Real-Time Discovery and Geospatial Visualization of Mobility and Industry Events from Large-Scale, Heterogeneous Data Streams

Abstract

Monitoring mobility-and industryrelevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific companies, transit routes and locations from heterogeneous, high-volume text streams remains a significant challenge. We present Spree, a scalable system for real-time, automatic event extraction from social media, news and domain-specific RSS feeds. Our system is tailored to a range of mobilityand industry-related events, and processes German texts within a distributed linguistic analysis pipeline implemented in Apache Flink. The pipeline detects and disambiguates highly ambiguous domain-relevant entities, such as street names, and extracts various events with their geo-locations. Event streams are visualized on a dynamic, interactive map for monitoring and analysis.

Time series big data: a survey on data stream frameworks, analysis and algorithms

“…We can find several works to monitor and improve urban mobility, monitor water consumption and detect water leaks [84], and forecast traffic flow [38], among many others. Leonhard Hennig et al [23] built a system to extract mobility and industry events from data streams. Qinglong Dai et al [13] used a data stream framework with customized changes to process data from smart grids.…”

SUDIR: An Approach of Sensing Urban Text Data From Internet Resources Based on Deep Learning

“…Most of the traditional research works focus on the rules-based, external knowledge-based and statistical learning-based methodologies of sensing urban text data. The first category of methods is the rule-based method (cf., [7]- [9]). [7] is based on rules and crawler technology to extract POI data and build an maps/location searching application from Internet resources.…”

“…[7] is based on rules and crawler technology to extract POI data and build an maps/location searching application from Internet resources. In addition, there are some rule-based methods used to extract urban data from specific data sources (cf., [8], [9]). The rules-based methods can achieve high-accuracy data collection, but due to the increasing update of Internet information, crawler tools or rules need to perform a lot of maintenance work.…”