Search results “Data mining oracle database” for the 2016
Mining Structured and Unstructured Data
Oracle Advanced Analytics (OAA) Database Option leverages Oracle Text, a free feature of the Oracle Database, to pre-process (tokenize) unstructured data for ingestion by the OAA data mining algorithms. By moving, parallelized implementations of machine learning algorithms inside the Oracle Database, data movement is eliminated and we can leverage other strengths of the Database such as Oracle Text (not to mention security, scalability, auditing, encryption, back up, high availability, geospatial data, etc.. This YouTube video presents an overview of the capabilities for combing and data mining structured and unstructured data, includes several brief demonstrations and instructions on how to get started--either on premise or on the Oracle Cloud.
Views: 2243 Charlie Berger
Oracle data mining tutorial, data mining techniques classification
What is data mining? The Oracle Data Miner tutorial presents data mining introduction. Learn data mining techniques.
Advanced Analytics using Oracle DB EE
If you’ve been wanting to expose your colleagues to the power of using the Oracle Database as a platform for predictive analytics, this special session will be perfect for you. We’ll use a combination of live demos and business use cases to explain that predictive analytics doesn’t require an advanced degree in mathematics, just a database and a few good business questions. We’ll share how to leverage the work you’ve done in building a data warehouse and collecting business data sets into solid evidence for business decisions. Predictive analytics is all about looking forward into the future and leveraging data to assess and evaluate alternative courses of action. Too often, executives and managers rely on gut instinct without using the data they already have to make better decisions. Here’s the outline for the session: - Key issues in leveraging the power of analytics - Using Oracle database as an analytics platform. - Oracle Advanced Analytics overview - Oracle Data Mining - Oracle R Enterprise - Common use cases for predictive analytics - Where to start when developing your analytics capabilities
Spatial database systems and their types
Spatial database systems and their types
3 Oracle ODM Cycle by Kareem
Views: 193 karim shaik
Database and Big Data
This course introduces important database concepts, including data modeling, database design, and data extraction. Students will also learn data analysis skills they need to transform raw data into useful business information and knowledge for decision-making and problem solving. Students explore relational design, data warehousing, data mining, data visualization, data search, knowledge management, business intelligence, data querying, basic analytics, and reporting.
Views: 715 [email protected]
Temporal Database in Hindi
A temporal database is a database with built-in support for handling data involving time, being related to the slowly changing dimension concept, for example a temporal data model and a temporal version of Structured Query Language (SQL). More specifically the temporal aspects usually include valid time and transaction time. These attributes can be combined to form bitemporal data. Valid time is the time period during which a fact is true in the real world. Transaction time is the time period during which a fact stored in the database was known. Bitemporal data combines both Valid and Transaction Time. It is possible to have timelines other than Valid Time and Transaction Time, such as Decision Time, in the database. In that case the database is called a multitemporal database as opposed to a bitemporal database. However, this approach introduces additional complexities such as dealing with the validity of (foreign) keys. Temporal databases are in contrast to current databases (at term that doesn't mean, currently available databases, some do have temporal features, see also below), which store only facts which are believed to be true at the current time. Temporal databases supports System-maintained transaction time. With the development of SQL and its attendant use in real-life applications, database users realized that when they added date columns to key fields, some issues arose. For example, if a table has a primary key and some attributes, adding a date to the primary key to track historical changes can lead to creation of more rows than intended. Deletes must also be handled differently when rows are tracked in this way. In 1992, this issue was recognized but standard database theory was not yet up to resolving this issue, and neither was the then-newly formalized SQL-92 standard. Richard Snodgrass proposed in 1992 that temporal extensions to SQL be developed by the temporal database community. In response to this proposal, a committee was formed to design extensions to the 1992 edition of the SQL standard (ANSI X3.135.