Some types of data analysis are challenging. In particular, applications such as fraud detection, intelligence, network management, intrusion detection, root cause analysis and portfolio management can be demanding because of the need to spot individual anomalies or small clusters of data among huge data volumes ­ the "needle in the haystack."

These cases are particularly tough because many existing data analysis techniques are not well-suited for them. For example, OLAP and pivot tables summarize detailed granular data into roll-ups and averages. Unfortunately, a single fraudulent insurance claim may become invisible when averaged in with thousands of legitimate claims.

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