Power conservative multi-attribute queries on data broadcast

Hu, Q.; Lee, W.-C.; Lee, Dik Lun

doi:10.1109/icde.2000.839402

Cited by 39 publications

(34 citation statements)

References 9 publications

(20 reference statements)

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“…Thus, both index search time and access latency are measured in terms of number of packet accesses [5,6]. The drawback of air indexing is that broadcast cycles are lengthened due to the additional indexing information.…”

Section: Wireless Data Broadcastmentioning

confidence: 99%

Search K Nearest Neighbors on Air

Zheng

Lee

2002

Mobile Data Management

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Abstract. While the K-Nearest-Neighbor (KNN) problem is well studied in the traditional wired, disk-based client-server environment, it has not been tackled in a wireless broadcast environment. In this paper, the problem of organizing location dependent data and answering KNN queries on air are investigated. The linear property of wireless broadcast media and power conserving requirement of mobile devices make this problem particularly interesting and challenging. An efficient data organization, called sorted list, and the corresponding search algorithm are proposed and compared with the well-known spatial index, R-Tree. In addition, we develop an approximate search scope to guide the search at the very beginning of the search process and a learning algorithm to adapt the search scope during the search to improve energy and access efficiency. Simulation based performance evaluation is conducted to compare sorted list and R-Tree. The results show that the utilization of search scope and learning algorithm improves search efficiency of both index mechanisms significantly. While R-Tree is more power efficient when a large number of nearest neighbors is requested, the sorted list has better access efficiency and less power consumption when the number of nearest neighbors is small.

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Section: Wireless Data Broadcastmentioning

confidence: 99%

Search K Nearest Neighbors on Air

Zheng

Lee

2002

Mobile Data Management

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“…In a doze mode the mobile device is consuming power orders of magnitude less than that in the active mode. Power conservation indexing methods for single-attribute and multi-attribute based queries in broadcast push environments appeared in [3,15,16].…”

Section: Broadcast-pullmentioning

confidence: 99%

“…Any number of clients can monitor the broadcast channel and retrieve data as it arrives on the broadcast channel. If data is properly organized to cater to the needs of the clients, such a scheme makes an effective use of the low wireless bandwidth and is ideal to achieve maximal scalability [1,15,16].…”

Section: Introductionmentioning

confidence: 99%

On-Demand Data Broadcasting for Mobile Decision Making

Sharaf

Chrysanthis

2004

Mobile Networks and Applications

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Abstract. The wide spread of mobile computing devices is transforming the newly emerged e-business world into a mobile e-business one, a world in which hand-held computers are the user's front-ends to access enterprise data. For good mobile decision making, users need to count on up-to-date, business-critical data. Such data are typically in the form of summarized information tailored to suit the user's analysis interests. In this paper, we are addressing the issue of time and energy efficient delivery of summary tables to mobile users with hand-held computers equipped with OLAP (On-Line Analytical Processing) front-end tools. Towards this, we propose a new on-demand scheduling algorithm, called STOBS, that exploits the derivation semantics among OLAP summary tables. It maximizes the aggregated data sharing between mobile users and reduces the broadcast length for satisfying a set of requests compared to the already existing techniques. The algorithm effectiveness with respect to access time and energy consumption is evaluated using simulation.

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“…Owing to this scalability feature, the wireless broadcast channel has been considered an alternative storage media (called "air storage") of the traditional hard disks [1,15]. Many studies addressing various problems in mobile broadcast environments, such as scheduling and bandwidth allocation, have appeared in the literature [1,12,15,22]. It is expected that in the near future a number of mobile LDISs (e.g., region-wide traffic reports and tourism information) will utilize broadcast for the dissemination of information to the rapidly increasing population of mobile users.…”

mentioning

confidence: 99%

“…Moreover, imbalanced indexing for skewed data accesses [6] and indexing for multiattribute queries [12] have also been investigated. However, these studies concentrated on one-dimensional indexes for equality-based queries, and thus are inapplicable to location-dependent query processing where point queries are involved.…”

mentioning

confidence: 99%