Multi-Criteria Decision Support Queries in Exploratory & Open World Settings

Research output: Book/anthology/dissertation/reportPh.D. thesis


Throughout the past decade, data sources have increased significantly in both their size, availability, richness, complexity and dynamics. This data surplus is not only enabling new businesses, scientific achievements and economic growth; it can also enable normal people to make better real-world decisions if provided with the right tools. The class of multi-criteria decision support queries is said to be one such set of tools, with skyline and top-$k$ queries being the main representatives. Over the past decades, skylines and top-$k$ queries have been extensively studied, yet due to a number of usability and trust issues, they have yet to enjoy wide adoption in either practical scientific or industrial applications. Simply put, the theoretical gain and intent of these tools do not match the reality of how users make decisions. In this thesis, we take a step forward in bridging the gap between the theory and intent of multi-criteria decision support queries and how users actually analyze their options and make decisions in real life. The thesis is separated into two parts.

In the first part, we investigate the use of skyline queries for exploratory search, in which users pose a string of related queries, exploring the options available to them. While this is a common usage pattern in real applications, utilizing skyline queries in such an interactive scenario is non-trivial. Specifically, we study the effects of exploratory search on the usability of skyline queries, and introduce caching-based methods for their efficient computation in those settings. We also present a method for the targeted sampling of $k$-representative skyline points, enabling a fixed size diverse and relevant overview of all options.

In the second part, we investigate the expansion of multi-criteria decision support queries into an open-world paradigm, where both unknown data and latent attributes are expected to exist. Specifically, we investigate the impact of open-world data on the conventional skyline paradigm and suggest a new probabilistic open-world paradigm for future research. We also study general open-world adaption by introducing a multi-criteria filtering method, capable of automatically filtering documents with latent attributes. Finally, we present an interdisciplinary work in Computer Science and Medicine, evaluating our filtering method for real systematic reviews in Evidence-Based Medicine.
Original languageEnglish
Number of pages228
Publication statusPublished - 2016

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