-1992 and ISO/IEC 9075:1992); those extensions, known as TSQL2, were developed during 1993 by this committee.[3] In late 1993, Snodgrass presented this work to the group responsible for the American National Standard for Database Language SQL, ANSI Technical Committee X3H2 (now known as NCITS H2). The preliminary language specification appeared in the March 1994 ACM SIGMOD Record. Based on responses to that specification, changes were made to the language, and the definitive version of the TSQL2 Language Specification was published in September, 1994[4] An attempt was made to incorporate parts of TSQL2 into the new SQL standard SQL:1999, called SQL3. Parts of TSQL2 were included in a new substandard of SQL3, ISO/IEC 9075-7, called SQL/Temporal.[3] The TSQL2 approach was heavily criticized by Chris Date and Hugh Darwen.[5] The ISO project responsible for temporal support was canceled near the end of 2001. As of December 2011, ISO/IEC 9075, Database Language SQL:2011 Part 2: SQL/Foundation included clauses in table definitions to define "application-time period tables" (valid time tables), "system-versioned tables" (transaction time tables) and "system-versioned application-time period tables" (bitemporal tables). A substantive difference between the TSQL2 proposal and what was adopted in SQL:2011 is that there are no hidden columns in the SQL:2011 treatment, nor does it have a new data type for intervals; instead two date or timestamp columns can be bound together using a PERIOD FOR declaration. Another difference is replacement of the controversial (prefix) statement modifiers from TSQL2 with a set of temporal predicates. For illustration, consider the following short biography of a fictional man, John Doe: John Doe was born on April 3, 1975 in the Kids Hospital of Medicine County, as son of Jack Doe and Jane Doe who lived in Smallville. Jack Doe proudly registered the birth of his first-born on April 4, 1975 at the Smallville City Hall. John grew up as a joyful boy, turned out to be a brilliant student and graduated with honors in 1993. After graduation he went to live on his own in Bigtown. Although he moved out on August 26, 1994, he forgot to register the change of address officially. It was only at the turn of the seasons that his mother reminded him that he had to register, which he did a few days later on December 27, 1994. Although John had a promising future, his story ends tragically. John Doe was accidentally hit by a truck on April 1, 2001. The coroner reported his date of death on the very same day.
Views: 8995 Introtuts
Indexing JSON Data in Oracle Database 12c
This video gives an overview of indexing JSON data in Oracle database 12c. For more information see: https://oracle-base.com/articles/12c/indexing-json-data-in-oracle-database-12cr1 Website: https://oracle-base.com Blog: https://oracle-base.com/blog Twitter: https://twitter.com/oraclebase Cameo by Bertrand Drouvot : Blog: https://bdrouvot.wordpress.com/ Twitter : https://twitter.com/BertrandDrouvot Cameo appearances are for fun, not an endorsement of the content of this video. All trademarks, product names and logos are the property of their respective owners.
Views: 1587 ORACLE-BASE.com
Detecting anomalies with Oracle Big Data Spatial and Graph
Detecting fraud and anomalies with Oracle Big Data Spatial and Graph Read more: 1. Oracle Big Data Spatial and Graph on Oracle.com: https://www.oracle.com/database/big-data-spatial-and-graph/index.html 2. OTN product page (trial software downloads, documentation): http://www.oracle.com/technetwork/database/database-technologies/bigdata-spatialandgraph/overview/index.html 3. Blog (technical examples and tips): https://blogs.oracle.com/bigdataspatialgraph/ 4. Big Data Lite Virtual Machine (a free sandbox environment to get started): http://www.oracle.com/technetwork/database/bigdata-appliance/oracle-bigdatalite-2104726.html
Views: 323 Oracle Big Data
Oracle SQL Developer Tutorial For Beginners  76   Filtering Data using WHERE clause
Oracle SQL Developer Tutorial For Beginners Series. This course introduces Oracle SQL Development for its subscribers. Currently this is based on Oracle 12c. The test environment is in Windows 10.
Views: 868 Sam Dhanasekaran
Oracle Big Data Discovery - Technical Introduction
This video covers very high-level detail regarding the architecture, deployment configurations, and starting the services on a Linux VM. If you have any questions, please leave a comment below. I usually respond within 24 hours.
