Semantically Diverse Path Search

The Best Paper Award Runner-Up at IEEE MDM 2020 were awarded to the authors of "Semantically Diverse Path Search"! 

Location-Based Services are often used to find proximal Points of Interest (PoI) – e.g., nearby restaurants and museums, police stations, hospitals, etc. – in a plethora of applications. An important recently addressed variant of the problem not only considers the distance/proximity aspect, but also desires semantically diverse locations in the answer-set. For instance, rather than picking several close-by attractions with similar features – e.g., restaurants with similar menus; museums with similar art exhibitions – a tourist may be more interested in a result set that could potentially provide more diverse types of experiences, for as long as they are within an acceptable distance from a given (current) location. Towards that goal, in this work we propose a novel approach to efficiently retrieve a path that will maximize the semantic diversity of the visited PoIs that are within distance limits along a given road network. We introduce a novel indexing structure – the Diversity Aggregated R-tree, based on which we devise efficient algorithms to generate the answer-set – i.e., the recommended locations among a set of given PoIs – relying on a greedy search strategy. Our experimental evaluations conducted on real datasets demonstrate the benefits of proposed methodology over the baseline alternative approaches.

The nice presentation was given by Xu Teng, Iowa State University, at the IEEE MDM 2020.

X. Teng, G. Trajcevski, J.-S. Kim, and A. Züfle, “Semantically Diverse Path Search,” In Proceedings of IEEE International Conference on Mobile Data Management (MDM 2020), July 2020, pp. 69-78

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