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  • BIP Enterprise Patches

    - by Tim Dexter
    Got some input from the support team yesterday BIP patching. I dont have any control over the patches but have a voice to get information out there. Just to clarify for you all, the recent 'rollup' patch, 9546699 that I blogged a while back supersedes all other patches for the standalone release. If you have an issue and log an SR, please ensure you have applied 9546699 and re-checked the issue. There are so many fixes and enhancements in that patch that your issue may be fixed or addressed. If you are a new customer and download the latest release, 10.1.3.4.1 from oracle.com. Please get onto the support site and get 9546699 applied straight away. For more information check out Pieter's Note on BIP patching - 797057.1

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  • Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned what is HDFS. In this article we will take a quick look at the importance of the Relational Database in Big Data world. A Big Question? Here are a few questions I often received since the beginning of the Big Data Series - Does the relational database have no space in the story of the Big Data? Does relational database is no longer relevant as Big Data is evolving? Is relational database not capable to handle Big Data? Is it true that one no longer has to learn about relational data if Big Data is the final destination? Well, every single time when I hear that one person wants to learn about Big Data and is no longer interested in learning about relational database, I find it as a bit far stretched. I am not here to give ambiguous answers of It Depends. I am personally very clear that one who is aspiring to become Big Data Scientist or Big Data Expert they should learn about relational database. NoSQL Movement The reason for the NoSQL Movement in recent time was because of the two important advantages of the NoSQL databases. Performance Flexible Schema In personal experience I have found that when I use NoSQL I have found both of the above listed advantages when I use NoSQL database. There are instances when I found relational database too much restrictive when my data is unstructured as well as they have in the datatype which my Relational Database does not support. It is the same case when I have found that NoSQL solution performing much better than relational databases. I must say that I am a big fan of NoSQL solutions in the recent times but I have also seen occasions and situations where relational database is still perfect fit even though the database is growing increasingly as well have all the symptoms of the big data. Situations in Relational Database Outperforms Adhoc reporting is the one of the most common scenarios where NoSQL is does not have optimal solution. For example reporting queries often needs to aggregate based on the columns which are not indexed as well are built while the report is running, in this kind of scenario NoSQL databases (document database stores, distributed key value stores) database often does not perform well. In the case of the ad-hoc reporting I have often found it is much easier to work with relational databases. SQL is the most popular computer language of all the time. I have been using it for almost over 10 years and I feel that I will be using it for a long time in future. There are plenty of the tools, connectors and awareness of the SQL language in the industry. Pretty much every programming language has a written drivers for the SQL language and most of the developers have learned this language during their school/college time. In many cases, writing query based on SQL is much easier than writing queries in NoSQL supported languages. I believe this is the current situation but in the future this situation can reverse when No SQL query languages are equally popular. ACID (Atomicity Consistency Isolation Durability) – Not all the NoSQL solutions offers ACID compliant language. There are always situations (for example banking transactions, eCommerce shopping carts etc.) where if there is no ACID the operations can be invalid as well database integrity can be at risk. Even though the data volume indeed qualify as a Big Data there are always operations in the application which absolutely needs ACID compliance matured language. The Mixed Bag I have often heard argument that all the big social media sites now a days have moved away from Relational Database. Actually this is not entirely true. While researching about Big Data and Relational Database, I have found that many of the popular social media sites uses Big Data solutions along with Relational Database. Many are using relational databases to deliver the results to end user on the run time and many still uses a relational database as their major backbone. Here are a few examples: Facebook uses MySQL to display the timeline. (Reference Link) Twitter uses MySQL. (Reference Link) Tumblr uses Sharded MySQL (Reference Link) Wikipedia uses MySQL for data storage. (Reference Link) There are many for prominent organizations which are running large scale applications uses relational database along with various Big Data frameworks to satisfy their various business needs. Summary I believe that RDBMS is like a vanilla ice cream. Everybody loves it and everybody has it. NoSQL and other solutions are like chocolate ice cream or custom ice cream – there is a huge base which loves them and wants them but not every ice cream maker can make it just right  for everyone’s taste. No matter how fancy an ice cream store is there is always plain vanilla ice cream available there. Just like the same, there are always cases and situations in the Big Data’s story where traditional relational database is the part of the whole story. In the real world scenarios there will be always the case when there will be need of the relational database concepts and its ideology. It is extremely important to accept relational database as one of the key components of the Big Data instead of treating it as a substandard technology. Ray of Hope – NewSQL In this module we discussed that there are places where we need ACID compliance from our Big Data application and NoSQL will not support that out of box. There is a new termed coined for the application/tool which supports most of the properties of the traditional RDBMS and supports Big Data infrastructure – NewSQL. Tomorrow In tomorrow’s blog post we will discuss about NewSQL. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • What is the correlation between the quality of the software development process and the quality of the product?

    - by Ophir Yoktan
    I used to believe the practicing "good" software development methods tends to yield a better product in the long run. However, I've seen quite a few cases where "quick-and-dirty" \ "brute-force" \ "copy-paste" programming appeared to give decent results quicker, and cheaper. This appears especially in cases where time to market is more critical then maintenance overhead. Is there a correlation between the quality of the development process and techniques and the quality of the product?

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  • Enterprise Manager 12c: New DSS Demos Available

