Hajj, the annual pilgrimage to Makkah, is one of the most massive gathering events in the world. It is a mandatory religious activity, once in a lifetime for every sane well-off Muslim. Every year around 3 million Muslims across the globe visit Makkah to perform this spatiotemporally constrained set of religious activities, like circumambulation around the Kaaba and the encampment at Mina, Arafat and Muzdalifah. The key ritual at Rami-al-Jamarat (the stoning-of-the-pillars) is a place seen particularly crowded. The diversified nature of the crowd in terms of race, age, language and culture brings several administrative challenges for local organizers whose responsibility is to ensure the smooth organization of the event. The advent of digital technologies has helped researchers to explore and propose modern methods for better crowd management and devise control strategies to organize such a mass gathering. In this paper, we present a taxonomy that summarizes our survey on the utilization of technology areas for providing needful services and improving crowd management during the Hajj season. These technologies are Wireless, Computer Vision, Spatial Computing, Data Analytics, Mobile Applications, Immersive Technologies, and Crowd Modelling and Simulation. We have also included the current research work and its implementation plan for Hajj 2020. One dimension of the taxonomy is to understand different possible technologies for Crowd management. Another dimension is to understand the impact of technologies to handle such a massive crowd. The discussion section covers a comparison of technologies with multiple aspects that may help researchers to contribute more towards dealing with future mass gathering events.

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Digital Revolution for Hajj Crowd Management: A Technology Survey

With the advancement of social sensing technologies, digital maps have recently witnessed a tremendous evolution with the aim of integrating enriched semantic layers from heterogeneous and diverse data sources. Current generations of digital maps are often crowd-sourced, allow interactive route planning, and may contain live updates, such as traffic congestion states. Within this context, we believe that the next generation of maps will introduce the concept of extracting Events of Interest (EoI) from crowdsourced data, and displaying them at different spatial scales based on their significance. This paper introduces Hadath1, a scalable and efficient system that extracts social events from unstructured data streams, e.g. Twitter. Hadath applies natural language processing and multi-dimensional clustering techniques to extract relevant events of interest at different map scales, and to infer the spatio-temporal scope of detected events. Hadath also implements a hierarchical in-memory spatio-temporal indexing scheme to allow efficient and scalable access to raw data, as well as to extracted clusters of events. Initially, data packets are processed to discover events at a local scale, then, the proper spatio-temporal scope and the significance of detected events at a global scale is determined. As a result, live events can be displayed at different spatio-temporal resolutions, thus allowing a smooth and unique browsing experience. Finally, to validate our proposed system, we conducted experiments on real-time and historical social media streams.

Building socially-enabled event-enriched maps

Geospatial Big Data analytics are changing the way that businesses operate in many industries Although a good number of research works have reported in the literature on geospatial data analytics and real-time data processing of large spatial data streams, only a few have addressed the full geospatial big data analytics project lifecycle and geospatial data science project lifecycle Big data analysis differs from traditional data analysis primarily due to the volume, velocity and variety characteristics of the data being processed One of a motivation of introducing new framework is to address these big data analysis challenges Geospatial data science projects differ from most traditional data analysis projects because they could be complex and in need of advanced technologies in comparison to the traditional data analysis projects For this reason, it is essential to have a process to govern the project and ensure that the project participants are competent enough to carry on the process To this end, this paper presents, new geospatial big data mining and machine learning framework for geospatial data acquisition, data fusion, data storing, managing, processing, analysing, visualising and modelling and evaluation Having a good process for data analysis and clear guidelines for comprehensive analysis is always a plus point for any data science project It also helps to predict required time and resources early in the process to get a clear idea of the business problem to be solved

A new data science framework for analysing and mining geospatial big data

Web of Things (WoT) uses current Web protocols and languages as an integration platform of physical devices on the Internet. Previsions estimate that by 2020, 20 to 50 billion such devices will be connected - producing, collecting, and transmitting data over the Internet. The amount of data associated with such a gigantic number of available devices represents a real challenge in data visualization. This paper proposes a Data Visualization model for WoT, based on Fog Computing concepts, more specifically the Fog of Things paradigm. The aim of our proposal is to enable the creation of different data visualizations that take into account the hierarchical levels in an architecture involving Fog and Cloud. In order to demonstrate our proposal, a prototype of the Web of Things application was developed, which is used to perform usability evaluation through the USE questionnaire technique.

Web of Things Data Visualization: From Devices to Web Via Fog and Cloud Computing

Cultural diversity of large crowds is one of the major concerns when participants overstep the predefined guidelines. Such behaviors eradicate crowds' safety, resulting massive casualties. Advent of tracking devices and smartphones with multiple sensing abilities can leverage to capture crowds' real-time spatio-temporal (ST) data to serve emergency service plans. In this paper, we present a Spatio-Temporal Service Model (STSM) that can detect and predict anomalies with in a very large crowd. The model correlates crowd's real-time information with past user's ST-Data and their traffic mobility, and alerts all stockholders and recommends optimized path to shelter points and safe exits for any possible disaster. As a case study, tracking devices were deployed to capture spatio-temporal information of about 2970 vehicles used for pilgrims' mobility during Hajj 2016 event. The data analysis is summarized and basis the functionalities of the model for future pilgrimage.

STSM- a model to detect and predict large crowd anomalies for optimized path recomendation

Analyzing and visualizing large datasets generated by real-time spatio-temporal activities (e.g. vehicle mobility or large crowd movement) are a very challenging task. Recursive delays both at middleware and front end applications limit the of usefulness of the real-time analysis. In this paper, we present a framework “Spatial-Crowd” that first handles spatial-temporal data acquisition and processing by scaling up the middleware components and its infrastructure. Then, it enables filtering, fixing, enriching and summarising the acquired dataset, readily available for client interfaces which usually are not scalable or built to manage such large datasets. This framework follows published subscriber model and allows users to subscribe to aggregated data streams instead of requesting data in real time. The framework is tested with data generated by a very large simulated dataset and performance showed a significant data reduction on the client side to enhance data visualisation.

Spatial-crowd: A big data framework for efficient data visualization

To manage large crowded events is a very challenging task nowadays. People are looking for solutions in which they can easily monitor the crowd, trace individuals, study formation and deformation patterns of the crowd. Millions of Muslims from around the world gather and perform Hajj in Makkah, Saudi Arabia in the 12th month of every lunar year. To monitor the crowd during Hajj is a great challenge. In this paper, we are proposing an identifiable crowd monitoring framework named iCrowd. This framework collect, identify, infer, analyze, store and retrieve the real-time location information of each of the pre-registered participants (pilgrims), and provides a digital view of crowd in near real-time. iCrowd framework is designed on a three-layer approach, device layer, middleware layer, and the application layer. We demonstrate our proposed framework with simulated data in the context of the event of Hajj.

Shahbaz Atta

Papers

Spatial-crowd: A big data framework for efficient data visualization

iCrowd — A framework for monitoring of identifiable crowd

STSM- a model to detect and predict large crowd anomalies for optimized path recomendation