Merge branch 'master' of https://github.com/ielab/ielab.github.io

_people/rischan-mafrur.md

@@ -1,11 +1,15 @@
 ---
 name: Rischan Mafrur
 image: /images/rischan.jpeg
-twitter: //twitter.com/PyDataScienceID
+twitter:
 github: //github.com/rischanlab
-website: //pydatascience.org
+website: //rischanlab.github.io
 scholar: //scholar.google.com.au/citations?user=2_Za2fYAAAAJ&hl=en
 orcid: 0000-0003-4424-3736
-role: phd
-description: PhD student, UQ, Insights Recommendation for Exploring IoT Data.
+role: alumni
+alumni: true
+links: 
+ - url: //doi.org/10.14264/812c680
+   name: Download PhD Thesis
+description: Graduated from UQ; Thesis -- Recommending data visualizations- tackling diversification and data quality challenges.
 ---

_publications/mafrur-2023-DiVE-extended.md

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+---
+authors: ["Mohamed A. Sharaf", rischan-mafrur, guido-zuccon]
+title: "Efficient Diversification for Recommending Aggregate Data Visualizations"
+venue: IEEE Access
+year: 2023
+pdf: /publications/pdfs/mafrur-2023-DiVE-extended.pdf
+---
+
+## Abstract 
+
+Visual data exploration is ubiquitous in nearly every industry and organization to support discovering data-driven actionable insights. However, unlocking those insights requires analysts to manually construct a prohibitively large number of aggregate queries and visually explore their results, looking for those valuable and insightful visualizations. Such a challenge naturally motivated the development of novel solutions that automate the visual exploration process, and recommend to analysts those particular queries that best visualize their data and reveal interesting actionable insights. In such automated solutions, there is a clear need for providing analysts with a diversified and concise set of recommended visualizations, which cover and represent a large combinatorial high-dimensional space of possible visualizations. However, directly incorporating existing diversification methods leads to a “process-first-diversify-next” approach, in which all possible data visualizations are generated first through executing a large number of aggregate queries. To address this challenge and minimize the incurred query processing costs, in this work, we propose novel optimization techniques for the efficient diversification of recommended insightful visualizations. The key idea underlying our proposed techniques is to identify and eliminate the processing of a large number of low-utility insignificant visualizations. Meanwhile, for the potentially high-utility insightful visualizations, shared multi-query optimization techniques are proposed for further reduction in data processing cost. Our extensive experimental evaluation on real datasets demonstrates the performance gains provided by our proposed techniques, in terms of minimizing the query processing cost (i.e., efficiency), as well as maximizing the quality of recommendations (i.e., effectiveness).

_publications/mafrur-2023-VizPut.md

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+---
+authors: [rischan-mafrur, "Mohamed A. Sharaf", guido-zuccon]
+title: "VizPut: Insight-Aware Imputation of Incomplete Data for Visualization Recommendation"
+venue: arXiv
+year: 2023
+pdf: /publications/pdfs/mafrur-2023-VizPut.pdf
+---
+
+## Abstract 
+
+In insight recommendation systems, obtaining timely and high-quality recommended visual analytics over incomplete data is challenging due to the difficulties in cleaning and processing such data. Failing to address data incompleteness results in diminished recommendation quality, compelling users to impute the incomplete data to a cleaned version through a costly imputation strategy. This paper introduces VizPut scheme, an insight-aware selective imputation technique capable of determining which missing values should be imputed in incomplete data to optimize the effectiveness of recommended visualizations within a specified imputation budget. The VizPut scheme determines the optimal allocation of imputation operations with the objective of achieving maximal effectiveness in recommended visual analytics. We evaluate this approach using real-world datasets, and our experimental results demonstrate that VizPut effectively maximizes the efficacy of recommended visualizations within the user-defined imputation budget.