2306448
The TheaterLoc Virtual Application
Although much has been written about various information integration technologies, little has been said regarding how to combine these technologies together to build an entire “virtual” application. In this paper, we describe the design and implementation of TheaterLoc, an information integration application that allows users to retrieve information about theaters and restaurants for a variety of cities in the United States, including an interactive map depicting their relative locations and video trailers of the movies playing at the selected theaters. The data retrieved by TheaterLoc comes from five distinct heterogeneous and distributed sources. The enabling technology used to achieve the integration includes the Ariadne information mediator and wrappers for each of the web-based data sources. We focus in detail on the mediator technologies, such as data modeling, source axiom compilation, and query planning. We also describe how the wrappers present an interface for querying data on web sites, aiding in information retrieval used during data integration. Finally, we discuss some of the major integration challenges we encountered and our plans to address them.
2628080
Web Information Personalization: Challenges and Approaches
As the number of web pages increases dramatically, the problem of the information overload becomes more severe when browsing and searching the WWW. To alleviate this problem, personalization becomes a popular remedy to customize the Web environment towards a user’s preference. To date, recommendation systems and personalized web search systems are the most successful examples of Web personalization. By focusing on these two types of systems, this paper reviews the challenges and the corresponding approaches proposed in the past ten years.
17575068
Yoda, an adaptive soft classification model: content-based similarity queries and beyond
Abstract. Providing a customized result set based upon a user preference is the ultimate objective of many content-based image retrieval systems. There are two main challenges in meeting this objective: First, there is a gap between the physical characteristics of digital images and the semantic meaning of the images. Secondly, different people may have different perceptions on the same set of images. To address both these challenges, we propose a model, named Yoda, that conceptualizes content-based querying as the task of soft classifying images into classes. These classes can overlap, and their members are different for different users. The “soft” classification is hence performed for each and every image feature, including both physical and semantic features. Subsequently, each image will be ranked based on the weighted aggregation of its classification memberships. The weights are user-dependent, and hence different users would obtain different result sets for the same query. Yoda employs a fuzzy-logic based aggregation function for ranking images. We show that, in addition to some performance benefits, fuzzy aggregation is less sensitive to noise and can support disjunctive queries as compared to weighted-average aggregation used by other content-based image retrieval systems. Finally, since Yoda heavily relies on user-dependent weights (i.e., user profiles) for the aggregation task, we utilize the users' relevance feedback to improve the profiles using genetic algorithms (GA). Our learning mechanism requires fewer user interactions, and results in a faster convergence to the user's preferences as compared to other learning techniques.
2852278
An Adaptive Recommendation System without Explicit Acquisition of User Relevance Feedback
Recommendation systems are widely adopted in e-commerce businesses for helping customers locate products they would like to purchase. In an earlier work, we introduced a recommendation system, termed Yoda, which employs a hybrid approach that combines collaborative filtering (CF) and content-based querying to achieve higher accuracy for large-scale Web-based applications. To reduce the complexity of the hybrid approach, Yoda is structured as a tunable model that is trained off-line and employed for real-time recommendation on-line. The on-line process benefits from an optimized aggregation function with low complexity that allows the real-time aggregation based on confidence values of an active user to pre-defined sets of recommendations. In this paper, we extend Yoda to include more recommendation sets. The recommendation sets can be obtained from different sources, such as human experts, web navigation patterns, and clusters of user evaluations.More over, the extended Yoda can learn the confidence values automatically by utilizing implicit users' relevance feedback through web navigations using genetic algorithms (GA). Our end-to-end experiments show while Yoda's complexity is low and remains constant as the number of users and/or items grow, its accuracy surpasses that of the basic nearest-neighbor method by a wide margin (in most cases more than 100%). The experimental results also indicate that the retrieval accuracy is significantly increased by using the GA-based learning mechanism.
1752675
Storing and Discovering Critical Workflows from Log in Scientific Exploration
This paper aims at effectively reproducing the results of previous scientific workflow executions. We first identify the information that needs to be recorded in a workflow execution log, based on which we then determine the data flow among experiments. To effectively record a workflow execution log in a relational database management system (RDBMS) when the workflow design is absent, we propose a generic relational storage schema. Then techniques have been designed to automatically discover the minimal set of experiments that must be performed in order to reproduce a scientific result by posing appropriate SQL queries. Although such SQL queries can be evaluated using an off-the-shelf database system, we investigate the unique characteristics of the workflow log data and optimization techniques for evaluating such SQL queries efficiently.
16524073
Partitioning of temporal planning problems in mixed space using the theory of extended saddle points
We study the partitioning of temporal planning problems formulated as mixed-integer nonlinear programming problems, develop methods to reduce the search space of partitioned subproblems, and propose algorithms for resolving unsatisfied global constraints. The algorithms are based on the necessary and sufficient extended saddle-point condition for constrained local minimization developed in this paper. When compared with the MIPS planner in solving some PDDL2.1 planning problems, our distributed implementation of MIPS shows significant improvements in time and quality.
12956058
Identifying meaningful return information for XML keyword search
Keyword search enables web users to easily access XML data without the need to learn a structured query language and to study possibly complex data schemas. Existing work has addressed the problem of selecting qualified data nodes that match keywords and connecting them in a meaningful way, in the spirit of inferring a where clause in XQuery. However, how to infer the return clause for keyword search is an open problem.
 To address this challenge, we present an XML keyword search engine, XSeek, to infer the semantics of the search and identify return nodes effectively. XSeek recognizes possible entities and attributes inherently represented in the data. It also distinguishes between search predicates and return specifications in the keywords. Then based on the analysis of both XML data structures and keyword match patterns, XSeek generates return nodes. Extensive experimental studies show the effectiveness of XSeek.
10510843
A SOA Based Pipeline System to Deal with Astronomy Telescope Data
In order to deal with telescope data more systematically and flexibly, this paper proposes a grid middleware pipeline system PMGrid based on service-oriented architecture (SOA) for efficient telescope data processing. This system allows users to dynamically compose data process application as a pipeline virtual service (PVS) using existing services for their special purposes. Furthermore, the configured PVS has the standard interface as services and can be published in PMGrid system as a single unit of service shared and reused by other users. Grid technologies and Web service technologies are aligned with each other in this system through standard-based interface and protocols. Based on SOA, the PMGrid system can provide not only more telescope data process functions to satisfy users' special purposes, but also more shared public functions. Combing SOA and grid, the PMGrid system is portable and has advantages in reducing cost and increasing the services utilization. This paper presents the design, analysis, and implementation of the PVS subsystem in the telescope data processing system
2396817
Automatic Pattern-Taxonomy Extraction for Web Mining
In this paper, we propose a model for discovering frequent sequential patterns, phrases, which can be used as profile descriptors of documents. It is indubitable that we can obtain numerous phrases using data mining algorithms. However, it is difficult to use these phrases effectively for answering what users want. Therefore, we present a pattern taxonomy extraction model which performs the task of extracting descriptive frequent sequential patterns by pruning the meaningless ones. The model then is extended and tested by applying it to the information filtering system. The results of the experiment show that pattern-based methods outperform the keyword-based methods. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the effectiveness of the system.