoke some application begin logic without having a global.asax?

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  • How can I fix this regex to allow a specific string?

    - by Sailing Judo
    This regex comes from Atwood and is used to filter out anchor tags with anything other than the href and a title: <a\shref="(\#\d+|(https?|ftp)://[-A-Za-z0-9+&@#/%?=~_|!:,.;]+)"(\stitle="[^"]+")?\s?> I need to allow am additional attribute that specifically matches: target="_blank". So the following url should be allowed: <a href="http://www.google.com" target="_blank"> I tried changing the pattern to these: <a\shref="(\#\d+|(https?|ftp)://[-A-Za-z0-9+&@#/%?=~_|!:,.;]+)"(\stitle="[^"]+")(\starget="_blank")?\s?> <a\shref="(\#\d+|(https?|ftp)://[-A-Za-z0-9+&@#/%?=~_|!:,.;]+)"(\stitle="[^"]+")(\starget=\"_blank\")?\s?> Clearly I don't know regex very well. How should the pattern be adjusted to allow the blank target and no other targets?

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  • How can I update a column in a table with the result of a select statement that uses row being updat

    - by Sailing Judo
    This SQL statement example is very close to what I think I need... update table1 set value1 = x.value1 from (select value1, code from table2 where code = something) as x However, what I need to do is change the "something" in the above example to a value from the row that is being updated. For example, I tried this but it didn't work: update table1 A set value1 = x.value1 from (select value1, code from table2 where code = A.something) as x This is a one time operation to update an existing table and I'm not really looking for high performance way to do this. Any solution that gets the task done is good enough.

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  • Fraud and Anomaly Detection using Oracle Data Mining YouTube-like Video

    - by chberger
    I've created and recorded another YouTube-like presentation and "live" demos of Oracle Advanced Analytics Option, this time focusing on Fraud and Anomaly Detection using Oracle Data Mining.  [Note:  It is a large MP4 file that will open and play in place.  The sound quality is weak so you may need to turn up the volume.] Data is your most valuable asset. It represents the entire history of your organization and its interactions with your customers.  Predictive analytics leverages data to discover patterns, relationships and to help you even make informed predictions.   Oracle Data Mining (ODM) automatically discovers relationships hidden in data.  Predictive models and insights discovered with ODM address business problems such as:  predicting customer behavior, detecting fraud, analyzing market baskets, profiling and loyalty.  Oracle Data Mining, part of the Oracle Advanced Analytics (OAA) Option to the Oracle Database EE, embeds 12 high performance data mining algorithms in the SQL kernel of the Oracle Database. This eliminates data movement, delivers scalability and maintains security.  But, how do you find these very important needles or possibly fraudulent transactions and huge haystacks of data? Oracle Data Mining’s 1 Class Support Vector Machine algorithm is specifically designed to identify rare or anomalous records.  Oracle Data Mining's 1-Class SVM anomaly detection algorithm trains on what it believes to be considered “normal” records, build a descriptive and predictive model which can then be used to flags records that, on a multi-dimensional basis, appear to not fit in--or be different.  Combined with clustering techniques to sort transactions into more homogeneous sub-populations for more focused anomaly detection analysis and Oracle Business Intelligence, Enterprise Applications and/or real-time environments to "deploy" fraud detection, Oracle Data Mining delivers a powerful advanced analytical platform for solving important problems.  With OAA/ODM you can find suspicious expense report submissions, flag non-compliant tax submissions, fight fraud in healthcare claims and save huge amounts of money in fraudulent claims  and abuse.   This presentation and several brief demos will show Oracle Data Mining's fraud and anomaly detection capabilities.  

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  • Advanced Analytics Oracle Data Mining - NEW 2-Day Training Course

    - by Mike.Hallett(at)Oracle-BI&EPM
    A NEW 2-Day Oracle University (OU) Instructor Led Course on Oracle Data Mining has been developed for partners and customers to learn more about data mining, predictive analytics and knowledge discovery inside the Oracle Database. Oracle Data Mining, provides data mining algorithms that run native for high performance in-database model building and model deployment. This OU course is a great way to learn the advantages and benefits of "big data analytics"; mining data, building and deploying "predictive analytics" all inside the Oracle Database and to work with OBI. To register for a class, click here, then click on View Schedule to see the latest scheduled classes and/or submit your information expressing interest in attending a class.

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  • Segmentation and Targeting: Your Tools for Personalizing the Online Customer Experience

    - by Christie Flanagan
    In order to deliver the kind of personalized and engaging online experiences that customers expect today, look to segmentation and targeting.  Segmentation is the practice of dividing your site visitors into distinct groups based on shared characteristics or behavior – for example, a segment may consist of site visitors who have visited pages related to certain product type, or they may consist of visitors within the same age group or geographic area.  The idea is that those within a segment are more likely to have common needs, problems or interests that can be served by your business. Targeting is the process by which the most relevant content, whether an article promotion or other piece of content, is delivered to your visitors based on their segment membership. Segmentation and targeting are used to drive greater engagement on your web presence by delivering content to your site visitors that is tailored to their interests, behavior or other attributes.  You may have a number of different goals for your segmentation and targeting efforts: Up-sell or cross-sell to your customers Conduct A/B testing on your offers and creative Offer discounts, promotions or other incentives for the time and duration that you specify Make is easier to find relevant information about products and services Create premium content model There are two different approaches you can take toward segmentation and targeting for you online customer experience initiatives. The first is more of a manual process, in which marketers manage the process of determining which segments to create and which content to target to those segments. The benefit of this approach is that it gives marketers a high level of control over the whole process which works well when you have a thorough understanding of your segments and which content is most likely to serve their needs.  Tools for marketer managed segmentation and targeting are often built right in to your WEM platform, as they are with Oracle WebCenter Sites. The downside is that the more segments and content that you have, the more time consuming and complicated in can be to manage manually.The second approach relies on predictive intelligence to automate the segmentation and targeting process.  This allows optimization of the process to occur in real time. This approach helps reduce the burden of manual segmentation and targeting and can result in new insights into segments that you may never have thought of on your own.  It also provides you with the capability to quickly test new offers and promotions on your site.  Predictive segmentation and targeting can be achieved by using Oracle WebCenter Sites and Oracle Real-Time Decisions together. *****Get a taste for how Oracle WebCenter Sites and Oracle Real-Time Decisions combine to deliver powerful capabilities for predictive segmentation and targeting by watching this on demand webcast introducing Oracle WebCenter Sites 11g or by reading IDC’s take on the latest release of Oracle’s web experience management solution.  Be sure to return to the Oracle WebCenter blog on Thursday for a closer look at how to optimize the online customer experience using these two products together.

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  • Today's $10 Deal from APress - Next-Generation Business Intelligence Software with Silverlight 3

    - by TATWORTH
    Today's $10 deal from Apress is " Next-Generation Business Intelligence Software with Silverlight 3 Business intelligence (BI) software is the code and tools that allow you to view different components of a business using a single visual platform, making comprehending mountains of data easier. BI is everywhere. Applications that include reports, analytics, statistics, and historical and predictive modeling are all examples of BI applications. Currently, we are in the second generation of BI software, called BI 2.0. This generation is focused on writing BI software that is predictive, adaptive, simple, and interactive. Next-Generation Business Intelligence Software with Rich Internet Applications brings you up to speed with the latest BI concepts."

