Exploiting Parallelism to Accelerate Keyword Search on Deep-Web Sources
Increasingly, biological data is being shared over the deep web. Many biological queries can only be answered by successively searching a number of distinct web-sites. This paper introduces a system that exploits parallelization for accelerating search over multiple deep web data sources. An interactive, two-stage multi-threading system is developed to achieve task parallelization, thread parallelization, and pipelined parallelization. We show the effectiveness of our system by considering a number of queries involving SNP datasets. We show that most of the queries can be accelerated significantly by exploiting these three forms of parallelism.

title: SEEDEEP: A System for Exploring and Querying Scientific Deep Web Data Sources
abstract: A recent and emerging trend in scientific data dissemination involves online databases that are hidden behind query forms, thus forming what is referred to as the deep web . In this paper, we propose SEEDEEP, a System for Exploring and quErying scientific DEEP web data sources. SEEDEEP is able to automatically mine deep web data source schemas, integrate heterogeneous data sources, answer cross-source keyword queries, and incorporates features like caching and fault-tolerance. Currently, SEEDEEP integrates 16 deep web data sources in the biological domain. We demonstrate how an integrated model for correlated deep web data sources is constructed, how a complex cross-source keyword query is answered efficiently and correctly, and how important performance issues are addressed.

title: SNPMiner: A Domain-Specific Deep Web Mining Tool
abstract: In this paper, we propose a novel query-oriented, mediator-based biological data querying tool, SNPMiner. The system searches and queries single nucleotide polymorphisms (SNPs) data from eight widely used web accessible databases. The system provides a domain-specific search utility, which can access and collect data from the deep web. This is a web-based system, so any user can use the system by accessing our server from their own computers. The system includes three important components, which are the Web server interface, the dynamic query planner, and the Web page parser. The Web server interface can provide end users a unified and friendly interface. The dynamic query planner can automatically schedule an efficient query order on all available databases according to user's query request. The Web page parser analyzes the layout of HTML files and extracts desired data from those files. The final results of the query are organized in a tabular format, which can be reviewed by a biological researcher.

title: Query Planning for Searching Inter-dependent Deep-Web Databases
abstract: Increasingly, many data sources appear as online databases, hidden behind query forms, thus forming what is referred to as the deep web. It is desirable to have systems that can provide a high-level and simple interface for users to query such data sources, and can automate data retrieval from the deep web. However, such systems need to address the following challenges. First, in most cases, no single database can provide all desired data, and therefore, multiple different databases need to be queried for a given user query. Second, due to the dependencies present between the deep-web databases, certain databases must be queried before others. Third, some database may not be available at certain times because of network or hardware problems, and therefore, the query planning should be capable of dealing with unavailable databases and generating alternative plans when the optimal one is not feasible. 
 
This paper considers query planning in the context of a deep-web integration system. We have developed a dynamic query planner to generate an efficient query order based on the database dependencies. Our query planner is able to select the topKquery plans. We also develop cost models suitable for query planning for deep web mining. Our implementation and evaluation has been made in the context of a bioinformatics system, SNPMiner. We have compared our algorithm with a naive algorithm and the optimal algorithm. We show that for the 30 queries we used, our algorithm outperformed the naive algorithm and obtained very similar results as the optimal algorithm. Our experiments also show the scalability of our system with respect to the number of data sources involved and the number of query terms.

title: Extracting Output Metadata from Scientific Deep Web Data Sources
abstract: Increasingly, many data sources appear as online databases, hidden behind query forms, thus forming the deep web. The popularity of this new medium for data dissemination is leading to new problems in data integration. Particularly, to enable data integration from multiple deep web data sources, one needs to obtain the metadata for each of the data sources. Obtaining the metadata, particularly, the output schema, can be very challenging. This is because, given an input query, many deep web data sources only return a subset of the output schema attributes, i.e, the ones that have a non-NULL value for the corresponding input. In this paper, we propose two approaches, which are the sampling model approach and the mixture model approach, respectively, to efficiently obtain an approximately complete set of output schema attributes from a deep web data source. Our experiments show while each of the above two approaches has limitations, a hybrid strategy, where we combine the two approaches, achieves high recall with good precision for most data sources.