    - by Javier Puerta
    Enterprise Manager Cloud Control 12c Application Replay Demo Now Available! User Experience Monitoring with Enterprise Manager Cloud Control 12c and Real User Experience Insight 12R1 Now Available! Oracle Enterprise Manager Cloud Control 12c: Database Management Packs demo upgrade     Enterprise Manager Cloud Control 12c Application Replay Demo Now Available! We are pleased to announce the availability of the Oracle Application Replay demo that showcases some of the key capabilities of performing realistic, production scale testing of your web and packaged Oracle applications. This demo specifically focuses on capturing production web traffic from an E-Business Suite application and replaying the captured workload on a test E-Business Suite application to assess the impact of an application infrastructure change on the workload. The target audiences are application developers, quality assurance teams, IT managers and production control staff that deal in day-to-day change management activities and trouble shooting of production environments. Demo Highlights: Enterprise Manager 12c workflows for capturing application workload Seamless integration of Application Replay with Real User Experience Insight for application workload capture Enterprise Manager 12c centralized workflows for replaying captured application workloads in a test environment Demonstrates how to minimize risk when deploying a complex EBusiness Suite application infrastructure change. Rich reporting capability for performance analysis and problem detection User Experience Monitoring with Enterprise Manager Cloud Control 12c and Real User Experience Insight 12R1 Now Available! We are pleased to announce the availability of the Oracle Real User Experience Insight demo that showcases some of the key capabilities of user experience monitoring. This demo specifically focuses on business reporting, integrated performance diagnostics, tracking of customer journey’s through RUEI’s userflow tracking capabilities and it’s Key Performance Indicators tracking and configuration. Demo Highlights: Application-centric dashboard Integration with Oracle Enterprise Manager 12c – JVMD, ADP and BTM Session diagnostics and user session replay Monitoring through “Key Performance Indicators” (KPI) --- create alerts/incidents FUSION Application centric dashboards & integrated BI Oracle Enterprise Manager Cloud Control 12c: Database Management Packs demo upgrade DSS is pleased to announce an upgrade to the Oracle Enterprise Manager Cloud Control 12c: Database Management Packs demo. While retaining the content from the initial release of the demo—Diagnostic and Tuning Packs, Test Data Management and Data Masking, and Real Application Testing—the demo now includes a new Data Masking for Real Application Testing scenario. Demo Features: Diagnostic and Tuning Packs SQL Performance Analyzer Database Replay Data Masking Masking Real Application Testing workloads Testing pending Optimizer statistics Test Data Management

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  • To sample or not to sample...

    - by [email protected]
    Ideally, we would know the exact answer to every question. How many people support presidential candidate A vs. B? How many people suffer from H1N1 in a given state? Does this batch of manufactured widgets have any defective parts? Knowing exact answers is expensive in terms of time and money and, in most cases, is impractical if not impossible. Consider asking every person in a region for their candidate preference, testing every person with flu symptoms for H1N1 (assuming every person reported when they had flu symptoms), or destructively testing widgets to determine if they are "good" (leaving no product to sell). Knowing exact answers, fortunately, isn't necessary or even useful in many situations. Understanding the direction of a trend or statistically significant results may be sufficient to answer the underlying question: who is likely to win the election, have we likely reached a critical threshold for flu, or is this batch of widgets good enough to ship? Statistics help us to answer these questions with a certain degree of confidence. This focuses on how we collect data. In data mining, we focus on the use of data, that is data that has already been collected. In some cases, we may have all the data (all purchases made by all customers), in others the data may have been collected using sampling (voters, their demographics and candidate choice). Building data mining models on all of your data can be expensive in terms of time and hardware resources. Consider a company with 40 million customers. Do we need to mine all 40 million customers to get useful data mining models? The quality of models built on all data may be no better than models built on a relatively small sample. Determining how much is a reasonable amount of data involves experimentation. When starting the model building process on large datasets, it is often more efficient to begin with a small sample, perhaps 1000 - 10,000 cases (records) depending on the algorithm, source data, and hardware. This allows you to see quickly what issues might arise with choice of algorithm, algorithm settings, data quality, and need for further data preparation. Instead of waiting for a model on a large dataset to build only to find that the results don't meet expectations, once you are satisfied with the results on the initial sample, you can  take a larger sample to see if model quality improves, and to get a sense of how the algorithm scales to the particular dataset. If model accuracy or quality continues to improve, consider increasing the sample size. Sampling in data mining is also used to produce a held-aside or test dataset for assessing classification and regression model accuracy. Here, we reserve some of the build data (data that includes known target values) to be used for an honest estimate of model error using data the model has not seen before. This sampling transformation is often called a split because the build data is split into two randomly selected sets, often with 60% of the records being used for model building and 40% for testing. Sampling must be performed with care, as it can adversely affect model quality and usability. Even a truly random sample doesn't guarantee that all values are represented in a given attribute. This is particularly troublesome when the attribute with omitted values is the target. A predictive model that has not seen any examples for a particular target value can never predict that target value! For other attributes, values may consist of a single value (a constant attribute) or all unique values (an identifier attribute), each of which may be excluded during mining. Values from categorical predictor attributes that didn't appear in the training data are not used when testing or scoring datasets. In subsequent posts, we'll talk about three sampling techniques using Oracle Database: simple random sampling without replacement, stratified sampling, and simple random sampling with replacement.

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  • Software Architecture: Quality Attributes