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  • Oracle Data Mining a Star Schema: Telco Churn Case Study

    - by charlie.berger
    There is a complete and detailed Telco Churn case study "How to" Blog Series just posted by Ari Mozes, ODM Dev. Manager.  In it, Ari provides detailed guidance in how to leverage various strengths of Oracle Data Mining including the ability to: mine Star Schemas and join tables and views together to obtain a complete 360 degree view of a customer combine transactional data e.g. call record detail (CDR) data, etc. define complex data transformation, model build and model deploy analytical methodologies inside the Database  His blog is posted in a multi-part series.  Below are some opening excerpts for the first 3 blog entries.  This is an excellent resource for any novice to skilled data miner who wants to gain competitive advantage by mining their data inside the Oracle Database.  Many thanks Ari! Mining a Star Schema: Telco Churn Case Study (1 of 3) One of the strengths of Oracle Data Mining is the ability to mine star schemas with minimal effort.  Star schemas are commonly used in relational databases, and they often contain rich data with interesting patterns.  While dimension tables may contain interesting demographics, fact tables will often contain user behavior, such as phone usage or purchase patterns.  Both of these aspects - demographics and usage patterns - can provide insight into behavior.Churn is a critical problem in the telecommunications industry, and companies go to great lengths to reduce the churn of their customer base.  One case study1 describes a telecommunications scenario involving understanding, and identification of, churn, where the underlying data is present in a star schema.  That case study is a good example for demonstrating just how natural it is for Oracle Data Mining to analyze a star schema, so it will be used as the basis for this series of posts...... Mining a Star Schema: Telco Churn Case Study (2 of 3) This post will follow the transformation steps as described in the case study, but will use Oracle SQL as the means for preparing data.  Please see the previous post for background material, including links to the case study and to scripts that can be used to replicate the stages in these posts.1) Handling missing values for call data recordsThe CDR_T table records the number of phone minutes used by a customer per month and per call type (tariff).  For example, the table may contain one record corresponding to the number of peak (call type) minutes in January for a specific customer, and another record associated with international calls in March for the same customer.  This table is likely to be fairly dense (most type-month combinations for a given customer will be present) due to the coarse level of aggregation, but there may be some missing values.  Missing entries may occur for a number of reasons: the customer made no calls of a particular type in a particular month, the customer switched providers during the timeframe, or perhaps there is a data entry problem.  In the first situation, the correct interpretation of a missing entry would be to assume that the number of minutes for the type-month combination is zero.  In the other situations, it is not appropriate to assume zero, but rather derive some representative value to replace the missing entries.  The referenced case study takes the latter approach.  The data is segmented by customer and call type, and within a given customer-call type combination, an average number of minutes is computed and used as a replacement value.In SQL, we need to generate additional rows for the missing entries and populate those rows with appropriate values.  To generate the missing rows, Oracle's partition outer join feature is a perfect fit.  select cust_id, cdre.tariff, cdre.month, minsfrom cdr_t cdr partition by (cust_id) right outer join     (select distinct tariff, month from cdr_t) cdre     on (cdr.month = cdre.month and cdr.tariff = cdre.tariff);   ....... Mining a Star Schema: Telco Churn Case Study (3 of 3) Now that the "difficult" work is complete - preparing the data - we can move to building a predictive model to help identify and understand churn.The case study suggests that separate models be built for different customer segments (high, medium, low, and very low value customer groups).  To reduce the data to a single segment, a filter can be applied: create or replace view churn_data_high asselect * from churn_prep where value_band = 'HIGH'; It is simple to take a quick look at the predictive aspects of the data on a univariate basis.  While this does not capture the more complex multi-variate effects as would occur with the full-blown data mining algorithms, it can give a quick feel as to the predictive aspects of the data as well as validate the data preparation steps.  Oracle Data Mining includes a predictive analytics package which enables quick analysis. begin  dbms_predictive_analytics.explain(   'churn_data_high','churn_m6','expl_churn_tab'); end; /select * from expl_churn_tab where rank <= 5 order by rank; ATTRIBUTE_NAME       ATTRIBUTE_SUBNAME EXPLANATORY_VALUE RANK-------------------- ----------------- ----------------- ----------LOS_BAND                                      .069167052          1MINS_PER_TARIFF_MON  PEAK-5                   .034881648          2REV_PER_MON          REV-5                    .034527798          3DROPPED_CALLS                                 .028110322          4MINS_PER_TARIFF_MON  PEAK-4                   .024698149          5From the above results, it is clear that some predictors do contain information to help identify churn (explanatory value > 0).  The strongest uni-variate predictor of churn appears to be the customer's (binned) length of service.  The second strongest churn indicator appears to be the number of peak minutes used in the most recent month.  The subname column contains the interior piece of the DM_NESTED_NUMERICALS column described in the previous post.  By using the object relational approach, many related predictors are included within a single top-level column. .....   NOTE:  These are just EXCERPTS.  Click here to start reading the Oracle Data Mining a Star Schema: Telco Churn Case Study from the beginning.    

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  • Great Blogs About Oracle Solaris 11

    - by Markus Weber
    Now that Oracle Solaris 11 has been released, why not blog about blogs. There is of course a tremendous amount of resource and information available, but valuable insights directly from people actually building the product is priceless. Here's a list of such great blogs. NOTE: If you think we missed some good ones, please let us know in the comments section !  Topic Title Author Top 11 Things My 11 favourite Solaris 11 features Darren Moffat Top 11 Things These are 11 of my favorite things! Mike Gerdts Top 11 Things 11 reason to love Solaris 11     Jim Laurent SysAdmin Resources Solaris 11 Resources for System Administrators Rick Ramsey Overview Oracle Solaris 11: The First Cloud OS Larry Wake Overview What's a "Cloud Operating System"? Harry Foxwell Overview What's New in Oracle Solaris 11 Jeff Victor Try it ! Virtually the fastest way to try Solaris 11 (and Solaris 10 zones) Dave Miner Upgrade Upgrading Solaris 11 Express b151a with support to Solaris 11 Alan Hargreaves IPS The IPS System Repository Tim Foster IPS Building a Solaris 11 repository without network connection Jim Laurent IPS IPS Self-assembly – Part 1: overlays Tim Foster IPS Self assembly – Part 2: multiple packages delivering configuration Tim Foster Security Immutable Zones on Encrypted ZFS Darren Moffat Security User home directory encryption with ZFS Darren Moffat Security Password (PAM) caching for Solaris su - "a la sudo" Darren Moffat Security Completely disabling root logins on Solaris 11 Darren Moffat Security OpenSSL Version in Solaris Darren Moffat Security Exciting Crypto Advances with the T4 processor and Oracle Solaris 11 Valerie Fenwick Performance Critical Threads Optimization Rafael Vanoni Performance SPARC T4-2 Delivers World Record SPECjvm2008 Result with Oracle Solaris 11 BestPerf Blog Performance Recent Benchmarks Using Oracle Solaris 11 BestPerf Blog Predictive Self Healing Introducing SMF Layers Sean Wilcox Predictive Self Healing Oracle Solaris 11 - New Fault Management Features Gavin Maltby Desktop What's new on the Solaris 11 Desktop? Calum Benson Desktop S11 X11: ye olde window system in today's new operating system Alan Coopersmith Desktop Accessible Oracle Solaris 11 - released! Peter Korn

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  • Closed-loop Recommendation Engines: Analyst Insight report on Oracle Real-Time Decisions (RTD)

    - by Mike.Hallett(at)Oracle-BI&EPM
    In November 2011, Helena Schwenk of MWD Advisors, published her analysis on Oracle Real-Time Decisions.  She summarizes as follows: "In contrast to other popular approaches to implementing predictive analytics, RTD focuses on learning from each interaction and using these insights to adjust what is presented, offered or displayed to a customer. Likewise its capabilities for optimising decisions within the context of specific business goals and a report-driven framework for assessing the performance of models and decisions make it a strong contender for organisations that want to continuously improve decision making as part of a customer experience marketing, e-commerce optimisation and operational process efficiency initiative." This is an outstanding report to share with a prospect or client as it goes into great detail about the product and its capabilities.  It also highlights the differences in Oracle's Real-Time Decisions product vs. other closed loop recommendation engines. I encourage you to share this report with your clients and prospects. It can be downloaded directly from here - MWD Advisors Vendor Profile: Oracle Real-Time Decisions. (expires in November 2012) Highlights: "At the core of RTD lies a learning engine that combines business rules and adaptive predictive models to deliver recommendations to operational systems while simultaneously learning from experiences." "While closed-loop recommendation engines are becoming more prevalent... there are a number of features that distinguish RTD: It makes its decisions in the context of the business objectives, such as maximising customer revenue or reducing service costs Its support for operational integration offers organisations some flexibility in how they implement the offering."