title: Graph and Topological Structure Mining on Scientific Articles
abstract: In this paper, we investigate a new approach for literature mining. We use frequent subgraph mining, and its generalization topological structure mining, for finding interesting relationships between gene names and other key biological terms from the text of scientific articles. We show how we can find keywords of interest and represent them as nodes of the graphs. We also propose several methods for inserting edges between these nodes. Our study initially focused on comparing: 1) different methods for constructing edges, and 2) patterns found from sub-graph mining and topological structure mining. Subsequently, we analyzed several frequent topological minors reported by our experiments, and explained their scientific significance. Overall, our study shows the following. First, a simple method of constructing edges, which is based on sliding windows, seems to provide the best results. Second, we are able to find much larger number of well-known and meaningful topological patterns with high support values, as compared to sub-graphs. Overall, the frequent topological minors our algorithm found correspond well to known relationships between genes and biological terms. Thus, we believe that topological structure mining can be a very valuable tool for researchers who are not deeply familiar with the existing literature, and want to obtain a quick summary about known relationships among key scientific names or terms.

title: Inequivalent manifold ranking for content-based image retrieval
abstract: We propose to improve the effectiveness and scalability of graph based manifold ranking methods in image retrieval applications by emphasizing reliable images while damping the effect of noisy or irregular ones. Label information is firstly passed between most reliable data points, then propagated to less reliable ones on manifold structure. By treating these images inequivalently, undesirable effect of noisy samples is greatly reduced, thus effectiveness of manifold ranking algorithms is enhanced. Also, graph size in terms of number of nodes and edges is dramatically reduced, resulting in a great speed-up of the algorithm. Our experiment on real world image data set demonstrates the effectiveness of the proposed approach.

title: WiMAX System Performance with Multiple Transmit and Multiple Receive Antennas
abstract: In this paper, the performance of WiMAX networks using multiple transmit and multiple receiver antennas are analyzed via system simulations. For downlink, two kinds of technologies are studied: i) open loop: where the base station has no knowledge of channel and uses mobile mode selection feedback to switch between single data stream STBC and multi-stream spatial multiplexing MIMO (SM-MIMO); ii) closed loop: where base station has explicit knowledge of downlink channel and transmits using adaptive beamforming. This paper compares the WiMAX downlink system performance using open loop and closed loop MIMO technologies with various frequency reuse factors and different data traffic models. The performance of WiMAX uplink is also analyzed with multiple receive antennas and using both single input multiple output (SIMO) and multi-user MIMO (MU-MIMO, a.k.a. SDMA).

title: A smoothed dynamic tabu search embedded GRASP for m-VRPTW
abstract: Vehicle routing problem with both time window and limited number of vehicles (m-VRPTW) is an useful extension of VRPTW problem in real applications. We propose an improved greedy randomized adaptive search procedure (GRASP) framework by techniques including multiple initialization and solution reuse. Furthermore, a new technique of smoothed dynamic tabu search is embedded into the GRASP to improve the performance. The experimental results for benchmark data show that the new algorithm can solve the m-VRPTW problem better than the published algorithm in accuracy.

title: Fault accommodation for nonlinear systems using fuzzy adaptive sliding control
abstract: An active fault accommodation control law is developed for a class of nonlinear systems to guarantee the closed-loop stability in the presence of a fault, based on a fuzzy logic system representation of the dynamics due to faults. It uses fuzzy logic system to approximate the dynamic caused by the fault. Through the adaptive process of the parameters, the dynamics caused by the fault is counteracted. The fuzzy sliding mode control is introduced to attenuate the fuzzy approximation error. Simultaneity, the closed-loop system is stable in Lyapunov sense and the tracking error converges to a neighbourhood of zero. The example of the proposed design indicates that the fault accommodation control law is effective for a nonlinear system.

title: The one-commodity pickup and delivery travelling salesman problem on a path or a tree
abstract: Optimization algorithms for both path and tree topology classes of the one-commodity pickup and delivery travelling salesman problem (1-PDTSP) are proposed in this article, which focus on minimizing the route distance to transport products among pickup and delivery customers by a single vehicle with a limited capacity of k . Each pickup customer provides one unit volume of the product while each delivery customer requires one unit volume of the product. For the path case, we propose an O(n2/ min (k , n)) algorithm for any arbitrary k , and two O(n) algorithms for k = 1 and k = ∞. For the tree case, O(n2) and O(n) algorithms are proposed for k = 1 and k = ∞, respectively. Moreover, when k is arbitrary, the problem becomes NP-hard in the strong sense.