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• Big Data – Beginning Big Data Series Next Month in 21 Parts

  - by Pinal Dave

  Big Data is the next big thing. There was a time when we used to talk in terms of MB and GB of the data. However, the industry is changing and we are now moving to a conversation where we discuss about data in Petabyte, Exabyte and Zettabyte. It seems that the world is now talking about increased Volume of the data. In simple world we all think that Big Data is nothing but plenty of volume. In reality Big Data is much more than just a huge volume of the data. When talking about the data we need to understand about variety and volume along with volume. Though Big data look like a simple concept, it is extremely complex subject when we attempt to start learning the same. My Journey I have recently presented on Big Data in quite a few organizations and I have received quite a few questions during this roadshow event. I have collected all the questions which I have received and decided to post about them on the blog. In the month of October 2013, on every weekday we will be learning something new about Big Data. Every day I will share a concept/question and in the same blog post we will learn the answer of the same. Big Data – Plenty of Questions I received quite a few questions during my road trip. Here are few of the questions. I want to learn Big Data – where should I start? Do I need to know SQL to learn Big Data? What is Hadoop? There are so many organizations talking about Big Data, and every one has a different approach. How to start with big Data? Do I need to know Java to learn about Big Data? What is different between various NoSQL languages. I will attempt to answer most of the questions during the month long series in the next month. Big Data – Big Subject Big Data is a very big subject and I no way claim that I will be covering every single big data concept in this series. However, I promise that I will be indeed sharing lots of basic concepts which are revolving around Big Data. We will discuss from fundamentals about Big Data and continue further learning about it. I will attempt to cover the concept so simple that many of you might have wondered about it but afraid to ask. Your Role! During this series next month, I need your one help. Please keep on posting questions you might have related to big data as blog post comments and on Facebook Page. I will monitor them closely and will try to answer them as well during this series. Now make sure that you do not miss any single blog post in this series as every blog post will be linked to each other. You can subscribe to my feed or like my Facebook page or subscribe via email (by entering email in the blog post). Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Big Data, PostADay, SQL, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Big Data – Role of Cloud Computing in Big Data – Day 11 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the NewSQL. In this article we will understand the role of Cloud in Big Data Story What is Cloud? Cloud is the biggest buzzword around from last few years. Everyone knows about the Cloud and it is extremely well defined online. In this article we will discuss cloud in the context of the Big Data. Cloud computing is a method of providing a shared computing resources to the application which requires dynamic resources. These resources include applications, computing, storage, networking, development and various deployment platforms. The fundamentals of the cloud computing are that it shares pretty much share all the resources and deliver to end users as a service.  Examples of the Cloud Computing and Big Data are Google and Amazon.com. Both have fantastic Big Data offering with the help of the cloud. We will discuss this later in this blog post. There are two different Cloud Deployment Models: 1) The Public Cloud and 2) The Private Cloud Public Cloud Public Cloud is the cloud infrastructure build by commercial providers (Amazon, Rackspace etc.) creates a highly scalable data center that hides the complex infrastructure from the consumer and provides various services. Private Cloud Private Cloud is the cloud infrastructure build by a single organization where they are managing highly scalable data center internally. Here is the quick comparison between Public Cloud and Private Cloud from Wikipedia:   Public Cloud Private Cloud Initial cost Typically zero Typically high Running cost Unpredictable Unpredictable Customization Impossible Possible Privacy No (Host has access to the data Yes Single sign-on Impossible Possible Scaling up Easy while within defined limits Laborious but no limits Hybrid Cloud Hybrid Cloud is the cloud infrastructure build with the composition of two or more clouds like public and private cloud. Hybrid cloud gives best of the both the world as it combines multiple cloud deployment models together. Cloud and Big Data – Common Characteristics There are many characteristics of the Cloud Architecture and Cloud Computing which are also essentially important for Big Data as well. They highly overlap and at many places it just makes sense to use the power of both the architecture and build a highly scalable framework. Here is the list of all the characteristics of cloud computing important in Big Data Scalability Elasticity Ad-hoc Resource Pooling Low Cost to Setup Infastructure Pay on Use or Pay as you Go Highly Available Leading Big Data Cloud Providers There are many players in Big Data Cloud but we will list a few of the known players in this list. Amazon Amazon is arguably the most popular Infrastructure as a Service (IaaS) provider. The history of how Amazon started in this business is very interesting. They started out with a massive infrastructure to support their own business. Gradually they figured out that their own resources are underutilized most of the time. They decided to get the maximum out of the resources they have and hence  they launched their Amazon Elastic Compute Cloud (Amazon EC2) service in 2006. Their products have evolved a lot recently and now it is one of their primary business besides their retail selling. Amazon also offers Big Data services understand Amazon Web Services. Here is the list of the included services: Amazon Elastic MapReduce – It processes very high volumes of data Amazon DynammoDB – It is fully managed NoSQL (Not Only SQL) database service Amazon Simple Storage Services (S3) – A web-scale service designed to store and accommodate any amount of data Amazon High Performance Computing – It provides low-tenancy tuned high performance computing cluster Amazon RedShift – It is petabyte scale data warehousing service Google Though Google is known for Search Engine, we all know that it is much more than that. Google Compute Engine – It offers secure, flexible computing from energy efficient data centers Google Big Query – It allows SQL-like queries to run against large datasets Google Prediction API – It is a cloud based machine learning tool Other Players Besides Amazon and Google we also have other players in the Big Data market as well. Microsoft is also attempting Big Data with the Cloud with Microsoft Azure. Additionally Rackspace and NASA together have initiated OpenStack. The goal of Openstack is to provide a massively scaled, multitenant cloud that can run on any hardware. Thing to Watch The cloud based solutions provides a great integration with the Big Data’s story as well it is very economical to implement as well. However, there are few things one should be very careful when deploying Big Data on cloud solutions. Here is a list of a few things to watch: Data Integrity Initial Cost Recurring Cost Performance Data Access Security Location Compliance Every company have different approaches to Big Data and have different rules and regulations. Based on various factors, one can implement their own custom Big Data solution on a cloud. Tomorrow In tomorrow’s blog post we will discuss about various 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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• 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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• 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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• 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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• Difference Procedural Generation and Random Generation

