9515152
Similarity Search in Arbitrary Subspaces Under Lp-Norm
Similarity search has been widely used in many applications such as information retrieval, image data analysis, and time-series matching. Specifically, a similarity query retrieves all data objects in a data set that are similar to a given query object. Previous work on similarity search usually consider the search problem in the full space. In this paper, however, we propose a novel problem, subspace similarity search, which finds all data objects that match with a query object in the subspace instead of the original full space. In particular, the query object can specify arbitrary subspace with arbitrary number of dimensions. Since traditional approaches for similarity search cannot be applied to solve the proposed problem, we introduce an efficient and effective pruning technique, which assigns scores to data objects with respect to pivots and prunes candidates via scores. We propose an effective multipivot-based method to pre-process data objects by selecting appropriate pivots, where the entire procedure is guided by a formal cost model, such that the pruning power is maximized. Finally, scores of each data object are organized in sorted list to facilitate an efficient subspace similarity search. Extensive experiments have verified the correctness of our cost model and demonstrated the efficiency and effectiveness of our proposed approach for the subspace similarity search.
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.
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.
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.
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.
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.
17397024
Multi-level secure video streaming over SRTP
In this paper, we propose a set of solutions to four different levels on data selection for encrypting Motion JPEG video frames over SRTP. These solutions are based on our intensive analysis of the JPEG image compression procedure. We select the most important data which contains most information of an image for encryption; in consequence provide a set of balances between security and computation for different hardware environments. Extensive experiments are conducted and performance data has been analyzed to evaluate the proposed solutions. Results show that these solutions are well suited in different scenarios with various computation power and security requirements.
18449881
Tracking Articulated Hand Underlying Graphical Model with Depth Cue
Visual tracking of articulated objects in real 3D space is challenging with applications in advanced human–computer interfaces and gesture semantic understanding. In this paper, graphical model, constructing articulated human hand, and NBP algorithm embedded with CAMSHIFT, inferencing hand configuration in 3D space, are applied for visual hand tracking. We also introduce image depth cue captured by two calibrated cameras together with color and edge as observation model. Depth cue allows our method to track human hand incomparatively accurate distance from cameras, especially in cluttered scenes with objects of similar color or edge. All those image cues are converted necessarily to probability distribution applied for the graphical model frame. The proposed method promotes the tracking efficiency and robustness proved by experiments and theory analysis.
7400847
Auction-based congestion management for target tracking in wireless sensor networks
This paper addresses the problem of providing congestion-management for a shared wireless sensor network-based target tracking system. In many large-scale wireless sensor network target tracking scenarios (e.g., a surveillance system for tracking vehicles in urban environments), multiple targets may converge within close proximity of each other. Such scenarios may cause network congestion as nearby sensors attempt to concurrently send updates to a data aggregation point (e.g., base station). We consider the case in which this problem is further complicated by two factors. First, such a large-scale sensor network may very well be deployed to serve multiple target tracking applications with different and dynamic priorities and interests in different (types of) targets. Second, each application will most likely place a different premium on the timeliness of the target information (principally defined by some quality metric) they receive. All the above challenges introduce formidable challenges in providing the expeditious delivery of target information to all prioritized applications. In this paper, we advocate the use of a distributed auction-based approach to locally manage network bandwidth allocation in the described context. We use the Second Price Auction mechanism (to ensure incentive compatibility) in which the congested node acts as the auctioneer and the packets carrying target updates act as bidders. Their bid values are defined by the loss of information utility to the applications associated with the packets. The winning packet receives the current transmission slot of the auctioneer node. We demonstrate through simulation that the resulting auction allocates bandwidth efficiently, maximizing the collective applications' goals, even when the application priorities change dynamically.
38456559
Testing Technology of Torsional Vibration Spring Static Stiffness
The principle and method of testing static stiffness of torsional vibration spring are put forward based on the structure of dual-mass flywheel with torsional vibration spring, and the test bench is designed for these. The testing data is collected by using the PXI-6259 data collection card of National instrument and the data is processed by the LabVIEW software system. The elastic characteristic of the torsional vibration spring is obtained by analyzing the data.
15196694
End-to-end diagnosis of QoS violations with neural network
In this paper, we introduce a novel end-to-end approach to QoS management with respect to the diagnosis of QoS violations. We first use a set of end-to-end flow traffic statistics to describe a QoS violation. Subsequently, neural network techniques are engaged to identify and differentiate QoS violations through classification of the collected statistics. Through experiments, we find that our scheme outperforms traditional rule-based methods which require clear margins of QoS parameters in asserting a QoS violation.