Graphs have become increasingly important in modeling structures with broad applications like Chemical informatics, Bioinformatics, Web page retrieval and World Wide Web. Frequent graph pattern mining plays an important role in many data mining tasks to find interesting patterns from graph databases. Among different graph patterns, frequent substructures are the very basic patterns that can be discovered in a collection of graphs. We extended the problem of mining frequent subgraph patterns to the problem of mining sequential patterns in a graph database. In this paper, we introduce the concept of Sequential Graph-Pattern Mining and proposed two novel algorithms SFG(Sequential Frequent Graph Pattern Mining) and TCSFG(Top-k Closed Sequential Frequent Graph Pattern Mining). SFG generates all the frequent sequences from the graph database, whereas TCSFG generates top-k frequent closed sequences. We have applied these algorithms on synthetic graph database and generated top-k frequent graph sequences.

Money laundering is a criminal activity to disguise black money as white money. It is a process by which illegal funds and assets are converted into legitimate funds and assets. Money Laundering occurs in three stages: Placement, Layering, and Integration. It leads to various criminal activities like Political corruption, smuggling, financial frauds, etc. In India there is no successful Anti Money laundering techniques which are available. The Reserve Bank of India (RBI), has issued guidelines to identify the suspicious transactions and send it to Financial Intelligence Unit (FIU). FIU verifies if the transaction is actually suspicious or not. This process is time consuming and not suitable to identify the illegal transactions that occurs in the system. To overcome this problem we propose an efficient Anti Money Laundering technique which can able to identify the traversal path of the Laundered money using Hash based Association approach and successful in identifying agent and integrator in the layering stage of Money Laundering by Graph Theoretic Approach.

The massive increases in data have paved a path for distributed computing, which in turn can reduce the data processing time. Though there are various approaches in distributed computing, Hadoop is one of the most efficient among the existing ones. Hadoop consists of different elements out of which Map Reduce is a scalable tool that enables to process a huge data in parallel. We proposed a Novel and Efficient User Profile Characterization under distributed environment. In this frame work the network anomalies are detected by using Hadoop Map Reduce technique. The experimental results clearly show that the proposed technique shows better performance.