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I am a software architect working in service hosting area. I am interested and specialized in SaaS, Cloud computing and Parallel processing. Ricky is a DZone MVB and is not an employee of DZone and has posted 88 posts at DZone. You can read more from them at their website. View Full User Profile

BI at large scale

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As more and more data being collected everywhere from pretty much everything a user do, such as transactions activities, social interactions, information search ... enterprises has been actively looking into ways to turn these vast amount of raw data into useful information.

BI process flow

It include the following stages of processing
  1. ETL: Extract operational data (inside enterprise or external sources) into data warehouse (typically organized in Star/Snowflake schema with Fact and Dimension tables).
  2. Data exploration: Get insight into data using simple visualization tools (e.g. histogram, summary statistics) or sophisticated OLAP tools (slice, dice, rollup, drilldown)
  3. Report generation: Produce executive reports
  4. Data mining: Extract patterns of the underlying data to form models (e.g. bayesian networks, linear regression, neural networks, decision trees, support vector machines, nearest neighbors, association rules, principal component analysis)
  5. Feedback: The model will be used to assist business decision making (predicting the future)
The gap of processing BIG data
Many data mining and machine learning algorithms are available in both commercial packages (e.g. SAS, SPSS) as well as open source libraries (e.g. Weka, R). Nevertheless, most of these ML algorithms implementation are based on fitting al data in memory and not designed to process big data (e.g. Tera byte data volume).

On the other hand, massively parallel processing platform such as Hadoop, Map/Reduce, over the last few years, has been proven in processing Terabyte or even Petabyte range of data. Although many sequential algorithm can be restructured to run in map reduce, including a big portion of machine learning algorithm, there isn't a corresponding parallel implementation of ML available in massively parallel form.

Approach 1: Apache Mahout
One approach is to "re-implement" the ML algorithm in Map/Reduce and this is the path of Apache Mahout project. Mahout seems to have implemented an impressive list of algorithms although I haven't used them for my projects yet.

Approach 2: Ensemble of parallel independent learners
This is an alternative path that doesn't require re-implementation of existing algorithms. It works in the following way.
  1. Draw samples from the Big data into many sample data sets, which can fit into the memory of a single, individual learner.
  2. Assign each sample data set to an individual learner, who use existing algorithms to learn the model. After learning, each individual learner keep their own learned model
  3. When a decision / prediction request is received, each individual learner will come up with its own prediction and then combine their results in some ways. (e.g. for classification task, the learners will vote for the predicted class and the majority wins. for regression, the average of the estimate values will be used to predict the output value)

I also found this approach can smoothly fade out outdated model. As user's behavior may change over time, same happens to the validity of a learned model. With this ensemble approach, I can have multiple learners each learn their model periodically. Everytime when a prediction is needed, I will pick the latest k models and combine the final prediction based on a time-decayed weighted voting model. Outdated model will automatically slide out the k-size window automatically.

One gotchas of sampling approach is the handling of rare events (since you may lost those rare events in sampling). In this case, stratified sampling (instead of simple random sampling) should be used.
Published at DZone with permission of Ricky Ho, author and DZone MVB. (source)

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Thomas Bernhardt replied on Mon, 2011/03/07 - 2:07pm

One possible way of dealing with large amounts of data (in real-time or stored) is to use a complex event processing (CEP). CEP engines are designed to derive information from large amounts of data that can arrive continuously as an event stream or analyzing past data by means of replay. Filtering, aggregating, detecting patterns and keeping only the data beeing looked for in memory or on disk can be done easier via CEP engine  since the CEP engine provides a (sometimes SQL-like) domain-specific language to declare what data is being looked for or derived. 

One such CEP engine is Esper (


Mb Junior replied on Tue, 2011/03/08 - 6:35pm

Well there are also other approaches. Some of implementations of Data Mining models can read instances directly from databases, such that it is nor required to store whole dataset at memory ex. see RapidMiner. Another approach is to filter data and leave just the important vectors. Of course you can re-sample data, that can be done on various sophisticated ways ex. see Motoda, Liu "Instance Selection and Construction for Data Mining". Of course suggested approach is even better then building one model, which for very large datasets may have problems with convergence.

Jessica Trishy replied on Fri, 2014/11/14 - 4:02pm

 see Motoda, Liu "Instance Selection and Construction for Data Mining". Of course suggested approach is even better then building one model, which for very large datasets may have problems with convergence.

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