Views: 504 Eric Sundby
Oracle Communications Data Model
The Oracle Communications Data Model is helping CSPs shave costs and eliminate complexity. http://medianetwork.oracle.com/video/player/4586715228001
R tutorial: connecting to a database
Learn more about connecting to databases with R: https://www.datacamp.com/courses/importing-data-in-r-part-2 Welcome to part two of importing data in R! The previous course dealt with accessing data stored in flat files or excel files. In a professional setting, you'll also encounter data stored in relational databases. In this video, I'll briefly talk about what a relational database is and then I'll explain how you can connect to it. In the next video, I'll explain how you can import data from it! So, what's a relational database? There's no better way to show this than with an example. Take this database, called company. It contains three tables, employees, products and sales. Like a flat file, information is displayed in a table format. The employees table has 5 records and three fields, namely id, name and started_at. The id here serves as a unique key for each row or record. Next, the products table contains the details on four products. We're dealing with data from a telecom company that's selling both with and without a contract. Also here, each product has an identifier. Finally, there's the sales table. It lists what products were sold by who, when and for what price. Notice here that the ids in employee_id and product_id correspond to the ids that you can find in the employees and products table respectively. The third sale for example, was done by the employee with id 6, so Julie. She sold the product with id 9, so the Biz Unlimited contract. These relations make this database very powerful. You only store all necessary information once in nicely separated tables, but can connect the dots between different records to model what's happening. How the data in a relational database is stored and shuffled around when you make adaptations, depends on the so-called database management system, or DBMS you're using. Open-source implementations such as MySQL, postgreSQL and SQLite are very popular, but there are also proprietary implementations such as Oracle Database and Microsoft SQL server. Practically all of these implementations use SQL, or sequel, as the language for querying and maintaining the database. SQL stands for Structured Query Language. Depending on the type of database you want to connect to, you'll have to use different packages. Suppose the company database I introduced before is a MySQL database. This means you'll need the RMySQL package. For postgreSQL you'll need RpostgreSQL, for Oracle, you'll use ROracle and so on. How you interact with the database, so which R functions you use to access and manipulate the database, is specified in another R package called DBI. In more technical terms, DBI is an interface, and RMySQL is the implementation. Let's install the RMySQL package, which automatically installs the DBI package as well. Loading only the DBI package will be enough to get started. The first step is creating a connection to the remote MySQL database. You do this with dbConnect(), as follows. The first argument specifies the driver that you will use to connect to the MySQL database. It sure looks a bit strange, but the MySQL() function from the RMySQL package simply constructs a driver for us that dbConnect can use. Next, you have to specify the database name, where the database is hosted, through which port you want to connect, and finally the credentials to authenticate yourself. This is an actual database that we're hosting, so you can try these commands yourself! The result of the dbConnect call, con, is a DBI connection object. You'll need to pass this object to whatever function you're using to interact with the database. Before we do that, let's get familiar with this connection object in the exercises!
Views: 35081 DataCamp
What is partition and why use it? Creating a Partition, Partitioning method
What is partition and why use it? Creating a Partition, Partitioning method - ETIT 427 - ADBA - IP University Syllabus For Students of B.Tech, B.E, MCA, BCA, B.Sc., M.Sc., Courses - As Per IP University Syllabus and Other Engineering Courses
Data Warehouse - 1 - Setting Up Oracle Source Module
Setting Up Oracle Source Module
Views: 1038 BscIT
MiCORE Solutions: Data Warehouse and Database Challenges
Jim McHugh, VP of Consulting Services
Basic concept of NORMALIZATION | DBMS
This video contains the basic concepts normalization ie what is the problem which is solved by nozmalization and will help you in various competitive exams like GATE , NET, PSU'S etc
Views: 410046 KNOWLEDGE GATE
Building Data Mining Model with IBM SPSS Modeler
Building Data Mining Model with IBM SPSS Modeler
Views: 3942 Chuc Nguyen Van
DATA MINING   1 Data Visualization   4 1 3  Database Visualization Part 1
Views: 52 Ryo Eng
Generating Recommendations with Oracle Big Data Spatial and Graph
Recommendations with Oracle Big Data Spatial and Graph Read more about Big Data Spatial and Graph: 1. Oracle Big Data Spatial and Graph on Oracle.com:  https://www.oracle.com/database/big-data-spatial-and-graph 2. OTN product page (trial software downloads, documentation):  http://www.oracle.com/technetwork/database/database-technologies/bigdata-spatialandgraph 3. Blog  (technical examples and tips):   https://blogs.oracle.com/bigdataspatialgraph/ 4. Big Data Lite Virtual Machine (a free sandbox environment to get started):   http://www.oracle.com/technetwork/database/bigdata-appliance/oracle-bigdatalite-2104726.html
Oracle SQL Developer Tutorial For Beginners   23   SQL Commands Classification
Oracle SQL Developer Tutorial For Beginners Series. This course introduces Oracle SQL Development for its subscribers. Currently this is based on Oracle 12c. The test environment is in Windows 10.
