2641288
A novel spectral coding in a large graph database
Retrieving related graphs containing a query graph from a large graph database is a key issue in many graph-based applications, such as drug discovery and structural pattern recognition. Because sub-graph isomorphism is a NP-complete problem [4], we have to employ a filter-and-verification framework to speed up the search efficiency, that is, using an effective and efficient pruning strategy to filter out the false positives (graphs that are not possible in the results) as many as possible first, then validating the remaining candidates by subgraph isomorphism checking. In this paper, we propose a novel filtering method, a spectral encoding method, i.e. GCoding. Specifically, we assign a signature to each vertex based on its local structures. Then, we generate a spectral graph code by combining all vertex signatures in a graph. Based on spectral graph codes, we derive a necessary condition for sub-graph isomorphism. Then we propose two pruning rules for sub-graph search problem, and prove that they satisfy the no-false-negative requirement (no dismissal in answers). Since graph codes are in numerical space, we take this advantage and conduct efficient filtering over graph codes. Extensive experiments show that GCoding outperforms existing counterpart methods.
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.
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.
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.
7600762
Automatic Scoring of Children's Read-Aloud Text Passages and Word Lists
Assessment of reading proficiency is typically done by asking subjects to read a text passage silently and then answer questions related to the text. An alternate approach, measuring reading-aloud proficiency, has been shown to correlate well with the aforementioned common method and is used as a paradigm in this paper. 
 
We describe a system that is able to automatically score two types of children's read speech samples (text passages and word lists), using automatic speech recognition and the target criterion "correctly read words per minute". Its performance is dependent on the data type (passages vs. word lists) as well as on the relative difficulty of passages or words for individual readers. Pearson correlations with human assigned scores are around 0.86 for passages and around 0.80 for word lists.
1592504
Analog Error-Control Coding Based on Dimension-Expanding Shannon Mapping for Robust Image Communication
Analog transmission has the advantage of allowing the quality of recovered data to vary gracefully as the channel conditions vary. For the purpose of robust image communication, two analog error-control coding schemes based on dimension-expanding Shannon mapping are discussed in this paper, including the encoding processes, decoding processes and joint source channel optimizing design methods. In addition, a method of getting the upper and lower bounds of the theoretically optimal coder is presented. In comparison with the optimal coder, these two analog error-control coders are easy to implement and simulations show that they both perform well and are robust to channel and source mismatch. It can be concluded that they are both good candidates for image subband coding to protect important source messages and suitable for robust image communication where channel conditions vary.
11705945
A TCP Based Performance Test Method and Its Application
With the increasing scale of software and the exploding amount of data, software performance problem has become more and more serious, drawing more and more people to study the way of testing softwarepsilas performance. Although there are many performance test methods focusing on B/S structured system, other systems are ignored. In this paper, the common performance test procedure is given first, and then a method is presented to show how to perform such a procedure on an application based on the TCP. This method, which involves three main phases including ldquoCapture and filter out useless packagesrdquo, ldquoParameterizationrdquo, and ldquoTesting and monitoringrdquo, provides an adaptive approach to test a system built upon the TCP. At last, the methodpsilas application is based on a typical C/S structure validation is proved in an experiment, in which the target application is based on a typical C/S structure.
18409692
An Interference Free Data Aggregation Protocol for Clustered Wireless Sensor Networks
An interference free cluster-based TDMA protocol (IFCT) for wireless sensor networks was proposed in previous study. IFCT uses a distributed frame assignment algorithm (FA) to assign different frames for neighboring clusters. In this paper, we propose a interference-free data aggregation protocol (IFDA) for clustered based TDMA wireless sensor networks based on IFCT. IFDA alters the FA algorithm of IFCT and uses a channel expansion algorithm CE to increase average per cluster channel usage ratio. The expected message cost for both FA and CE algorithms of IFDA is O(1). The expected running time complexity for both FA and CE is bound to polynomial of D, where D is the maximum neighboring cluster count of a cluster. Simulation results show that FA algorithm of IFDA requires less message cost than that of IFCT. Besides, IFDA improves the average per cluster channel usage ratio by about 80%compared with IFCT.
16340504
Research on Telerobotic System Based on ARM Core Processor
This paper discusses an embedded and teleoperation technology-based, the use of Internet to achieve real-time remote device control system,embedded ARM processor S3C2410 as a network server, CAN bus as a multi-robot remote control bus operating system.It realizes bilateral communication between control command and feedback data of the master-slave robot, and enhances the performance of stability. The simulation experimental results demonstrate that the system is accurate and available.