  - by U-No-Poo

  Today, I got into an argument about the term "procedural generation". My point was that its different from "classic" random generation in the way that procedural is based on a more mathematical, fractal based, algorithm leading to a more "realistic" distribution and the usual randomness of most languages are based on a pseudo-random-number generator, leading to an "unrealistic", in a way, ugly, distribution. This discussion was made with a heightmap in mind. The discussion left me somehow unconvinced about my own arguments though, so, is there more to it? Or am I the one who is, in fact, simply wrong?

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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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• 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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• 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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• Oracle Financial Analytics for SAP Certified with Oracle Data Integrator EE

  - by denis.gray

  Two days ago Oracle announced the release of Oracle Financial Analytics for SAP.  With the amount of press this has garnered in the past two days, there's a key detail that can't be missed.  This release is certified with Oracle Data Integrator EE - now making the combination of Data Integration and Business Intelligence a force to contend with.  Within the Oracle Press Release there were two important bullets: ·         Oracle Financial Analytics for SAP includes a pre-packaged ABAP code compliant adapter and is certified with Oracle Data Integrator Enterprise Edition to integrate SAP Financial Accounting data directly with the analytic application.  ·         Helping to integrate SAP financial data and disparate third-party data sources is Oracle Data Integrator Enterprise Edition which delivers fast, efficient loading and transformation of timely data into a data warehouse environment through its high-performance Extract Load and Transform (E-LT) technology. This is very exciting news, demonstrating Oracle's overall commitment to Oracle Data Integrator EE.   This is a great way to start off the new year and we look forward to building on this momentum throughout 2011.   The following links contain additional information and media responses about the Oracle Financial Analytics for SAP release. IDG News Service (Also appeared in PC World, Computer World, CIO: "Oracle is moving further into rival SAP's turf with Oracle Financial Analytics for SAP, a new BI (business intelligence) application that can crunch ERP (enterprise resource planning) system financial data for insights." Information Week: "Oracle talks a good game about the appeal of an optimized, all-Oracle stack. But the company also recognizes that we live in a predominantly heterogeneous IT world" CRN: "While some businesses with SAP Financial Accounting already use Oracle BI, those integrations had to be custom developed. The new offering provides pre-built integration capabilities." ECRM Guide:  "Among other features, Oracle Financial Analytics for SAP helps front-line managers improve financial performance and decision-making with what the company says is comprehensive, timely and role-based information on their departments' expenses and revenue contributions."   SAP Getting Started Guide for ODI on OTN: http://www.oracle.com/technetwork/middleware/data-integrator/learnmore/index.html For more information on the ODI and its SAP connectivity please review the Oracle® Fusion Middleware Application Adapters Guide for Oracle Data Integrator11g Release 1 (11.1.1)

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• What is the definition of "Big Data"?

  - by Ben

  Is there one? All the definitions I can find describe the size, complexity / variety or velocity of the data. Wikipedia's definition is the only one I've found with an actual number Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. However, this seemingly contradicts the MIKE2.0 definition, referenced in the next paragraph, which indicates that "big" data can be small and that 100,000 sensors on an aircraft creating only 3GB of data could be considered big. IBM despite saying that: Big data is more simply than a matter of size. have emphasised size in their definition. O'Reilly has stressed "volume, velocity and variety" as well. Though explained well, and in more depth, the definition seems to be a re-hash of the others - or vice-versa of course. I think that a Computer Weekly article title sums up a number of articles fairly well "What is big data and how can it be used to gain competitive advantage". But ZDNet wins with the following from 2012: “Big Data” is a catch phrase that has been bubbling up from the high performance computing niche of the IT market... If one sits through the presentations from ten suppliers of technology, fifteen or so different definitions are likely to come forward. Each definition, of course, tends to support the need for that supplier’s products and services. Imagine that. Basically "big data" is "big" in some way shape or form. What is "big"? Is it quantifiable at the current time? If "big" is unquantifiable is there a definition that does not rely solely on generalities?

