Liu, Ling

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Now showing 1 - 5 of 5
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    Safe Region Techniques for Fast Spatial Alarm Evaluation
    (Georgia Institute of Technology, 2008) Bamba, Bhuvan ; Liu, Ling ; Iyengar, Arun ; Yu, Philip S.
    Spatial alarms are personalized location-based triggers installed by mobile users to serve as a reminder of a location of interest to be encountered in their future trips. Unlike continuous spatial queries, spatial alarms do not require immediate processing and periodic reevaluation upon installation. Thus, a critical challenge for efficient processing of spatial alarms is to determine when to evaluate each spatial alarm, while ensuring the demanding requirements of high accuracy and system scalability. In this paper, we compare alternative approaches for evaluation of spatial alarms: periodic evaluation, safe period-based processing and safe region-based processing. We argue that the safe region-based approach provides highly efficient processing of spatial alarms at the server. Furthermore, it reduces wireless communication costs and energy consumption on the client side by reducing the number of location updates to be transmitted to the server without sacrificing accuracy of spatial alarm evaluation. We develop safe region computation techniques based on different heuristics, namely, Maximum Perimeter Rectangular Safe Region (MPSR), Largest Component Rectangles Safe Region (LCSR) and Bitmap Encoded Safe Region (BSR) approach, and present an in-depth study on trade-offs involved in the selection of an appropriate safe region computation strategy. Our experimental evaluation shows that the best optimization strategy requires an approach which adapts to changing system load conditions and resource constraints, as none of the safe region computation techniques outperforms the others on all relevant evaluation metrics. Experimental evaluation also validates our conjecture that safe region-based processing offers close to optimal performance in terms of CPU load on the server and wireless communication costs at the mobile clients.
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    LIRA: Lightweight, Region-aware Load Shedding in Mobile CQ Systems
    (Georgia Institute of Technology, 2006) Gedik, Bugra ; Liu, Ling ; Wu, Kun-Lung ; Yu, Philip S.
    Position updates and query re-evaluations are two predominant, costly components of processing location-based, continual queries (CQs) in mobile systems. To obtain high-quality query results, the query processor usually demands receiving frequent position updates from the mobile nodes. However, processing frequent updates oftentimes causes the query processor to become overloaded, under which updates must be dropped randomly, bringing down the quality of query results, negating the benefits of frequent position updates. In this paper, we develop LIRA − a lightweight, region-aware load-shedding technique for preventively reducing the position-update load of a query processor, while maintaining high-quality query results. Instead of having to receive too many updates and then randomly drop some of them, LIRA uses a region-aware partitioning mechanism to identify the most beneficial shedding regions to cut down the position updates sent by the mobile nodes within those regions. Based on the number of mobile nodes and queries in a region, LIRA judiciously applies different amounts of update reduction for different regions, maintaining better overall accuracy of query results. Experimental results show that LIRA is vastly superior to random update dropping and clearly outperforms other alternatives that do not possess full-scale, region-aware load-shedding capabilities. Moreover, due to its lightweight nature, LIRA introduces very little overhead.
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    Adaptive Load Shedding for Windowed Stream Joins
    (Georgia Institute of Technology, 2005) Gedik, Bugra ; Wu, Kun-Lung ; Yu, Philip S. ; Liu, Ling
    We present an adaptive load shedding approach for windowed stream joins. In contrast to the conventional approach of dropping tuples from the input streams, we explore the concept of selective processing for load shedding, focusing on costly stream joins such as those over set-valued or weighted set-valued attributes. The main idea of our adaptive load shedding approach is two-fold. First, we allow stream tuples to be stored in the windows and shed excessive CPU load by performing the stream join operations, not on the entire set of tuples within the windows, but on a dynamically changing subset of tuples that are highly beneficial. Second, we support such dynamic selective processing through three forms of runtime adaptations: By adaptation to input stream rates, we perform partial processing based load shedding and dynamically determine the fraction of the windows to be processed by comparing the tuple consumption rate of join operation to the incoming stream rates. By adaptation to time correlation between the streams, we dynamically determine the number of basic windows to be used and prioritize the tuples for selective processing, encouraging CPU-limited execution of stream joins in high priority basic windows. By adaptation to join directions, we dynamically determine the most beneficial direction to perform stream joins in order to process more useful tuples under heavy load conditions and boost the utility or number of output tuples produced. Our load shedding framework not only enables us to integrate utility-based load shedding with time correlation-based load shedding, but more importantly, it also allows load shedding to be adaptive to various dynamic stream properties. Inverted indexes are used to further speed up the execution of stream joins based on set-valued attributes. Experiments are conducted to evaluate the effectiveness of our adaptive load shedding approach in terms of output rate and utility.
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    GRUBJOIN: An Adaptive Multi-Way Windowed Stream Join with Time Correlation-Aware CPU Load Shedding
    (Georgia Institute of Technology, 2005) Gedik, Bugra ; Wu, Kun-Lung ; Yu, Philip S. ; Liu, Ling
    Dropping tuples has been commonly used for load shedding. However, tuple dropping generally is inadequate to shed load for multiway windowed stream joins. The output rate can be unnecessarily and severely degraded because tuple dropping does not recognize time correlations likely to exist among the streams. This paper introduces GrubJoin: an adaptive multi-way windowed stream join that efficiently performs time correlation-aware CPU load shedding. GrubJoin maximizes the output rate by achieving nearoptimal window harvesting within an operator throttling framework, i.e., regulating the fractions of the join windows that are processed by the multi-way join. Window harvesting performs the join using only certain more useful segments of the join windows. Due mainly to the combinatorial explosion of possible multi-way join sequences involving various segments of individual join windows, GrubJoin faces a set of unique challenges, such as determining the optimal window harvesting configuration and learning the time correlations among the streams. To tackle these challenges, we formalize window harvesting as an optimization problem, develop greedy heuristics to determine near-optimal window harvesting configurations and use approximation techniques to capture the time correlations among the streams. Experimental results show that GrubJoin is vastly superior to tuple dropping when time correlations exist among the streams and is equally effective as tuple dropping in the absence of time correlations.
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    Processing Moving Queries over Moving Objects Using Motion Adaptive Indexes
    (Georgia Instiute of Technology, 2004) Gedik, Bugra ; Wu, Kun-Lung ; Yu, Philip S. ; Liu, Ling
    This paper describes a motion adaptive indexing scheme for efficient evaluation of moving queries (MQs) over moving objects. It uses the concept of motion-sensitive bounding boxes to model the dynamic behavior of both moving objects and moving queries. Instead of indexing frequently changing object positions, we index less frequently changing motion sensitive bounding boxes together with the motion functions of the objects. This significantly decreases the number of update operations performed on the indexes. We use predictive query results to optimistically precalculate query results, thus decreasing the number of search operations performed on the indexes. More importantly, we propose a motion adaptive indexing method. Instead of using fixed parameters for motion sensitive bounding boxes, we automatically adapt the sizes of the motion sensitive bounding boxes to the dynamic motion behaviors of the corresponding individual objects. As a result, the moving queries can be evaluated faster by performing fewer IOs. Furthermore, we introduce the concept of guaranteed safe radius and optimistic safe radius to extend our motion adaptive indexing scheme to evaluating moving continual k-nearest neighbor (kNN) queries. Our experiments show that the proposed motion adaptive indexing scheme is efficient for evaluation of both moving continual range queries and moving continual kNN queries.