Wednesday Jul 15, 2015
Monday May 04, 2015
By Charlie Berger, Advanced Analytics-Oracle on May 04, 2015
To download, visit:
New Data Miner Features in SQL Developer 4.1
In response to the growing popularity of JSON data and its use in Big Data configurations, Data Miner now provides an easy to use JSON Query node. The JSON Query node allows you to select and aggregate JSON data without entering any SQL commands. The JSON Query node opens up using all of the existing Data Miner features with JSON data. The enhancements include:
Data Source Node
o Automatically identifies columns containing JSON data by identifying those with the IS_JSON constraint.
o Generates JSON schema for any selected column that contain JSON data.
o Imports a JSON schema for a given column.
o JSON schema viewer.
Create Table Node
o Ability to select a column to be typed as JSON.
o Generates JSON schema in the same manner as the Data Source node.
JSON Data Type
o Columns can be specifically typed as JSON data.
JSON Query Node (see related JSON node blog posting)
o Ability to utilize any of the selection and aggregation features without having to enter SQL commands.
o Ability to select data from a graphical layout of the JSON schema, making data selection as easy as it is with scalar relational data columns.
o Ability to partially select JSON data as standard relational scalar data while leaving other parts of the same JSON document as JSON data.
o Ability to aggregate JSON data in combination with relational data. Includes the Sub-Group By option, used to generate nested data that can be passed into mining model build nodes.
o Improved database session management resulting in less database sessions being generated and a more responsive user interface.
o Filter Columns Node - Combined primary Editor and associated advanced panel to improve usability.
o Explore Data Node - Allows multiple row selection to provide group chart display.
o Classification Build Node - Automatically filters out rows where the Target column contains NULLs or all Spaces. Also, issues a warning to user but continues with Model build.
o Workflow - Enhanced workflows to ensure that Loading, Reloading, Stopping, Saving operations no longer block the UI.
o Online Help - Revised the Online Help to adhere to topic-based framework.
Selected Bug Fixes (does not include 4.0 patch release fixes)
o GLM Model Algorithm Settings: Added GLM feature identification sampling option (Oracle Database 12.1 and above).
o Filter Rows Node: Custom Expression Editor not showing all possible available columns.
o WebEx Display Issues: Fixed problems affecting the display of the Data Miner UI through WebEx conferencing.
For More Information and Support, please visit the Oracle Data Mining Discussion Forum on the Oracle Technology Network (OTN)
Return to Oracle Data Miner page on OTN
Tuesday Jan 01, 2013
By Charlie Berger, Advanced Analytics-Oracle on Jan 01, 2013
Turkcell İletişim Hizmetleri A.S. Successfully Combats Communications Fraud with Advanced In-Database Analytics
[Original link available on oracle.com http://www.oracle.com/us/corporate/customers/customersearch/turkcell-1-exadata-ss-1887967.html]
- Oracle Customer: Turkcell İletişim Hizmetleri A.Ş.
- Location: Istanbul, Turkey
- Industry: Communications
- Employees: 3,583
- Annual Revenue: Over $5 Billion
Turkcell İletişim Hizmetleri A.Ş. is a leading provider of mobile communications in Turkey with more than 34 million subscribers. Established in 1994, Turkcell created the first global system for a mobile communications (GSM) network in Turkey. It was the first Turkish company listed on the New York Stock Exchange.
Communications fraud, or the use of telecommunications products or services without intention to pay, is a major issue for the organization. The practice is fostered by prepaid card usage, which is growing rapidly. Anonymous network-branded prepaid cards are a tempting vehicle for money launderers, particularly since these cards can be used as cash vehicles—for example, to withdraw cash at ATMs. It is estimated that prepaid card fraud represents an average loss of US$5 per US$10,000 in transactions. For a communications company with billions of transactions, this could result in millions of dollars lost through fraud every year.
