4440052
An Optimal Divide-Conquer Algorithm for 2D Skyline Queries
Skyline query processing is fundamental to many applications including multi-criteria decision making. In this paper, we will present an optimal algorithm for computing skyline in the two dimensional space. The algorithm has the progressive nature and adopts the divide-conquer paradigm. It can be shown that our algorithm achieves the minimum I/O costs, and is more efficient and scalable than the existing techniques. The experiment results demonstrated that our algorithm greatly improves the performance of the existing techniques.
17501980
Term Graph Model for Text Classification
Most existing text classification methods (and text mining methods at large) are based on representing the documents using the traditional vector space model. We argue that important information, such as the relationship among words, is lost. We propose a term graph model to represent not only the content of a document but also the relationship among the keywords. We demonstrate that the new model enables us to define new similarity functions, such as considering rank correlation based on PageRank-style algorithms, for the classification purpose. Our preliminary results show promising results of our new model.
14038708
Efficiently computing frequent tree-like topology patterns in a Web environment
Data mining has recently emerged in a number of real life applications, such as banking, supermarketing, and interneting. The authors investigate a novel data mining problem derived from the applications of WWW. They present an efficient algorithm for solving the problem. The initial implementation suggests that the algorithm is very efficient and scalable in practice.
6221918
Efficiently Matching Proximity Relationships in Spatial Databases
Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate a proximity matching problem among clusters and features. The investigation involves proximity relationship measurement between clusters and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. An efficient algorithm, for solving the problem, is proposed and evaluated. The algorithm applies a standard multistep paradigm in combining with novel lower and upper proximity bounds. The algorithm is implemented in several different modes. Our experiment results do not only give a comparison among them but also illustrate the efficiency of the algorithm.
1993887
Locating Motifs in Time-Series Data
Finding motifs in time-series is proposed to make clustering of time-series subsequences meaningful, because most existing algorithms of clustering time-series subsequences are reported meaningless in recent studies. The existing motif finding algorithms emphasize the efficiency at the expense of quality, in terms of the number of time-series subsequences in a motif and the total number of motifs found. In this paper, we formalize the problem as a continuous top-k motif balls problem in an m-dimensional space, and propose heuristic approaches that can significantly improve the quality of motifs with reasonable overhead, as shown in our experimental studies.
4018890
Clustering Moving Objects for Spatio-temporal Selectivity Estimation
Many spatio-temporal applications involve managing and querying moving objects. In such an environment, predictive spatio-temporal queries become an important query class to be processed to capture the nature of moving objects. In this paper, we investigated the problem of selectivity estimation for predictive spatio-temporal queries. We propose a novel histogram technique based on a clustering paradigm. To avoid expensive computation costs, we developed linear time heuristics to construct such a histogram. Our performance study indicated that the new techniques improve the accuracy of the existing techniques by one order of magnitude.
9661555
Multicast in Fat-Tree-Based InfiniBand Networks
The multicast operation is a very commonly used operation in parallel applications. With the hardware supported multicast of the InfiniBand architecture (IBA), we propose a cyclic multicast scheme for fat-tree-based (m-port n-tree) InfiniBand networks. The basic concept of the proposed cyclic multicast scheme is to find the union sets of the output ports of switches in the paths between the source processing node and each destination processing node in a multicast group. Based on the union sets and the path selection scheme, the forwarding table for a given multicast group can be constructed. We implement the proposed multicast scheme along with the OpenSM multicast scheme and the unicast scheme on an m-port n-tree InfiniBand network simulator. The simulation results show that the proposed multicast scheme outperforms the unicast scheme for all simulated cases. For many-to-many and all-to-many cases, the cyclic multicast scheme outperforms the OpenSM multicast scheme. For many-to-all case, the performance of the cyclic multicast scheme is a little better than that of the OpenSM multicast scheme
11787758
Simplification of network dynamics in large systems
We show that when networks are large significant simplicity can be achieved for pricing-based control. We first consider a general loss network with Poisson arrivals and arbitrary holding time distributions. In dynamic pricing schemes, the network provider can charge different prices to the user according to the current utilization level of the network and also other factors. We show that when the system becomes large the performance (in terms of expected revenue) of an appropriately chosen static pricing scheme, whose price is independent of the current network utilization, will approach that of the optimal dynamic pricing scheme. Further, we show that under certain conditions, this static price is independent of the route that the flows take. We then extend the result to the case of dynamic routing, and show that the performance of an appropriately chosen static pricing scheme with bifurcation probability determined by average parameters can also approach that of the optimal dynamic routing scheme when the system is large. These results deepen our understanding of pricing-based network control. In particular, they provide us with the insight that, when the system is large, an appropriate pricing strategy based on the average network conditions (hence, slowly changing) can approach optimality.
7373402
Co-cited author maps as interfaces to digital libraries: designing Pathfinder Networks in the humanities
Using data from the Arts and Humanities Citation Index for 1988-1997, we are attempting to generate co-cited author maps in real time that can serve as interfaces for document retrieval. Our novel approach is to start the maps by entering the name of a single author. Our system obtains a rank-ordered list of the other authors who are most often co-cited with the input name (e.g., the top 50) and then systematically pairs these other authors in turn to obtain their co-citation counts. The resulting matrix can be passed to a mapping program which groups the authors meaningfully to show the structure of the field implied by the input author's name. At present we are experimenting with Pathfinder Networks (PFNETs) as the mapping program, and show a PFNET for 25 authors in hermeneutics, a major field of the humanities. The documents co-citing the mapped authors are candidates for retrieval. We discuss desirable features and design considerations in retrieval interfaces based on PFNETs.
1863895
Low-Complexity and Distributed Energy Minimization in Multihop Wireless Networks
In this work, we study the problem of minimizing the total power consumption in a multihop wireless network subject to a given offered load. It is well-known that the total power consumption of multihop wireless networks can be substantially reduced by jointly optimizing power control, link scheduling, and routing. However, the known optimal cross-layer solution to this problem is centralized and with high computational complexity. In this paper, we develop a low-complexity and distributed algorithm that is provably power-efficient. In particular, under the node-exclusive interference model and with suitable assumptions on the power-rate function, we can show that the total power consumption of our algorithm is at most (2+¿) times as large as the power consumption of the optimal (but centralized and complex) algorithm, where ¿ is an arbitrarily small positive constant. Our algorithm is not only the first such distributed solution with provable performance bound, but its power-efficiency ratio is also tighter than that of another suboptimal centralized algorithm in the literature.
2200679
A Distributed Joint Channel-Assignment, Scheduling and Routing Algorithm for Multi-Channel Ad-hoc Wireless Networks
The capacity of ad hoc wireless networks can be substantially increased by equipping each network node with multiple radio interfaces that can operate on multiple non-overlapping channels. However, new scheduling, channel-assignment, and routing algorithms are required to fully utilize the increased bandwidth in multi-channel multi-radio ad hoc networks. In this paper, we develop a fully distributed algorithm that jointly solves the channel-assignment, scheduling and routing problem. Our algorithm is an online algorithm, i.e., it does not require prior information on the offered load to the network, and can adapt automatically to the changes in the network topology and offered load. We show that our algorithm is provably efficient. That is, even compared with the optimal centralized and offline algorithm, our proposed distributed algorithm can achieve a provable fraction of the maximum system capacity. Further, the achievable fraction that we can guarantee is larger than that of some other comparable algorithms in the literature.