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• Big Data – Operational Databases Supporting Big Data – Columnar, Graph and Spatial Database – Day 14 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the Key-Value Pair Databases and Document Databases in the Big Data Story. In this article we will understand the role of Columnar, Graph and Spatial Database supporting Big Data Story. Now we will see a few of the examples of the operational databases. Relational Databases (The day before yesterday’s post) NoSQL Databases (The day before yesterday’s post) Key-Value Pair Databases (Yesterday’s post) Document Databases (Yesterday’s post) Columnar Databases (Tomorrow’s post) Graph Databases (Today’s post) Spatial Databases (Today’s post) Columnar Databases  Relational Database is a row store database or a row oriented database. Columnar databases are column oriented or column store databases. As we discussed earlier in Big Data we have different kinds of data and we need to store different kinds of data in the database. When we have columnar database it is very easy to do so as we can just add a new column to the columnar database. HBase is one of the most popular columnar databases. It uses Hadoop file system and MapReduce for its core data storage. However, remember this is not a good solution for every application. This is particularly good for the database where there is high volume incremental data is gathered and processed. Graph Databases For a highly interconnected data it is suitable to use Graph Database. This database has node relationship structure. Nodes and relationships contain a Key Value Pair where data is stored. The major advantage of this database is that it supports faster navigation among various relationships. For example, Facebook uses a graph database to list and demonstrate various relationships between users. Neo4J is one of the most popular open source graph database. One of the major dis-advantage of the Graph Database is that it is not possible to self-reference (self joins in the RDBMS terms) and there might be real world scenarios where this might be required and graph database does not support it. Spatial Databases  We all use Foursquare, Google+ as well Facebook Check-ins for location aware check-ins. All the location aware applications figure out the position of the phone with the help of Global Positioning System (GPS). Think about it, so many different users at different location in the world and checking-in all together. Additionally, the applications now feature reach and users are demanding more and more information from them, for example like movies, coffee shop or places see. They are all running with the help of Spatial Databases. Spatial data are standardize by the Open Geospatial Consortium known as OGC. Spatial data helps answering many interesting questions like “Distance between two locations, area of interesting places etc.” When we think of it, it is very clear that handing spatial data and returning meaningful result is one big task when there are millions of users moving dynamically from one place to another place & requesting various spatial information. PostGIS/OpenGIS suite is very popular spatial database. It runs as a layer implementation on the RDBMS PostgreSQL. This makes it totally unique as it offers best from both the worlds. Courtesy: mushroom network Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Hive. 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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• Can't save data for a member in a data form

  - by RahulS

  Implied sharing is an old thing everyone knows the reasons and solutions of that, still little theory about that: With Essbase implied sharing, some members are shared even if you do not explicitly set them as shared. These members are implied shared members. When an implied share relationship is created, each implied member assumes the other member’s value. Essbase assumes (or implies) a shared member relationship in these situations: 1. A parent has only one child 2. A parent has only one child that consolidates to the parent In a Planning form that contains members with an implied sharing relationship, when a value is added for the parent, the child assumes the same value after the form is saved. Likewise, if a value is added for the child, the parent usually assumes the same value after a form is saved.For example, when a calculation script or load rule populates an implied share member, the other implied share member assumes the value of the member populated by the calculation script or load rule. The last value calculated or imported takes precedence. The result is the same whether you refer to the parent or the child as a variable in a calculation script. For more information have a look at: http://docs.oracle.com/cd/E17236_01/epm.1112/hp_admin_11122/ch14s11.html Now the issue which we are going to talk about is We loose data on save even when the parent is dynamic calc and has a single child. A dynamic calc parent to a single child:  If we design the form with following selection: In the data form we will find parent below the member and this is by design whenever you make a selection using commands to select all the member below parent, always children will appear before the parent: Lets try to enter data, Save it Now, try to change the way we selected members Here we go: Now the question again why this behavior: 1. Data from Planning data form passes to Essbase row by row, 2. Because in data form the child member appears before the parent, 3. First, data goes to Essbase for child (SingleStoreChild), 4. Then when Planning passes the data for parent there was #Missing or No data,  5. Over writes the data to #missing. PS: As we know that dynamic calc members are calculated on the fly they are not allocated with any memory in the Essbase, here the parent was dynamic calc and it was pointing to same memory as child in the background, when Planning was passing data to Essbase for second row it has updated the child with missing data.(Little confusing, let me know if you need more explanation) 6. As one of the solutions just change the order of appearance of parent and child. Cheers..!!! Rahul S. https://www.facebook.com/pages/HyperionPlanning/117320818374228

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• Is Data Science “Science”?

