121925502
Similarity search over time series and trajectory data
Time series data have been used in many applications, such as financial data analysis and weather forecasting. Similarly, trajectories of moving objects are often used to perform movement pattern analysis in surveillance video and sensor monitoring systems. All these applications are closely related to similarity-based time series or trajectory data retrieval. 
In this dissertation, various similarity models are proposed to capture the similarities among time series and trajectory data under various circumstances and requirements, such as the appearance of noise and local time shifting. A novel representation, called multi-scale time series histograms , is proposed to answer pattern existence queries and shape match queries. Earlier proposals generally address one or the other; multi-scale time series histograms can answer both types, which offers users more flexibility. A metric distance function, called Edit distance with Real Penalty (ERP), is proposed that can support local time shifting in time series and trajectory data. A second distance function, Edit Distance on Real sequence (EDR) is proposed to measure the similarity between time series or trajectories with local time shifting and noise. 
Since the proposed similarity models are computationally expensive, several indexing and pruning methods are proposed to improve the retrieval efficiency. For multi-scale time series histograms, A multi-step filtering process is introduced to improve the retrieval efficiency without introducing false dismissals. For ERP, a framework is developed to index time series or trajectory data under a metric distance function, which exploits the pruning power of lower bounding and triangle inequality. For EDR, three pruning techniques—mean value Q-grams, near triangle inequality, and trajectory histograms—are developed to improve the retrieval efficiency.
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.
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.
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.
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.
45287977
Distance paired-domination problems on subclasses of chordal graphs
Let G=(V,E) be a graph without isolated vertices. For a positive integer k, a set S@?V is a k-distance paired-dominating set if each vertex in V-S is within distance k of a vertex in S and the subgraph induced by S contains a perfect matching. In this paper, we present two linear time algorithms to find a minimum cardinality k-distance paired-dominating set in interval graphs and block graphs, which are two subclasses of chordal graphs. In addition, we present a characterization of trees with unique minimum k-distance paired-dominating set.
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.
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.
616754
Automated Planning of CPICH Power for Enhancing HSDPA Performance at Cell Edges with Preserved Control of R99 Soft Handover
We present and demonstrate a novel approach for automated planning of common pilot channel (CPICH) power in mobile networks with co-existing HSDPA and R99 services. CPICH power allocation greatly influences the cell coverage pattern. A conventional strategy is to uniformly allocate a constant proportion of the total power to CPICH. We study non-uniform CPICH and optimize its allocation for enhancing HSDPA performance. We focus on HSDPA performance at cell edges, where user throughput is typically very low. Our approach is based on a linear-integer mathematical model; solving the model results in a power allocation that both ensures CPICH coverage and optimizes HSDPA performance at cell edges. Moreover, the model allows for precise control of soft handover (SHO) regions for R99 service. Experimental results show that our approach yields significant enhancement of HSDPA performance at cell edges with preserved R99 SHO control.