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  • Combined Likelihood Models

    - by Lukas Vermeer
    In a series of posts on this blog we have already described a flexible approach to recording events, a technique to create analytical models for reporting, a method that uses the same principles to generate extremely powerful facet based predictions and a waterfall strategy that can be used to blend multiple (possibly facet based) models for increased accuracy. This latest, and also last, addition to this sequence of increasing modeling complexity will illustrate an advanced approach to amalgamate models, taking us to a whole new level of predictive modeling and analytical insights; combination models predicting likelihoods using multiple child models. The method described here is far from trivial. We therefore would not recommend you apply these techniques in an initial implementation of Oracle Real-Time Decisions. In most cases, basic RTD models or the approaches described before will provide more than enough predictive accuracy and analytical insight. The following is intended as an example of how more advanced models could be constructed if implementation results warrant the increased implementation and design effort. Keep implemented statistics simple! Combining likelihoods Because facet based predictions are based on metadata attributes of the choices selected, it is possible to generate such predictions for more than one attribute of a choice. We can predict the likelihood of acceptance for a particular product based on the product category (e.g. ‘toys’), as well as based on the color of the product (e.g. ‘pink’). Of course, these two predictions may be completely different (the customer may well prefer toys, but dislike pink products) and we will have to somehow combine these two separate predictions to determine an overall likelihood of acceptance for the choice. Perhaps the simplest way to combine multiple predicted likelihoods into one is to calculate the average (or perhaps maximum or minimum) likelihood. However, this would completely forgo the fact that some facets may have a far more pronounced effect on the overall likelihood than others (e.g. customers may consider the product category more important than its color). We could opt for calculating some sort of weighted average, but this would require us to specify up front the relative importance of the different facets involved. This approach would also be unresponsive to changing consumer behavior in these preferences (e.g. product price bracket may become more important to consumers as a result of economic shifts). Preferably, we would want Oracle Real-Time Decisions to learn, act upon and tell us about, the correlations between the different facet models and the overall likelihood of acceptance. This additional level of predictive modeling, where a single supermodel (no pun intended) combines the output of several (facet based) models into a single prediction, is what we call a combined likelihood model. Facet Based Scores As an example, we have implemented three different facet based models (as described earlier) in a simple RTD inline service. These models will allow us to generate predictions for likelihood of acceptance for each product based on three different metadata fields: Category, Price Bracket and Product Color. We will use an Analytical Scores entity to store these different scores so we can easily pass them between different functions. A simple function, creatively named Compute Analytical Scores, will compute for each choice the different facet scores and return an Analytical Scores entity that is stored on the choice itself. For each score, a choice attribute referring to this entity is also added to be returned to the client to facilitate testing. One Offer To Predict Them All In order to combine the different facet based predictions into one single likelihood for each product, we will need a supermodel which can predict the likelihood of acceptance, based on the outcomes of the facet models. This model will not need to consider any of the attributes of the session, because they are already represented in the outcomes of the underlying facet models. For the same reason, the supermodel will not need to learn separately for each product, because the specific combination of facets for this product are also already represented in the output of the underlying models. In other words, instead of learning how session attributes influence acceptance of a particular product, we will learn how the outcomes of facet based models for a particular product influence acceptance at a higher level. We will therefore be using a single All Offers choice to represent all offers in our combined likelihood predictions. This choice has no attribute values configured, no scores and not a single eligibility rule; nor is it ever intended to be returned to a client. The All Offers choice is to be used exclusively by the Combined Likelihood Acceptance model to predict the likelihood of acceptance for all choices; based solely on the output of the facet based models defined earlier. The Switcheroo In Oracle Real-Time Decisions, models can only learn based on attributes stored on the session. Therefore, just before generating a combined prediction for a given choice, we will temporarily copy the facet based scores—stored on the choice earlier as an Analytical Scores entity—to the session. The code for the Predict Combined Likelihood Event function is outlined below. // set session attribute to contain facet based scores. // (this is the only input for the combined model) session().setAnalyticalScores(choice.getAnalyticalScores); // predict likelihood of acceptance for All Offers choice. CombinedLikelihoodChoice c = CombinedLikelihood.getChoice("AllOffers"); Double la = CombinedLikelihoodAcceptance.getChoiceEventLikelihoods(c, "Accepted"); // clear session attribute of facet based scores. session().setAnalyticalScores(null); // return likelihood. return la; This sleight of hand will allow the Combined Likelihood Acceptance model to predict the likelihood of acceptance for the All Offers choice using these choice specific scores. After the prediction is made, we will clear the Analytical Scores session attribute to ensure it does not pollute any of the other (facet) models. To guarantee our combined likelihood model will learn based on the facet based scores—and is not distracted by the other session attributes—we will configure the model to exclude any other inputs, save for the instance of the Analytical Scores session attribute, on the model attributes tab. Recording Events In order for the combined likelihood model to learn correctly, we must ensure that the Analytical Scores session attribute is set correctly at the moment RTD records any events related to a particular choice. We apply essentially the same switching technique as before in a Record Combined Likelihood Event function. // set session attribute to contain facet based scores // (this is the only input for the combined model). session().setAnalyticalScores(choice.getAnalyticalScores); // record input event against All Offers choice. CombinedLikelihood.getChoice("AllOffers").recordEvent(event); // force learn at this moment using the Internal Dock entry point. Application.getPredictor().learn(InternalLearn.modelArray, session(), session(), Application.currentTimeMillis()); // clear session attribute of facet based scores. session().setAnalyticalScores(null); In this example, Internal Learn is a special informant configured as the learn location for the combined likelihood model. The informant itself has no particular configuration and does nothing in itself; it is used only to force the model to learn at the exact instant we have set the Analytical Scores session attribute to the correct values. Reporting Results After running a few thousand (artificially skewed) simulated sessions on our ILS, the Decision Center reporting shows some interesting results. In this case, these results reflect perfectly the bias we ourselves had introduced in our tests. In practice, we would obviously use a wider range of customer attributes and expect to see some more unexpected outcomes. The facetted model for categories has clearly picked up on the that fact our simulated youngsters have little interest in purchasing the one red-hot vehicle our ILS had on offer. Also, it would seem that customer age is an excellent predictor for the acceptance of pink products. Looking at the key drivers for the All Offers choice we can see the relative importance of the different facets to the prediction of overall likelihood. The comparative importance of the category facet for overall prediction might, in part, be explained by the clear preference of younger customers for toys over other product types; as evident from the report on the predictiveness of customer age for offer category acceptance. Conclusion Oracle Real-Time Decisions' flexible decisioning framework allows for the construction of exceptionally elaborate prediction models that facilitate powerful targeting, but nonetheless provide insightful reporting. Although few customers will have a direct need for such a sophisticated solution architecture, it is encouraging to see that this lies within the realm of the possible with RTD; and this with limited configuration and customization required. There are obviously numerous other ways in which the predictive and reporting capabilities of Oracle Real-Time Decisions can be expanded upon to tailor to individual customers needs. We will not be able to elaborate on them all on this blog; and finding the right approach for any given problem is often more difficult than implementing the solution. Nevertheless, we hope that these last few posts have given you enough of an understanding of the power of the RTD framework and its models; so that you can take some of these ideas and improve upon your own strategy. As always, if you have any questions about the above—or any Oracle Real-Time Decisions design challenges you might face—please do not hesitate to contact us; via the comments below, social media or directly at Oracle. We are completely multi-channel and would be more than glad to help. :-)

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  • PRUEBAS DE ESPECIALIZACION 2013/2014