Consequently, Turkcell wanted to combat communications fraud and money laundering by introducing advanced analytical solutions to monitor key parameters of prepaid card usage and issue alerts or block fraudulent activity. This type of fraud prevention would require extremely fast analysis of the company’s one petabyte of uncompressed customer data to identify patterns and relationships, build predictive models, and apply those models to even larger data volumes to make accurate fraud predictions.
To achieve this, Turkcell deployed Oracle Exadata Database Machine X2-2 HC Full Rack, so that data analysts can build predictive antifraud models inside the Oracle Database and deploy them into Oracle Exadata for scoring, using Oracle Data Mining, a component of Oracle Advanced Analytics, leveraging Oracle Database11g technology. This enabled the company to create predictive antifraud models faster than with any other machine, as models can be built using search and query language (SQL) inside the database, and Oracle Exadata can access raw data without summarized tables, thereby achieving extremely fast analyses.
A word from Turkcell İletişim Hizmetleri A.Ş.
“Turkcell manages 100 terabytes of compressed data—or one petabyte of uncompressed raw data—on Oracle Exadata. With Oracle Data Mining, a component of the Oracle Advanced Analytics Option, we can analyze large volumes of customer data and call-data records easier and faster than with any other tool and rapidly detect and combat fraudulent phone use.” – Hasan Tonguç Yılmaz, Manager, Turkcell İletişim Hizmetleri A.Ş.
- Combat communications fraud and money laundering by introducing advanced analytical solutions to monitor prepaid card usage and alert or block suspicious activity
- Monitor numerous parameters for up to 10 billion daily call-data records and value-added service logs, including the number of accounts and cards per customer, number of card loads per day, number of account loads over time, and number of account loads on a subscriber identity module card at the same location
- Enable extremely fast sifting through huge data volumes to identify patterns and relationships, build predictive antifraud models, and apply those models to even larger data volumes to make accurate fraud predictions
- Detect fraud patterns as soon as possible and enable quick response to minimize the negative financial impact
Oracle Product and Services
- Used Oracle Exadata Database Machine X2-2 HC Full Rack to create predictive antifraud models more quickly than with previous solutions by accessing raw data without summarized tables and providing unmatched query speed, which optimizes and shortens the project design phases for creating predictive antifraud models
- Leveraged SQL for the preparation and transformation of one petabyte of uncompressed raw communications data, using Oracle Data Mining, a feature of Oracle Advanced Analytics to increase the performance of predictive antifraud models
- Deployed Oracle Data Mining models on Oracle Exadata to identify actionable information in less time than traditional methods—which would require moving large volumes of customer data to a third-party analytics software—and achieve an average gain of four hours and more, taking into consideration the absence of any system crash (as occurred in the previous environment) during data import
- Achieved extreme data analysis speed with in-database analytics performed inside Oracle Exadata, through a row-wise information search—including day, time, and duration of calls, as well as number of credit recharges on the same day or at the same location—and query language functions that enabled analysts to detect fraud patterns almost immediately
- Implemented a future-proof solution that could support rapidly growing data volumes that tend to double each year with Oracle Exadata’s massively scalable data warehouse performance
“We selected Oracle because in-database mining to support antifraud efforts will be a major focus for Turkcell in the future. With Oracle Exadata Database Machine and the analytics capabilities of Oracle Advanced Analytics, we can complete antifraud analysis for large amounts of call-data records in just a few hours. Further, we can scale the solution as needed to support rapid communications data growth,” said Hasan Tonguç Yılmaz, datawarehouse/data mining developer, Turkcell Teknoloji Araştırma ve Geliştirme A.Ş.
Oracle Partner: Turkcell Teknoloji Araştırma ve Geliştirme A.Ş.
All development and test processes were performed by Turkcell Teknoloji. The company also made significant contributions to the configuration of numerous technical analyses which are carried out regularly by Turkcell İletişim Hizmetleri's antifraud specialists.