    Quality is what all software engineers should strive for when building a new system or adding new functionality. Dictonary.com ambiguously defines quality as a grade of excellence. Unfortunately, quality must be defined within the context of a situation in that each engineer must extract quality attributes from a project’s requirements. Because quality is defined by project requirements the meaning of quality is constantly changing base on the project. Software architecture factors that indicate the relevance and effectiveness The relevance and effectiveness of architecture can vary based on the context in which it was conceived and the quality attributes that are required to meet. Typically when evaluating architecture for a specific system regarding relevance and effectiveness the following questions should be asked.   Architectural relevance and effectiveness questions: Does the architectural concept meet the needs of the system for which it was designed? Out of the competing architectures for a system, which one is the most suitable? If we look at the first question regarding meeting the needs of a system for which it was designed. A system that answers yes to this question must meet all of its quality goals. This means that it consistently meets or exceeds performance goals for the system. In addition, the system meets all the other required system attributers based on the systems requirements. The suitability of a system is based on several factors. In order for a project to be suitable the necessary resources must be available to complete the task. Standard Project Resources: Money Trained Staff Time Life cycle factors that affect the system and design The development life cycle used on a project can drastically affect how a system’s architecture is created as well as influence its design. In the case of using the software development life cycle (SDLC) each phase must be completed before the next can begin.  This waterfall approach does not allow for changes in a system’s architecture after that phase is completed. This can lead to major system issues when the architecture for the system is not as optimal because of missed quality attributes. This can occur when a project has poor requirements and makes misguided architectural decisions to name a few examples. Once the architectural phase is complete the concepts established in this phase must move on to the design phase that is bound to use the concepts and guidelines defined in the previous phase regardless of any missing quality attributes needed for the project. If any issues arise during this phase regarding the selected architectural concepts they cannot be corrected during the current project. This directly has an effect on the design of a system because the proper qualities required for the project where not used when the architectural concepts were approved. When this is identified nothing can be done to fix the architectural issues and system design must use the existing architectural concepts regardless of its missing quality properties because the architectural concepts for the project cannot be altered. The decisions made in the design phase then preceded to fall down to the implementation phase where the actual system is coded based on the approved architectural concepts established in the architecture phase regardless of its architectural quality. Conversely projects using more of an iterative or agile methodology to implement a system has more flexibility to correct architectural decisions based on missing quality attributes. This is due to each phase of the SDLC is executed more than once so any issues identified in architecture of a system can be corrected in the next architectural phase. Subsequently the corresponding changes will then be adjusted in the following design phase so that when the project is completed the optimal architectural and design decision are applied to the solution. Architecture factors that indicate functional suitability Systems that have function shortcomings do not have the proper functionality based on the project’s driving quality attributes. What this means in English is that the system does not live up to what is required of it by the stakeholders as identified by the missing quality attributes and requirements. One way to prevent functional shortcomings is to test the project’s architecture, design, and implementation against the project’s driving quality attributes to ensure that none of the attributes were missed in any of the phases. Another way to ensure a system has functional suitability is to certify that all its requirements are fully articulated so that there is no chance for misconceptions or misinterpretations by all stakeholders. This will help prevent any issues regarding interpreting the system requirements during the initial architectural concept phase, design phase and implementation phase. Consider the applicability of other architectural models When considering an architectural model for a project is also important to consider other alternative architectural models to ensure that the model that is selected will meet the systems required functionality and high quality attributes. Recently I can remember talking about a project that I was working on and a coworker suggested a different architectural approach that I had never considered. This new model will allow for the same functionally that is offered by the existing model but will allow for a higher quality project because it fulfills more quality attributes. It is always important to seek alternatives prior to committing to an architectural model. Factors used to identify high-risk components A high risk component can be defined as a component that fulfills 2 or more quality attributes for a system. An example of this can be seen in a web application that utilizes a remote database. One high-risk component in this system is the TCIP component because it allows for HTTP connections to handle by a web server and as well as allows for the server to also connect to a remote database server so that it can import data into the system. This component allows for the assurance of data quality attribute and the accessibility quality attribute because the system is available on the network. If for some reason the TCIP component was to fail the web application would fail on two quality attributes accessibility and data assurance in that the web site is not accessible and data cannot be update as needed. Summary As stated previously, quality is what all software engineers should strive for when building a new system or adding new functionality. The quality of a system can be directly determined by how closely it is implemented when compared to its desired quality attributes. One way to insure a higher quality system is to enforce that all project requirements are fully articulated so that no assumptions or misunderstandings can be made by any of the stakeholders. By doing this a system has a better chance of becoming a high quality system based on its quality attributes

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  • Kipróbálható az ingyenes új Oracle Data Miner 11gR2 grafikus workflow-val

    - by Fekete Zoltán
    Oracle Data Mining technológiai információs oldal. Oracle Data Miner 11g Release 2 - Early Adopter oldal. Megjelent, letöltheto és kipróbálható az Oracle Data Mining, az Oracle adatbányászat új grafikus felülete, az Oracle Data Miner 11gR2. Az Oracle Data Minerhez egyszeruen az SQL Developer-t kell letöltenünk, mivel az adatbányászati felület abból indítható. Az Oracle Data Mining az Oracle adatbáziskezelobe ágyazott adatbányászati motor, ami az Oracle Database Enterprise Edition opciója. Az adatbányászat az adattárházak elemzésének kifinomult eszköze és folyamata. Az Oracle Data Mining in-database-mining elonyeit felvonultatja: - nincs felesleges adatmozgatás, a teljes adatbányászati folyamatban az adatbázisban maradnak az adatok - az adatbányászati modellek is az Oracle adatbázisban vannak - az adatbányászati eredmények, cluster adatok, döntések, valószínuségek, stb. szintén az adatbázisban keletkeznek, és ott közvetlenül elemezhetoek Az új ingyenes Data Miner felület "hatalmas gazdagodáson" ment keresztül az elozo verzióhoz képest. - grafikus adatbányászati workflow szerkesztés és futtatás jelent meg! - továbbra is ingyenes - kibovült a felület - új elemzési lehetoségekkel bovült - az SQL Developer 3.0 felületrol indítható, ez megkönnyíti az adatbányászati funkciók meghívását az adatbázisból, ha épp nem a grafikus felületetet szeretnénk erre használni Az ingyenes Data Miner felület az Oracle SQL Developer kiterjesztéseként érheto el, így az elemzok közvetlenül dolgozhatnak az adatokkal az adatbázisban és a Data Miner grafikus felülettel is, építhetnek és kiértékelhetnek, futtathatnak modelleket, predikciókat tehetnek és elemezhetnek, támogatást kapva az adatbányászati módszertan megvalósítására. A korábbi Oracle Data Miner felület a Data Miner Classic néven fut és továbbra is letöltheto az OTN-rol. Az új Data Miner GUI-ból egy képernyokép: Milyen feladatokra ad megoldási lehetoséget az Oracle Data Mining: - ügyfél viselkedés megjövendölése, prediktálása - a "legjobb" ügyfelek eredményes megcélzása - ügyfél megtartás, elvándorlás kezelés (churn) - ügyfél szegmensek, klaszterek, profilok keresése és vizsgálata - anomáliák, visszaélések felderítése - stb.

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  • Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21