    - by agallego
    Consigue  tu Certificado de Especialista Oracle  de forma GRATUITA , 27 y 28 de Noviembre de 2013  Ahora puedes realizar los exámenes de implementación de las especializaciones de Oracle y convertirte en especialista. Podrás realizar cualquiera de los exámenes de implementación de la siguiente lista: Oracle Fusion Customer Relationship Management 11g Sales Certified Implementation Specialist (1Z0-456) Oracle Fusion Customer Relationship Management 11g Incentive Compensation Certified Implementation Specialist (1Z0-472) Oracle ATG Web Commerce 10 Implementation Developer Certified Implementation Specialist (1Z0-510) Oracle RightNow CX Cloud Service 2012 Certified Implementation Specialist (1Z0-465) Oracle RightNow CX Cloud Service 2012 Developer Certified Implementation Specialist (1Z0-480) Oracle Fusion Human Capital Management 11g Human Resources Certified Implementation Specialist (1Z0-584) Oracle Fusion Human Capital Management 11g Talent Management Certified Implementation Specialist (1Z0-585) Oracle Taleo Recruiting Cloud Service 2013 Certified Implementation Specialist  (1Z0-474) Oracle Fusion Financials 11g Accounts Payable Certified Implementation Specialist(1Z0-507) Oracle Fusion Financials 11g Accounts Receivable Certified Implementation Specialist(1Z0-506) Oracle Fusion Financials 11g General Ledger Certified Implementation Specialist (1Z0-508) Oracle Fusion Distributed Order Orchestration 11g Essentials (1Z0-469) Oracle Documaker Standard Edition 12 Implementation Essentials (1Z0-570) Oracle Hyperion Planning 11 Essentials (1Z0-533) Oracle Hyperion Financial Management 11 Essentials (1Z0-532) Oracle Business Intelligence Foundation Suite 11g Essentials (1Z0-591) Oracle Essbase 11 Essentials (1Z0-531) Oracle GoldenGate 10 Essentials (1Z0-539) Oracle GoldenGate 11g Certified Implementation Exam Essentials Oracle Business Intelligence Applications 7.9.6 for CRM Essentials (1Z0-524) Oracle Business Intelligence Applications 7.9.6 for ERP Essentials (1Z0-525) Oracle Oracle Endeca Information Discovery 2.3 Certified Implementation Specialist (1Z0-461) Oracle SOA Suite 11g Essentials (1Z0-478) Oracle Service-Oriented Architecture Certified Implementation Specialist (1Z0-451) Oracle Unified Business Process Management Suite 11g Certified Implementation Specialist (1Z0-560) Oracle WebLogic Server 12c Certified Implementation Specialist (1Z0-599) Oracle Application Grid Certified Implementation Specialist(1Z0-523) Oracle WebCenter Content 11g Essentials (1Z0-542) Oracle WebCenter Portal 11g Essentials (1Z0-541) Oracle Application Development Framework Essentials (1Z1-554) Oracle Identity Governance Suite 11g Essentials(1z0-459) Oracle Access Management Suite Plus 11g Essentials Exam(1z0-479) M2M Platform Certified Architecture Essentials (1Z0-467) Oracle WebCenter Sites 11g Certified Implementation Specialist (1Z0-462)  Oracle Cloud Application Foundation Essentials(1Z0-468) Oracle Exadata 11g Essentials (1Z0-536) Exadata Database Machine Models X3-2 and X3-8 Certified Implementation Specialist (1Z0-485) Oracle Certified Expert, Oracle Exadata X3 Administration(1Z0-027) Exalogic Elastic Cloud X2-2 Certified Implementation Specialist (1Z0-569) Oracle Linux System Administration (1Z0-403) Oracle Linux Fundamentals (1Z0-402) Oracle Linux 6 Certified Implementation Specialist (1Z0-460) Oracle VM 3 for x86 Certified Implementation Specialist (1Z0-590) Oracle Enterprise Manager 11g Essentials  (1Z0-530 ) Oracle Enterprise Manager 12c Essentials (1Z0-457) SPARC T4-Based Server Installation Essentials (1Z0-597) 1Z0-821 Oracle Solaris 11 System Administration 1Z0-822 Oracle Solaris 11 Advanced System Administration Oracle Solaris 11 Installation and Configuration Essentials (1Z0-580) StorageTek Tape Libraries Certified Implementation Specialist(1Z0-546) Sun ZFS Storage Appliance Certified Implementation Specialist The Primavera P6 Enterprise Project Portfolio Management 8 Essentials (1Z0-567) The Primavera Portfolio Management Essentials (1Z0-544) Primavera Contract Management 14 Certified Implementation Specialist (1Z0-582) Oracle Utilities Customer Care and Billing 2 Certified Implementation Specialist (1Z0-562) Oracle Policy Automation 10 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Implementation Specialist (1Z0-455) Oracle Flexcube Universal Banking 11 Technical Implementation Essentials (1Z0-579) Oracle FlexCube Universal Banking 11 Basic Implementation Essentials (1Z0-561) Oracle Flexcube Universal Banking 11 Technical Implementation Essentials (1Z0-579) Oracle FLEXCUBE Direct Banking 6 Implementation Essentials (1Z0-594)   Puedes consultar la información acerca de los examenes en cada uno de los enlaces. Para prepararte los examenes sigue la Guia de estudio que encontrarás en la página de cada examen. Requisitos: ser  Partner Gold, Platinum o Diamond de Oracle y tener un usuario de Oracle Pearson Vue.  ¿Cuándo?: 27 y 28 de noviembre  a las (9:00, 12:00, 16:00)  ¿Dónde?: Core Networks, C.E.Parque Norte, Edificio Olmo, Planta 1 Serrano Galvache 56 | 28033, Madrid Para inscribirte: Create una cuenta en Pearson Vue (www.pearsonvue.com/oracle). Para Registrarte aquí. Para más información sobre el programa de especializaciones, haz clic aquí. No pierdas esta oportunidad e inscríbete hoy.  Para cualquier duda contactar con [email protected]. Ana María Gallego Partner Enablement Manager Spain and Portugal        

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  • LSI 9285-8e and Supermicro SC837E26-RJBOD1 duplicate enclosure ID and slot numbers

