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Oracle Advanced Analytics OverviewUNIXBusinessApplication

Oracle Advanced Analytics is the #7 ranked solution in our list of top Data Mining tools. It is most often compared to SAS Analytics: Oracle Advanced Analytics vs SAS Analytics

What is Oracle Advanced Analytics?

Oracle Advanced Analytics 12c delivers parallelized in-database implementations of data mining algorithms and integration with open source R. Data analysts use Oracle Data Miner GUI and R to build and evaluate predictive models and leverage R packages and graphs. Application developers deploy Oracle Advanced Analytics models using SQL data mining functions and R. With the Oracle Advanced Analytics option, Oracle extends the Oracle Database to an sclable analytical platform that mines more data and data types, eliminates data movement, and preserves security to anticipate customer behavior, detect patterns, and deliver actionable insights. Oracle Big Data SQL adds new big data sources and Oracle R Advanced Analytics for Hadoop provides algorithms that run on Hadoop. 

Oracle Advanced Analytics is also known as OAA.

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Oracle Advanced Analytics Customers

Orbitz, Marriott, SGS Life Science, Masdar, AlliantEnergy Corporation, British Standards Institute, Skybox Security, Triple PointTechnology, and Coca Cola.

Oracle Advanced Analytics Video

Archived Oracle Advanced Analytics Reviews (more than two years old)

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ITCS user
CEO with 1,001-5,000 employees
Vendor
First Look – Oracle Advanced Analytics
Oracle Advanced Analytics is a new Oracle database option (announced today) that bundles Oracle R Enterprise and Oracle Data Mining (reviewed previously). With this release, R becomes a first class native interface for the Oracle database along with SQL and the graphic interface that ships with Oracle Data Mining.  This allows analytic modeling code to be written in 100% R with the tables and views in the Oracle database appearing as R objects directly. There is no need for modelers to write SQL – they can just write R code and manipulate the data in the database. This makes it easier for R programmers to access the database (no extracting the data to a file, no writing SQL) and the availability of data extends to Oracle OLAP Cubes that can also be accessed from the R code. Performance…

Oracle Advanced Analytics is a new Oracle database option (announced today) that bundles Oracle R Enterprise and Oracle Data Mining (reviewed previously). With this release, R becomes a first class native interface for the Oracle database along with SQL and the graphic interface that ships with Oracle Data Mining.  This allows analytic modeling code to be written in 100% R with the tables and views in the Oracle database appearing as R objects directly. There is no need for modelers to write SQL – they can just write R code and manipulate the data in the database. This makes it easier for R programmers to access the database (no extracting the data to a file, no writing SQL) and the availability of data extends to Oracle OLAP Cubes that can also be accessed from the R code.

Performance is good with the approach for a number of reasons. First, with this set up, the database computing hardware is used and all the R packages are being executed on the database server. The approach further improves performance by allowing the data to be accessed without extracting or moving it. Finally all the ODM algorithms are available to Oracle R Enterprise so that R packages can use the ODM algorithms already deeply embedded and optimized for the Oracle database as well as the Oracle Exadata and Oracle Exalytics machines.

Besides improving data access and performance for R, Oracle R Enterprise also allows a piece of R code that builds a model, makes a forecast or scores a customer to be treated as a database function. Once deployed to an Oracle database function this R code can then be called by any piece of SQL (in a BI tool like OBIEE or Java code or business rules). Any arbitrary R code can be executed in this way with no constraint on inputs or outputs or the code/packages being used. This supports the increasing focus of modelers on real-time scoring by making it easy to embed R code as SQL-friendly functions that can be called to calculate a score or make a prediction right when the decision is being made.

This release builds on previous work around making Big Data available to R. The Oracle R connector for Hadoop is available in conjunction with the support for R in the Oracle Big Data appliance. These allow you to run R against both Oracle’s own Big Data appliance and against a generic Hadoop/HDFS installation with no need to convert it to MapReduce for execution. This means that a user can develop a single R script that brings in data from an Oracle database, Oracle Big Data Appliance and HDFS and have it all look like R objects to the script. These scripts can then be deployed to the Oracle Database or Oracle Big Data infrastructure for real-time scoring against this diverse set of data sources.

This will clearly be a product considered in the forthcoming Decision Management Systems Platform Report.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
it_user7308
Consultant with 51-200 employees
Vendor
Oracle Data Mining vs. SAS is like Support Vector Machines vs. Neural Nets
I’m part of a small group of mathematics enthusiasts in Kansas City who meet about once a month on Saturday mornings to drink coffee and discuss mathematics. This past weekend it was my turn to do a presentation to the rest of the group and I chose to speak on the mathematical foundations of the Support Vector Machine algorithm in Oracle Data Mining. While I wasn’t surprised that some in the group had a better handle on Vapnik-Chervonenkis theory than I and gently “guided” me a few times, I was somewhat surprised at their positive reaction to my characterization of the “Oracle” approach to data mining in contrast with the “SAS” approach. While gross simplifications are always “gross”, here is my take on what I believe to be very different philosophies. Let’s use classification as an…

I’m part of a small group of mathematics enthusiasts in Kansas City who meet about once a month on Saturday mornings to drink coffee and discuss mathematics. This past weekend it was my turn to do a presentation to the rest of the group and I chose to speak on the mathematical foundations of the Support Vector Machine algorithm in Oracle Data Mining. While I wasn’t surprised that some in the group had a better handle on Vapnik-Chervonenkis theory than I and gently “guided” me a few times, I was somewhat surprised at their positive reaction to my characterization of the “Oracle” approach to data mining in contrast with the “SAS” approach. While gross simplifications are always “gross”, here is my take on what I believe to be very different philosophies. Let’s use classification as an example since we’re talking about SVMs.

