$$\partial u\partial u$$ Multi-Tenanted Framework: Distributed Near Duplicate Detection for Big Data

Abstract

Near duplicate detection algorithms have been proposed and implemented in order to detect and eliminate duplicate entries from massive datasets. Due to the differences in data representation (such as measurement units) across different data sources, potential duplicates may not be textually identical, even though they refer to the same real-world entity. As data warehouses typically contain data coming from several heterogeneous data sources, detecting near duplicates in a data warehouse requires a considerable memory and processing power. Traditionally, near duplicate detection algorithms are sequential and operate on a single computer. While parallel and distributed frameworks have recently been exploited in scaling the existing algorithms to operate over larger datasets, they are often focused on distributing a few chosen algorithms using frameworks such as MapReduce. A common distribution strategy and framework to parallelize the execution of the existing similarity join algorithms is still lacking. In-Memory Data Grids (IMDG) offer a distributed storage and execution, giving the illusion of a single large computer over multiple computing nodes in a cluster. This paper presents the research, design, and implementation of $$\partial u\partial u$$ , a distributed near duplicate detection framework, with preliminary evaluations measuring its performance and achieved speed up. $$\partial u\partial u$$ leverages the distributed shared memory and execution model provided by IMDG to execute existing near duplicate detection algorithms in a parallel and multi-tenanted environment. As a unified near duplicate detection framework for big data, $$\partial u\partial u$$ efficiently distributes the algorithms over utility computers in research labs and private clouds and grids.