    - by Andy Shinn
    I am working with 2 x Supermicro SC837E26-RJBOD1 chassis connected to a single LSI 9285-8e card in a Supermicro 1U host. There are 28 drives in each chassis for a total of 56 drives in 28 RAID1 mirrors. The problem I am running in to is that there are duplicate slots for the 2 chassis (the slots list twice and only go from 0 to 27). All the drives also show the same enclosure ID (ID 36). However, MegaCLI -encinfo lists the 2 enclosures correctly (ID 36 and ID 65). My question is, why would this happen? Is there an option I am missing to use 2 enclosures effectively? This is blocking me rebuilding a drive that failed in slot 11 since I can only specify enclosure and slot as parameters to replace a drive. When I do this, it picks the wrong slot 11 (device ID 46 instead of device ID 19). Adapter #1 is the LSI 9285-8e, adapter #0 (which I removed due to space limitations) is the onboard LSI. Adapter information: Adapter #1 ============================================================================== Versions ================ Product Name : LSI MegaRAID SAS 9285-8e Serial No : SV12704804 FW Package Build: 23.1.1-0004 Mfg. Data ================ Mfg. Date : 06/30/11 Rework Date : 00/00/00 Revision No : 00A Battery FRU : N/A Image Versions in Flash: ================ BIOS Version : 5.25.00_4.11.05.00_0x05040000 WebBIOS Version : 6.1-20-e_20-Rel Preboot CLI Version: 05.01-04:#%00001 FW Version : 3.140.15-1320 NVDATA Version : 2.1106.03-0051 Boot Block Version : 2.04.00.00-0001 BOOT Version : 06.253.57.219 Pending Images in Flash ================ None PCI Info ================ Vendor Id : 1000 Device Id : 005b SubVendorId : 1000 SubDeviceId : 9285 Host Interface : PCIE ChipRevision : B0 Number of Frontend Port: 0 Device Interface : PCIE Number of Backend Port: 8 Port : Address 0 5003048000ee8e7f 1 5003048000ee8a7f 2 0000000000000000 3 0000000000000000 4 0000000000000000 5 0000000000000000 6 0000000000000000 7 0000000000000000 HW Configuration ================ SAS Address : 500605b0038f9210 BBU : Present Alarm : Present NVRAM : Present Serial Debugger : Present Memory : Present Flash : Present Memory Size : 1024MB TPM : Absent On board Expander: Absent Upgrade Key : Absent Temperature sensor for ROC : Present Temperature sensor for controller : Absent ROC temperature : 70 degree Celcius Settings ================ Current Time : 18:24:36 3/13, 2012 Predictive Fail Poll Interval : 300sec Interrupt Throttle Active Count : 16 Interrupt Throttle Completion : 50us Rebuild Rate : 30% PR Rate : 30% BGI Rate : 30% Check Consistency Rate : 30% Reconstruction Rate : 30% Cache Flush Interval : 4s Max Drives to Spinup at One Time : 2 Delay Among Spinup Groups : 12s Physical Drive Coercion Mode : Disabled Cluster Mode : Disabled Alarm : Enabled Auto Rebuild : Enabled Battery Warning : Enabled Ecc Bucket Size : 15 Ecc Bucket Leak Rate : 1440 Minutes Restore HotSpare on Insertion : Disabled Expose Enclosure Devices : Enabled Maintain PD Fail History : Enabled Host Request Reordering : Enabled Auto Detect BackPlane Enabled : SGPIO/i2c SEP Load Balance Mode : Auto Use FDE Only : No Security Key Assigned : No Security Key Failed : No Security Key Not Backedup : No Default LD PowerSave Policy : Controller Defined Maximum number of direct attached drives to spin up in 1 min : 10 Any Offline VD Cache Preserved : No Allow Boot with Preserved Cache : No Disable Online Controller Reset : No PFK in NVRAM : No Use disk activity for locate : No Capabilities ================ RAID Level Supported : RAID0, RAID1, RAID5, RAID6, RAID00, RAID10, RAID50, RAID60, PRL 11, PRL 11 with spanning, SRL 3 supported, PRL11-RLQ0 DDF layout with no span, PRL11-RLQ0 DDF layout with span Supported Drives : SAS, SATA Allowed Mixing: Mix in Enclosure Allowed Mix of SAS/SATA of HDD type in VD Allowed Status ================ ECC Bucket Count : 0 Limitations ================ Max Arms Per VD : 32 Max Spans Per VD : 8 Max Arrays : 128 Max Number of VDs : 64 Max Parallel Commands : 1008 Max SGE Count : 60 Max Data Transfer Size : 8192 sectors Max Strips PerIO : 42 Max LD per array : 16 Min Strip Size : 8 KB Max Strip Size : 1.0 MB Max Configurable CacheCade Size: 0 GB Current Size of CacheCade : 0 GB Current Size of FW Cache : 887 MB Device Present ================ Virtual Drives : 28 Degraded : 0 Offline : 0 Physical Devices : 59 Disks : 56 Critical Disks : 0 Failed Disks : 0 Supported Adapter Operations ================ Rebuild Rate : Yes CC Rate : Yes BGI Rate : Yes Reconstruct Rate : Yes Patrol Read Rate : Yes Alarm Control : Yes Cluster Support : No BBU : No Spanning : Yes Dedicated Hot Spare : Yes Revertible Hot Spares : Yes Foreign Config Import : Yes Self Diagnostic : Yes Allow Mixed Redundancy on Array : No Global Hot Spares : Yes Deny SCSI Passthrough : No Deny SMP Passthrough : No Deny STP Passthrough : No Support Security : No Snapshot Enabled : No Support the OCE without adding drives : Yes Support PFK : Yes Support PI : No Support Boot Time PFK Change : Yes Disable Online PFK Change : No PFK TrailTime Remaining : 0 days 0 hours Support Shield State : Yes Block SSD Write Disk Cache Change: Yes Supported VD Operations ================ Read Policy : Yes Write Policy : Yes IO Policy : Yes Access Policy : Yes Disk Cache Policy : Yes Reconstruction : Yes Deny Locate : No Deny CC : No Allow Ctrl Encryption: No Enable LDBBM : No Support Breakmirror : No Power Savings : Yes Supported PD Operations ================ Force Online : Yes Force Offline : Yes Force Rebuild : Yes Deny Force Failed : No Deny Force Good/Bad : No Deny Missing Replace : No Deny Clear : No Deny Locate : No Support Temperature : Yes Disable Copyback : No Enable JBOD : No Enable Copyback on SMART : No Enable Copyback to SSD on SMART Error : Yes Enable SSD Patrol Read : No PR Correct Unconfigured Areas : Yes Enable Spin Down of UnConfigured Drives : Yes Disable Spin Down of hot spares : No Spin Down time : 30 T10 Power State : Yes Error Counters ================ Memory Correctable Errors : 0 Memory Uncorrectable Errors : 0 Cluster Information ================ Cluster Permitted : No Cluster Active : No Default Settings ================ Phy Polarity : 0 Phy PolaritySplit : 0 Background Rate : 30 Strip Size : 64kB Flush Time : 4 seconds Write Policy : WB Read Policy : Adaptive Cache When BBU Bad : Disabled Cached IO : No SMART Mode : Mode 6 Alarm Disable : Yes Coercion Mode : None ZCR Config : Unknown Dirty LED Shows Drive Activity : No BIOS Continue on Error : No Spin Down Mode : None Allowed Device Type : SAS/SATA Mix Allow Mix in Enclosure : Yes Allow HDD SAS/SATA Mix in VD : Yes Allow SSD SAS/SATA Mix in VD : No Allow HDD/SSD Mix in VD : No Allow SATA in Cluster : No Max Chained Enclosures : 16 Disable Ctrl-R : Yes Enable Web BIOS : Yes Direct PD Mapping : No BIOS Enumerate VDs : Yes Restore Hot Spare on Insertion : No Expose Enclosure Devices : Yes Maintain PD Fail History : Yes Disable Puncturing : No Zero Based Enclosure Enumeration : No PreBoot CLI Enabled : Yes LED Show Drive Activity : Yes Cluster Disable : Yes SAS Disable : No Auto Detect BackPlane Enable : SGPIO/i2c SEP Use FDE Only : No Enable Led Header : No Delay during POST : 0 EnableCrashDump : No Disable Online Controller Reset : No EnableLDBBM : No Un-Certified Hard Disk Drives : Allow Treat Single span R1E as R10 : No Max LD per array : 16 Power Saving option : Don't Auto spin down Configured Drives Max power savings option is not allowed for LDs. Only T10 power conditions are to be used. Default spin down time in minutes: 30 Enable JBOD : No TTY Log In Flash : No Auto Enhanced Import : No BreakMirror RAID Support : No Disable Join Mirror : No Enable Shield State : Yes Time taken to detect CME : 60s Exit Code: 0x00 Enclosure information: # /opt/MegaRAID/MegaCli/MegaCli64 -encinfo -a1 Number of enclosures on adapter 1 -- 3 Enclosure 0: Device ID : 36 Number of Slots : 28 Number of Power Supplies : 2 Number of Fans : 3 Number of Temperature Sensors : 1 Number of Alarms : 1 Number of SIM Modules : 0 Number of Physical Drives : 28 Status : Normal Position : 1 Connector Name : Port B Enclosure type : SES VendorId is LSI CORP and Product Id is SAS2X36 VendorID and Product ID didnt match FRU Part Number : N/A Enclosure Serial Number : N/A ESM Serial Number : N/A Enclosure Zoning Mode : N/A Partner Device Id : 65 Inquiry data : Vendor Identification : LSI