The unprecedented rise of social media platforms, combined with location-aware technologies, has led to continuously producing a significant amount of geo-social data that flows as a user-generated data stream. This data has been exploited in several important use cases in various application domains. This article supports geo-social personalized queries in streaming data environments. We define temporal geo-social queries that provide users with real-time personalized answers based on their social graph. The new queries allow incorporating keyword search to get personalized results that are relevant to certain topics. To efficiently support these queries, we propose an indexing framework that provides lightweight and effective real-time indexing to digest geo-social data in real time. The framework distinguishes highly dynamic data from relatively stable data and uses appropriate data structures and a storage tier for each. Based on this framework, we propose a novel geo-social index and adopt two baseline indexes to support the addressed queries. The query processor then employs different types of pruning to efficiently access the index content and provide a real-time query response. The extensive experimental evaluation based on real datasets has shown the superiority of our proposed techniques to index real-time data and provide low-latency queries compared to existing competitors.
This content will become publicly available on August 23, 2024
A Scalable Unified System for Seeding Regionalization Queries
Spatial regionalization is the process of combining a collection of spatial polygons into contiguous regions that satisfy user-defined criteria and objectives. Numerous techniques for spatial regionalization have been proposed in the literature, which employs varying methods for region growing, seeding, optimization, and enforce different user-defined constraints and objectives. This paper introduces a scalable unified system for addressing seeding spatial
regionalization queries efficiently. The proposed system provides a usable and scalable framework that employs a wide-range of existing spatial regionalization techniques and allows users to submit novel combinations of queries that have not been previously explored. This represents a significant step forward in the field of spatial regionalization as it provides a robust platform for addressing different regionalization queries. The system is mainly composed of three components: query parser, query planner, and query executor. Preliminary evaluations of the system demonstrate its efficacy in efficiently addressing various regionalization queries.
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- Award ID(s):
- 2237348
- NSF-PAR ID:
- 10494685
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- The 18th International Symposium on Spatial and Temporal Data (SSTD '23)
- Page Range / eLocation ID:
- 96 to 105
- Format(s):
- Medium: X
- Location:
- Calgary, AB, Canada
- Sponsoring Org:
- National Science Foundation
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