    - by Pinal Dave
    In yesterday’s blog post we learned the importance of the Cloud in the Big Data Story. In this article we will understand the role of Operational Databases Supporting Big Data Story. Even though we keep on talking about Big Data architecture, it is extremely crucial to understand that Big Data system can’t just exist in the isolation of itself. There are many needs of the business can only be fully filled with the help of the operational databases. Just having a system which can analysis big data may not solve every single data problem. Real World Example Think about this way, you are using Facebook and you have just updated your information about the current relationship status. In the next few seconds the same information is also reflected in the timeline of your partner as well as a few of the immediate friends. After a while you will notice that the same information is now also available to your remote friends. Later on when someone searches for all the relationship changes with their friends your change of the relationship will also show up in the same list. Now here is the question – do you think Big Data architecture is doing every single of these changes? Do you think that the immediate reflection of your relationship changes with your family member is also because of the technology used in Big Data. Actually the answer is Facebook uses MySQL to do various updates in the timeline as well as various events we do on their homepage. It is really difficult to part from the operational databases in any real world business. Now we will see a few of the examples of the operational databases. Relational Databases (This blog post) NoSQL Databases (This blog post) Key-Value Pair Databases (Tomorrow’s post) Document Databases (Tomorrow’s post) Columnar Databases (The Day After’s post) Graph Databases (The Day After’s post) Spatial Databases (The Day After’s post) Relational Databases We have earlier discussed about the RDBMS role in the Big Data’s story in detail so we will not cover it extensively over here. Relational Database is pretty much everywhere in most of the businesses which are here for many years. The importance and existence of the relational database are always going to be there as long as there are meaningful structured data around. There are many different kinds of relational databases for example Oracle, SQL Server, MySQL and many others. If you are looking for Open Source and widely accepted database, I suggest to try MySQL as that has been very popular in the last few years. I also suggest you to try out PostgreSQL as well. Besides many other essential qualities PostgreeSQL have very interesting licensing policies. PostgreSQL licenses allow modifications and distribution of the application in open or closed (source) form. One can make any modifications and can keep it private as well as well contribute to the community. I believe this one quality makes it much more interesting to use as well it will play very important role in future. Nonrelational Databases (NOSQL) We have also covered Nonrelational Dabases in earlier blog posts. NoSQL actually stands for Not Only SQL Databases. There are plenty of NoSQL databases out in the market and selecting the right one is always very challenging. Here are few of the properties which are very essential to consider when selecting the right NoSQL database for operational purpose. Data and Query Model Persistence of Data and Design Eventual Consistency Scalability Though above all of the properties are interesting to have in any NoSQL database but the one which most attracts to me is Eventual Consistency. Eventual Consistency RDBMS uses ACID (Atomicity, Consistency, Isolation, Durability) as a key mechanism for ensuring the data consistency, whereas NonRelational DBMS uses BASE for the same purpose. Base stands for Basically Available, Soft state and Eventual consistency. Eventual consistency is widely deployed in distributed systems. It is a consistency model used in distributed computing which expects unexpected often. In large distributed system, there are always various nodes joining and various nodes being removed as they are often using commodity servers. This happens either intentionally or accidentally. Even though one or more nodes are down, it is expected that entire system still functions normally. Applications should be able to do various updates as well as retrieval of the data successfully without any issue. Additionally, this also means that system is expected to return the same updated data anytime from all the functioning nodes. Irrespective of when any node is joining the system, if it is marked to hold some data it should contain the same updated data eventually. As per Wikipedia - Eventual consistency is a consistency model used in distributed computing that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. In other words -  Informally, if no additional updates are made to a given data item, all reads to that item will eventually return the same value. Tomorrow In tomorrow’s blog post we will discuss about various other Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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  • timetable in a jTable

    - by chandra
    I want to create a timetable in a jTable. For the top row it will display from monday to sunday and the left colume will display the time of the day with 2h interval e.g 1st colume (0000 - 0200), 2nd colume (0200 - 0400) .... And if i click a button the timing will change from 2h interval to 1h interval. I do not want to hardcode it because i need to do for 2h, 1h, 30min , 15min, 1min, 30sec and 1 sec interval and it will take too long for me to hardcode. Can anyone show me an example or help me create an example for the 2h to 1h interval so that i know what to do? The data array is for me to store data and are there any other easier or shortcuts for me to store them because if it is in 1 sec interval i got thousands of array i need to type it out. private void oneHour() //1 interval functions { if(!once) { initialize(); once = true; } jTable.setModel(new javax.swing.table.DefaultTableModel( new Object [][] { {"0000 - 0100", data[0][0], data[0][1], data[0][2], data[0][3], data[0][4], data[0][5], data[0][6]}, {"0100 - 0200", data[2][0], data[2][1], data[2][2], data[2][3], data[2][4], data[2][5], data[2][6]}, {"0200 - 0300", data[4][0], data[4][1], data[4][2], data[4][3], data[4][4], data[4][5], data[4][6]}, {"0300 - 0400", data[6][0], data[6][1], data[6][2], data[6][3], data[6][4], data[6][5], data[6][6]}, {"0400 - 0600", data[8][0], data[8][1], data[8][2], data[8][3], data[8][4], data[8][5], data[8][6]}, {"0600 - 0700", data[10][0], data[4][1], data[10][2], data[10][3], data[10][4], data[10][5], data[10][6]}, {"0700 - 0800", data[12][0], data[12][1], data[12][2], data[12][3], data[12][4], data[12][5], data[12][6]}, {"0800 - 0900", data[14][0], data[14][1], data[14][2], data[14][3], data[14][4], data[14][5], data[14][6]}, {"0900 - 1000", data[16][0], data[16][1], data[16][2], data[16][3], data[16][4], data[16][5], data[16][6]}, {"1000 - 1100", data[18][0], data[18][1], data[18][2], data[18][3], data[18][4], data[18][5], data[18][6]}, {"1100 - 1200", data[20][0], data[20][1], data[20][2], data[20][3], data[20][4], data[20][5], data[20][6]}, {"1200 - 1300", data[22][0], data[22][1], data[22][2], data[22][3], data[22][4], data[22][5], data[22][6]}, {"1300 - 1400", data[24][0], data[24][1], data[24][2], data[24][3], data[24][4], data[24][5], data[24][6]}, {"1400 - 1500", data[26][0], data[26][1], data[26][2], data[26][3], data[26][4], data[26][5], data[26][6]}, {"1500 - 1600", data[28][0], data[28][1], data[28][2], data[28][3], data[28][4], data[28][5], data[28][6]}, {"1600 - 1700", data[30][0], data[30][1], data[30][2], data[30][3], data[30][4], data[30][5], data[30][6]}, {"1700 - 1800", data[32][0], data[32][1], data[32][2], data[32][3], data[32][4], data[32][5], data[32][6]}, {"1800 - 1900", data[34][0], data[34][1], data[34][2], data[34][3], data[34][4], data[34][5], data[34][6]}, {"1900 - 2000", data[36][0], data[36][1], data[36][2], data[36][3], data[36][4], data[36][5], data[36][6]}, {"2000 - 2100", data[38][0], data[38][1], data[38][2], data[38][3], data[38][4], data[38][5], data[38][6]}, {"2100 - 2200", data[40][0], data[40][1], data[40][2], data[40][3], data[40][4], data[40][5], data[40][6]}, {"2200 - 2300", data[42][0], data[42][1], data[42][2], data[42][3], data[42][4], data[42][5], data[42][6]}, {"2300 - 2400", data[44][0], data[44][1], data[44][2], data[44][3], data[44][4], data[44][5], data[44][6]}, {"2400 - 0000", data[46][0], data[46][1], data[46][2], data[46][3], data[46][4], data[46][5], data[46][6]}, }, new String [] { "Time/Day", "(Mon)", "(Tue)", "(Wed)", "(Thurs)", "(Fri)", "(Sat)", "(Sun)" } )); } private void twoHour() //2 hour interval functions { if(!once) { initialize(); once = true; } jTable.setModel(new javax.swing.table.DefaultTableModel( new Object [][] { {"0000 - 0200", data[0][0], data[0][1], data[0][2], data[0][3], data[0][4], data[0][5], data[0][6]}, {"0200 - 0400", data[4][0], data[4][1], data[4][2], data[4][3], data[4][4], data[4][5], data[4][6]}, {"0400 - 0600", data[8][0], data[8][1], data[8][2], data[8][3], data[8][4], data[8][5], data[8][6]}, {"0600 - 0800", data[12][0], data[12][1], data[12][2], data[12][3], data[12][4], data[12][5], data[12][6]}, {"0800 - 1000", data[16][0], data[16][1], data[16][2], data[16][3], data[16][4], data[16][5], data[16][6]}, {"1000 - 1200", data[20][0], data[20][1], data[20][2], data[20][3], data[20][4], data[20][5], data[20][6]}, {"1200 - 1400", data[24][0], data[24][1], data[24][2], data[24][3], data[24][4], data[24][5], data[24][6]}, {"1400 - 1600", data[28][0], data[28][1], data[28][2], data[28][3], data[28][4], data[28][5], data[28][6]}, {"1600 - 1800", data[32][0], data[32][1], data[32][2], data[32][3], data[32][4], data[32][5], data[32][6]}, {"1800 - 2000", data[36][0], data[36][1], data[36][2], data[36][3], data[36][4], data[36][5], data[36][6]}, {"2000 - 2200", data[40][0], data[40][1], data[40][2], data[40][3], data[40][4], data[40][5], data[40][6]}, {"2200 - 2400",data[44][0], data[44][1], data[44][2], data[44][3], data[44][4], data[44][5], data[44][6]} },

