**TECHNOLOGY:** JAVA

**DOMAIN:** HADOOP

S. No. |
IEEE TITLE |
ABSTRACT |
IEEE YEAR |

1. | An Iterative MapReduce Based Frequent Subgraph Mining Algorithm | Frequent subgraph mining (FSM) is an important task for exploratory data analysis on graph data. Over the years, many algorithms have been proposed to solve this task. These algorithms assume that the data structure of the mining task is small enough to fit in the main memory of a computer. However, as the real-world graph data grows, both in size and quantity, such an assumption does not hold any longer. To overcome this, some graph database-centric methods have been proposed in recent years for solving FSM; however, a distributed solution using MapReduce paradigm has not been explored extensively. Since MapReduce is becoming the de-facto paradigm for computation on massive data, an efficient FSM algorithm on this paradigm is of huge demand. In this work, we propose a frequent subgraph mining algorithm called FSM-H which uses an iterative MapReduce based framework. FSM-H is complete as it returns all the frequent subgraphs for a given user-defined support, and it is efficient as it applies all the optimizations that the latest FSM algorithms adopt. Our experiments with real life and large synthetic datasets validate the effectiveness of FSM-H for mining frequent subgraphs from large graph datasets. | 2015 |

2. | Efficient Parallel Processing of Distance Join Queries Over Distributed Graphs | Distance join queries have recently been recognized as a particularly useful operation over graph data, since they capture graph similarity in a meaningful way. Consequently, they have been studied extensively in recent years [1], [2]. However, current methods are designed for centralized systems, and rely on the graph embedding for effective pruning and indexing. As graph sizes become very large and graph data must be deployed in the distributed environment, these techniques become impractical. In this work, we propose a solution for efficient parallel processing of distance join queries over distributed large graphs. There have been emerging efforts devoted to managing large graphs in distributed and parallel systems. Programming models like Pregel [3] and iterative computing framework like HaLoop [4] have been proposed to handle queries over distributed graphs. However, they are designed in the perspective of functionality instead of the query efficiency. In this work, we define an optimization problem: combining the iterative join and the graph exploration method to minimize the evaluation time of distance join queries. Without sacrificing a system’s scalability, our technique exploits a light-weight vertex centric encoding schema built on a distance-aware partition of the entire graph. Extensive experiments over both real and synthetic large graphs show that, by employing an adaptive query plan generation and scheduling method, we can effectively reduce the redundant message passing and I/O costs. Compared to simply using iterative join or graph exploration method, our solution achieves as many as one order of magnitude of time saving for the query evaluation. | 2015 |

3. | A Parallel Matrix-Based Method for Computing Approximations in Incomplete Information Systems | As the volume of data grows at an unprecedented rate, large-scale data mining and knowledge discovery present a tremendous challenge. Rough set theory, which has been used successfully in solving problems in pattern recognition, machine learning, and data mining, centers around the idea that a set of distinct objects may be approximated via a lower and upper bound. In order to obtain the benefits that rough sets can provide for data mining and related tasks, efficient computation of these approximations is vital. The recently introduced cloud computing model, MapReduce, has gained a lot of attention from the scientific community for its applicability to large-scale data analysis. In previous research, we proposed a MapReduce-based method for computing approximations in parallel, which can efficiently process complete data but fails in the case of missing (incomplete) data. To address this shortcoming, three different parallel matrix-based methods are introduced to process large-scale, incomplete data. All of them are built on MapReduce and implemented on Twister that is a lightweight MapReduce runtime system. The proposed parallel methods are then experimentally shown to be efficient for processing large-scale data. | 2015 |

4. | Real-Time Semiparametric Regression for Distributed Data Sets | This paper proposes a method for semiparametric regression analysis of large-scale data which are distributed over multiple hosts. This enables modeling of nonlinear relationships and both the batch approach, where analysis starts after all data have been collected, and the real-time setting are addressed. The methodology is extended to operate in evolving environments, where it can no longer be assumed that model parameters remain constant overtime. Two areas of application for the methodology are presented: regression modeling when there are multiple data owners and regression modeling within the MapReduce framework. A website, realtime-semiparametric-regression.net, illustrates the use of the proposed method on United States domestic airline data in real-time. | 2015 |