  - by BuckWoody

  I hold the term “science” in very high esteem. I grew up on the Space Coast in Florida, and eventually worked at the Kennedy Space Center, surrounded by very intelligent people who worked in various scientific fields. Recently a new term has entered the computing dialog – “Data Scientist”. Since it’s not a standard term, it has a lot of definitions, and in fact has been disputed as a correct term. After all, the reasoning goes, if there’s no such thing as “Data Science” then how can there be a Data Scientist? This argument has been made before, albeit with a different term – “Computer Science”. In Peter Denning’s excellent article “Is Computer Science Science” (April  2005/Vol. 48, No. 4 COMMUNICATIONS OF THE ACM) there are many points that separate “science” from “engineering” and even “art”.  I won’t repeat the content of that article here (I recommend you read it on your own) but will leverage the points he makes there. Definition of Science To ask the question “is data science ‘science’” then we need to start with a definition of terms. Various references put the definition into the same basic areas: Study of the physical world Systematic and/or disciplined study of a subject area ...and then they include the things studied, the bodies of knowledge and so on. The word itself comes from Latin, and means merely “to know” or “to study to know”. Greek divides knowledge further into “truth” (episteme), and practical use or effects (tekhne). Normally computing falls into the second realm. Definition of Data Science And now a more controversial definition: Data Science. This term is so new and perhaps so niche that the major dictionaries haven’t yet picked it up (my OED reference is older – can’t afford to pop for the online registration at present). Researching the term's general use I created an amalgam of the definitions this way: “Studying and applying mathematical and other techniques to derive information from complex data sets.” Using this definition, data science certainly seems to be science - it's learning about and studying some object or area using systematic methods. But implicit within the definition is the word “application”, which makes the process more akin to engineering or even technology than science. In fact, I find that using these techniques – and data itself – part of science, not science itself. I leave out the concept of studying data patterns or algorithms as part of this discipline. That is actually a domain I see within research, mathematics or computer science. That of course is a type of science, but does not seek for practical applications. As part of the argument against calling it “Data Science”, some point to the scientific method of creating a hypothesis, testing with controls, testing results against the hypothesis, and documenting for repeatability.  These are not steps that we often take in working with data. We normally start with a question, and fit patterns and algorithms to predict outcomes and find correlations. In this way Data Science is more akin to statistics (and in fact makes heavy use of them) in the process rather than starting with an assumption and following on with it. So, is Data Science “Science”? I’m uncertain – and I’m uncertain it matters. Even if we are facing rampant “title inflation” these days (does anyone introduce themselves as a secretary or supervisor anymore?) I can tolerate the term at least from the intent that we use data to study problems across a wide spectrum, rather than restricting it to a single domain. And I also understand those who have worked hard to achieve the very honorable title of “scientist” who have issues with those who borrow the term without asking. What do you think? Science, or not? Does it matter?

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• Data Modeling Resources

  - by Dejan Sarka

  You can find many different data modeling resources. It is impossible to list all of them. I selected only the most valuable ones for me, and, of course, the ones I contributed to. Books Chris J. Date: An Introduction to Database Systems – IMO a “must” to understand the relational model correctly. Terry Halpin, Tony Morgan: Information Modeling and Relational Databases – meet the object-role modeling leaders. Chris J. Date, Nikos Lorentzos and Hugh Darwen: Time and Relational Theory, Second Edition: Temporal Databases in the Relational Model and SQL – all theory needed to manage temporal data. Louis Davidson, Jessica M. Moss: Pro SQL Server 2012 Relational Database Design and Implementation – the best SQL Server focused data modeling book I know by two of my friends. Dejan Sarka, et al.: MCITP Self-Paced Training Kit (Exam 70-441): Designing Database Solutions by Using Microsoft® SQL Server™ 2005 – SQL Server 2005 data modeling training kit. Most of the text is still valid for SQL Server 2008, 2008 R2, 2012 and 2014. Itzik Ben-Gan, Lubor Kollar, Dejan Sarka, Steve Kass: Inside Microsoft SQL Server 2008 T-SQL Querying – Steve wrote a chapter with mathematical background, and I added a chapter with theoretical introduction to the relational model. Itzik Ben-Gan, Dejan Sarka, Roger Wolter, Greg Low, Ed Katibah, Isaac Kunen: Inside Microsoft SQL Server 2008 T-SQL Programming – I added three chapters with theoretical introduction and practical solutions for the user-defined data types, dynamic schema and temporal data. Dejan Sarka, Matija Lah, Grega Jerkic: Training Kit (Exam 70-463): Implementing a Data Warehouse with Microsoft SQL Server 2012 – my first two chapters are about data warehouse design and implementation. Courses Data Modeling Essentials – I wrote a 3-day course for SolidQ. If you are interested in this course, which I could also deliver in a shorter seminar way, you can contact your closes SolidQ subsidiary, or, of course, me directly on addresses [email protected] or [email protected]. This course could also complement the existing courseware portfolio of training providers, which are welcome to contact me as well. Logical and Physical Modeling for Analytical Applications – online course I wrote for Pluralsight. Working with Temporal data in SQL Server – my latest Pluralsight course, where besides theory and implementation I introduce many original ways how to optimize temporal queries. Forthcoming presentations SQL Bits 12, July 17th – 19th, Telford, UK – I have a full-day pre-conference seminar Advanced Data Modeling Topics there.

