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Begoli, Edmon

Publications and source records attributed to Begoli, Edmon.

43 records · Page 3

An Emerging Role for Polystores in Precision Medicine

Medical data is organically heterogeneous, and it usually varies significantly in both size and composition. Yet, this data is also a key for the recent and promising field of precision medicine, which focuses on identifying and tailoring appropriate medical treatments for the needs of the individual patients, based on their specific conditions, their medical history, lifestyle, genetic, and other individual factors. As we, and a database community at large, recognize that a “one size does not fit all” solution is required to work with such data, we present in this paper our observations based on our experiences, and the applications in the field of precision medicine. Finally, we make the case for the use of polystore architecture; how it applies for precision medicine; we discuss the reference architecture; describe some of its critical components (array database); and discuss the specific types of analysis that directly benefit from this database architecture, and the ways it serves the data.

60 APPLIED LIFE SCIENCES↗

Storage and Read-Optimized Data Placement Structures for High-Performance Analysis [Book Chapter]

Here, we present state-of-art structures and methods for efficient data preparation and representation for analysis. Our intent is to introduce the data science and analytics communities to open source data placements, structures, and methods. These practices can make the foundational processes of data preparation and access dramatically more efficient than typical raw file or database representations and use more conservative storage. To illustrate this, we introduce two highly efficient data placement structures. We then present a tutorial, supported by step-by-step examples, of how to create, use, and access data, structured by Parquet or ORC (Optimized Row Columnar), using Apache Spark. Finally, we illustrate the benefits of using these structures with computational and storage volume benchmarks.

97 MATHEMATICS AND COMPUTING↗

Towards a Heterogeneous, Polystore-like Data Architecture for the US Department of Veteran Affairs (VA) Enterprise Analytics

The Polystore architecture revisits the federated approach to access and querying of the standalone, independent databases in the uniform and optimized fashion, but this time in the context of heterogeneous data and specialized analyses. In the light of this architectural philosophy, and in the light of the major data architecture development efforts at the US Department of Veterans Administration (VA), we discuss the need for the heterogeneous data store consisting of the large relational data warehouse, an image and text datastore, and a peta-scale genomic repository. The VA's heterogeneous datastore would, to a larger or smaller degree, follow the architectural blueprint proposed by the polystore architecture. To this end, we discuss the current state of the data architecture at VA, architectural alternatives for development of the heterogeneous datastore, the anticipated challenges, and the drawbacks and benefits of adopting the polystore architecture.

Federated Databases↗

Real-Time Discovery Services over Large, Heterogeneous and Complex Healthcare Datasets Using Schema-Less, Column-Oriented Methods

We present a service platform for schema-leess exploration of data and discovery of patient-related statistics from healthcare data sets. The architecture of this platform is motivated by the need for fast, schema-less, and flexible approaches to SQL-based exploration and discovery of information embedded in the common, heterogeneously structured healthcare data sets and supporting components (electronic health records, practice management systems, etc.) The motivating use cases described in the paper are clinical trials candidate discovery, and a treatment effectiveness analysis. Following the use cases, we discuss the key features and software architecture of the platform, the underlying core components (Apache Parquet, Drill, the web services server), and the runtime profiles and performance characteristics of the platform. We conclude by showing dramatic speedup with some approaches, and the performance tradeoffs and limitations of others.

Apache Drill Apache Parquet Column Oriented Stores↗

Hierarchical Cluster Analysis of Service Usage and Geographic Variation in Medicare Spending

Per capita Medicare spending varies considerably throughout the Unites States, even after taking into account factors such as cost of living and pre-existing conditions of the Medicare populations. Additionally, per capita spending may vary up to four times as much under Medicare as it does under private insurance. Several studies have explored the Medicare-costs differences between regions with respect to how Medicare services are used. The recent public release of a large, relatively fine-grained set of Medicare data over four annual periods (2007–2010) by the Centers for Medicare and Medicaid Services (CMS), allowed us to perform a broad-scale exploratory analysis of Medicare service-usage patterns. The CMS data set contains data on the Health Care Referral Region level, which subsets the United States into 306 distinct regions. To identify potential trends in service usage over these regions, we used hierarchical agglomerative cluster analysis (HACA) to group regions into clusters based on similarity of service-usage patterns. Much previous work on the subject of cost variation starts with analysis of cost. By beginning with service-usage data, HACA effectively uncoupled patterns of service-usage from overall patterns of cost. We found that while some Medicare services were used in a fairly consistent manner over all clusters, other services varied considerably. Variation in how frequently services are used may provide insight into practices that are particularly expensive or particularly cost effective. It also may be possible to develop recommendations for more cost-efficient high-quality medical care by taking such information into account. In addition, the variance among the frequencies with which some services are used may point to inconsistent standards of medical practice that are not necessarily directly reflected in total cost, or to more deeply underlying geographic or demographic factors that are not necessarily reflected directly in total cost.

Vacha, Linnet↗

Performance implications from sizing a VM on multi-core systems: A Data analytic application s view

In this paper, we present a quantitative performance analysis of data analytics applications running on multi-core virtual machines. Such environments form the core of cloud computing. In addition, data analytics applications, such as Cassandra and Hadoop, are becoming increasingly popular on cloud computing platforms. This convergence necessitates a better understanding of the performance and cost implications of such hybrid systems. For example, the very rst step in hosting applications in virtualized environments, requires the user to con gure the number of virtual processors and the size of memory. To understand performance implications of this step, we benchmarked three Yahoo Cloud Serving Benchmark (YCSB) workloads in a virtualized multi-core environment. Our measurements indicate that the performance of Cassandra for YCSB workloads does not heavily depend on the processing capacity of a system, while the size of the data set is critical to performance relative to allocated memory. We also identi ed a strong relationship between the running time of workloads and various hardware events (last level cache loads, misses, and CPU migrations). From this analysis, we provide several suggestions to improve the performance of data analytics applications running on cloud computing environments.

Lim, Seung-Hwan↗