35965067
Dynamic Layer Management in Superpeer Architectures
Superpeer unstructured P2P systems have been found to be very effective by dividing the peers into two layers, superlayer and leaf-layer, in which message flooding is only conducted among superlayer and all leaf-peers are represented by corresponding superpeers. However, current superpeer systems do not employ any effective layer management schemes, so the transient and low-capacity peers are allowed to act as superpeers. Moreover, the lack of an appropriate size ratio maintenance mechanism on superlayer to leaf-layer makes the system's search performance far from being optimal. We present one workload model aimed at reducing the weighted overhead of a network. Using our proposed workload model, a network can determine an optimal layer size ratio between leaf-layer and superlayer. We then propose a dynamic layer management algorithm, DLM, which can maintain an optimal layer size ratio and adaptively elect and adjust peers between superlayer and leaf-layer. DLM is completely distributed in the sense that each peer decides to be a superpeer or a leaf-peer independently without global knowledge. DLM could effectively help a superpeer P2P system maintain the optimal layer size ratio and designate peers with relatively long lifetime and large capacities as superpeers, and the peers with short lifetime and low capacities as leaf-peers under highly dynamic network situations. We demonstrate that the quality of a superpeer system is significantly improved under the DLM scheme by comprehensive simulations.
848425
Location awareness in unstructured peer-to-peer systems
Peer-to-peer (P2P) computing has emerged as a popular model aiming at further utilizing Internet information and resources. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a serious topology mismatch between the P2P overlay network and the physical underlying network. The topology mismatch problem brings great stress in the Internet infrastructure. It greatly limits the performance gain from various search or routing techniques. Meanwhile, due to the inefficient overlay topology, the flooding-based search mechanisms cause a large volume of unnecessary traffic. Aiming at alleviating the mismatching problem and reducing the unnecessary traffic, we propose a location-aware topology matching (LTM) technique. LTM builds an efficient overlay by disconnecting slow connections and choosing physically closer nodes as logical neighbors while still retaining the search scope and reducing response time for queries. LTM is scalable and completely distributed in the sense that it does not require any global knowledge of the whole overlay network. The effectiveness of LTM is demonstrated through simulation studies.
132343
Approaching optimal peer-to-peer overlays
In unstructured peer-to-peer (P2P) systems, there exists a serious topology mismatch problem between physical and logical network. We first analyze the relationship between the property of the overlay and the corresponding message duplications incurred by queries in a given overlay, and prove that computing an optimal overlay with global knowledge is an NP-hard problem. Motivated by the analysis results, we design a distributed overlay optimization algorithm, THANCS, to attack topology mismatch. We demonstrate its performance by comprehensive simulations in dynamic environments. The proposed THANCS has three major strengths. First, it does not need any global knowledge. Second, its optimization convergent speed is fast. Third, it is orthogonal with other types of advanced search approaches.
18230
Indexable PLA for Efficient Similarity Search
Similarity-based search over time-series databases has been a hot research topic for a long history, which is widely used in many applications, including multimedia retrieval, data mining, web search and retrieval, and so on. However, due to high dimensionality (i.e. length) of the time series, the similarity search over directly indexed time series usually encounters a serious problem, known as the "dimensionality curse". Thus, many dimensionality reduction techniques are proposed to break such curse by reducing the dimensionality of time series. Among all the proposed methods, only Piecewise Linear Approximation (PLA) does not have indexing mechanisms to support similarity queries, which prevents it from efficiently searching over very large time-series databases. Our initial studies on the effectiveness of different reduction methods, however, show that PLA performs no worse than others. Motivated by this, in this paper, we re-investigate PLA for approximating and indexing time series. Specifically, we propose a novel distance function in the reduced PLA-space, and prove that this function indeed results in a lower bound of the Euclidean distance between the original time series, which can lead to no false dismissals during the similarity search. As a second step, we develop an effective approach to index these lower bounds to improve the search efficiency. Our extensive experiments over a wide spectrum of real and synthetic data sets have demonstrated the efficiency and effectiveness of PLA together with the newly proposed lower bound distance, in terms of both pruning power and wall clock time, compared with two state-of-the-art reduction methods, Adaptive Piecewise Constant Approximation (APCA) and Chebyshev Polynomials (CP).
6911930
LANDMARC: Indoor Location Sensing Using Active RFID
Growing convergence among mobile computing devices and embedded technology sparks the development and deployment of “context-aware” applications, where location is the most essential context. In this paper we present LANDMARC, a location sensing prototype system that uses Radio Frequency Identification (RFID) technology for locating objects inside buildings. The major advantage of LANDMARC is that it improves the overall accuracy of locating objects by utilizing the concept of reference tags. Based on experimental analysis, we demonstrate that active RFID is a viable and cost-effective candidate for indoor location sensing. Although RFID is not designed for indoor location sensing, we point out three major features that should be added to make RFID technologies competitive in this new and growing market.