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• Deploying Data Mining Models using Model Export and Import

  - by [email protected]

  In this post, we'll take a look at how Oracle Data Mining facilitates model deployment. After building and testing models, a next step is often putting your data mining model into a production system -- referred to as model deployment. The ability to move data mining model(s) easily into a production system can greatly speed model deployment, and reduce the overall cost. Since Oracle Data Mining provides models as first class database objects, models can be manipulated using familiar database techniques and technology. For example, one or more models can be exported to a flat file, similar to a database table dump file (.dmp). This file can be moved to a different instance of Oracle Database EE, and then imported. All methods for exporting and importing models are based on Oracle Data Pump technology and found in the DBMS_DATA_MINING package. Before performing the actual export or import, a directory object must be created. A directory object is a logical name in the database for a physical directory on the host computer. Read/write access to a directory object is necessary to access the host computer file system from within Oracle Database. For our example, we'll work in the DMUSER schema. First, DMUSER requires the privilege to create any directory. This is often granted through the sysdba account. grant create any directory to dmuser; Now, DMUSER can create the directory object specifying the path where the exported model file (.dmp) should be placed. In this case, on a linux machine, we have the directory /scratch/oracle. CREATE OR REPLACE DIRECTORY dmdir AS '/scratch/oracle'; If you aren't sure of the exact name of the model or models to export, you can find the list of models using the following query: select model_name from user_mining_models; There are several options when exporting models. We can export a single model, multiple models, or all models in a schema using the following procedure calls: BEGIN   DBMS_DATA_MINING.EXPORT_MODEL ('MY_MODEL.dmp','dmdir','name =''MY_DT_MODEL'''); END; BEGIN   DBMS_DATA_MINING.EXPORT_MODEL ('MY_MODELS.dmp','dmdir',              'name IN (''MY_DT_MODEL'',''MY_KM_MODEL'')'); END; BEGIN   DBMS_DATA_MINING.EXPORT_MODEL ('ALL_DMUSER_MODELS.dmp','dmdir'); END; A .dmp file can be imported into another schema or database using the following procedure call, for example: BEGIN   DBMS_DATA_MINING.IMPORT_MODEL('MY_MODELS.dmp', 'dmdir'); END; As with models from any data mining tool, when moving a model from one environment to another, care needs to be taken to ensure the transformations that prepare the data for model building are matched (with appropriate parameters and statistics) in the system where the model is deployed. Oracle Data Mining provides automatic data preparation (ADP) and embedded data preparation (EDP) to reduce, or possibly eliminate, the need to explicitly transport transformations with the model. In the case of ADP, ODM automatically prepares the data and includes the necessary transformations in the model itself. In the case of EDP, users can associate their own transformations with attributes of a model. These transformations are automatically applied when applying the model to data, i.e., scoring. Exporting and importing a model with ADP or EDP results in these transformations being immediately available with the model in the production system.

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• SQL SERVER – Data and Page Compressions – Data Storage and IO Improvement

  - by pinaldave

  The performance of SQL Server is primarily decided by the disk I/O efficiency. Improving I/O definitely improves the performance. SQL Server 2008 introduced Data and Backup compression features to improve the disk I/O. Here, I will explain Data compression. Data compression implies the reduction in the disk space reserved by data. Therefore, data compression can be [...]

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• Why Oracle Data Integrator for Big Data?

  - by Mala Narasimharajan

  Big Data is everywhere these days - but what exactly is it? It’s data that comes from a multitude of sources – not only structured data, but unstructured data as well.  The sheer volume of data is mindboggling – here are a few examples of big data: climate information collected from sensors, social media information, digital pictures, log files, online video files, medical records or online transaction records.  These are just a few examples of what constitutes big data.   Embedded in big data is tremendous value and being able to manipulate, load, transform and analyze big data is key to enhancing productivity and competitiveness.  The value of big data lies in its propensity for greater in-depth analysis and data segmentation -- in turn giving companies detailed information on product performance, customer preferences and inventory.  Furthermore, by being able to store and create more data in digital form, “big data can unlock significant value by making information transparent and usable at much higher frequency." (McKinsey Global Institute, May 2011) Oracle's flagship product for bulk data movement and transformation, Oracle Data Integrator, is a critical component of Oracle’s Big Data strategy. ODI provides automation, bulk loading, and validation and transformation capabilities for Big Data while minimizing the complexities of using Hadoop.  Specifically, the advantages of ODI in a Big Data scenario are due to pre-built Knowledge Modules that drive processing in Hadoop. This leverages the graphical UI to load and unload data from Hadoop, perform data validations and create mapping expressions for transformations.  The Knowledge Modules provide a key jump-start and eliminate a significant amount of Hadoop development.  Using Oracle Data Integrator together with Oracle Big Data Connectors, you can simplify the complexities of mapping, accessing, and loading big data (via NoSQL or HDFS) but also correlating your enterprise data – this correlation may require integrating across heterogeneous and standards-based environments, connecting to Oracle Exadata, or sourcing via a big data platform such as Oracle Big Data Appliance. To learn more about Oracle Data Integration and Big Data, download our resource kit to see the latest in whitepapers, webinars, downloads, and more… or go to our website on www.oracle.com/bigdata

