Main Themes

Big Data is increasingly becoming an important part of scientific research across multiple disciplines. Researchers, IT specialists and data scientists have to design and employ  discipline-specific solutions for Big Data management that efficiently extract maximum knoweldge from raw data. This process of obtaining insight from Big Data follows the same three major steps for all disciplines: (i) an IT infrastructure needs to be designed such that it supports data storage, processing and archiving, (ii) data needs to be structured (via metadata) to support further analysis, and (iii) extracted information needs to be represented and visualized in a way that leads to maximum knowledge extraction. The Target conference aims to bring together scientists and computer specialists from a variety of disciplines and explore how each community handles the challenges of Big Data management for each of the three main steps. Ultimately, we seek to arrive at a model for Big Data solutions that caters to the needs of individual disciplines while employing shared hardware and computing infrastructure. 

Big Data stages across disciplines

Theme 1: Big Data Technology

In these sessions, we will examine the current trends in technologies supporting the processing of Big Data. In particular, we will look at high speed data acquisition systems, data storage, data networks and processing facilities. Contributions from technology suppliers, hardware and software, together with projects which are driving this technology will be featured. The conflicting requirements of performance and high availability, are of particular interest to a number of large-scale projects on the horizon. Similarly, the requirement to ensure data privacy in medical research represents an increasing challenge to existing technological solutions.

Theme 2: Big Data Metadata

Metadata or "data about data" becomes increasingly important in managing Big Data. When data has been acquired the use of metadata allows the imposition of structure, so that subsequent analysis can take place. In these sessions, we will focus on examining the role that detailed data models can play in a number of disciplines from astronomy to social sciences that deal with Big Data. Topics which will be covered include data mining, querying large and ultra-large catalogues, application of relation and object-orientated databases, data providence and data lineage.

Theme 3: Big Data Visualization

The final phase in Big Data management, following data processing, is gaining insight and understanding from Big Data. In these sessions, the role of visualization techniques in extracting knowledge from data will be examined. We expect visual analytics (VA) to play an increasing valuable role in the future interpretation of Big Data. VA methodologies can allow the user to discover interesting and unexpected relationships in complex multi-dimensional datasets. In addition there will be contributions on the role that visualization can play in presenting complex science principles to the general public.

Big data management phases