12060317
DiCAS: An Efficient Distributed Caching Mechanism for P2P Systems
Peer-to-peer networks are widely criticized for their inefficient flooding search mechanism. Distributed hash table (DHT) algorithms have been proposed to improve the search efficiency by mapping the index of a file to a unique peer based on predefined hash functions. However, the tight coupling between indices and hosting peers incurs high maintenance cost in a highly dynamic network. To properly balance the tradeoff between the costs of indexing and searching, we propose the distributed caching and adaptive search (DiCAS) algorithm, where indices are passively cached in a group of peers based on a predefined hash function. Guided by the same function, adaptive search selectively forwards queries to "matched" peers with a high probability of caching the desired indices. The search cost is reduced due to shrunk searching space. Different from the DHT solutions, distributed caching loosely maps the index of a file to a group of peers in a passive fashion, which saves the cost of updating indices. Our simulation study shows that the DiCAS protocol can significantly reduce the network search traffic with the help of small cache space contributed by each individual peer
14575854
Query Translation Disambiguation as Graph Partitioning
Resolving ambiguity in the process of query translation is crucial to cross-language information retrieval when only a bilingual dictionary is available. In this paper we propose a novel approach for query translation disambiguation, named "spectral query translation model". The proposed approach views the problem of query translation disambiguation as a graph partitioning problem. For a given query, a weighted graph is first created for all possible translations of query words based on the co-occurrence statistics of the translation words. The best translation of the query is then determined by the most strongly connected component within the graph. The proposed approach distinguishes from previous approaches in that the translations of all query words are estimated simultaneously. Furthermore, translation probabilities are introduced in the proposed approach to capture the uncertainty in translating queries. Empirical studies with TREC datasets have shown that the spectral query translation model achieves a relative 20% -50% improvement in cross-language information retrieval, compared to other approaches that also exploit word co-occurrence statistics for query translation disambiguation.
8733679
Stochastic Fluid Theory for P2P Streaming Systems
We develop a simple stochastic fluid model that seeks to expose the fundamental characteristics and limitations of P2P streaming systems. This model accounts for many of the essential features of a P2P streaming system, including the peers' realtime demand for content, peer churn (peers joining and leaving), peers with heterogeneous upload capacity, limited infrastructure capacity, and peer buffering and playback delay. The model is tractable, providing closed-form expressions which can be used to shed insight on the fundamental behavior of P2P streaming systems. The model shows that performance is largely determined by a critical value. When the system is of moderate-to-large size, if a certain ratio of traffic loads exceeds the critical value, the system performs well; otherwise, the system performs poorly. Furthermore, large systems have better performance than small systems since they are more resilient to bandwidth fluctuations caused by peer churn. Finally, buffering can dramatically improve performance in the critical region, for both small and large systems. In particular, buffering can bring more improvement than can additional infrastructure bandwidth.
39241507
Feature selection using a proximity-index optimization model
Abstract One of the recurring issues in pattern recognition problems has been feature extraction and selection. This paper addresses this issue from a different pespective. Without assuming any particular classificaiton algorithm, it first suggests that one extract as much information as conveniently possible in several pattern-information domains. This paper later suggests applying the proposed Proximity-Index method, to select a significantly smaller, yet optimal feature subset. This method is formally described and is successfully applied to a waveform classification problem. The features selected by the algorithm are used to classify ten signal classes and produce a very encouraging recognition performance of 87.00% on 200 samples. This method is computationally inexpensive and particularly useful for large data set problems.
101051
Simultaneous training of negatively correlated neural networks in an ensemble
This paper presents a new cooperative ensemble learning system (CELS) for designing neural network ensembles. The idea behind CELS is to encourage different individual networks in an ensemble to learn different parts or aspects of a training data so that the ensemble can learn the whole training data better. In CELS, the individual networks are trained simultaneously rather than independently or sequentially. This provides an opportunity for the individual networks to interact with each other and to specialize. CELS can create negatively correlated neural networks using a correlation penalty term in the error function to encourage such specialization. This paper analyzes CELS in terms of bias-variance-covariance tradeoff. CELS has also been tested on the Mackey-Glass time series prediction problem and the Australian credit card assessment problem. The experimental results show that CELS can produce neural network ensembles with good generalization ability.
5940493
Efficient schema design for a pharmaceutical data repository
Large scientific databases often consist of heterogeneous components that differ in the number of attributes and the number of tuples. In this paper, we show that increased performance is achieved when a complex schema is used to represent different components of a data repository. The driving application used in this study is a pharmaceutical drug repository. The types of access patterns associated with this repository consist of the retrieval of a selected set of attribute values for a consecutive or a random set of tuples. These consecutive and random access patterns, which are typical of a drug discovery experiment, are used to measure the data retrieval throughput for different schemas.