arXiv ·
The Symposium on Data Mining and Applications (SDMA 2014) was organized by MEGDAM to foster collaboration among data mining and machine learning researchers in Saudi Arabia, GCC countries, and the Middle East. The symposium covered areas such as statistics, computational intelligence, pattern recognition, databases, Big Data Mining and visualization. Acceptance was based on originality, significance and quality of contribution.
KAUST ·
KAUST Associate Professor Xiangliang Zhang leads the Machine Intelligence and Knowledge Engineering (MINE) group, focusing on machine learning and data mining algorithms for AI applications. The MINE group researches complex graph data to profile nodes, predict links, detect computing communities, and understand their connections. Zhang's team also works on graph alignment and recommender systems. Why it matters: This research contributes to advancing machine learning techniques at a leading GCC institution, potentially impacting various AI applications in the region.
KAUST ·
KAUST Associate Professor Xiangliang Zhang presented her work on mining streaming and temporal data at the International Joint Conference on Artificial Intelligence and the European Conference on Artificial Intelligence (IJCAI-ECAI-18) in Stockholm. Her talk, "Mining Streaming and Temporal Data: from Representation to Knowledge," summarized her research on mining data streams. Zhang directs the KAUST Machine Intelligence and kNowledge Engineering (MINE) group, which focuses on knowledge discovery from large-scale data. Why it matters: Showcases KAUST's contributions to AI research and highlights the university's growing recognition within the international AI community.
arXiv ·
This paper focuses on analyzing surveys of women entrepreneurs in the UAE using machine learning techniques. The goal is to extract relevant insights from the data to understand the current landscape and predict future trends. The study aims to support better business decisions related to women in entrepreneurship.
arXiv ·
This study analyzes the evolution of data science vocabulary using 16,018 abstracts containing "data science" over 13 years. It identifies new vocabulary introduction and its integration into scientific literature using techniques like EDA, LSA, LDA, and N-grams. The research compares overall scientific publications with those specific to Saudi Arabia, identifying representative articles based on vocabulary usage. Why it matters: The work provides insights into the development of data science terminology and its specific adoption within the Saudi Arabian research landscape.
MBZUAI ·
A new mini-batch strategy using aggregated relational data is proposed to fit the mixed membership stochastic blockmodel (MMSB) to large networks. The method uses nodal information and stochastic gradients of bipartite graphs for scalable inference. The approach was applied to a citation network with over two million nodes and 25 million edges, capturing explainable structure. Why it matters: This research enables more efficient community detection in massive networks, which is crucial for analyzing complex relationships in various domains, but this article has no clear connection to the Middle East.
arXiv ·
This paper introduces a unified deep autoregressive model (UAE) for cardinality estimation that learns joint data distributions from both data and query workloads. It uses differentiable progressive sampling with the Gumbel-Softmax trick to incorporate supervised query information into the deep autoregressive model. Experiments show UAE achieves better accuracy and efficiency compared to state-of-the-art methods.
MBZUAI ·
The IEEE International Conference on Data Mining (ICDM) opened in Abu Dhabi, marking the emirate as a growing hub for global AI conferences. Three faculty members from Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) hold key positions in the ICDM organizing committee. Abu Dhabi has recently hosted IROS in October and will host COLING in January 2025, with MBZUAI faculty heavily involved in these events. Why it matters: Hosting top-tier AI conferences in Abu Dhabi, with strong MBZUAI participation, enhances the region's reputation and visibility in the global AI community.