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T6 Information Quality in Information Fusion and Decision Making

Length: 3 hours

Intended Audience: This tutorial is intended for both researchers and practitioners from a wide variety of fields such as communication, intelligence, business processes, high performance computing, health care, and databases, who are interested in understanding the problems of information quality in information fusion and building methods for solution of these problems.

Description: Designing fusion systems for decision support in complex dynamic situations requires fusion of a large amount of multimedia and multispectral information to produce estimates about objects and gain knowledge of the entire domain of interest. Data and information to be processed and made sense of includes but is not limited to data obtained from physical sensors (infrared imagers, radars, chemical, etc.), human intelligence reports, and information obtained from open sources (traditional such as newspapers, radio, TV as well as social media such as Twitter, Facebook, Instagram).

The problem of building such fusion-based systems is complicated by the fact that data and information obtained from observations and reports as well as information produced by both human and automatic processes are of variable quality and may be unreliable, of low fidelity, insufficient resolution, contradictory, and/or redundant. It can come from a broken sensor or a sensor improperly used in the environmental context. A message obtained from a human sensor can contain a human error or is intentionally sent to skew the information. Furthermore, there is often no guarantee that evidence obtained from the sources is based on direct, independent observations. Sources may provide unverified reports obtained from other sources (e.g., replicating information in social networks), resulting in correlations and bias. In the more malicious setting, some sources may coordinate to provide similar information in order to reinforce their opinion in the system. The fusion methods used can be insufficient to achieve the required rigor.

The success of decision making in a complex fusion driven human-machine system depends on how well knowledge produced by fusion processes represents reality, which in turn depends on how adequate data are, how good and adequate is the fusion model used, and how accurate, appropriate or applicable prior and contextual knowledge is.

The tutorial will discuss major challenges and some possible approaches addressing the problem of representing and incorporating information quality into fusion processes. In particular it will present an ontology of quality of information and identify potential methods of representing and assessing the values of quality attributes and their combination. It will also examine the relation between information quality and context, and suggest possible approaches to quality control compensating for insufficient information and model quality.

Presenter: Galina Rogova

Dr. Rogova is a research professor at the State University at Buffalo as well as an independent consultant (DBA Encompass Consulting). She is a recognized expert in information fusion and decision making, and lectured internationally on this topic. Her other research expertise includes reasoning under uncertainty, information quality, machine learning, and image understanding. She has worked on a wide range of defense and non-defense problems such as situation and threat assessment, information quality in information fusion, computer-aided diagnosis, and understanding of volcanic eruption patters, among others. Her research was funded by multiple government agencies as well as commercial companies. She published numerous papers and co-edited 5 books. She served as a committee member, session chair and organizer, and tutorial lecturer for numerous International Conferences on Information Fusion. Dr. Rogova was a member of organizing committee of multiple NATO ASI and NATO ARWs on information fusion and decision support.

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