7048128
Database Systems for Advanced Applications
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.
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.
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.
37631893
Stereo Vision Based Floor Plane Extraction and Camera Pose Estimation
In this paper, we present a unified approach for floor plane extraction and camera pose estimation. A histogram based method is performed on the reconstructed 3-D points obtained by an onboard video-rate stereo vision system to extract candidate points of the floor plane and determine the pose of vision system simultaneously. The obstacle area is easily localized given the floor plane region. In order to improve reliability and accuracy of camera pose estimation results, the Least Median of Squares (LMedS) based fitting method is applied to estimate the floor plane parameters with the extracted candidate points. The precise pose of the onboard stereo vision system is directly acquired related to the floor plane parameters. Experimental results in real indoor environments are discussed and show the good performance.
24767232
The Research and Application of Human Detection Based on Support Vector Machine Using in Intelligent Video Surveillance System
This paper presented a special design and implementation of human detection based on SVM (support vector machine) and this method is used in intelligent video surveillance system. In order to simplify the design of the SVM classifier and improve efficiency of machine learning, both a grid vector representation and a center radiating vector representation are proposed to abstract features of the object. The sample data is obtained through processing and analysis including human and no-human which forms the training input to SVM. Finally, we used the trained recognizer to identify whether there is somebody broken into the object region. If there is, the automatic warning device gives the alarm, which guarantees a real-time surveillance.
16623985
Building an Adaptive Multimedia System using the Utility Model
We present our experience of building a prototype system based on the Utility Model for adaptive multimedia. The Utility Model is proposed to capture the issues and dynamics in multi-session multimedia systems where the quality of service (QoS) of individual sessions is adapted to dynamic changes of available resources and of user preferences. We present the design and implementation of our prototype multimedia system, and report experimental results. We demonstrate that the Utility Model supports two types of adaptation: reactive adaptation for systems where only a subset of the applications follows the adaptation model, and proactive adaptation for systems where all the applications follow the adaptation model. Our results demonstrate that the Utility Model may be effectively used for dynamic quality adaptation in real-time multimedia systems.
16274066
Density-based multipath secure communication over mobile ad hoc networks
Security is a critical issue in mobile ad hoc networks (MANETs), where mobile nodes communicate with each other over relatively unreliable wireless links with no fixed infrastructure. One example of this occurs on battle fields, where soldiers often wear cameras and wireless mobile devices to send or receive images/video of different angles or locations in order to obtain better observation of the area. Such an environment could be highly insecure since an enemy could place devices to eavesdrop on or compromise the soldier nodes. In this paper, we consider a case where time sensitive multimedia is sent using multiple paths which can make better use of load balancing strategies, increase bandwidth and save node energy. However, an enemy node could listen in on more than one path at the same time, allowing it to collect sufficient shares of an image or video frame to recover the image. Our proposed algorithm is based on a quantitative analysis of security to protect against such multi-path eavesdropping. We prove that our algorithm can achieve better level of security by detouring traffic via paths without or with less vulnerable areas at the same amount of redundancy. On the other hand, we also prove, using simulation results and mathematical derivation, our algorithm can provide much more redundancy for better multimedia quality purpose while at the same time guaranteeing the same level of security.
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.