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• General Policies and Procedures for Maintaining the Value of Data Assets

  Here is a general list for policies and procedures regarding maintaining the value of data assets. Data Backup Policies and Procedures Backups are very important when dealing with data because there is always the chance of losing data due to faulty hardware or a user activity. So the need for a strategic backup system should be mandatory for all companies. This being said, in the real world some companies that I have worked for do not really have a good data backup plan. Typically when companies tend to take this kind of approach in data backups usually the data is not really recoverable.  Unfortunately when companies do not regularly test their backup plans they get a false sense of security because they think that they are covered. However, I can tell you from personal and professional experience that a backup plan/system is never fully implemented until it is regularly tested prior to the time when it actually needs to be used. Disaster Recovery Plan Expanding on Backup Policies and Procedures, a company needs to also have a disaster recovery plan in order to protect its data in case of a catastrophic disaster.  Disaster recovery plans typically encompass how to restore all of a company’s data and infrastructure back to a restored operational status.  Most Disaster recovery plans also include time estimates on how long each step of the disaster recovery plan should take to be executed.  It is important to note that disaster recovery plans are never fully implemented until they have been tested just like backup plans. Disaster recovery plans should be tested regularly so that the business can be confident in not losing any or minimal data due to a catastrophic disaster. Firewall Policies and Content Filters One way companies can protect their data is by using a firewall to separate their internal network from the outside. Firewalls allow for enabling or disabling network access as data passes through it by applying various defined restrictions. Furthermore firewalls can also be used to prevent access from the internal network to the outside by these same factors. Common Firewall Restrictions Destination/Sender IP Address Destination/Sender Host Names Domain Names Network Ports Companies can also desire to restrict what their network user’s view on the internet through things like content filters. Content filters allow a company to track what webpages a person has accessed and can also restrict user’s access based on established rules set up in the content filter. This device and/or software can block access to domains or specific URLs based on a few factors. Common Content Filter Criteria Known malicious sites Specific Page Content Page Content Theme  Anti-Virus/Mal-ware Polices Fortunately, most companies utilize antivirus programs on all computers and servers for good reason, virus have been known to do the following: Corrupt/Invalidate Data, Destroy Data, and Steal Data. Anti-Virus applications are a great way to prevent any malicious application from being able to gain access to a company’s data.  However, anti-virus programs must be constantly updated because new viruses are always being created, and the anti-virus vendors need to distribute updates to their applications so that they can catch and remove them. Data Validation Policies and Procedures Data validation is very important to ensure that only accurate information is stored. The existence of invalid data can cause major problems when businesses attempt to use data for knowledge based decisions and for performance reporting. Data Scrubbing Policies and Procedures Data scrubbing is valuable to companies in one of two ways. The first can be used to clean data prior to being analyzed for report generation. The second is that it allows companies to remove things like personally Identifiable information from its data prior to transmit it between multiple environments or if the information is sent to an external location. An example of this can be seen with medical records in regards to HIPPA laws that prohibit the storage of specific personal and medical information. Additionally, I have professionally run in to a scenario where the Canadian government does not allow any Canadian’s personal information to be stored on a server not located in Canada. Encryption Practices The use of encryption is very valuable when a company needs to any personal information. This allows users with the appropriated access levels to view or confirm the existence or accuracy of data within a system by either decrypting the information or encrypting a piece of data and comparing it to the stored version.  Additionally, if for some unforeseen reason the data got in to the wrong hands then they would have to first decrypt the data before they could even be able to read it. Encryption just adds and additional layer of protection around data itself. Standard Normalization Practices The use of standard data normalization practices is very important when dealing with data because it can prevent allot of potential issues by eliminating the potential for unnecessary data duplication. Issues caused by data duplication include excess use of data storage, increased chance for invalidated data, and over use of data processing. Network and Database Security/Access Policies Every company has some form of network/data access policy even if they have none. These policies help secure data from being seen by inappropriate users along with preventing the data from being updated or deleted by users. In addition, without a good security policy there is a large potential for data to be corrupted by unassuming users or even stolen. Data Storage Policies Data storage polices are very important depending on how they are implemented especially when a company is trying to utilize them in conjunction with other policies like Data Backups. I have worked at companies where all network user folders are constantly backed up, and if a user wanted to ensure the existence of a piece of data in the form of a file then they had to store that file in their network folder. Conversely, I have also worked in places where when a user logs on or off of the network there entire user profile is backed up. Training Policies One of the biggest ways to prevent data loss and ensure that data will remain a company asset is through training. The practice of properly train employees on how to work with in systems that access data is crucial when trying to ensure a company’s data will remain an asset. Users need to be trained on how to manipulate a company’s data in order to perform their tasks to reduce the chances of invalidating data.

