18844092
Extracting Output Metadata from Scientific Deep Web Data Sources
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.
9156262
SEEDEEP: A System for Exploring and Querying Scientific Deep Web Data Sources
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.
15473023
Query Planning for Searching Inter-dependent Deep-Web Databases
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.
6932857
SNPMiner: A Domain-Specific Deep Web Mining Tool
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.
15711911
A stochastic beam search for the berth allocation problem
In this paper, the optimization of the Berth Allocation Problem (BAP) is transformed into a multiple stage decision making procedure and a new multiple stage search method, namely stochastic beam search algorithm, is proposed to solve it. New techniques such as an improved beam search scheme, a two-phase node goodness estimation, and a stochastic node selection criteria are proposed. Real-life information provided by Singapore Port was collected as our test data. Experimental results show that the proposed stochastic beam search is more accurate and efficient than both the state-of-the-art meta-heuristic and the traditional determinist beam search.
15643548
Robust airport gate assignment
In this paper, we propose a new strategy for the robust constraint resource assignment problem and apply it to solve the robust airport gate assignment (RAGA). RAGA attempts to accurately build an evaluation criteria for the ability of an aircraft-to-gate assignment to handle uncertainty on aircraft schedule; and to accurately and effectively search the most robust airport gate assignment. We model the RAGA by a stochastic programming model and transform it into a binary programming model by introducing the unsupervised estimation functions without knowing any information on the real-time arrival and departure time of aircrafts in advance. Moreover, a partition-based search space encoding, two neighborhood operators for single or multiple aircrafts reassignment, and a hybrid meta-heuristic combining a tabu search and a local search are proposed to solve RAGA efficiently. Experimental results on the real-life test data from Hong Kong International Airport demonstrate that the proposed RAGA model provides a valuable tool for the airport to improve its robustness in uncertain operations
137346482
Even aberration measurement of lithographic projection system based on optimized phase-shifting marks
In the present paper, we propose a novel method for measuring the even aberrations of lithographic projection optics by use of optimized phase-shifting marks on the test mask. The line/space ratio of the phase-shifting marks is optimized to obtain the maximum sensitivities of Zernike coefficients corresponding to even aberrations. Spherical aberration and astigmatism can be calculated from the focus shifts of phase-shifting gratings oriented at 0^o, 45^o, 90^o and 135^o at multiple illumination settings. The PROLITH simulation results show that, the measurement accuracy of spherical aberration and astigmatism obviously increase, after the optimization of the measurement mark.
28791718
Fault accommodation for nonlinear systems using fuzzy adaptive sliding control
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.
62479173
Integrating expert vision systems and spatial databases by unifying knowledge representation schemes: development in remote sensing image analysis expert systems and geographical information systems
Integration of remote sensing and geographical information system (GIS) technologies can significantly strengthen our capability to handle geographical information. However, so far little practical integration has been achieved. The major barriers to the integration include the problems with data format, data accuracy and system communication. Previous efforts made on these problems could not provide satisfied solutions. 
This research explores a new path for achieving the integration. The three problems, which at first may seem to be independent of each other, are considered as different aspects of a problem with knowledge representation and are tackled by unifying the systems to be integrated on a common knowledge representation framework. Three new techniques--the communication subschema, the relational-linear quadtree, and the fuzzy representation method--have been developed to provide such a framework. A unified data model has been defined to implement the techniques. 
The three techniques have been successfully applied to the integration of GIS and remote sensing systems. Encouraging results have been achieved in the practice of designing, implementing, and integrating a GIS and two remote sensing image analysis systems.