Category Archives: Data Science

Elastic Search or Solr Search Result Quality Evaluation with NCDG on Spark

You have built an enterprise search engine with Elastic Search or Solr. You have tweaked all the knobs in the search engine to get the best possible quality for the search results. But how do you know how well your … Continue reading

Posted in Big Data, Data Science, elastic search, Log Analysis, Scala, Search Analytic, Solr, Spark | Tagged , , , | Leave a comment

Plugin Framework Based Data Transformation on Spark

Data transformation is one of the key components in most ETL process. It is well known, that in most data projects, more than 50% of the time in spent in data pre processing. In my earlier blog, a Hadoop based … Continue reading

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Normal Distribution Fitness Test with Chi Square on Spark

Many Machine Learning models is based on certain assumptions made about the data. For example, in ZScore basedĀ  anomaly detection, it isĀ  assumed that the data has normal distribution. Your Machine Learning model will be as good as how those … Continue reading

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Learning Alarm Threshold from User Feedback using Decision Tree on Spark

Alarm fatigue is a phenomena where some one is exposed to large number of alarms, become desensitized to them and start ignoring them. It’s been reported that security professionals ignore 32% of alarms because they are thought to be false. … Continue reading

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Contextual Outlier Detection with Statistical Modeling on Spark

Sometimes an outlier is defined with respect to a context. Whether a data point should be labeled as an outlier depends on the associated context. For a bank ATM, transactions that are considered normal between 6 AM and 10 PM, … Continue reading

Posted in Anomaly Detection, Big Data, Data Science, Spark | Tagged , , | 1 Comment

Pluggable Rule Driven Data Validation with Spark

Data validation is an essential component in any ETL data pipeline. As we all know most Data Engineers and Scientist spend most of their time cleaning and preparing their data before they can even get to the core processing of … Continue reading

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Leave One Out Encoding for Categorical Feature Variables on Spark

Categorical feature variables is a thorny issue for many supervised Machine Learning algorithms. Many learning algorithms can not handle categorical feature variables. In this post, we will go over an encoding scheme called Leave One Out Encoding, as implemented with … Continue reading

Posted in Big Data, Data Science, ETL, Spark | Tagged | 2 Comments