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).
9576587
Non-Threshold based Event Detection for 3D Environment Monitoring in Sensor Networks
Event detection is a crucial task for wireless sensor network applications, especially environment monitoring. Existing approaches for event detection are mainly based on some predefined threshold values, and thus are often inaccurate and incapable of capturing complex events. For example, in coal mine monitoring scenarios, gas leakage or water osmosis can hardly be described by the overrun of specified attribute thresholds, but some complex pattern in the full-scale view of the environmental data. To address this issue, we propose a non-threshold based approach for the real 3D sensor monitoring environment. We employ energy-efficient methods to collect a time series of data maps from the sensor network and detect complex events through matching the gathered data to spatio-temporal data patterns. Finally, we conduct trace driven simulations to prove the efficacy and efficiency of this approach on detecting events of complex phenomena from real-life records.
7517195
Spotting Significant Changing Subgraphs in Evolving Graphs
Graphs are popularly used to model structural relationships between objects. In many application domains such as social networks, sensor networks and telecommunication, graphs evolve over time. In this paper, we study a new problem of discovering the subgraphs that exhibit significant changes in evolving graphs. This problem is challenging since it is hard to define changing regions that are closely related to the actual changes (i.e., additions/deletions of edges/nodes) in graphs. We formalize the problem, and design an efficient algorithm that is able to identify the changing subgraphs incrementally. Our experimental results on real datasets show that our solution is very efficient and the resultant subgraphs are of high quality.
262231
Monochromatic and bichromatic reverse skyline search over uncertain databases
Reverse skyline queries over uncertain databases have many important applications such as sensor data monitoring and business planning. Due to the existence of uncertainty in many real-world data, answering reverse skyline queries accurately and efficiently over uncertain data has become increasingly important. In this paper, we model the probabilistic reverse skyline query on uncertain data, in both monochromatic and bichromatic cases, and propose effective pruning methods to reduce the search space of query processing. Moreover, efficient query procedures have been presented seamlessly integrating the proposed pruning methods. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach with various experimental settings.
8704395
Summarization Graph Indexing: Beyond Frequent Structure-Based Approach
Graph is an important data structure to model complex structural data, such as chemical compounds, proteins, and XML documents. Among many graph data-based applications, sub-graph search is a key problem, which is defined as given a query Q, retrieving all graphs containing Q as a sub-graph in the graph database. Most existing sub-graph search methods try to filter out false positives (graphs that are not possible in the results) as many as possible by indexing some frequent sub-structures in graph database, such as [20, 22, 4, 23]. However, due to ignoring the relationships between sub-structures, these methods still admit a high percentage of false positives. In this paper, we propose a novel concept, Summarization Graph, which is a complete graph and captures most topology information of the original graph, such as sub-structures and their relationships. Based on Summarization Graphs, we convert the filtering problem into retrieving objects with set-valued attributes. Moreover, we build an efficient signature file-based index to improve the filtering process. We prove theoretically that the pruning power of our method is larger than existing structure-based approaches. Finally, we show by extensive experimental study on real and synthetic data sets that the size of candidate set generated by Summarization Graph-based approach is only about 50% of that left by existing graph indexing methods, and the total response time of our method is reduced 2-10 times.
8931794
Speed up semantic search in p2p networks
Peer-to-peer architectures become popular in modern massively distributed systems, which are often in very large scale and contain a huge volume of heterogeneous data. To facilitate the information retrieval process in P2P networks, we consider semantic search approach, where syntax-based queries are shipped to peers based on semantic correlations. Motivated by an interesting experience in Web information retrieval, we propose a novel ontology-based scheme to measure similarity of peer interests accurately and consistently in a decentralized way, and group peers under a scalable hierarchical overlay network. Given queries, our approach either floods them within local peer groups or guides them towards remote groups based on the similarity of interests. Our work overcomes the limitations of the existing P2P hybrid-search approaches by avoiding costly data popularity measurement. Performance evaluation and comparison against baseline algorithms show that our approach provides a better solution for information retrieval in large-scale P2P networks.
