Research Projects

During my second postdoc at HBKU Qatar, I explored a new research area: Digital Wellbeing. An increasing body of research has examined the potential mental health risks associated with problematic internet use. The growing time people spend on smartphones, along with the wide range of activities available on these devices, has led researchers to investigate their impact on users' mental health and well-being. In the literature, smartphone addiction is defined as excessive smartphone use that interferes with daily life priorities, including sleep.

Sleep is crucial for overall health, daily functioning, and performance. It is a particularly important and potentially modifiable health risk in teens. Sleep deprivation has been linked to numerous physical and mental health issues, such as weakened immune response, mental illness, reduced fertility, impaired cognition, hypertension, and hyperglycemia. Excessive smartphone use can disrupt the sleep-wake cycle, potentially contributing to these health problems.

Personality traits are also believed to influence human behavior, particularly in relation to internet and digital device use. Studies have shown that individuals with problematic smartphone use, characterized by difficulty controlling their internet habits, are more likely to delay sleep. Similarly, using smartphones near bedtime can negatively affect sleep quality. The goal of this project was to investigate how personality traits, problematic internet use, and smartphone usage impact sleep quality.

Achievements

Under this project, we have successfully published the following journals and two more in the review process.

• Alam, Aftab*, Sameha Al-Shakhsi, Dena Al-Thani, and Raian Ali. "Do near-bedtime usage of smartphones and problematic internet usage impact sleep? A study based on objectively recorded usage data." Behavior & Information Technology (2023): 1-16.

• Alam, Aftab*, Sameha Alshakhsi, Dena Al-Thani, and Raian Ali. "The role of objectively recorded smartphone usage and personality traits in sleep quality." PeerJ Computer Science 9 (2023): e1261.

• Tauseef Ur Rahman, Zahoor Jan, Aftab Alam*, Vasilis Katos, Raian Ali. Problematic Internet Usage, and smartphone use: their effects on sleep quality across groups using mediation analysis (Journal of Behaviour and Information Technology)

• Rahman, Tauseef Ur, Zahoor Jan, Aftab Alam*, and Raian Ali. "Smartphone use and personality: Their effects on sleep quality across groups using mediation analysis." Digital Health 10 (2024): 20552076241295797.

Deep Learning is effective at detecting hidden patterns in Euclidean data (images, text, videos). But what about applications in which data is provided from non-Euclidean domains and represented as graphs with complex object relationships and interdependencies? That’s where Graph Neural Networks (GNN) come as a significant solution. Because of the implicit data dependence in the graphs, it is hard for industrial communities to exploit these methods to address real-world challenges at scale. Furthermore, the graphs are typically large, containing hundreds of millions of nodes and several billions of edges.  The main drawbacks in the existing system are: 

• They are unable to scale due to memory capacity and bandwidth constraints between graph stores and workers.

• They require extra development of graph stores without well exploiting mature infrastructures such as MapReduce that guarantee good system properties. 

• They focus on training but ignore the optimization of inference over graphs.

This makes them an unintegrated system and also does not provide an open-source abstraction to assist developers. Under this project, we will design a Distributed Graph Learning Library (DGLL) a scalable, fault-tolerance, and integrated system, with fully functional training and inference for GMLs and GNNs. Our system will follow a layered design architecture and consist of three main layers, i.e., Graph Data Layer. Graph Optimization Layer, Graph Machine Learning Layer. The first layer will utilize a state-of-the-art distributed file system and a scalable graph data store. The second layer will provide distributed graph processing using graph programming models while optimizing and hiding the underlying complexity of advanced message passing, graph partitioning, and graph sampling techniques. Finally, we will develop graph learning and graph neural network modules on top of the second layer for fast and memory-efficient training and inference.

Achievements

• Under this project, we have successfully published the following paper.

• Van, D. T. T., Khan, M. N., Afridi, T. H., Ullah, I., Alam, A., & Lee, Y. K. (2022). GDLL: A Scalable and Share Nothing Architecture Based Distributed Graph Neural Networks Framework. IEEE Access, 10, 21684-21700

During Ph.D., I was supposed to work in the domain of shared-nothing architecture-based distributed computing solutions for big data analytics in the cloud, i.e., designing industrial-scale distributed computing architectures for big data analytics in the cloud. Subsequently, addressing the research issues and challenges being identified in the designed video big data system. I proposed a reference architecture for video big data analytics in the cloud called Cloud-based Video Analytics System (CVAS)  and a distributed data curation framework called TORNADO  for video big data analytics in the cloud. An abstract view of the proposed CVAS and TORNADO, an abstract overview, and a short description are given bellow.