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  • How to Code Faster (Without Sacrificing Quality)

    - by ashes999
    I've been a professional coder for a several years. The comments about my code have generally been the same: writes great code, well-tested, but could be faster. So how do I become a faster coder, without sacrificing quality? For the sake of this question, I'm going to limit the scope to C#, since that's primarily what I code (for fun) -- or Java, which is similar enough in many ways that matter. Things that I'm already doing: Write the minimal solution that will get the job done Write a slew of automated tests (prevents regressions) Write (and use) reusable libraries for all kinds of things Use well-known technologies where they work well (eg. Hibernate) Use design patterns where they fit into place (eg. Singleton) These are all great, but I don't feel like my speed is increasing over time. I do care, because if I can do something to increase my productivity (even by 10%), that's 10% faster than my competitors. (Not that I have any.) Besides which, I've consistently gotten this feeback from my managers -- whether it was small-scale Flash development or enterprise Java/C++ development. Edit: There seem to be a lot of questions about what I mean by fast, and how I know I'm slow. Let me clarify with some more details. I worked in small and medium-sized teams (5-50 people) in various companies over various projects and various technologies (Flash, ASP.NET, Java, C++). The observation of my managers (which they told me directly) is that I'm "slow." Part of this is because a significant number of my peers sacrificed quality for speed; they wrote code that was buggy, hard to read, hard to maintain, and difficult to write automated tests for. My code generally is well-documented, readable, and testable. At Oracle, I would consistently solve bugs slower than other team-members. I know this, because I would get comments to that effect; this means that other (yes, more senior and experienced) developers could do my work in less time than it took me, at nearly the same quality (readability, maintainability, and testability). Why? What am I missing? How can I get better at this? My end goal is simple: if I can make product X in 40 hours today, and I can improve myself somehow so that I can create the same product at 20, 30, or even 38 hours tomorrow, that's what I want to know -- how do I get there? What process can I use to continually improve? I had thought it was about reusing code, but that's not enough, it seems.

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  • Looking for Cutting-Edge Data Integration: 2010 Innovation Awards

    - by dain.hansen
    This year's Oracle Fusion Middleware Innovation Awards will honor customers and partners who are creatively using to various products across Oracle Fusion Middleware. Brand new to this year's awards is a category for Data Integration. Think you have something unique and innovative with one of our Oracle Data Integration products? We'd love to hear from you! Please submit today The deadline for the nomination is 5 p.m. PT Friday, August 6th 2010, and winning organizations will be notified by late August 2010. What you win! FREE pass to Oracle OpenWorld 2010 in San Francisco for select winners in each category. Honored by Oracle executives at awards ceremony held during Oracle OpenWorld 2010 in San Francisco. Oracle Middleware Innovation Award Winner Plaque 1-3 meetings with Oracle Executives during Oracle OpenWorld 2010 Feature article placement in Oracle Magazine and placement in Oracle Press Release Customer snapshot and video testimonial opportunity, to be hosted on oracle.com Podcast interview opportunity with Senior Oracle Executive

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  • Tips on ensuring Model Quality

    - by [email protected]
    Given enough data that represents well the domain and models that reflect exactly the decision being optimized, models usually provide good predictions that ensure lift. Nevertheless, sometimes the modeling situation is less than ideal. In this blog entry we explore the problems found in a few such situations and how to avoid them.1 - The Model does not reflect the problem you are trying to solveFor example, you may be trying to solve the problem: "What product should I recommend to this customer" but your model learns on the problem: "Given that a customer has acquired our products, what is the likelihood for each product". In this case the model you built may be too far of a proxy for the problem you are really trying to solve. What you could do in this case is try to build a model based on the result from recommendations of products to customers. If there is not enough data from actual recommendations, you could use a hybrid approach in which you would use the [bad] proxy model until the recommendation model converges.2 - Data is not predictive enoughIf the inputs are not correlated with the output then the models may be unable to provide good predictions. For example, if the input is the phase of the moon and the weather and the output is what car did the customer buy, there may be no correlations found. In this case you should see a low quality model.The solution in this case is to include more relevant inputs.3 - Not enough cases seenIf the data learned does not include enough cases, at least 200 positive examples for each output, then the quality of recommendations may be low. The obvious solution is to include more data records. If this is not possible, then it may be possible to build a model based on the characteristics of the output choices rather than the choices themselves. For example, instead of using products as output, use the product category, price and brand name, and then combine these models.4 - Output leaking into input giving the false impression of good quality modelsIf the input data in the training includes values that have changed or are available only because the output happened, then you will find some strong correlations between the input and the output, but these strong correlations do not reflect the data that you will have available at decision (prediction) time. For example, if you are building a model to predict whether a web site visitor will succeed in registering, and the input includes the variable DaysSinceRegistration, and you learn when this variable has already been set, you will probably see a big correlation between having a Zero (or one) in this variable and the fact that registration was successful.The solution is to remove these variables from the input or make sure they reflect the value as of the time of decision and not after the result is known. 