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• Map Generation Algorithms for Minecraft Clone

  - by Danjen

  I'm making a Minecraft clone for the sake of it (with some inspriation from Dwarf Fortress) and had a few questions about the way the world generation is handled. Things I want it to cover: Biomes such as hills, mountains, forests, etc. Caves/caverns/tunnels Procedural (so it stretches to infinity... is wrap-around a possibility?) Breaking the map into smaller chunks Moddable (ie, new terrain types) Multiplayer compatible In particular, I've seen things such as Perlin Noise, Heightmaps, and Marching Cubes thrown around. These are like different tools to use, but I don't know when or why I would use them. Are there any other techniques that are useful for map generation? I realize this is borderline subjective and open-ended, but I am looking for some more insight into the processes involved.

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• Realistic planetary terrain generation with weights

  - by Programmdude

  I need terrain generation for a planet. The planet will be divided up into several hundred hexes, and I need it to be realistic and based on weights. I have dabbled in terrain generation before, but nothing like this. So I figure it would be a good idea to ask the community for answers, recommended articles or the like. By realistic, I mean not just random hexes, but continent shaped things with a few islands. More desert around the equator and more ice around the poles. I also have two weights I need to base it around: ice percentage and water percentage. That means that around XX% of the planet will need to be water. Does anyone have any advice or places to start? Generating arbitrary terrain is easy, but something a bit more "organic" like this seems rather difficult. It also needs to be seamless. Should be obvious since it's a planet, but no harm in pointing it out.

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• Importing Multiple Schemas to a Model in Oracle SQL Developer Data Modeler

  - by thatjeffsmith

  Your physical data model might stretch across multiple Oracle schemas. Or maybe you just want a single diagram containing tables, views, etc. spanning more than a single user in the database. The process for importing a data dictionary is the same, regardless if you want to suck in objects from one schema, or many schemas. Let’s take a quick look at how to get started with a data dictionary import. I’m using Oracle SQL Developer in this example. The process is nearly identical in Oracle SQL Developer Data Modeler – the only difference being you’ll use the ‘File’ menu to get started versus the ‘File – Data Modeler’ menu in SQL Developer. Remember, the functionality is exactly the same whether you use SQL Developer or SQL Developer Data Modeler when it comes to the data modeling features – you’ll just have a cleaner user interface in SQL Developer Data Modeler. Importing a Data Dictionary to a Model You’ll want to open or create your model first. You can import objects to an existing or new model. The easiest way to get started is to simply open the ‘Browser’ under the View menu. The Browser allows you to navigate your open designs/models You’ll see an ‘Untitled_1′ model by default. I’ve renamed mine to ‘hr_sh_scott_demo.’ Now go back to the File menu, and expand the ‘Data Modeler’ section, and select ‘Import – Data Dictionary.’ This is a fancy way of saying, ‘suck objects out of the database into my model’ Connect! If you haven’t already defined a connection to the database you want to reverse engineer, you’ll need to do that now. I’m going to assume you already have that connection – so select it, and hit the ‘Next’ button. Select the Schema(s) to be imported Select one or more schemas you want to import The schemas selected on this page of the wizard will dictate the lists of tables, views, synonyms, and everything else you can choose from in the next wizard step to import. For brevity, I have selected ALL tables, views, and synonyms from 3 different schemas: HR SCOTT SH Once I hit the ‘Finish’ button in the wizard, SQL Developer will interrogate the database and add the objects to our model. The Big Model and the 3 Little Models I can now see ALL of the objects I just imported in the ‘hr_sh_scott_demo’ relational model in my design tree, and in my relational diagram. Quick Tip: Oracle SQL Developer calls what most folks think of as a ‘Physical Model’ the ‘Relational Model.’ Same difference, mostly. In SQL Developer, a Physical model allows you to define partitioning schemes, advanced storage parameters, and add your PL/SQL code. You can have multiple physical models per relational models. For example I might have a 4 Node RAC in Production that uses partitioning, but in test/dev, only have a single instance with no partitioning. I can have models for both of those physical implementations. The list of tables in my relational model Wouldn’t it be nice if I could segregate the objects based on their schema? Good news, you can! And it’s done by default Several of you might already know where I’m going with this – SUBVIEWS. You can easily create a ‘SubView’ by selecting one or more objects in your model or diagram and add them to a new SubView. SubViews are just mini-models. They contain a subset of objects from the main model. This is very handy when you want to break your model into smaller, more digestible parts. The model information is identical across the model and subviews, so you don’t have to worry about making a change in one place and not having it propagate across your design. SubViews can be used as filters when you create reports and exports as well. So instead of generating a PDF for everything, just show me what’s in my ‘ABC’ subview. But, I don’t want to do any work! Remember, I’m really lazy. More good news – it’s already done by default! The schemas are automatically used to create default SubViews Auto-Navigate to the Object in the Diagram In the subview tree node, right-click on the object you want to navigate to. You can ask to be taken to the main model view or to the SubView location. If you haven’t already opened the SubView in the diagram, it will be automatically opened for you. The SubView diagram only contains the objects from that SubView Your SubView might still be pretty big, many dozens of objects, so don’t forget about the ‘Navigator‘ either! In summary, use the ‘Import’ feature to add existing database objects to your model. If you import from multiple schemas, take advantage of the default schema based SubViews to help you manage your models! Sometimes less is more!