Views: 1833 Sam Dhanasekaran
Getting Started with Orange 04: Loading Your Data
Loading your data in Orange from Google sheets or Excel. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 51688 Orange Data Mining
Market Basket Analysis
https://www.experfy.com/training/courses/clustering-and-association-rule-mining Clustering and Association Rule Mining are two of the most frequently used Data Mining technique for various functional needs, especially in Marketing, Merchandising, and Campaign efforts. Clustering helps find natural and inherent structures amongst the objects, where as Association Rule is a very powerful way to identify interesting relations between objects in large commercial databases Affinity analysis and association rule learning encompasses a broad set of analytics techniques. Of these, “market basket analysis” is perhaps the most famous example and has emerged as the next step in the evolution of retail merchandising and promotion. Follow us on: https://www.facebook.com/experfy https://twitter.com/experfy https://experfy.com
Views: 8428 Experfy
Why Won't Oracle 12c Use My Index - 12c Attribute Clustering - Lesson 2 Demonstration
Sometimes a poor clustering factor is the cause when Oracle Database cost based optimizer does not choose to use an index. With Oracle 12c ( EE) offers a new feature that can really help - Learn 12c CREATE TABLE - "CLUSTERING BY LINEAR ORDER". In this lesson 2 (of 4), you see a great demonstration of CREATE TABLE - "CLUSTERING BY LINEAR ORDER" - so the CBO will use your index! To see all free lessons, visit http://www.skillbuilders.com/12c-attribute-clustering.
Views: 251 SkillBuilders
Keith Griffiths: Cyber Security
http://www.keith-griffiths.com I provide a range of IT services for a broad range of businesses. If you have an IT related issue call me on 07411 835 390. //Cyber Security I protect information systems from theft or damage to hardware, software, and to the information on them, as well as from disruption and intrusion. //Data Management Specialising in data analysis, data mining, organising data and performing research. I also work with spreadsheets SQL, MySQL, and Oracle databases.
Views: 31 Keith Griffiths
Oracle Data Visualisation - Revenue Assurance Analysis
Oracle Data Visualisation for workgroup collaboration. SuperTel Revenue Assurance user brings in their own data to complete an analysis started by a colleague.
Views: 499 Chris Hathaway
Fast Database and Data Streaming Operations using Graphics Processors
We present novel techniques to utilize the high computational power of graphics processing units (GPUs) to significantly accelerate many of the traditional general purpose algorithms on CPUs. As graphics processors are primarily designed to perform fast display of geometric primitives, we abstract many of the essential database and data mining algorithms using basic graphics operations. Our algorithms use efficient data representations and utilize the inherent parallelism in the single instruction multiple data (SIMD) units and the vector processing functionalities of the GPUs to efficiently evaluate the boolean combinations of predicates, aggregates, and join queries. Graphics processors are optimized for processing data streams. We present deterministic algorithms to efficiently estimate quantiles and frequencies in large data streams. We utilize the high computational power and the memory bandwidth on a GPU to perform sorting on a GPU. The sorting algorithm is used as a main computational component for the construction of epsilon-approximate quantile and frequency summaries. We have applied our algorithm to data streams consisting of more than 100 million elements on a 3.4GHz PC with a NVIDIA 6800 Ultra GPU and achieved 2-4 times performance improvement over optimized CPU-based algorithms. Our recent research focuses on using GPUs for sorting very large databases composed of hundreds of gigabytes of data using low-end commodity PCs. Experimental studies on the SortBenchmark indicate that external sorting is highly memory-intensive. As the GPUs internally have a dedicated memory interface, we present an efficient hybrid sorting algorithm to perform the computation on both the GPU and CPU, in parallel. Experimental results on a low-end PC with a NVIDIA 7800 GTX graphics co-processor indicate higher performance than optimized CPU-based algorithms on a high-end PC with 3.6 GHz Dual Xeon processors.
