Chris Hillman

Chris Hillman

Kingston upon Thames, United Kingdom
Information Technology and Services

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Chris Hillman's Overview


500+ connections

Chris Hillman's Publications

  • Near Real-Time Processing of Proteomics Data Using Hadoop

    • Liebert
    • March 14, 2014
    Authors: Chris Hillman

    This article presents a near real-time processing solution using MapReduce and Hadoop. The solution is aimed at some of the data management and processing challenges facing the life sciences community. Research into genes and their product proteins generates huge volumes of data that must be extensively preprocessed before any biological insight can be gained. In order to carry out this processing in a timely manner, we have investigated the use of techniques from the big data field. These are applied specifically to process data resulting from mass spectrometers in the course of proteomic experiments. Here we present methods of handling the raw data in Hadoop, and then we investigate a process for preprocessing the data using Java code and the MapReduce framework to identify 2D and 3D peaks.

Chris Hillman's Skills & Expertise

  1. Data Warehousing
  2. Hadoop
  3. Dimensional Modeling
  4. Big Data
  5. OLAP
  6. Business Intelligence
  7. Analytics
  8. Data Modeling
  9. ETL
  10. Data Warehouse Architecture
  11. Data Integration
  12. Predictive Analytics
  13. Business Intelligence Tools
  14. Teradata
  15. Database Design
  16. Data Mining
  17. Business Analysis
  18. Data Quality
  19. Master Data Management
  20. Dashboard
  21. Data Marts
  22. Business Analytics

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Chris Hillman's Projects

  • MSc Thesis: Investigation of the Extraction, Transformation and Loading of Mass Spectrometer RAW Files

    • July 2011 to September 2011
    Team Members: Chris Hillman

    Proteomic experiments produce large quantities of data, the volume of this data and the complexity of the analysis conducted on it is constantly increasing. It has been predicted that the technological capabilities in this field will continue to double each year for the foreseeable future, a so called Moore’s Law for proteomics (Kuster et al., 2005). Furthermore proteomics will shift from a pure discovery phase to a re-measurement mode as the data regarding specific proteins is captured and stored in databases. This will lead to increasingly comprehensive but also faster and more accessible whole-proteome quantitation (Cox and Mann, 2007).
    In order to facilitate the efficient analysis of this data, techniques from the field of business intelligence (BI) have been previously proposed and studied (Boulon et al., 2010). Both proteomics and BI are wide and varied subjects, in this thesis we begin with a brief description of each along with the principal instrument of experimentation, the Mass Spectrometer. Following this description we will investigate the data produced from the experiments and how the source files containing this data can be processed and stored. A core BI methodology is followed, namely Extract, Transform and Load (ETL) which is described by Ralph Kimball, one of the key innovators in BI, as “The foundation of the data warehouse”(Kimball, 2004). Finally, we investigate how the data may need to be reprocessed in the future or stored in frameworks, such as Hadoop, which are substantially different to the standard relational database and are designed to support large scale analysis and data mining.

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