Parallel extraction of Regions‐of‐Interest from social media data

Abstract

Geotagged data gathered from social media can be used to discover places-of-interest (PoIs) that have attracted many visitors. Since a PoI is generally identified by geographical coordinates of a single point, it is hard to match it with people trajectories. Therefore, we define an area, called region-of-interest (RoI), represented by the boundaries of a PoI. The main goal of this study is to discover RoIs from PoIs using spatial data mining techniques. In this paper, we propose a new parallel method for extracting RoIs from social media datasets. It consists of two main steps: (i) automatic keyword extraction and data grouping and (ii) parallel RoI extraction. The first step extracts keywords identifying the PoIs; these keywords are used to group social media items according to the places they refer to. The second step uses a Parallel Clustering Approach (ParCA) of spatial dataset to identify RoIs. ParCA exploits a parallel execution of DBSCAN on subsets of data to generate subclusters on each processing node and then merge overlapping subclusters to form global clusters. ParCA was implemented using the MapReduce model. Experiments performed over a set of PoIs in the city of Rome using social media data show that our approach is highly scalable and reaches an accuracy of 79% in detecting RoIs. On a parallel computer with 50 cores, we obtained a speedup of 52 by processing large datasets divided into 32 splits, compared with the execution time registered when each dataset is not partitioned.

Knowledge Discovery from Large Amounts of Social Media Data

“…For this step, our method turns out to be the most accurate one in finding trajectories, achieving an overall improvement of the F1 score up to 0.39 (i.e., in comparison to that of DSets-DBSCAN). Also from the point of view of scalability, increasing the number of cores dedicated to the execution of the application leads to a significant reduction in overall execution times, which demonstrates the scalability of our method [32].…”

Using social media for sub-event detection during disasters

“…Following the same approach proposed in [39], we used a clustering algorithm to aggregate the posts that refer to the same sub-event and discover the area where it occurred. In particular, DBSCAN [40] has been chosen for its ability to detect clusters with different sizes and shapes, tolerate noise, and be applicable on small or large data sets.…”

Autotuning of Exascale Applications With Anomalies Detection

“…C. Automatic keywords extraction and data grouping: the keywords that identify the places-of-interests are extracted; these keywords will be used to group social media items according to the places they refer to. D. RoIs detection using a parallel clustering approach: A dataparallel approach is used to detect Regions-of-Interest (RoIs) starting from social media data grouped by keywords (Belcastro et al, 2020). RoIs represent a way to partition the space into meaningful areas; they are the boundaries of Points-of-Interest (e.g., a city square).…”