I think of the “SAS” approach to be similar to that of a “statistician” or classic data scientist. That is, there is a desire to understand the algorithm in context of the data set. The main objective is to identify and understand the source(s) of error in the model and to characterize the algorithm through the use various coefficients and ratios. A good deal of effort is spent in the evaluation process of the algorithm and in understanding the impact of different choices in methodology. The SAS perspective emphasizes understanding the data preparation and the algorithm. The more detail, the better.

The “Oracle” approach to data mining is characterized by a broader set of business concerns from data security and processing time to the business value of results. An “ODM” approach would be to invest time to develop and test several different models and see which ones have more predictive power and to spend relatively less time in evaluation of the individual models. Another way to say this is that error is error and rather than understanding sources of error, we should take that same time and try to understand business implications of the positive “working” part of the model. The more business value, the better.

The fundamental concept behind Support Vector Machines is to take a highly dimensional data set and to separate two classes of data by a hyperplane that maximizes the marginal distance between the data points (i.e. perfectly in the middle). Let’s contrast this with a “neural net” algorithm which is another classification approach. Neural nets use iteration and a wide variety of statistical techniques to “tune” their algorithm to minimize the predictive error across a particular training data set. Support Vector Machines tend to be more robust and work well across a broad range of new data sets. Neural Nets are more precise, but also are prone to “over fitting” their training data set and typically are less robust. I think of the “Oracle” data mining approach to be like the Support Vector Machine. It uses a very strong mathematical foundation that is highly generalizable and works well across a broad range of data sets. The “SAS” data mining approach uses highly complex mathematical techniques specific to a given situation. The “Oracle” approach minimizes the “structural risk” of classification or it uses an approach that is least likely to produce error. The “SAS” approach minimizes the “empirical risk” of classification or it uses an approach minimizes the total error.

I have nothing but respect for SAS practitioners. They are true experts and tend to personify the “if it’s worth doing, it’s worth doing right” approach to analytics. Of course the contrasting position is, “the perfect is the enemy of the good.” Time spent perfecting a model is forever lost and often more value is delivered by moving more quickly and accomplishing more. The Support Vector Machine algorithm deployment in Oracle Data Mining is good, very good. It recognizes that there is an inherent tradeoff between algorithmic complexity and the ability to generalize across new data sets. It uses a sensible automatic data preparation process that makes good choices and then leverages a replicable, explainable, foundationally solid methodology for balancing tradeoffs. In short, even proof-obsessed, dyed-in-the-wool mathematicians can recognize the inherent value of Oracle’s Support Vector Machine strategy.

Disclosure: My company is a Oracle Gold Partner

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Find out what your peers are saying about Oracle, SAS, IBM and others in Data Mining. Updated: October 2021.
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it_user3882
IT Administrator at a tech services company with 501-1,000 employees
Consultant
Powerful, but problems with huge data sets

Valuable Features:

• Oracle Data Mining provides a powerful functionality as it enables users to discover new insights hidden in data. • It enables building of predictive models. • Its model can be included in SQL queries and entrenched in applications to offer improved business intelligence. • It has more than ten data mining algorithms that can be used in data mining.

Room for Improvement:

• Problems in handling huge data sets • Cannot be used to process statistical data • Performance benchmarks are difficult in data mining

Other Advice:

I use the models of this product to derive predictions and descriptions of behavior from the database system. It eases work of moving data from relational tables into the analytical workbenches. It simplifies model implementation by submitting Oracle SQL functions to score data stored within the database.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
ITCS user
-- at a university
Vendor
Simple, quick, easy to use, but capabilities are very basic.

Valuable Features:

Easy to use, simple. Quick learning curve.

Room for Improvement:

Basic capabilities, less professional than would be expected from a company dealing with databases as a core business.

Valuable Features:

Easy to use, simple. Quick learning curve.

Room for Improvement:

Basic capabilities, less professional than would be expected from a company dealing with databases as a core business.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
it_user1221
Database Expert at a healthcare company with 501-1,000 employees
Vendor
Use this if you need to create adaptive data models for trending analysis.

Valuable Features:

Oracle Data Mining (ODM) provides data mining functionality as a part of Oracle Advanced Analytics Option. I have used this tool to model database login activity which was used to establish database activity trends and help in fraud detection and prevention. Once the data was loaded, used Oracle supplied analytical functions like Regression and “attribute Importance” to create a predictive model. Viewing the data on the Dashboard with charts is a great option this tool provides. Data Mining models can be included in SQL queries and embedded in applications to offer improved business intelligence.

Room for Improvement:

Since a separate Oracle database is needed, the data loading and cleansing the raw data can be a time consuming process.Licensing can be tricky with all the options in the Oracle OLAP suite.It is better to get the whole OLAP suite and use the features one needs rather than buying piece meal.

Other Advice:

One should try to automate the whole data loading and cleansing process using scripts to get a quick turnaround. Using shell and sql scripts one can automate this process. This product was chosen to establish database login patterns as a custom solution for a DAM (Data Access Monitoring) tool.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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