Thursday, July 9, 2020

Managing Uncertainty in Evolving Geo-Spatial Data

Andreas Züfle, Goce Trajcevski, Dieter Pfoser, Joon-Seok Kim

Our ability to extract knowledge from evolving spatial phenomena and make it actionable is often impaired by unreliable, erroneous, obsolete, imprecise, sparse, and noisy data. Integrating the impact of this uncertainty is a paramount when estimating the reliability/confidence of any time-varying query result from the underlying input data. The goal of this advanced seminar is to survey solutions for managing, querying and mining uncertain spatial and spatio-temporal data. We survey different models and show examples of how to efficiently enrich query results with reliability information. We discuss both analytical solutions as well as approximate solutions based on geosimulation.

The Advanced Seminar of IEEE MDM 2020 was featured with four parts as follows:

  • Part I: Introduction and Motivation
  • Part II: Uncertainty in Spatial Data
    1. Uncertainty Models and Possible World Semantics
    2. Representative Query Processing using Monte-Carlo Sampling
  • Part III: Uncertainty in Evolving Spatial Data
    1. Sources, Models and Contexts
    2. Non-point Evolving Entities
  • Part IV: Geospatial Simulation 

Among them, I share the video of "Part IV: Geospatial Simulation" that I presented at the conference.

The whole video for the advanced seminar can be found here.

A. Züfle, G. Trajcevski, D. Pfoser, and J.-S. Kim, “Managing Uncertainty in Evolving Geo-Spatial Data,” In Proceedings of IEEE International Conference on Mobile Data Management (MDM 2020), July 2020, pp. 5-8