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

Location-Based Social Network Data Generation Based on Patterns of Life

Joon-Seok Kim, Hyunjee Jin, Hamdi Kavak, Ovi Chris Rouly, Andrew Crooks, Dieter Pfoser, Carola Wenk, Andreas Züfle

Abstract

Location-based social networks (LBSNs) have been studied extensively in recent years. However, utilizing real-world LBSN data sets yields several weaknesses: sparse and small data sets, privacy concerns, and a lack of authoritative ground-truth. To overcome these weaknesses, we leverage a large-scale LBSN simulation to create a framework to simulate human behavior and to create synthetic but realistic LBSN data based on human patterns of life. Such data not only captures the location of users over time but also their interactions via social networks. Patterns of life are simulated by giving agents (i.e., people) an array of "needs" that they aim to satisfy, e.g., agents go home when they are tired, to restaurants when they are hungry, to work to cover their financial needs, and to recreational sites to meet friends and satisfy their social needs. While existing real-world LBSN data sets are trivially small, the proposed framework provides a source for massive LBSN benchmark data that closely mimics the real-world. As such, it allows us to capture 100% of the (simulated) population without any data uncertainty, privacy-related concerns, or incompleteness. It allows researchers to see the (simulated) world through the lens of an omniscient entity having perfect data. Our framework is made available to the community. In addition, we provide a series of simulated benchmark LBSN data sets using different synthetic towns and real-world urban environments obtained from OpenStreetMap. The simulation software and data sets, which comprise gigabytes of spatio-temporal and temporal social network data, are made available to the research community.

Please check out the video that I presented at the 21st IEEE International Conference on Mobile Data Management (MDM 2020)!

Source code: https://github.com/gmuggs/pol
LSBN Data: https://osf.io/e24th/
Additional materials: https://mdm2020.joonseok.org


J.-S. Kim, H. Jin, H. Kavak, O. Rouly, A. Crooks, D. Pfoser, C. Wenk, and A. Züfle, “Location-Based Social Network Data Generation Based on Patterns of Life,” In Proceedings of IEEE International Conference on Mobile Data Management (MDM 2020), July 2020, pp. 158-167

Call for Paper GeoSim 2020

The GeoSim 2020 workshop focuses on all aspects of simulation as a general paradigm to model and predict spatial systems and generate spatial data. New simulation methodologies and frameworks, not necessarily coming from the SIGSPATIAL community, are encouraged to participate. Also, this workshop is of interest to everyone who works with spatial data. The simulation methods that will be presented and discussed in the workshop should find a wide application across the community by producing benchmark datasets that can be parameterized and scaled. Simulated data sets will be made available to the community via the website.

The workshop seeks high-quality full (8-10 pages) and short (up to 4 pages) papers that will be peer-reviewed. Once accepted, at least one author is required to register for the workshop and the ACM SIGSPATIAL conference, as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library.

• We solicit novel and previously unpublished research on all topics related to geospatial simulation including, but not limited to:
• Disease Spread Simulation
• Urban Simulation
• Agent Based Models for Spatial Simulation
• Multi-Agent Based Spatial Simulation
• Big Spatial Data Simulation
• Spatial Data/Trajectory Generators
• Road Traffic Simulation
• Environmental Simulation
• GIS using Spatial Simulation
• Modeling and Simulation of COVID-19
• Interactive Spatial Simulation
• Spatial Simulation Parallelization and Distribution
• Geo-Social Simulation and Data Generators
• Social Unrest and Riot Prediction using Simulation
• Spatial Analysis based on Simulation
• Behavioral Simulation
• Verifying, and Validating Spatial Simulations
• Applications for Spatial Simulation

Special Topic:

The special topic for GeoSim 2020 brings focus to current trends in disease spread simulations, their practicality in predictive and prescriptive analytics, and the challenges they face in their use.

Paper Format:

Manuscripts should be submitted in PDF format and formatted using the ACM camera-ready templates available at http://www.acm.org/publications/proceedings-template.

Submission Site:

https://easychair.org/my/conference?conf=geosim2020


For more information, please visit our workshop website: at https://www.geosim.org/