Graph Indexing for Efficient Evaluation of Label-constrained Reachability Queries

Given a directed edge labeled graph G , to check whether vertex v is reachable from vertex u under a label set S is to know if there is a path from u to v whose edge labels across the path are a subset of S . Such a query is referred to as a label-constrained reachability ( LCR ) query. In this article, we present a new approach to store a compressed transitive closure of G in the form of intervals over spanning trees (forests). The basic idea is to associate each vertex v with two sequences of some other vertices: one is used to check reachability from v to any other vertex, by using intervals, while the other is used to check reachability to v from any other vertex. We will show that such sequences are in general much shorter than the number of vertices in G. Extensive experiments have been conducted, which demonstrates that our method is much better than all the previous methods for this problem in all the important aspects, including index construction times, index sizes, and query times.

Efficient Algorithms for Cleaning and Indexing of Graph data

Information extraction and analysis from the enormous graph data is expanding rapidly. From the survey, it is observed that 80% of researchers spend more than 40% of their project time in data cleaning. This signifies a huge need for data cleaning. Due to the characteristics of big data, the storage and retrieval is another major concern and is addressed by data indexing. The existing data cleaning techniques try to clean the graph data based on information like structural attributes and event log sequences. The cleaning of graph data on a single piece of information alone will not increase the performance of computation. Along with node, the label can also be inconsistent, so it is highly desirable to clean both to improve the performance. This paper addresses aforesaid issue by proposing graph data cleaning algorithm to detect the unstructured information along with inconsistent labeling and clean the data by applying rules and verify based on data inconsistency. The authors propose an indexing algorithm based on CSS-tree to build an efficient and scalable graph indexing on top of Hadoop.

VFG – INDEX: A NOVEL GRAPH INDEXING METHOD

The latest advancements in science and technology have observed large quantity of complicated structures and schema less data such as proteins, circuits, images, Web, and XML documents which can be modeled into various types of which one of the most dominant now a days are graphs. This has caused Graph Mining, one of the budding areas of research happening throughout. In this paper, we investigate the various algorithms for Graph Indexing which makes use of Frequent Sub graph as a key term for indexing, of which one the most dominant is FG-index algorithm. This algorithm is tested with AIDS dataset and an improved algorithm is proposed for effective indexing.