1546807
MINDEX: An efficient index structure for salient-object-based queries in video databases
Abstract.Several salient-object-based data models have been proposed to model video data. However, none of them addresses the development of an index structure to efficiently handle salient-object-based queries. There are several indexing schemes that have been proposed for spatiotemporal relationships among objects, and they are used to optimize timestamp and interval queries, which are rarely used in video databases. Moreover, these index structures are designed without consideration of the granularity levels of constraints on salient objects and the characteristics of video data. In this paper, we propose a multilevel index structure (MINDEX) to efficiently handle the salient-object-based queries with different levels of constraints. We present experimental results showing the performance of different methods of MINDEX construction.
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.
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.
6162473
The Effects of Mobility and Redundancy on Wireless Video Streaming over MANETs
This study examines the extended use of different coding schemes (Scalable vs. Multiple Description or MD) in different video formats (MPEG vs. Motion JPEG or MJPEG) in a Mobile Ad Hoc Network (MANET) environment and proposes a set of redundancy solutions at different coding levels. Simulations and analysis demonstrate and explain the relationship among the coding schemes, video formats, level and amount of redundancy, mobility of mobile devices, CPU load and battery life and frame loss rate at the receiver side. Finally, two practical scenarios are used to illustrate these findings and proposed solutions.
18482512
Remote equipment monitoring system based on ARM9 and CAN bus
In this paper, the remote monitoring system based on ARM9 and CAN bus is studied. It focuses on the ARM9 and Ethernet interface methods, CAN / Ethernet protocol conversion methods, and the construction of the network database. Adopting the technology, which is embedded Network interface device in the network environment of self-identification, self-description and interoperability, it will make the network sensor dynamic configuration and reconfiguration. Test results show that the system meets the requirements of automation equipment maintenance, real-time monitoring and reliability, so that it makes the effective realization of the remote monitoring equipment come true.
22510443
Universal Approximation and QoS Violation Application of Extreme Learning Machine
Neural networks have been successfully applied to many applications due to their approximation capability. However, complicated network structures and algorithms will lead to computational and time-consuming burdens. In order to satisfy demanding real-time requirements, many fast learning algorithms were explored in the past. Recently, a fast algorithm, Extreme Learning Machine (ELM) (Huang et al. 70:489–501, 2006) was proposed. Unlike conventional algorithms whose neurons need to be tuned, the input-to-hidden neurons of ELM are randomly generated. Though a large number of experimental results have shown that input-to-hidden neurons need not be tuned, there lacks a rigorous proof whether ELM possesses the universal approximation capability. In this paper, based on the universal approximation property of an orthonormal method, we firstly illustrate the equivalent relationship between ELM and the orthonormal method, and further prove that neural networks with ELM are also universal approximations. We also successfully apply ELM to the identification of QoS violation in the multimedia transmission.
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.
31409147
Systemical convergence rate analysis of convex incremental feedforward neural networks
In this paper, we systemically investigate several convex incremental feedforward neural networks. Firstly, we prove the universal approximation and the convergence rate of a generalized convex incremental (GCI) structure, which provides us a wider parameter selection. Second, according to the convergence rate proof of GCI, we further prove the convergence rate of a best convex incremental (BCI) structure, moreover its proof also illustrates that BCI can achieve a better generalization performance than GCI. But we should note that the hidden neurons of BCI and GCI both are constructed on the maximum principle (not random). Next, we introduce the random neuron conception based on CI-ELM (convex incremental extreme learning machines), and further propose an alternative algorithm (improved CI-ELM, ICI-ELM) between CI-ELM and BCI, which removes the ''useless'' neurons in CI-ELM and improves the efficiency of neural networks. ICI-ELM randomly generates a group of parameters, among which we determine the best parameters leading to the smallest residual error. Therefore ICI-ELM can achieve a faster convergence rate than CI-ELM, meanwhile it still retains the same convergence rate as BCI. On the other hand, ICI-ELM also provides an alternative scheme to replace conventional gradient methods, which are only suitable for differential functions and often achieves local minima. The experimental results based on several benchmark regression problems support our claims.