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.
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.
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.
26734510
Estimate Test Execution Effort at an Early Stage: An Empirical Study
Software testing is becoming more and more important as it is a widely used activity to ensure software quality. Test execution becomes an activity in the critical path of a project. In this case, early estimation of test execution effort can benefit both tester managers and software projects. This paper reports an empirical study on early test execution effort estimation. In the study, we propose an approach which mainly consists of two parts: test case number prediction from use cases and test effort estimation based on the test suite execution vector model which combines test case number, test execution complexity and its tester together. For each part, we evaluate it with the data of real projects from a financial software company.
20112388
The shortest route cut and fill problem in linear topological structure
In this paper, we study the Shortest Route Cut and Fill (SRCF) problem where a fleet of vehicles with fixed capacities travel between cut and fill points to finish all cut and fill requirements so that the travel time of the fleet is minimized. We state and classify the SRCF problem and present a complete view of its computational complexities with NP-hardness proof for the basic linear topological structure. Moreover, a simple cycle-based optimization method is proposed to solve the SRCF in linear time complexity, for the version of single vehicle in linear structure without time window constraint.
10478649
WiMAX Overview and System Performance
In this paper, the performance of WiMax network with DL SIMO, DL MIMO and UL SIMO is analyzed via system simulations. Two data traffic models are considered, namely Web browsing (HTTP) and full buffer sessions, operating in multi path fading channels such as the ITU pedestrian-B and vehicular-A channels. The control channel coverage and reliability of WiMAX using realistic overhead is also analyzed in this paper.
1118388
HRPD-A downlink voice/IP and data throughput and delay analysis
HRPD-A adds new forward and reverse link rates and a forward link multi-user packet capability at the MAC layer. Completed simulations reveal the performance characteristics of this system in the 1.9 GHz frequency band. These characteristics consist of capacities of voice/IP traffic and mixed data and voice/IP traffic. Downlink data traffic models consist of FTP, HTTP (Web browsing), WAP, and video streaming models. The simulation assumptions include the ITU channel models and 3GPP2 data traffic models
39708575
A New Multi-view Learning Algorithm Based on ICA Feature for Image Retrieval
In content-based image retrieval (CBIR), most techniques involve an important issue of how to efficiently bridge the gap between the high-level concepts and low-level visual features. We propose a novel semi-supervised learning method for image retrieval, which makes full use of both ICA feature and general low-level feature. Our approach can be characterized by the following three aspects: (1) The ICA feature, as proved to be representative of human vision, is adopted as a view to describe human perception; (2) A multi-view learning algorithm is introduced to make the most use of different features and dramatically reduce human relevance feedback needed to achieve a satisfactory result; (3) A new semi-supervised learning algorithm is proposed to adapt to the small sample problem and other special constrains of our multi-view learning algorithm. Our experimental results and comparisons are presented to demonstrate the effectiveness of the proposed approach.