Jeffrey Xu Yu

Spatial Join Strategies in Distributed Spatial DBMS

In a distributed spatial database system, a user may issue a query that relates two spatial relations that are stored at different sites. Because of the sheer volume and complexity of spatial data, spatial joins between two spatial relations at different sites are expensive in terms of computation and transmission cost. In this paper, we examine the problems of spatial joins between sites, and present spatial join processing strategies used in a heterogeneous spatial database system. Preliminary experimental results are reported.

An analysis of selective tuning schemes for nonuniform broadcast

Abstract Broadcasting is an effective means of disseminating information in a wireless environment to a large number of clients with powerful palmtops. However, it requires the clients to be actively listening to the communication channels for the desired information. Because of the high power consumption of the active mode, it is crucial for the battery-operated palmtops to conserve their energy in order to extend their effective battery life. This calls for selective tuning mechanisms that allow the clients to operate in the less energy-consuming doze mode, and to operate only in active mode when the desirable portion of the information is broadcast. Most of the existing work focuses on uniform broadcast. In practice, only a small amount of information is highly in demand by a large number of clients while the remainder is less popular. This nonuniform access pattern poses several new issues. In this paper, we examine these issues and look at how a nonuniform broadcast can be organized for selective tuning by the clients. We describe several indexing schemes to facilitate selective tuning which are variations of existing techniques on uniform broadcast. We analyze the performance of the schemes based on the average tuning time and average access time.

Energy efficient filtering of nonuniform broadcast

In a wireless environment, information is broadcast on communication channels to clients using powerful, battery-operated palmtops. To conserve the usage of energy, the information to be broadcast must be organized so that the client can selectively tune in at the desirable portion of the broadcast. Most of the existing work focus on uniform broadcast. However very often, a small amount of information is more frequently accessed by a large number of clients while the remainder are less in demand. This nonuniform access pattern poses several new issues. In this paper we examine these issues and look at how a nonuniform broadcast can be organized for selective tuning by the clients. We propose several new indexing schemes to facilitate selective tuning. A performance study is conducted to study and demonstrate the effectiveness of the proposed schemes.

Statistical estimation of access frequencies in data broadcasting environments

In a data publishing environment, the server periodically broadcasts data to users based on a broadcast program. The program is constructed using knowledge of access frequencies, which is assumed to be available and accurate, on the broadcast data. For example, the program may broadcast frequently accessed data more often in a broadcast cycle. However, it remains an open question as to how to obtain such access frequencies. The difficulty of obtaining such access frequencies is that in such an environment, mobile users are only listening to the channel they are interested in and do not request for the data items from the server. A promising approach in the literature is to make use of broadcast misses to understand the access patterns in a data publishing environment. In this case, mobile users may decide whether to wait for the required item to arrive or to make an explicit request for it even though it will be published. However, estimation of access frequencies based on broadcast misses may not be accurate because the number of broadcast misses to the data depends on how frequently the data is broadcast: if a piece of data is more frequently broadcast than the others, then the broadcast misses to that piece of data will be low because the average waiting time is low. In this paper, we propose a statistical estimation model that is based on maximum likelihood estimation to estimate the access frequencies. Our approach is novel in that it exploits knowledge that is available – broadcast misses and broadcast frequencies – to refine the program to better meet the needs of the user population. We report our simulation study that demonstrates the effectiveness of our approach.

Mining inter-transaction associations with templates

Multi-dimensional, inter-transaction association rules extend the traditional association rules to describe more general associations among items with multiple properties cross transactions. “After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away” is an example of such rules. Since the number of potential inter-transaction association rules tends to be extremely large, mining inter-transaction associations poses more challenges on efficient processing than mining intra-transaction associations. In order to make such association mining truly practical and computationally tractable, in this study, we present a template model to help users declare the interesting inter-transaction associations to be mined. With the guidance of templates, several optimization techniques are devised to speed up the discovery of inter-transaction association rules. We show, through a series of experiments, that these optimization techniques can yield significant performance benefits.

Optimizing multiple dimensional queries simultaneously in multidimensional databases

Abstract. Some significant progress related to multidimensional data analysis has been achieved in the past few years, including the design of fast algorithms for computing datacubes, selecting some precomputed group-bys to materialize, and designing efficient storage structures for multidimensional data. However, little work has been carried out on multidimensional query optimization issues. Particularly the response time (or evaluation cost) for answering several related dimensional queries simultaneously is crucial to the OLAP applications. Recently, Zhao et al. first exploited this problem by presenting three heuristic algorithms. In this paper we first consider in detail two cases of the problem in which all the queries are either hash-based star joins or index-based star joins only. In the case of the hash-based star join, we devise a polynomial approximation algorithm which delivers a plan whose evaluation cost is

Supporting range queries in a wireless environment with nonuniform broadcast

O(n^{\epsilon }

Using Stem Rules to Refine Document Retrieval Queries

) times the optimal, where n is the number of queries and

A dynamic scheduler for the infinite air-cache

\epsilon

A Performance Study of Declustering Strategies for Parallel Spatial Databases

is a fixed constant with