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• Deploying Data Mining Models using Model Export and Import, Part 2

  - by [email protected]

  In my last post, Deploying Data Mining Models using Model Export and Import, we explored using DBMS_DATA_MINING.EXPORT_MODEL and DBMS_DATA_MINING.IMPORT_MODEL to enable moving a model from one system to another. In this post, we'll look at two distributed scenarios that make use of this capability and a tip for easily moving models from one machine to another using only Oracle Database, not an external file transport mechanism, such as FTP. The first scenario, consider a company with geographically distributed business units, each collecting and managing their data locally for the products they sell. Each business unit has in-house data analysts that build models to predict which products to recommend to customers in their space. A central telemarketing business unit also uses these models to score new customers locally using data collected over the phone. Since the models recommend different products, each customer is scored using each model. This is depicted in Figure 1.Figure 1: Target instance importing multiple remote models for local scoring In the second scenario, consider multiple hospitals that collect data on patients with certain types of cancer. The data collection is standardized, so each hospital collects the same patient demographic and other health / tumor data, along with the clinical diagnosis. Instead of each hospital building it's own models, the data is pooled at a central data analysis lab where a predictive model is built. Once completed, the model is distributed to hospitals, clinics, and doctor offices who can score patient data locally.Figure 2: Multiple target instances importing the same model from a source instance for local scoring Since this blog focuses on model export and import, we'll only discuss what is necessary to move a model from one database to another. Here, we use the package DBMS_FILE_TRANSFER, which can move files between Oracle databases. The script is fairly straightforward, but requires setting up a database link and directory objects. We saw how to create directory objects in the previous post. To create a database link to the source database from the target, we can use, for example: create database link SOURCE1_LINK connect to <schema> identified by <password> using 'SOURCE1'; Note that 'SOURCE1' refers to the service name of the remote database entry in your tnsnames.ora file. From SQL*Plus, first connect to the remote database and export the model. Note that the model_file_name does not include the .dmp extension. This is because export_model appends "01" to this name.  Next, connect to the local database and invoke DBMS_FILE_TRANSFER.GET_FILE and import the model. Note that "01" is eliminated in the target system file name.  connect <source_schema>/<password>@SOURCE1_LINK; BEGIN  DBMS_DATA_MINING.EXPORT_MODEL ('EXPORT_FILE_NAME' || '.dmp',                                 'MY_SOURCE_DIR_OBJECT',                                 'name =''MY_MINING_MODEL'''); END; connect <target_schema>/<password>; BEGIN  DBMS_FILE_TRANSFER.GET_FILE ('MY_SOURCE_DIR_OBJECT',                               'EXPORT_FILE_NAME' || '01.dmp',                               'SOURCE1_LINK',                               'MY_TARGET_DIR_OBJECT',                               'EXPORT_FILE_NAME' || '.dmp' );  DBMS_DATA_MINING.IMPORT_MODEL ('EXPORT_FILE_NAME' || '.dmp',                                 'MY_TARGET_DIR_OBJECT'); END; To clean up afterward, you may want to drop the exported .dmp file at the source and the transferred file at the target. For example, utl_file.fremove('&directory_name', '&model_file_name' || '.dmp');

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• Abstract Data Type and Data Structure

  - by mark075

  It's quite difficult for me to understand these terms. I searched on google and read a little on Wikipedia but I'm still not sure. I've determined so far that: Abstract Data Type is a definition of new type, describes its properties and operations. Data Structure is an implementation of ADT. Many ADT can be implemented as the same Data Structure. If I think right, array as ADT means a collection of elements and as Data Structure, how it's stored in a memory. Stack is ADT with push, pop operations, but can we say about stack data structure if I mean I used stack implemented as an array in my algorithm? And why heap isn't ADT? It can be implemented as tree or an array.

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