Achievements

Under this project, we have successfully published the following journals and conferences.

• Aftab Alam, Irfan Ullah, and Young-Koo Lee. "Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues." IEEE Access 8 (2020): 152377-152422.

• Aftab Alam, and Young-Koo Lee. "TORNADO: Intermediate results orchestration-based service-oriented data curation framework for intelligent video big data analytics in the cloud." Sensors 20.12 (2020): 3581.

• Uddin, Md Azher, Aftab Alam, Nguyen Anh Tu, Md Siyamul Islam, and Young-Koo Lee. "SIAT: A distributed video analytics framework for intelligent video surveillance." Symmetry 11, no. 7 (2019): 911.

• Khan, Muhammad Numan, Aftab Alam, and Young-Koo Lee. "FALKON: Large-Scale Content-Based Video Retrieval Utilizing Deep-Features and Distributed In-memory Computing." In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 36-43. IEEE, 2020.

• Alam, Aftab, Muhammad Numan Khan, Jawad Khan, and Young-Koo Lee. "IntelliBVR-Intelligent large-scale video retrieval for objects and events utilizing distributed deep-learning and semantic approaches." In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 28-35. IEEE, 2020.

• Alam, Aftab, et al. "Architecture for Intelligent CCTV Cloud Platform." 한국정보과학회 학술발표논문집 (2016): 152-154.

• Alam, Aftab, et al. "Distributed Data Model for Managing Large-Scale Video Data in the Cloud Using Hadoop-HBase." 한국정보과학회 학술발표논문집 (2017): 79-81.

Individuals use smartphones for a variety of purposes like photography, schedule planning, playing games, and so on, apart from benefiting from the core tasks of call-making and short messaging. These services are sources of personal data generation. Therefore, any application that utilizes the personal data of a user from his/her smartphone is truly a great witness of his/her interests and this information can be used for various personalized services. Under this project, we were supposed to develop a personalized application for mining the lifestyle patterns of a smartphone user. The proposed app uses the personal photograph collections of a user, which reflect the day-to-day photos taken by a smartphone, to recognize scenes (called objects of interest in our work). These are then mined to discover lifestyle patterns. Modeling data in the form of graphs is effective in preserving the lifestyle behavior maintained over time. Graph-modelled lifestyle data enabled us to apply a variety of graph-mining techniques for pattern discovery. To demonstrate the effectiveness of our proposal, we have developed a prototype system for LPaMI to implement its end-to-end pipeline. We have also conducted an extensive evaluation for various phases of LPaMI using different real-world datasets. We understand that the output of LPaMI can be utilized for a variety of pattern discovery application areas like trip and food recommendations, shopping, and so on. The outcome of this project was:

Figure. Pipeline of Lifestyle Pattern Mining from personal image collections in a smartphone.

Large graphs such as social networks, web graphs, and biological networks, are complex and face the challenges of processing and visualization. Motivated by such issues, Taivonen et al. [1] proposed models and sequential algorithms for weighted graphs with the intention to generate a candidate compress graph. The proposed compression algorithm is expensive in terms of computation time because of the sequential process. The weighted graph compression algorithms can be made faster while adopting parallel processing techniques. In this paper, we adopt a parallel processing technique for the weighted graph compression problem while using multi-selection nodes to perform a merge-able technique with various graph clustering algorithms to avoid overlapping between nodes from different threads. For the performance evaluation purposes of the proposed method, we carry out a series of tests on the real networks. We perform extensive experiments on parallel graph summarization while using different graph clustering algorithms. Our results demonstrate their effectiveness for parallel graph compression on real networks.

My first research project focused on developing, researching, and proposing a user reputation model for social question-and-answer (SQA) systems. I introduced a dynamic points-based user reputation model (as shown in the given equation), which leverages the graph structure of the SQA community and takes user ratings and social network analysis as input. The impact weight of each relationship and user ratings depends on the current levels of both the asker and answerer and the difficulty level of the question.