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  • What's New in Database Lifecycle Management in Enterprise Manager 12c Release 3

    - by HariSrinivasan
    Enterprise Manager 12c Release 3 includes improvements and enhancements across every area of the product. This blog provides an overview of the new and enhanced features in the Database Lifecycle Management area. I will deep dive into specific features more in depth in subsequent posts. "What's New?"  In this release, we focused on four things: 1. Lifecycle Management Support for new Database12c - Pluggable Databases 2. Management of long running processes, such as a security patch cycle (Change Activity Planner) 3. Management of large number of systems by · Leveraging new framework capabilities for lifecycle operations, such as the new advanced ‘emcli’ script option · Refining features such as configuration search and compliance 4. Minor improvements and quality fixes to existing features · Rollback support for Single instance databases · Improved "OFFLINE" Patching experience · Faster collection of ORACLE_HOME configurations Lifecycle Management Support for new Database 12c - Pluggable Databases Database 12c introduces Pluggable Databases (PDBs), the brand new addition to help you achieve your consolidation goals. Pluggable databases offer unprecedented consolidation at database level and native lifecycle verbs for creating, plugging and unplugging the databases on a container database (CDB). Enterprise Manager can supplement the capabilities of pluggable databases by offering workflows for migrating, provisioning and cloning them using the software library and the deployment procedures. For example, Enterprise Manager can migrate an existing database to a PDB or clone a PDB by storing a versioned copy in the software library. One can also manage the planned downtime related to patching by  migrating the PDBs to a new CDB. While pluggable databases offer these exciting features, it can also pose configuration management and compliance challenges if not managed properly. Enterprise Manager features like inventory management, topology associations and configuration search can mitigate the sprawl of PDBs and also lock them to predefined golden standards using configuration comparison and compliance rules. Learn More ... Management of Long Running datacenter processes - Change Activity Planner (CAP) Currently, customers resort to cumbersome methods to create, execute, track and monitor change activities within their data center. Some customers use traditional tools such as spreadsheets, project planners and in-house custom built solutions. Customers often have weekly sync up meetings across stake holders to collect status and updates. Some of the change activities, for example the quarterly patch set update (PSU) patch rollouts are not single tasks but processes with multiple tasks. Some of those tasks are performed within Enterprise Manager Cloud Control (for example Patch) and some are performed outside of Enterprise Manager Cloud Control. These tasks often run for a longer period of time and involve multiple people or teams. Enterprise Manger Cloud Control supports core data center operations such as configuration management, compliance management, and automation. Enterprise Manager Cloud Control release 12.1.0.3 leverages these capabilities and introduces the Change Activity Planner (CAP). CAP provides the ability to plan, execute, and track change activities in real time. It covers the typical datacenter activities that are spread over a long period of time, across multiple people and multiple targets (even target types). Here are some examples of Change Activity Process in a datacenter: · Patching large environments (PSU/CPU Patching cycles) · Upgrading large number of database environments · Rolling out Compliance Rules · Database Consolidation to Exadata environments CAP provides user flows for Compliance Officers/Managers (incl. lead administrators) and Operators (DBAs and admins). Managers can create change activity plans for various projects, allocate resources, targets, and groups affected. Upon activation of the plan, tasks are created and automatically assigned to individual administrators based on target ownership. Administrators (DBAs) can identify their tasks and understand the context, schedules, and priorities. They can complete tasks using Enterprise Manager Cloud Control automation features such as patch plans (or in some cases outside Enterprise Manager). Upon completion, compliance is evaluated for validations and updates the status of the tasks and the plans. Learn More about CAP ...  Improved Configuration & Compliance Management of a large number of systems Improved Configuration Comparison:  Get to the configuration comparison results faster for simple ad-hoc comparisons. When performing a 1 to 1 comparison, Enterprise Manager will perform the comparison immediately and take the user directly to the results without having to wait for a job to be submitted and executed. Flattened system comparisons reduce comparison setup time and reduce complexity. In addition to the previously existing topological comparison, users now have an option to compare using a “flattened” methodology. Flattening means to remove duplicate target instances within the systems and remove the hierarchy of member targets. The result are much easier to spot differences particularly for specific use cases like comparing patch levels between complex systems like RAC and Fusion Apps. Improved Configuration Search & Advanced EMCLI Script option for Mass Automation Enterprise manager 12c introduces a new framework level capability to be able to script and stitch together multiple tasks using EMCLI. This powerful capability can be leveraged for lifecycle operations, especially when executing a task over a large number of targets. Specific usages of this include, retrieving a qualified list of targets using Configuration Search and then using the resultset for automation. Another example would be executing a patching operation and then re-executing on targets where it may have failed. This is complemented by other enhancements, such as a better usability for designing reusable configuration searches. IN EM 12c Rel 3, a simplified UI makes building adhoc searches even easier. Searching for missing patches is a common use of configuration search. This required the use of the advanced options which are now clearly defined and easy to use. Perform “Configuration Search” using the EMCLI. Users can find and execute Configuration Searches from the EMCLI which can be extremely useful for building sophisticated automation scripts. For an example, Run the Search named “Oracle Databases on Exadata” which finds all Database targets running on top of Exadata. Further filter the results by refining by options like name, host, etc.. emcli get_targets -config_search="Databases on Exadata" –target_name="exa%“ Use this in powerful mass automation operations using the new emcli script option. For example, to solve the use case of – Finding all DBs running on Exadata and housing E-Biz and Patch them. Create a Python script with emcli functions and invoke it in the new EMCLI script option shell. Invoke the script in the new EMCLI with script option directly: $<path to emcli>/emcli @myPSU_Patch.py Richer compliance content:  Now over 50 Oracle Provided Compliance Standards including new standards for Pluggable Database, Fusion Applications, Oracle Identity Manager, Oracle VM and Internet Directory. 9 Oracle provided Real Time Monitoring Standards containing over 900 Compliance Rules across 500 Facets. These new Real time Compliance Standards covers both Exadata Compute nodes and Linux servers. The result is increased Oracle software coverage and faster time to compliance monitoring on Exadata. Enhancements to Patch Management: Overhauled "OFFLINE" Patching experience: Simplified Patch uploads UI to improve the offline experience of patching. There is now a single step process to get the patches into software library. Customers often maintain local repositories of patches, sometimes called software depots, where they host the patches downloaded from My Oracle Support. In the past, you had to move these patches to your desktop then upload them to the Enterprise Manager's Software library through the Enterprise Manager Cloud Control user interface. You can now use the following EMCLI command to upload multiple patches directly from a remote location within the data center: $emcli upload_patches -location <Path to Patch directory> -from_host <HOSTNAME> The upload process filters all of the new patches, automatically selects the relevant metadata files from the location, and uploads the patches to software library. Other Improvements:  Patch rollback for single instance databases, new option in the Patch Plan to rollback the patches added to the patch plans. Upon execution, the procedure would rollback the patch and the SQL applied to the single instance Databases. Improved and faster configuration collection of Oracle Home targets can enable more reliable automation at higher level functions like Provisioning, Patching or Database as a Service. Just to recap, here is a list of database lifecycle management features:  * Red highlights mark – New or Enhanced in the Release 3. • Discovery, inventory tracking and reporting • Database provisioning including o Migration to Pluggable databases o Plugging and unplugging of pluggable databases o Gold image based cloning o Scaling of RAC nodes •Schema and data change management •End-to-end patch management in online and offline modes, including o Patch advisories in online (connected with My Oracle Support) and offline mode o Patch pre-deployment analysis, deployment and rollback (currently only for single instance databases) o Reporting • Upgrade planning and execution of the upgrade process • Configuration management including • Compliance management with out-of-box content • Change Activity Planner for planning, designing and tracking long running processes For more information on Enterprise Manager’s database lifecycle management capabilities, visit http://www.oracle.com/technetwork/oem/lifecycle-mgmt/index.html