CORP Product Identification : SAS2X36 Product Revision Level : 0718 Vendor Specific : x36-55.7.24.1 Number of Voltage Sensors :2 Voltage Sensor :0 Voltage Sensor Status :OK Voltage Value :5020 milli volts Voltage Sensor :1 Voltage Sensor Status :OK Voltage Value :11820 milli volts Number of Power Supplies : 2 Power Supply : 0 Power Supply Status : OK Power Supply : 1 Power Supply Status : OK Number of Fans : 3 Fan : 0 Fan Speed :Low Speed Fan Status : OK Fan : 1 Fan Speed :Low Speed Fan Status : OK Fan : 2 Fan Speed :Low Speed Fan Status : OK Number of Temperature Sensors : 1 Temp Sensor : 0 Temperature : 48 Temperature Sensor Status : OK Number of Chassis : 1 Chassis : 0 Chassis Status : OK Enclosure 1: Device ID : 65 Number of Slots : 28 Number of Power Supplies : 2 Number of Fans : 3 Number of Temperature Sensors : 1 Number of Alarms : 1 Number of SIM Modules : 0 Number of Physical Drives : 28 Status : Normal Position : 1 Connector Name : Port A Enclosure type : SES VendorId is LSI CORP and Product Id is SAS2X36 VendorID and Product ID didnt match FRU Part Number : N/A Enclosure Serial Number : N/A ESM Serial Number : N/A Enclosure Zoning Mode : N/A Partner Device Id : 36 Inquiry data : Vendor Identification : LSI CORP Product Identification : SAS2X36 Product Revision Level : 0718 Vendor Specific : x36-55.7.24.1 Number of Voltage Sensors :2 Voltage Sensor :0 Voltage Sensor Status :OK Voltage Value :5020 milli volts Voltage Sensor :1 Voltage Sensor Status :OK Voltage Value :11760 milli volts Number of Power Supplies : 2 Power Supply : 0 Power Supply Status : OK Power Supply : 1 Power Supply Status : OK Number of Fans : 3 Fan : 0 Fan Speed :Low Speed Fan Status : OK Fan : 1 Fan Speed :Low Speed Fan Status : OK Fan : 2 Fan Speed :Low Speed Fan Status : OK Number of Temperature Sensors : 1 Temp Sensor : 0 Temperature : 47 Temperature Sensor Status : OK Number of Chassis : 1 Chassis : 0 Chassis Status : OK Enclosure 2: Device ID : 252 Number of Slots : 8 Number of Power Supplies : 0 Number of Fans : 0 Number of Temperature Sensors : 0 Number of Alarms : 0 Number of SIM Modules : 1 Number of Physical Drives : 0 Status : Normal Position : 1 Connector Name : Unavailable Enclosure type : SGPIO Failed in first Inquiry commnad FRU Part Number : N/A Enclosure Serial Number : N/A ESM Serial Number : N/A Enclosure Zoning Mode : N/A Partner Device Id : Unavailable Inquiry data : Vendor Identification : LSI Product Identification : SGPIO Product Revision Level : N/A Vendor Specific : Exit Code: 0x00 Now, notice that each slot 11 device shows an enclosure ID of 36, I think this is where the discrepancy happens. One should be 36. But the other should be on enclosure 65. Drives in slot 11: Enclosure Device ID: 36 Slot Number: 11 Drive's postion: DiskGroup: 5, Span: 0, Arm: 1 Enclosure position: 0 Device Id: 48 WWN: Sequence Number: 11 Media Error Count: 0 Other Error Count: 0 Predictive Failure Count: 0 Last Predictive Failure Event Seq Number: 0 PD Type: SATA Raw Size: 2.728 TB [0x15d50a3b0 Sectors] Non Coerced Size: 2.728 TB [0x15d40a3b0 Sectors] Coerced Size: 2.728 TB [0x15d400000 Sectors] Firmware state: Online, Spun Up Is Commissioned Spare : YES Device Firmware Level: A5C0 Shield Counter: 0 Successful diagnostics completion on : N/A SAS Address(0): 0x5003048000ee8a53 Connected Port Number: 1(path0) Inquiry Data: MJ1311YNG6YYXAHitachi HDS5C3030ALA630 MEAOA5C0 FDE Enable: Disable Secured: Unsecured Locked: Unlocked Needs EKM Attention: No Foreign State: None Device Speed: 6.0Gb/s Link Speed: 6.0Gb/s Media Type: Hard Disk Device Drive Temperature :30C (86.00 F) PI Eligibility: No Drive is formatted for PI information: No PI: No PI Drive's write cache : Disabled Drive's NCQ setting : Enabled Port-0 : Port status: Active Port's Linkspeed: 6.0Gb/s Drive has flagged a S.M.A.R.T alert : No Enclosure Device ID: 36 Slot Number: 11 Drive's postion: DiskGroup: 19, Span: 0, Arm: 1 Enclosure position: 0 Device Id: 19 WWN: Sequence Number: 4 Media Error Count: 0 Other Error Count: 0 Predictive Failure Count: 0 Last Predictive Failure Event Seq Number: 0 PD Type: SATA Raw Size: 2.728 TB [0x15d50a3b0 Sectors] Non Coerced Size: 2.728 TB [0x15d40a3b0 Sectors] Coerced Size: 2.728 TB [0x15d400000 Sectors] Firmware state: Online, Spun Up Is Commissioned Spare : NO Device Firmware Level: A580 Shield Counter: 0 Successful diagnostics completion on : N/A SAS Address(0): 0x5003048000ee8e53 Connected Port Number: 0(path0) Inquiry Data: MJ1313YNG1VA5CHitachi HDS5C3030ALA630 MEAOA580 FDE Enable: Disable Secured: Unsecured Locked: Unlocked Needs EKM Attention: No Foreign State: None Device Speed: 6.0Gb/s Link Speed: 6.0Gb/s Media Type: Hard Disk Device Drive Temperature :30C (86.00 F) PI Eligibility: No Drive is formatted for PI information: No PI: No PI Drive's write cache : Disabled Drive's NCQ setting : Enabled Port-0 : Port status: Active Port's Linkspeed: 6.0Gb/s Drive has flagged a S.M.A.R.T alert : No Update 06/28/12: I finally have some new information about (what we think) the root cause of this problem so I thought I would share. After getting in contact with a very knowledgeable Supermicro tech, they provided us with a tool called Xflash (doesn't appear to be readily available on their FTP). When we gathered some information using this utility, my colleague found something very strange: root@mogile2 test]# ./xflash.dat -i get avail Initializing Interface. Expander: SAS2X36 (SAS2x36) 1) SAS2X36 (SAS2x36) (50030480:00EE917F) (0.0.0.0) 2) SAS2X36 (SAS2x36) (50030480:00E9D67F) (0.0.0.0) 3) SAS2X36 (SAS2x36) (50030480:0112D97F) (0.0.0.0) This lists the connected enclosures. You see the 3 connected (we have since added a 3rd and a 4th which is not yet showing up) with their respective SAS address / WWN (50030480:00EE917F). Now we can use this address to get information on the individual enclosures: [root@mogile2 test]# ./xflash.dat -i 5003048000EE917F get exp Initializing Interface. Expander: SAS2X36 (SAS2x36) Reading the expander information.......... Expander: SAS2X36 (SAS2x36) B3 SAS Address: 50030480:00EE917F Enclosure Logical Id: 50030480:0000007F IP Address: 0.0.0.0 Component Identifier: 0x0223 Component Revision: 0x05 [root@mogile2 test]# ./xflash.dat -i 5003048000E9D67F get exp Initializing Interface. Expander: SAS2X36 (SAS2x36) Reading the expander information.......... Expander: SAS2X36 (SAS2x36) B3 SAS Address: 50030480:00E9D67F Enclosure Logical Id: 50030480:0000007F IP Address: 0.0.0.0 Component Identifier: 0x0223 Component Revision: 0x05 [root@mogile2 test]# ./xflash.dat -i 500304800112D97F get exp Initializing Interface. Expander: SAS2X36 (SAS2x36) Reading the expander information.......... Expander: SAS2X36 (SAS2x36) B3 SAS Address: 50030480:0112D97F Enclosure Logical Id: 50030480:0112D97F IP Address: 0.0.0.0 Component Identifier: 0x0223 Component Revision: 0x05 Did you catch it? The first 2 enclosures logical ID is partially masked out where the 3rd one (which has a correct unique enclosure ID) is not. We pointed this out to Supermicro and were able to confirm that this address is supposed to be set during manufacturing and there was a problem with a certain batch of these enclosures where the logical ID was not set. We believe that the RAID controller is determining the ID based on the logical ID and since our first 2 enclosures have the same logical ID, they get the same enclosure ID. We also confirmed that 0000007F is the default which comes from LSI as an ID. The next pointer that helps confirm this could be a manufacturing problem with a run of JBODs is the fact that all 6 of the enclosures that have this problem begin with 00E. I believe that between 00E8 and 00EE Supermicro forgot to program the logical IDs correctly and neglected to recall or fix the problem post production. Fortunately for us, there is a tool to manage the WWN and logical ID of the devices from Supermicro: ftp://ftp.supermicro.com/utility/ExpanderXtools_Lite/. Our next step is to schedule a shutdown of these JBODs (after data migration) and reprogram the logical ID and see if it solves the problem. Update 06/28/12 #2: I just discovered this FAQ at Supermicro while Google searching for "lsi 0000007f": http://www.supermicro.com/support/faqs/faq.cfm?faq=11805. I still don't understand why, in the last several times we contacted Supermicro, they would have never directed us to this article :\