- Turkcell İletişim Hizmetleri Uses Engineered System to Analyze 10 Billion Daily, Call-Data Records and Service Logs and to Generate 100,000 Monthly Reports
- Turkcell Deploys Oracle Data Integrator to Drive Efficiency
- Turkcell Accelerates Reporting Tenfold, Saves on Storage and Energy Costs with Consolidated Oracle Exadata Platform
- Turkcell Superonline Transforms Its Order Management and Service Fulfillment with Oracle Communications Solutions
- Technologist of the Year
- Turkcell is an Exemplary Oracle Cross Stack Customer
- Turkcell Gets Three 10X Improvements with Oracle
- Oracle Exadata Changes the Rules of the Game for Turkcell
- Customers Discuss Benefits of Oracle Exadata
- Turkcell Technology Uses Oracle Complex Event Processing for Extreme Scale Mobile Networks
- Turkcell Technology Research & Development Inc. Achieves Substantial Savings with Fault Prevention
- Turkcell iletisim Hizmetleri A.S. Processes Mobile Network Data of 33 Million Subscribers in Real Time
- Kcell Boosts Business Intelligence with Data Warehouse Solution
- Turkcell Gets the Benefits of Oracle Exadata
- Turkcell Eliminates Manual Updates with Oracle IDM
Thursday May 10, 2012
Oracle Virtual SQL Developer Days DB May 15th - Session #3: 1Hr. Predictive Analytics and Data Mining Made Easy!
By Charlie Berger, Advanced Analytics-Oracle on May 10, 2012
Oracle Data Mining's SQL Developer based ODM'r GUI + ODM is being featured in this upcoming Virtual SQL Developer Day online event next Tuesday, May 15th. Several thousand people have already registered and registration is still growing. We recorded and uploaded presentations/demos and then anyone can view them "on demand", but at the specified date/time per the SQL DD event agenda. Anyone can also download a complete 11gR2 Database w/ SQL Developer 3.1 & Oracle Data Miner GUI extension VM installation for the Hands-on Labs and follow our 4 ODM Oracle by Examples e-training. We moderators monitor the online chat and answer questions.
Session #3: 1Hr. Predictive Analytics and Data Mining Made Easy!We're also included in the June 7th physical event in NYC and future virtual and physical events. Great event(s) and great "viz" for OAA/ODM.
Oracle Data Mining, a component of the Oracle Advanced Analytics database option, embeds powerful data mining algorithms in the SQL kernel of the Oracle Database for problems such as customer churn, predicting customer behavior, up-sell and cross-sell, detecting fraud, market basket analysis (e.g. beer & diapers), customer profiling and customer loyalty. Oracle Data Miner, SQL Developer 3.1 extension, provides data analysts a “workflow” paradigm to build analytical methodologies to explore data and build, evaluate and apply data mining models—all while keeping the data inside the Oracle Database. This workshop will teach the student the basics of getting started using Oracle Data Mining.
Tuesday Mar 22, 2011
Everything about Oracle Data Mining, a component of the Oracle Advanced Analytics Option - News, Technical Information, Opinions, Tips & Tricks. All in One Place
- Oracle Advanced Analytics Oracle University (OU) Classes in Cambridge, MA. September 28-Oct. 1, 2015
- Big Data Analytics with Oracle Advanced Analytics: Making Big Data and Analytics Simple white paper
- 2015 BIWA SIG Virtual Conference - Two Days of "Live" Talks by Experts - FREE
- Call for Abstracts at BIWA Summit'16 - The Oracle Big Data + Analytics User Conference
- Oracle Data Miner 4.1, SQL Developer 4.1 Extension Now Available!
- OpenWorld 2015 Call for Proposals Extended to Wed, May 6th, 11:59 p.m
- Use Repository APIs to Manage and Schedule Workflows to run
- Use Oracle Data Miner to Perform Sentiment Analysis inside Database using Twitter Data Demo
- How to import JSON data to Data Miner for Mining
- ORACLE BI, DW, ANALYTICS, BIG DATA AND SPATIAL USER COMMUNITY - BIWA Summit'15 www.biwasummit.org