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  • Quality Assurance tools discrepancies

    - by Roudak
    It is a bit ironic, yesterday I answered a question related to this topic that was marked to be good and today I'm the one who asks. These are my thoughts and a question: Also let's agree on the terms: QA is a set of activities that defines and implements processes during SW development. The common tool is the process audit. However, my colleague at work agrees with the opinion that reviews and inspections are also quality assurance tools, although most sources classify them as quality control. I would say both sides are partially right: during inspections, we evaluate a physical product (clearly QC) but we see it as a white box so we can check its compliance with set processes (QA). Do you think it is the reason of the dichotomy among the authors? I know it is more like an academic question but it deserves the answer :)

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  • September issue of the Enterprise Manager Indepth Newsletter

    - by Javier Puerta
    The September issue of the Enterprise Manager Indepth Newsletter is now available here  Featured articles include: Oracle OpenWorld Preview: Don't-Miss Sessions, Hands-on Labs, and MoreBecause of the rapid and widespread adoption of Oracle Enterprise Manager 12c since its launch at Oracle OpenWorld 2011, conference organizers are expecting Oracle Enterprise Manager sessions to attract record crowds at Oracle OpenWorld 2012. Read More Oracle Cloud Builder Summit—Zero to Enterprise Cloud in Two HoursIn August, Oracle launched the worldwide Oracle Cloud Builder Summit series, an event where attendees learn firsthand how to plan, deploy, and manage an enterprise private cloud using Oracle Enterprise Manager 12c—all in a few hours. Read More WEBCASTS Reduce Database Testing Efforts While Maximizing ROIWatch this on-demand Webcast demonstrating how to manage database and system changes with confidence using Oracle Real Application Testing. Viewers will be among the first to hear results from Forrester Consulting's commissioned, multicustomer study, “Total Economic Impact of Oracle Real Application Testing.”

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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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  • Data Networks Visualized via Light Paintings [Video]

    - by ETC
    All around you are wireless data networks: cellular networks, Wi-Fi networks, a world of wireless communication. Check out this awesome video of network signals mapped over a cityscape. What would happen if you made a device that allowed you to map signal strength onto film? In the following video electronics tinkerers craft an LED meter and use it to paint onto long exposure photographs with phenomenal results. Immaterials: light painting Wi-Fi [via Make] Latest Features How-To Geek ETC Learn To Adjust Contrast Like a Pro in Photoshop, GIMP, and Paint.NET Have You Ever Wondered How Your Operating System Got Its Name? Should You Delete Windows 7 Service Pack Backup Files to Save Space? What Can Super Mario Teach Us About Graphics Technology? Windows 7 Service Pack 1 is Released: But Should You Install It? How To Make Hundreds of Complex Photo Edits in Seconds With Photoshop Actions Add a “Textmate Style” Lightweight Text Editor with Dropbox Syncing to Chrome and Iron Is the Forcefield Really On or Not? [Star Wars Parody Video] Google Updates Picasa Web Albums; Emphasis on Sharing and Showcasing Uwall.tv Turns YouTube into a Video Jukebox Early Morning Sunrise at the Beach Wallpaper Data Networks Visualized via Light Paintings [Video]

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  • Read On Phone Pushes Data from Your Desktop to the Appropriate Android App

    - by ETC
    Read On Phone is a free Android application that intelligently pushes data to your phone from your bowser. Rather than simply opening the URL on your phone, it opens the appropriate application for the task and formats text. Most send-to-phone type tools simply take the URL of the web page you’re looking at on your computer and shuttle it to your phone. Read On Phone is a more active and effective tool. When you send a page that is text, it formats the text for easy reading on your phone. When you send a YouTube video, map, or telephone number, it opens up the appropriate tool on your phone such as your YouTube viewer, Google Maps, or your phone dialer. In addition to that handy functionality Read On Phone also includes adjustments for day and night reading, font size, auto-scrolling, and pagination. Read On Phone is available as both a Chrome extension and as a bookmarklet for cross-browser use. Hit up the link below for additional information. Read On Phone Latest Features How-To Geek ETC Should You Delete Windows 7 Service Pack Backup Files to Save Space? What Can Super Mario Teach Us About Graphics Technology? Windows 7 Service Pack 1 is Released: But Should You Install It? How To Make Hundreds of Complex Photo Edits in Seconds With Photoshop Actions How to Enable User-Specific Wireless Networks in Windows 7 How to Use Google Chrome as Your Default PDF Reader (the Easy Way) Read On Phone Pushes Data from Your Desktop to the Appropriate Android App MetroTwit is a Sleek Native Twitter Client for Your Windows System Make Efficient Use of Tab Bar Space by Customizing Tab Width in Firefox See the Geeky Work Done Behind the Scenes to Add Sounds to Movies [Video] Use a Crayon to Enhance Engraved Lettering on Electronics Adult Swim Brings Their Programming Lineup to iOS Devices

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  • What is enterprise software, exactly?

