3,643 research outputs found
Indexing range sum queries in spatio-temporal databases
No full textAlthough spatio-temporal databases have received considerable attention recently, there has been little work on processing range sum queries on the historical records of moving objects despite their importance. Since the direct access to a huge amount of data to answer range sum queries incurs prohibitive computation cost, materialization techniques based on existing index structures are suggested. A simple but effective solution is to apply the materialization technique to the MVR-tree known as the most efficient structure for window queries with spatio-temporal conditions. Aggregate structures based on other index structures such as the HR-tree and the 3DR-tree do not provide satisfactory query performance. In this paper, we propose a new index structure called the Adaptively Partitioned Aggregate R-Tree (APART) and query processing algorithms to efficiently process range sum queries in many situations. Our experimental results show that the performance of the APART is typically 1.3 times better than that of its competitor for a wide range of scenarios. (c) 2006 Elsevier B.V. All rights reserved.This research was supported by the Agency for Defense
Development, Korea, through the Image Information Research
Center at Korea Advanced Institute of Science &
Technology
Preparation and Electrical Properties of Ferroelectric Thin Films deposited by Liguid Source Misted Chemical Deposition (LSMCVD)
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An efficient and scalable approach to CNN queries in a road network
Get PDFInternational Conference on VLDB(Trondheim, Norway from August 30 to September 2, 2005)Image Information Research Center and Information Technology Research Cente
Effectds of Shape and Misalignment of Reinforcement on Load Transfer Efficiency of Metal Matrix Composites
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Motivation at work : A case study in Ammerchant Bank Berhad Sarawak / Chung Siew Mee @ Ellen Chung, Abang Sulaiman Abang Hj. Naim & Arrominy Hj. Arabi
Get PDFEmployee’s performance is frequently described as a joint function of ability and motivation, and one of the primary tasks facing a manager is motivating employees to perform to the best of their ability (Moorhead & Griffin, 1998). In fact, motivation has been described as “one of the most pivotal concerns of modem organizational research” (Baron, 1991: 1). But what exactly is work motivation? Pinder (1998) describes work motivation as the set of internal and external forces that initiate work-related behavior, and determine its form, direction, intensity, and duration. Work motivation is a middle-range concept that deals only with events and phenomena related to people in a work context. The definition recognizes the influence of both environmental forces (e.g., organizational reward systems, the nature of the work being performed) and forces inherent in the person (e.g., individual needs and motives) on work-related behavior
An adaptive indexing technique using spatio-temporal query workloads
No full textMany spatio-temporal access methods, such as the HR-tree, the 3DR-tree, and the MV3R-tree, have been proposed for timestamp and interval queries. However, these access methods have the following problems: the poor performance of the 3DR-tree for timestamp queries, the huge size and the poor performance of the HR-tree for interval queries, and the large size and the high update cost of the MV3R-tree. We address these problems by proposing an adaptive partitioning technique called the Adaptive Partitioned R-tree (APR-tree) using workloads with timestamp and interval queries. The APR-tree adaptively partitions the time domain using query workloads. Since the time domain of the APR-tree is automatically fitted to query workloads, the APR-tree outperforms the other access methods for various query workloads. The size of the APR-tree is on the average 1.3 times larger than that of the 3DR-tree which has the smallest size. The update cost of the APR-tree is on the average similar to that of the 3DR-tree which has the smallest update cost. (C) 2003 Elsevier B.V. All rights reserved
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