8353452
OPHMR: An Optimized Polymorphic Hybrid Multicast Routing Protocol for MANET
We propose in this paper an optimized, polymorphic, hybrid multicast routing protocol for MANET. This new polymorphic protocol attempts to benefit from the high efficiency of proactive behavior (in terms of quicker response to transmission requests) and the limited network traffic overhead of the reactive behavior, while being power, mobility, and vicinity-density (in terms of number of neighbor nodes per specified area around a mobile node) aware. The proposed protocol is based on the principle of adaptability and multibehavioral modes of operations. It is able to change behavior in different situations in order to improve certain metrics like maximizing battery life, reducing communication delays, improving deliverability, etc. The protocol is augmented by an optimization scheme, adapted from the one proposed for the optimized link state routing protocol (OLSR) in which only selected neighbor nodes propagate control packets to reduce the amount of control overhead. Extensive simulations and comparison to peer protocols demonstrated the effectiveness of the proposed protocol in improving performance and in extending battery power longevity
15477358
Cooperative Spectrum Sensing with Multi-Bits Local Sensing Decisions in Cognitive Radio Context
There are two important constraints for cooperative spectrum sensing in cognitive radio (CR) context. Firstly, as the CR system can only tolerate low transmitting overhead, the local sensing data must be compressed before transmitting. Secondly, many sophisticated data fusion techniques cannot be used in CR system because of the lack of the signal's prior knowledge. In this paper, we proposed a novel cooperative spectrum sensing scheme which adapts to different overhead tolerance and does not need any prior knowledge. This scheme consists of two main parts: the quantization schemes and the data fusion rule. Two different quantization schemes, according to whether or not the distribution functions of test statistic are known, are proposed. Correspondingly, the optimal data fusion rule of multi-bits decisions is derived. Furthermore, to make the optimal fusion rule more practical, an iterative scheme, which does not need any prior knowledge of the signal, is proposed to estimate the likelihood ratio of local decisions. Simulation results show that our scheme achieves better performance than the schemes with "OR" and "AND" combinations. Furthermore, it is also shown that the proposed scheme could achieve the theoretically optimal performance by only two or three bits quantization.
29666362
Algebraic Laws for BSP Programming
The Bulk-Synchronous Parallel (BSP) model of computation reflects the capabilities and costs of communication on a wide range of general-purpose parallel architectuers. Thus it allows general-purpose parallel software and hardware to be developed independently of one another; much as the von Neumann model provides the same facility for sequential computation. This paper presents a simple programming notation for shared-memory programming, based upon BSP constructs. The notation is defined formally by its effect upon process states. Algebraic laws are given which allow program derivation. A further set of laws allow transformation of finite programs to a normal form.
14606163
Gene intrusion on a society of Hill-Climbers
In this paper, a new algorithm of Gene Intrusion on a Society of Hill-Climbers (GI-SoHC) is introduced. In GI-SoHC, the genes which contribute most to the best fitness of the best climber in each generation are selected and inserted to other climbers for replacing their own genes, such that the society can have faster climbing speed and better climbing locations.The experiment results show that with Gene Intrusion, climbers generally and averagely perform better in that they climb more quickly than the original Historical Society of Hill-Climbers, especially in the early stage of the climbing process.
14979419
QoS-aware routing based on bandwidth estimation for mobile ad hoc networks
Routing protocols for mobile ad hoc networks (MANETs) have been explored extensively in recent years. Much of this work is targeted at finding a feasible route from a source to a destination without considering current network traffic or application requirements. Therefore, the network may easily become overloaded with too much traffic and the application has no way to improve its performance under a given network traffic condition. While this may be acceptable for data transfer, many real-time applications require quality-of-service (QoS) support from the network. We believe that such QoS support can be achieved by either finding a route to satisfy the application requirements or offering network feedback to the application when the requirements cannot be met. We propose a QoS-aware routing protocol that incorporates an admission control scheme and a feedback scheme to meet the QoS requirements of real-time applications. The novel part of this QoS-aware routing protocol is the use of the approximate bandwidth estimation to react to network traffic. Our approach implements these schemes by using two bandwidth estimation methods to find the residual bandwidth available at each node to support new streams. We simulate our QoS-aware routing protocol for nodes running the IEEE 802.11 medium access control. Results of our experiments show that the packet delivery ratio increases greatly, and packet delay and energy dissipation decrease significantly, while the overall end-to-end throughput is not impacted, compared with routing protocols that do not provide QoS support.