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  • Customer Experience Management : A conversation with world experts RTD

    - by David lefranc
    A conversation with world experts in Customer Experience Management in Rome, Italy - Wed, June 20, 2012 It is our pleasure to share the registration link below for your chance to meet active members of the Oracle Real-Time Decisions Customer Advisory Board. Join us to hear how leading brands across the world have achieved tremendous return on investment through their Oracle Real-Time Decisions deployments and do not miss this unique opportunity to ask them specific questions directly during our customer roundtable. Please share this information with anyone interested in real-time decision management and cross-channel predictive process optimization.http://www.oracle.com/goto/RealTimeDecisions

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  • Customer Experience Management : A conversation with world experts RTD

    - by David lefranc
    A conversation with world experts in Customer Experience Management in Rome, Italy - Wed, June 20, 2012 It is our pleasure to share the registration link below for your chance to meet active members of the Oracle Real-Time Decisions Customer Advisory Board. Join us to hear how leading brands across the world have achieved tremendous return on investment through their Oracle Real-Time Decisions deployments and do not miss this unique opportunity to ask them specific questions directly during our customer roundtable. Please share this information with anyone interested in real-time decision management and cross-channel predictive process optimization.http://www.oracle.com/goto/RealTimeDecisions

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  • A conversation with world experts in Customer Experience Management in Rome, Italy - Wed, June 20, 2012

    - by nicolasbonnet
    It is my pleasure to share the registration link below for your chance to meet active members of the Oracle Real-Time Decisions Customer Advisory Board. Join us to hear how leading brands across the world have achieved tremendous return on investment through their Oracle Real-Time Decisions deployments and do not miss this unique opportunity to ask them specific questions directly during our customer roundtable. Please share this information with anyone interested in real-time decision management and cross-channel predictive process optimization http://www.oracle.com/goto/RealTimeDecisions Nicolas Bonnet / Senior Director Product Management / Oracle Business Intelligence

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  • optimized search using ajax and keypress

    - by ooo
    i have the following code as i want to search a database as a user is typing into a textbox. This code below works fine but it seems a little inefficient as if a user is typing really fast, i am potentially doing many more searches than necessary. So if a user is typing in "sailing" i am searching on "sail", "saili", "sailin", and "sailing" i wanted to see if there was a way to detect any particular time between keypresses so only search if user stops typing for 500 milliseconds or something like this. is there a best practices for something like this? $('#searchString').keypress(function(e) { if (e.keyCode == 13) { var url = '/Tracker/Search/' + $("#searchString").val(); $.get(url, function(data) { $('div#results').html(data); $('#results').show(); }); } else { var existingString = $("#searchString").val(); if (existingString.length > 2) { var url = '/Tracker/Search/' + existingString; $.get(url, function(data) { $('div#results').html(data); $('#results').show(); }); } }

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  • Megacli is killing me, any help appreciated

    - by Stefan
    I run a server with 2 drives in raid0 configured through BIOS. I just added 2 more drives using hotplug (the server is dell r610 with RHEL 5.4 64bit) and I would like to configure a separate raid0 partition on these drives. I am getting the following error: /opt/MegaRAID/MegaCli/MegaCli64 -CfgLdAdd r0[32:2, 32:3] -a0 The specified physical disk does not have the appropriate attributes to complete the requested command. Exit Code: 0x26 All the parameters are correct and there is just no reason why this command could not work, see this (fujitsu is current raid, seagate is the new one I want to create): /opt/MegaRAID/MegaCli/MegaCli64 -PDList -aALL | egrep 'Adapter|Enclosure|Slot|Inquiry' Adapter #0 Enclosure Device ID: 32 Slot Number: 0 Enclosure position: 0 Inquiry Data: FUJITSU MBD2147RC D807D0A4PA101174 Enclosure Device ID: 32 Slot Number: 1 Enclosure position: 0 Inquiry Data: FUJITSU MBD2147RC D807D0A4PA10115T Enclosure Device ID: 32 Slot Number: 2 Enclosure position: 0 Inquiry Data: SEAGATE ST9300603SS FS033SE0TF5K Enclosure Device ID: 32 Slot Number: 3 Enclosure position: 0 Inquiry Data: SEAGATE ST9300603SS FS023SE070FK I also tried to set up the drive as hotspare, also some strange error: /opt/MegaRAID/MegaCli/MegaCli64 -PDHSP -Set -physdrv[32:3] -a0 Adapter: 0: Set Physical Drive at EnclId-32 SlotId-3 as Hot Spare Failed. FW error description: The specified device is in a state that doesn't support the requested command. Exit Code: 0x32 As you can see the disk is in Unconfigured, Good state: Enclosure Device ID: 32 Slot Number: 3 Enclosure position: 0 Device Id: 3 Sequence Number: 1 Media Error Count: 0 Other Error Count: 0 Predictive Failure Count: 0 Last Predictive Failure Event Seq Number: 0 PD Type: SAS Raw Size: 279.396 GB [0x22ecb25c Sectors] Non Coerced Size: 278.896 GB [0x22dcb25c Sectors] Coerced Size: 278.875 GB [0x22dc0000 Sectors] Firmware state: Unconfigured(good), Spun Up SAS Address(0): 0x5000c50005cd20b1 SAS Address(1): 0x0 Connected Port Number: 3(path0) Inquiry Data: SEAGATE ST9300603SS FS023SE070FK FDE Capable: Not Capable FDE Enable: Disable Secured: Unsecured Locked: Unlocked Needs EKM Attention: No Foreign State: Foreign Foreign Secure: Drive is not secured by a foreign lock key Device Speed: Unknown Link Speed: Unknown Media Type: Hard Disk Device Drive Temperature :30C (86.00 F)

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  • How can I get Haproxy to not log local requests?

    - by coneybeare
    I am trying to clean out some of the log clutter from my machines and am starting by removing requests that are generated from the server themselves. I have cache warmers running around the clock and I don't want these polluting the logs. I was able to get apache to stop logging local requests by adding a dontlog for the local IP: SetEnvIf Remote_Addr "RE\.DA\.CT\.ED" dontlog CustomLog "|logger -p local3.info -t http" combined env=!dontlog and now I am looking for something similar to put in a configuration for the Haproxy log. How can I prevent 127.0.0.1 requests from writing to the Haproxy log? UPDATE: 2/15/11 I use the excellent loggly service to pull out logs in the cloud, but I am seeing tons of logs like this: 2011 Feb 15 06:09:42.000 ip-10-251-194-96 http: RE.DA.CT.ED - - [15/Feb/2011:06:09:42 -0500] "HEAD /search/Nevad/predictive/txt HTTP/1.0" 200 - "-" "Wget/1.10.2 (Red Hat modified)" 2011 Feb 15 06:09:42.000 127.0.0.1 haproxy[10390]: 127.0.0.1:58408 [15/Feb/2011:06:09:42] www i-5dd7a331.0 0/0/0/8/8 200 210 - - --NI 0/0/0 0/0 "HEAD /search/Nevad/predictive/txt HTTP/1.1" and I want them gone. This question focuses on how to remove that haproxy log line from writing to the server side log in the first place.

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  • Microsoft&rsquo;s new technical computing initiative

