Mu-Woong Lee

Spatial Skyline Queries: An Efficient Geometric Algorithm

As more data-intensive applications emerge, advanced retrieval semantics, such as ranking and skylines, have attracted attention. Geographic information systems are such an application with massive spatial data. Our goal is to efficiently support skyline queries over massive spatial data. To achieve this goal, we first observe that the best known algorithm VS 2, despite its claim, may fail to deliver correct results. In contrast, we present a simple and efficient algorithm that computes the correct results. To validate the effectiveness and efficiency of our algorithm, we provide an extensive empirical comparison of our algorithm and VS 2 in several aspects.

Continuous Skylining on Volatile Moving Data

A dynamic skyline query retrieves the moving data objects that are not spatially dominated by any other object with respect to a given query point. Existing efforts on supporting such queries, however, supports location as a single dynamic attribute and one or more static dimensions. In a clear contrast, this paper focuses on the continuous skyline computation on moving data with an arbitrary number of dynamic queriable dimensions, e.g., to model both location and its volatility, with and without static dimension. Toward the goal, we investigate the relative positions and velocities of the initial skyline points with respect to the query, to derive a search region for skyline candidates. After retrieving these candidates, we further prune out some candidates and examine their spatial relations to monitor the changes in the skyline.

Integrating code search into the development session

To support rapid and efficient software development, we propose to demonstrate our tool, integrating code search into software development process. For example, a developer, right during writing a module, can find a code piece sharing the same syntactic structure from a large code corpus representing the wisdom of other developers in the same team (or in the universe of open-source code). While there exist commercial code search engines on the code universe, they treat software as text (thus oblivious of syntactic structure), and fail at finding semantically related code. Meanwhile, existing tools, searching for syntactic clones, do not focus on efficiency, focusing on “post-mortem” usage scenario of detecting clones “after” the code development is completed. In clear contrast, we focus on optimizing efficiency for syntactic code search and making this search “interactive” for large-scale corpus, to complement the existing two lines of research. From our demonstration, we will show how such interactive search supports rapid software development, as similarly claimed lately in SE and HCI communities [1], [2]. As an enabling technology, we design efficient index building and traversal techniques, optimized for code corpus and code search workload. Our tool can identify relevant code in the corpus of 1.7 million code pieces in a sub-second response time, without compromising any accuracy obtained by a state-of-the-art tool, as we report our extensive evaluation results in [3].

Spatial skyline queries: exact and approximation algorithms

As more data-intensive applications emerge, advanced retrieval semantics, such as ranking and skylines, have attracted the attention of researchers. Geographic information systems are a good example of an application using a massive amount of spatial data. Our goal is to efficiently support exact and approximate skyline queries over massive spatial datasets. A spatial skyline query, consisting of multiple query points, retrieves data points that are not father than any other data points, from all query points. To achieve this goal, we present a simple and efficient algorithm that computes the correct results, also propose a fast approximation algorithm that returns a desirable subset of the skyline results. In addition, we propose a continuous query algorithm to trace changes of skyline points while a query point moves. To validate the effectiveness and efficiency of our algorithm, we provide an extensive empirical comparison between our algorithms and the best known spatial skyline algorithms from several perspectives.

Robust distributed indexing for locality-skewed workloads

Multidimensional indexing is crucial for enabling a fast search over large-scale data. Owing to the unprecedented scale of data, extending such indexing technology has recently gained attention in distributed environments. The goal of existing efforts in distributed indexing has been the localization of queries to data residing at a small number of nodes (i.e., locality-preserving indexing) to minimize communication cost. However, considering that workloads often correlate with data locality, such indexing often generates hotspots. Location-based queries are typically skewed to disaster areas during certain periods of time, e.g., during Hurricane Irene, search traffic increased by more than 2000%. To alleviate such hotspots, we propose workload-balancing as an optimization goal. A cost model analytically supporting the need for load balancing is first developed, then a distributed index that evenly distributes the workload is presented. Our empirical study suggests that hotspots degrading search performance can be effectively alleviated. Specifically, when deployed to Amazon EC2, our proposed scheme showed maximum speed-up of 127.7%. Even in hostile settings where workload is not at all correlated with the search criteria, the proposed schemes performance is comparable to existing approaches optimized for such settings.

k-nearest dominant search on wireless sensor networks

The Network Centric Warfare(NCW) model is currently attracting attention in modern warfare research. In this model, data processing issues, such as efficiently determining enemies of high threat, are critical for optimizing war tactics. In this paper, by adopting the concept of skyline queries which have been extensively studied in database research, we address the problem of searching highly threatening enemies, named “dominants”. While skyline queries have been extensively studied in database research, most existing solutions assume centralized scenarios. However, during warfare, tactics should consider data distributed all over the field, where existing solutions for centralized scenarios cannot apply. In this paper, we treat each Forward Air Controller in Close Air Support operation as a sensor node and propose a novel algorithm to determine dominants. Our proposed algorithm works by considering the distance from the query to the enemies, and prioritizes the retrieval of enemies of high threat.