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Sukumar, Sreenivas R

Publications and source records attributed to Sukumar, Sreenivas R.

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Enabling Graph Mining in RDF Triplestores using SPARQL for Holistic In-situ Graph Analysis

The graph analysis is now considered as a promising technique to discover useful knowledge in data with a new perspective. We envi- sion that there are two dimensions of graph analysis: OnLine Graph Analytic Processing (OLGAP) and Graph Mining (GM) where each respectively focuses on subgraph pattern matching and automatic knowledge discovery in graph. Moreover, as these two dimensions aim to complementarily solve complex problems, holistic in-situ graph analysis which covers both OLGAP and GM in a single system is critical for minimizing the burdens of operating multiple graph systems and transferring intermediate result-sets between those systems. Nevertheless, most existing graph analysis systems are only capable of one dimension of graph analysis. In this work, we take an approach to enabling GM capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.) in RDF triplestores, which are originally developed to store RDF datasets and provide OLGAP capability. More specifically, to achieve our goal, we implemented six representative graph mining algorithms using SPARQL. The approach allows a wide range of available RDF data sets directly applicable for holistic graph analysis within a system. For validation of our approach, we evaluate performance of our implementations with nine real-world datasets and three different computing environments - a laptop computer, an Amazon EC2 instance, and a shared-memory Cray XMT2 URIKA-GD graph-processing appliance. The experimen- tal results show that our implementation can provide promising and scalable performance for real world graph analysis in all tested environments. The developed software is publicly available in an open-source project that we initiated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Fraud Detection in Healthcare

We discuss the problem of fraud detection in healthcare in this chapter. Given the recent scrutiny of the ineciencies in the US healthcare system, identifying fraud has been on the forefront of the eorts towards reducing the healthcare costs. In this chapter we will focus on understanding the issue of healthcare fraud in detail, and review methods that have been proposed in the literature to combat this issue using data driven approach.

data analytics↗
DOE OSTI2015

Graph processing platforms at scale: practices and experiences

Graph analysis unveils hidden associations of data in many phenomena and artifacts, such as road network, social networks, genomic information, and scientific collaboration. Unfortunately, a wide diversity in the characteristics of graphs and graph operations make it challenging to find a right combination of tools and implementation of algorithms to discover desired knowledge from the target data set. This study presents an extensive empirical study of three representative graph processing platforms: Pegasus, GraphX, and Urika. Each system represents a combination of options in data model, processing paradigm, and infrastructure. We benchmarked each platform using three popular graph operations, degree distribution, connected components, and PageRank over a variety of real-world graphs. Our experiments show that each graph processing platform shows different strength, depending the type of graph operations. While Urika performs the best in non-iterative operations like degree distribution, GraphX outputforms iterative operations like connected components and PageRank. In addition, we discuss challenges to optimize the performance of each platform over large scale real world graphs.

Lim, Seung-Hwan↗
DOE OSTI2015

Graph Mining Meets the Semantic Web

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today, data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. We address that need through implementation of three popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, and PageRank). We implement these algorithms as SPARQL queries, wrapped within Python scripts. We evaluate the performance of our implementation on 6 real world data sets and show graph mining algorithms (that have a linear-algebra formulation) can indeed be unleashed on data represented as RDF graphs using the SPARQL query interface.

Lee, Sangkeun↗
DOE OSTI2015

Open Research Challenges with Big Data - A Data-Scientist s Perspective

In this paper, we discuss data-driven discovery challenges of the Big Data era. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical data mining and machine learning under more scrutiny and evaluation for gleaning insights from the data than ever before. In that context, we pose and debate the question - Are data mining algorithms scaling with the ability to store and compute? If yes, how? If not, why not? We survey recent developments in the state-of-the-art to discuss emerging and outstanding challenges in the design and implementation of machine learning algorithms at scale. We leverage experience from real-world Big Data knowledge discovery projects across domains of national security, healthcare and manufacturing to suggest our efforts be focused along the following axes: (i) the data science challenge - designing scalable and flexible computational architectures for machine learning (beyond just data-retrieval); (ii) the science of data challenge the ability to understand characteristics of data before applying machine learning algorithms and tools; and (iii) the scalable predictive functions challenge the ability to construct, learn and infer with increasing sample size, dimensionality, and categories of labels. We conclude with a discussion of opportunities and directions for future research.

Big data↗
DOE OSTI2015

Using Clinical Data, Hypothesis Generation Tools and PubMed Trends to Discover the Association between Diabetic Retinopathy and Antihypertensive Drugs

Diabetic retinopathy (DR) is a leading cause of blindness and common complication of diabetes. Many diabetic patients take antihypertensive drugs to prevent cardiovascular problems, but these drugs may have unintended consequences on eyesight. Six common classes of antihypertensive drug are angiotensin converting enzyme (ACE) inhibitors, alpha blockers, angiotensin receptor blockers (ARBs), -blockers, calcium channel blockers, and diuretics. Analysis of medical history data might indicate which of these drugs provide safe blood pressure control, and a literature review is often used to guide such analyses. Beyond manual reading of relevant publications, we sought to identify quantitative trends in literature from the biomedical database PubMed to compare with quantitative trends in the clinical data. By recording and analyzing PubMed search results, we found wide variation in the prevalence of each antihypertensive drug in DR literature. Drug classes developed more recently such as ACE inhibitors and ARBs were most prevalent. We also identified instances of change-over-time in publication patterns. We then compared these literature trends to a dataset of 500 diabetic patients from the UT Hamilton Eye Institute. Data for each patient included class of antihypertensive drug, presence and severity of DR. Graphical comparison revealed that older drug classes such as diuretics, calcium channel blockers, and -blockers were much more prevalent in the clinical data than in the DR and antihypertensive literature. Finally, quantitative analysis of the dataset revealed that patients taking -blockers were statistically more likely to have DR than patients taking other medications, controlling for presence of hypertension and year of diabetes onset. This finding was concerning given the prevalence of -blockers in the clinical data. We determined that clinical use of -blockers should be minimized in diabetic patients to prevent retinal damage.

Big data↗
DOE OSTI2015

Enabling Graph Appliance for Genome Assembly

In recent years, there has been a huge growth in the amount of genomic data available as reads generated from various genome sequencers. The number of reads generated can be huge, ranging from hundreds to billions of nucleotide, each varying in size. Assembling such large amounts of data is one of the challenging computational problems for both biomedical and data scientists. Most of the genome assemblers developed have used de Bruijn graph techniques. A de Bruijn graph represents a collection of read sequences by billions of vertices and edges, which require large amounts of memory and computational power to store and process. This is the major drawback to de Bruijn graph assembly. Massively parallel, multi-threaded, shared memory systems can be leveraged to overcome some of these issues. The objective of our research is to investigate the feasibility and scalability issues of de Bruijn graph assembly on Cray s Urika-GD system; Urika-GD is a high performance graph appliance with a large shared memory and massively multithreaded custom processor designed for executing SPARQL queries over large-scale RDF data sets. However, to the best of our knowledge, there is no research on representing a de Bruijn graph as an RDF graph or finding Eulerian paths in RDF graphs using SPARQL for potential genome discovery. In this paper, we address the issues involved in representing a de Bruin graphs as RDF graphs and propose an iterative querying approach for finding Eulerian paths in large RDF graphs. We evaluate the performance of our implementation on real world ebola genome datasets and illustrate how genome assembly can be accomplished with Urika-GD using iterative SPARQL queries.

Big data↗