    - by good_computer
    I don't understand the difference between "normal" software and enterprise software. Even after reading these... "Enterprise Software" on Wikipedia "Enterprise Software Is Sexy Again" on Techcrunch "The Great Enterprise Software Swindle" on Coding Horror I can't really wrap my head around the real differences. Is there any difference at all between the two? Why do people say enterprise software sucks?

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  • Stay Connected with Oracle Enterprise 2.0

    - by kellsey.ruppel(at)oracle.com
    We want to be sure you stay connected and updated with the latest in Oracle Content Management, Portal and Collaboration technologies. We invite you to follow us on Twitter, become our friends on Facebook, check our blog frequently, and subscribe to the Enterprise 2.0 newsletter! Oracle Enterprise 2.0 Twitter Oracle Enterprise 2.0 Facebook Oracle Enterprise 2.0 Blog Oracle Enterprise 2.0 Newsletter We look forward to staying connected with you in 2011!

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  • How to Set Up Your Enterprise Social Organization?

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

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  • SQLAuthority News – Download SQL Azure Labs Codename “Data Explorer” Client

    - by pinaldave
    Microsoft SQL Azure labs has recently released Data Explorer client. I was looking forward to visualizing tool for quite a while and I am delighted to see this tool. I will be trying out this tool in coming week and will post here my experience. I have listed few of the resources which are related to Data Explorer at the end. Please let me know if I have missed any and I will add the same. With “Data Explorer” you can: Identify the data you care about from the sources you work with (e.g. Excel spreadsheets, files, SQL Server databases). Discover relevant data and services via automatic recommendations from the Windows Azure Marketplace. Enrich your data by combining it and visualizing the results. Collaborate with your colleagues to refine the data. Publish the results to share them with others or power solutions. The Data Explorer Client package contains the Data Explorer workspace as well as an Office plugin that integrates Data Explorer into Excel. Resources: Download Data Explorer Data Explorer Blog Desktop Client Video of  Contoso Bikes and Frozen Yogurt (Data Explorer) Please note that this is not the final release of the product. Please do not attempt this on production server. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Azure, SQL Documentation, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority News, T SQL, Technology

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  • Statistical Software Quality Control References

    - by Xodarap
    I'm looking for references about hypothesis testing in software management. For example, we might wonder whether "crunch time" leads to an increase in defect rate - this is a surprisingly difficult thing to do. There are many questions on how to measure quality - this isn't what I'm asking. And there are books like Kan which discuss various quality metrics and their utilities. I'm not asking this either. I want to know how one applies these metrics to make decisions. E.g. suppose we decide to go with critical errors / KLOC. One of the problems we'll have to deal with with that this is not a normally distributed data set (almost all patches have zero critical errors). And further, it's not clear that we really want to examine the difference in means. So what should our alternative hypothesis be? (Note: Based on previous questions, my guess is that I'll get a lot of answers telling me that this is a bad idea. That's fine, but I'd request that it's based on published data, instead of your own experience.)

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  • Oracle Enterprise Manager 12c R3 introduces advancements in cloud lifecycle and operations management

    - by Anand Akela
    Oracle Enterprise Manager 12c Release 3 (R3) was announced ( Press Release ) earlier today. It is now available for download at  OTN . This latest release features improvements in several areas, including: Improvements to Private Cloud and Engineered Systems Management Expanded Middleware and Application Management Capabilities Efficiency Gains for Enterprise manager Users in EM’s Enterprise-Ready Framework You can learn more about what's new in the Oracle Enterprise Manager 12c R3 in the Enterprise Manager 12c documentation . You will see more blogs and details about the new features during the next few weeks. Please let us what On July 18th, you can join us at a webcast to hear Thomas Kurian, EVP of Product Development on what Oracle Engineering has achieved with Oracle Enterprise Manager 12c Release 3 to address these challenges. Later, during this webcast, Oracle experts will discuss the latest capabilities in Oracle Enterprise Manager 12c Release 3 for cloud lifecycle and operations management. The presentation will be followed by a live Q&A session with Oracle experts. You can also join us online on Twitter to get your specific questions answered. Please use hash tag #em12c to join the conversation. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} Register Now for the Webcast! Stay Connected: Twitter |  Face book |  You Tube |  Linked in |  Newsletter

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  • SQL Rally Pre-Con: Data Warehouse Modeling – Making the Right Choices

    - by Davide Mauri
    As you may have already learned from my old post or Adam’s or Kalen’s posts, there will be two SQL Rally in North Europe. In the Stockholm SQL Rally, with my friend Thomas Kejser, I’ll be delivering a pre-con on Data Warehouse Modeling: Data warehouses play a central role in any BI solution. It's the back end upon which everything in years to come will be created. For this reason, it must be rock solid and yet flexible at the same time. To develop such a data warehouse, you must have a clear idea of its architecture, a thorough understanding of the concepts of Measures and Dimensions, and a proven engineered way to build it so that quality and stability can go hand-in-hand with cost reduction and scalability. In this workshop, Thomas Kejser and Davide Mauri will share all the information they learned since they started working with data warehouses, giving you the guidance and tips you need to start your BI project in the best way possible?avoiding errors, making implementation effective and efficient, paving the way for a winning Agile approach, and helping you define how your team should work so that your BI solution will stand the test of time. You'll learn: Data warehouse architecture and justification Agile methodology Dimensional modeling, including Kimball vs. Inmon, SCD1/SCD2/SCD3, Junk and Degenerate Dimensions, and Huge Dimensions Best practices, naming conventions, and lessons learned Loading the data warehouse, including loading Dimensions, loading Facts (Full Load, Incremental Load, Partitioned Load) Data warehouses and Big Data (Hadoop) Unit testing Tracking historical changes and managing large sizes With all the Self-Service BI hype, Data Warehouse is become more and more central every day, since if everyone will be able to analyze data using self-service tools, it’s better for him/her to rely on correct, uniform and coherent data. Already 50 people registered from the workshop and seats are limited so don’t miss this unique opportunity to attend to this workshop that is really a unique combination of years and years of experience! http://www.sqlpass.org/sqlrally/2013/nordic/Agenda/PreconferenceSeminars.aspx See you there!

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