Views: 670 Microsoft Research
Oracle Healthcare Foundation: Managing Big Data and Analytics
Through insight and innovation you can provide patients with the best quality of care, using the best therapies at a lower cost.
Oracle Data Cloud Summit Sessions 2016: Frito-Lay, Growing with Data & Insights
CPG marketers are embracing data and advanced analytics to fuel growth through digital. The Frito-Lay portfolio has long viewed digital as a core part of their marketing mix and strategically has revised their efforts to reflect that. To stay ahead of the curve, they had to quickly evolve internally and through their external partnerships to understand which channels and platforms are most effective for their brands. Hear directly from their Director of Marketing Portfolio Analytics on their measurement test & learn journey from audience creation & targeting to trying to crack the code on digital video.
Views: 72 Oracle Data Cloud
Session 5: Part 1 MongoDB and Data Mining Examples
For each week, relevant content covered will be placed on this YouTube channel
Views: 134 Mark Altaweel
Oracle Data Visualization Dashboard for EBS Order to Ship flow
Making sense of your data shouldn't be tough. Oracle Data Visualization Desktop gives decision-makers their own personal desktop application to access, explore, blend, and share data visualizations. Oracle Data Visualization is stunningly visual. You can create visual stories on data from a variety of sources including spreadsheets, databases and applications. Combine and blend your data using the intelligent visualizations to quickly see patterns. Selecting data in one visualization highlights related data in everything else on the screen. You can capture insights and comments to create an interactive story that you can share with colleagues. In this very Simple demo we have visualized Order to Ship data from Oracle EBS12i using Oracle Data Visualization Tool.
Data Mining: Assignment 1
Explain practical applications of data mining to a corporation in a domain of my choosing. I chose Business and Retail. Critiques are welcome as I'm constantly trying to improve.
Views: 96 Reggie Yamanaka
Oracle's Software in Silicon  - Data Analytics Accelerator - (DAX) Deep Dive
In this video you will get a quick overview of Dax as well as more information for developers.
Alumni Diaries | Dr. Jayant R. Haritsa
Jayant R. Haritsa is a faculty of SERC and CSA departments at Indian Institute of Science, Bangalore, India. He works on Database Systems (Query Optimization, Data Mining, XML Databases, Biological Databases, Multi-lingual Databases). He has won in 2009, prestigious Shanti Swarup Bhatnagar Prize sponsored by CSIR, India and also the Infosys Prize for Engineering in 2014. He did his SSLC from Vijaya High School, Jayanagar, Bangalore and Pre-University in Science, from National College (Basavanagudi), Bangalore. He did his B.Tech (Electronics) from Dept. of Electrical Engineering, Indian Institute of Technology, Madras and MS and PhD (Computer Science), Computer Sciences Department, University of Wisconsin, Madison. Later he became a Research Fellow in Institute for Systems Research, University of Maryland (College Park). Currently he is the chairman of CSA department at Indian Institute of Science.
Views: 1215 IITM TV
Oracle warehouse builder || Oracle warehouse builder(OWB) Part - 5
DURGASOFT is INDIA's No.1 Software Training Center offers online training on various technologies like JAVA, .NET , ANDROID,HADOOP,TESTING TOOLS , ADF, INFORMATICA,TALLEAU,IOS,OBIEE,ANJULAR JA, SAP... courses from Hyderabad & Bangalore -India with Real Time Experts. Mail us your requirements to [email protected] so that our Supporting Team will arrange Demo Sessions. Ph:Call +91-8885252627,+91-7207212428,+91-7207212427,+91-8096969696. http://durgasoft.com http://durgasoftonlinetraining.com https://www.facebook.com/durgasoftware http://durgajobs.com https://www.facebook.com/durgajobsinfo......

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