    - by Randy Walker
    I made a mental note from earlier in the year.  Microsoft literally buys computers by the truckload.  From what I understand, it’s a typical practice amongst large software vendors.  You plug a few wires in, you test it, and you instantly have mega tera tera flops (don’t hold me to that number).  Microsoft has been trying to plug away at their cloud services (named Azure).  Which, for the layman, means Microsoft runs your software on their computers, and as demand increases you can allocate more computing power on the fly. With this in mind, it doesn’t surprise me that I was recently sent an executive email concerning Microsoft’s new technical computing initiative.  I find it to be a great marketing idea with actual substance behind their real work.  From the programmer academic perspective, in college we dreamed about this type of processing power.  This has decades of computer science theory behind it. A copy of the email received.  (note that I almost deleted this email, thinking it was spam due to it’s length) We don't often think about how complex life really is. Take the relatively simple task of commuting to and from work: it is, in fact, a complicated interplay of variables such as weather, train delays, accidents, traffic patterns, road construction, etc. You can however, take steps to shorten your commute - using a good, predictive understanding of a few of these variables. In fact, you probably are already taking these inputs and instinctively building a predictive model that you act on daily to get to your destination more quickly. Now, when we apply the same method to very complex tasks, this modeling approach becomes much more challenging. Recent world events clearly demonstrated our inability to process vast amounts of information and variables that would have helped to more accurately predict the behavior of global financial markets or the occurrence and impact of a volcano eruption in Iceland. To make sense of issues like these, researchers, engineers and analysts create computer models of the almost infinite number of possible interactions in complex systems. But, they need increasingly more sophisticated computer models to better understand how the world behaves and to make fact-based predictions about the future. And, to do this, it requires a tremendous amount of computing power to process and examine the massive data deluge from cameras, digital sensors and precision instruments of all kinds. This is the key to creating more accurate and realistic models that expose the hidden meaning of data, which gives us the kind of insight we need to solve a myriad of challenges. We have made great strides in our ability to build these kinds of computer models, and yet they are still too difficult, expensive and time consuming to manage. Today, even the most complicated data-rich simulations cannot fully capture all of the intricacies and dependencies of the systems they are trying to model. That is why, across the scientific and engineering world, it is so hard to say with any certainty when or where the next volcano will erupt and what flight patterns it might affect, or to more accurately predict something like a global flu pandemic. So far, we just cannot collect, correlate and compute enough data to create an accurate forecast of the real world. But this is about to change. Innovations in technology are transforming our ability to measure, monitor and model how the world behaves. The implication for scientific research is profound, and it will transform the way we tackle global challenges like health care and climate change. It will also have a huge impact on engineering and business, delivering breakthroughs that could lead to the creation of new products, new businesses and even new industries. Because you are a subscriber to executive e-mails from Microsoft, I want you to be the first to know about a new effort focused specifically on empowering millions of the world's smartest problem solvers. Today, I am happy to introduce Microsoft's Technical Computing initiative. Our goal is to unleash the power of pervasive, accurate, real-time modeling to help people and organizations achieve their objectives and realize their potential. We are bringing together some of the brightest minds in the technical computing community across industry, academia and science at www.modelingtheworld.com to discuss trends, challenges and shared opportunities. New advances provide the foundation for tools and applications that will make technical computing more affordable and accessible where mathematical and computational principles are applied to solve practical problems. One day soon, complicated tasks like building a sophisticated computer model that would typically take a team of advanced software programmers months to build and days to run, will be accomplished in a single afternoon by a scientist, engineer or analyst working at the PC on their desktop. And as technology continues to advance, these models will become more complete and accurate in the way they represent the world. This will speed our ability to test new ideas, improve processes and advance our understanding of systems. Our technical computing initiative reflects the best of Microsoft's heritage. Ever since Bill Gates articulated the then far-fetched vision of "a computer on every desktop" in the early 1980's, Microsoft has been at the forefront of expanding the power and reach of computing to benefit the world. As someone who worked closely with Bill for many years at Microsoft, I am happy to share with you that the passion behind that vision is fully alive at Microsoft and is carried out in the creation of our new Technical Computing group. Enabling more people to make better predictions We have seen the impact of making greater computing power more available firsthand through our investments in high performance computing (HPC) over the past five years. Scientists, engineers and analysts in organizations of all sizes and sectors are finding that using distributed computational power creates societal impact, fuels scientific breakthroughs and delivers competitive advantages. For example, we have seen remarkable results from some of our current customers: Malaria strikes 300,000 to 500,000 people around the world each year. To help in the effort to eradicate malaria worldwide, scientists at Intellectual Ventures use software that simulates how the disease spreads and would respond to prevention and control methods, such as vaccines and the use of bed nets. Technical computing allows researchers to model more detailed parameters for more accurate results and receive those results in less than an hour, rather than waiting a full day. Aerospace engineering firm, a.i. solutions, Inc., needed a more powerful computing platform to keep up with the increasingly complex computational needs of its customers: NASA, the Department of Defense and other government agencies planning space flights. To meet that need, it adopted technical computing. Now, a.i. solutions can produce detailed predictions and analysis of the flight dynamics of a given spacecraft, from optimal launch times and orbit determination to attitude control and navigation, up to eight times faster. This enables them to avoid mistakes in any areas that can cause a space mission to fail and potentially result in the loss of life and millions of dollars. Western & Southern Financial Group faced the challenge of running ever larger and more complex actuarial models as its number of policyholders and products grew and regulatory requirements changed. The company chose an actuarial solution that runs on technical computing technology. The solution is easy for the company's IT staff to manage and adjust to meet business needs. The new solution helps the company reduce modeling time by up to 99 percent - letting the team fine-tune its models for more accurate product pricing and financial projections. Our Technical Computing direction Collaborating closely with partners across industry and academia, we must now extend the reach of technical computing even further to help predictive modelers and data explorers make faster, more accurate predictions. As we build the Technical Computing initiative, we will invest in three core areas: Technical computing to the cloud: Microsoft will play a leading role in bringing technical computing power to scientists, engineers and analysts through the cloud. Existing high- performance computing users will benefit from the ability to augment their on-premises systems with cloud resources that enable 'just-in-time' processing. This platform will help ensure processing resources are available whenever they are needed-reliably, consistently and quickly. Simplify parallel development: Today, computers are shipping with more processing power than ever, including multiple cores, but most modern software only uses a small amount of the available processing power. Parallel programs are extremely difficult to write, test and trouble shoot. However, a consistent model for parallel programming can help more developers unlock the tremendous power in today's modern computers and enable a new generation of technical computing. We are delivering new tools to automate and simplify writing software through parallel processing from the desktop... to the cluster... to the cloud. Develop powerful new technical computing tools and applications: We know scientists, engineers and analysts are pushing common tools (i.e., spreadsheets and databases) to the limits with complex, data-intensive models. They need easy access to more computing power and simplified tools to increase the speed of their work. We are building a platform to do this. Our development efforts will yield new, easy-to-use tools and applications that automate data acquisition, modeling, simulation, visualization, workflow and collaboration. This will allow them to spend more time on their work and less time wrestling with complicated technology. Thinking bigger There is so much left to be discovered and so many questions yet to be answered in the fascinating world around us. We believe the technical computing community will show us that we have not seen anything yet. Imagine just some of the breakthroughs this community could make possible: Better predictions to help improve the understanding of pandemics, contagion and global health trends. Climate change models that predict environmental, economic and human impact, accessible in real-time during key discussions and debates. More accurate prediction of natural disasters and their impact to develop more effective emergency response plans. With an ambitious charter in hand, this new team is ready to build on our progress to-date and execute Microsoft's technical computing vision over the months and years ahead. We will steadily invest in the right technologies, tools and talent, and work to bring together the technical computing community. I invite you to visit www.modelingtheworld.com today. We welcome your ideas and feedback. I look forward to making this journey with you and others who want to answer the world's biggest questions, discover solutions to problems that seem impossible and uncover a host of new opportunities to change the world we live in for the better. Bob

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  • Installed Ubuntu in VMware Player, Black side bars / broken GUI

    - by Eric
    I recently installed Ubuntu 12.10 in VMware Player and have came up with a black sidebar, missing icons, a pretty broken GUI. Everything works just fine though. I am able to run Firefox and open termainal and all that good stuff just fine, it's just that I can SEE them on the sidebar. I have to open up a seperate window on Windows with a picture of the Ubuntu 12.10 desktop in order for me to know what to click on, but once I do click on it, it's pretty much smooth sailing from there(not counting closing Firefox and several other things). Again, everything works just fine, but when it comes to the sidebar, the GUI, the dashboard (get a completely black screen for when I open dash board), they come up as completely black, broken (visual tears and what not), and hoving over them just brings up a big black bar (assuming it's the "zooming" in of the icon, but it just shows a black bar of where the icon should be). I'm not exactly sure what so do to get this to work (to fix the GUI), any ideas as to what I may do to fix this?

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  • How can I simulate objects floating on water without a physics engine?

    - by user1075940
    In my game the water movement is done in a shader using Gerstner equations. The water movement looks realistic enough for a school project but I encounter serious problem when I wanted to do sailing on waves (similar to this). I managed to do collision with land by calculating quad's vertices and normals beneath ship, however same method can not be applied to water because XZ are displaced and Y is calculated in a shader :( How to approach this problem ? Is it possible to retrieve transformed grid from shader? Unfortunately no external physics libraries can be used.

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