Jongwuk Lee

BSkyTree: scalable skyline computation using a balanced pivot selection

Skyline queries have gained a lot of attention for multi-criteria analysis in large-scale datasets. While existing skyline algorithms have focused mostly on exploiting data dominance to achieve efficiency, we propose that data incomparability should be treated as another key factor in optimizing skyline computation. Specifically, to optimize both factors, we first identify common modules shared by existing non-index skyline algorithms, and then analyze them to develop a cost model to guide a balanced pivot point selection. Based on the cost model, we lastly implement our balanced pivot selection in two algorithms, BSkyTree-S and BSkyTree-P, treating both dominance and incomparability as key factors. Our experimental results demonstrate that proposed algorithms outperform state-of-the-art skyline algorithms up to two orders of magnitude.

Scalable skyline computation using a balanced pivot selection technique

Skyline queries have recently received considerable attention as an alternative decision-making operator in the database community. The conventional skyline algorithms have primarily focused on optimizing the dominance of points in order to remove non-skyline points as efficiently as possible, but have neglected to take into account the incomparability of points in order to bypass unnecessary comparisons. To design a scalable skyline algorithm, we first analyze a cost model that copes with both dominance and incomparability, and develop a novel technique to select a cost-optimal point, called a pivot point, that minimizes the number of comparisons in point-based space partitioning. We then implement the proposed pivot point selection technique in the existing sorting- and partitioning-based algorithms. For point insertions/deletions, we also discuss how to maintain the current skyline using a skytree, derived from recursive point-based space partitioning. Furthermore, we design an efficient greedy algorithm for the k representative skyline using the skytree. Experimental results demonstrate that the proposed algorithms are significantly faster than the state-of-the-art algorithms.

Toward efficient multidimensional subspace skyline computation

Skyline queries have attracted considerable attention to assist multicriteria analysis of large-scale datasets. In this paper, we focus on multidimensional subspace skyline computation that has been actively studied for two approaches. First, to narrow down a full-space skyline, users may consider multiple subspace skylines reflecting their interest. For this purpose, we tackle the concept of a skycube, which consists of all possible non-empty subspace skylines in a given full space. Second, to understand diverse semantics of subspace skylines, we address skyline groups in which a skyline point (or a set of skyline points) is annotated with decisive subspaces. Our primary contributions are to identify common building blocks of the two approaches and to develop orthogonal optimization principles that benefit both approaches. Our experimental results show the efficiency of proposed algorithms by comparing them with state-of-the-art algorithms in both synthetic and real-life datasets.

Query result clustering for object-level search

Query result clustering has recently attracted a lot of attention to provide users with a succinct overview of relevant results. However, little work has been done on organizing the query results for object-level search. Object-level search result clustering is challenging because we need to support diverse similarity notions over object-specific features (such as the price and weight of a product) of heterogeneous domains. To address this challenge, we propose a hybrid subspace clustering algorithm called Hydra. Algorithm Hydra captures the user perception of diverse similarity notions from millions of Web pages and disambiguates different senses using feature-based subspace locality measures. Our proposed solution, by combining wisdom of crowds and wisdom of data, achieves robustness and efficiency over existing approaches. We extensively evaluate our proposed framework and demonstrate how to enrich user experiences in object-level search using a real-world product search scenarios.

Skyline ranking for uncertain databases

Abstract Skyline queries have been actively studied to effectively identify interesting tuples with low formulation overhead. This paper aims to support skyline queries for uncertain data with maybe confidence . Prior skyline work for uncertain data assumes that each tuple is exhaustively enumerated with all possible probabilities of alternative confidence . However, it is inappropriate to some real-life scenarios, e.g. , scientific Web data or privacy-preserving data, such that each tuple is associated with a probability of existence. We thus propose novel skyline algorithms that efficiently deal with maybe uncertainty, leveraging auxiliary indexes, i.e. , an R-tree or a dominance graph . We also discuss our proposed algorithms over data dependency . Our experiments demonstrate that the proposed algorithms are significantly faster than a naive method by orders of magnitude.

Navigation system for product search

We demonstrate Product EntityCube, a product recommendation and navigation system. While the unprecedented scale of a product search portal enables to satisfy users with diverse needs, this scale also complicates product recommendation. Specifically, our target application poses a unique challenge of overcoming insufficient user profiles and feedbacks. To address this problem, we organize query results into clusters representing different user perceptions of similarity, and provide a navigational UI to handle personal interests. Specifically, we first discuss hybrid object clustering capturing diverse user interests from millions of Web pages and disambiguating different perceptions using feature-based similarity. We then discuss skyline object ranking to highlight interesting items at each cluster. Our demonstration illustrates how Product EntityCube can enrich user product shopping experiences.

Dynamic Simulation of the Sour Water Stripping Process and Modified Structure for Effective Pressure Control

As a result of stringent government regulations regarding air and water Pollution, the sour water stripping process has emerged as an important process in petroleum refineries and coke-making works industries. The dynamics of the sour water stripping process have not been revealed as sour water is a weak electrolyte and has nonlinear characteristics. Moreover, in a real plant, there are several problems such as plugging, excessive use of steam, and difficulty in controlling the column pressure. This paper tries to solve these problems and examines controllability. Steady state simulation is used to find the optimal operating conditions. However, the results from the steady state simulation cannot be applied to the actual system directly since the concept of controllability is ignored. Dynamic simulation is used to examine the controllability and the capability of disturbance rejection and to cope with abnormal situations which may occur in real plant. In the actual system, when the ammonia composition in feed increases, the column pressure increases rapidly. The conventional process cannot control the column pressure properly and the system becomes unstable. The proposed structure, with an inter-cooler, gives better performance than the conventional one.

Hybrid entity clustering using crowds and data

Query result clustering has attracted considerable attention as a means of providing users with a concise overview of results. However, little research effort has been devoted to organizing the query results for entities which refer to real-world concepts, e.g., people, products, and locations. Entity-level result clustering is more challenging because diverse similarity notions between entities need to be supported in heterogeneous domains, e.g., image resolution is an important feature for cameras, but not for fruits. To address this challenge, we propose a hybrid relationship clustering algorithm, called Hydra, using co-occurrence and numeric features. Algorithm Hydra captures diverse user perceptions from co-occurrence and disambiguates different senses using feature-based similarity. In addition, we extend Hydra into

QSkycube: efficient skycube computation using point-based space partitioning

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