2673174
Automatic complex schema matching across Web query interfaces: A correlation mining approach
To enable information integration, schema matching is a critical step for discovering semantic correspondences of attributes across heterogeneous sources. While complex matchings are common, because of their far more complex search space, most existing techniques focus on simple 1:1 matchings. To tackle this challenge, this article takes a conceptually novel approach by viewing schema matching as correlation mining, for our task of matching Web query interfaces to integrate the myriad databases on the Internet. On this “deep Web ” query interfaces generally form complex matchings between attribute groups (e.g., {author} corresponds to {first name, last name} in the Books domain). We observe that the co-occurrences patterns across query interfaces often reveal such complex semantic relationships: grouping attributes (e.g., {first name, last name}) tend to be co-present in query interfaces and thus positively correlated. In contrast, synonym attributes are negatively correlated because they rarely co-occur. This insight enables us to discover complex matchings by a correlation mining approach. In particular, we develop the DCM framework, which consists of data preprocessing, dual mining of positive and negative correlations, and finally matching construction. We evaluate the DCM framework on manually extracted interfaces and the results show good accuracy for discovering complex matchings. Further, to automate the entire matching process, we incorporate automatic techniques for interface extraction. Executing the DCM framework on automatically extracted interfaces, we find that the inevitable errors in automatic interface extraction may significantly affect the matching result. To make the DCM framework robust against such “noisy” schemas, we integrate it with a novel “ensemble” approach, which creates an ensemble of DCM matchers, by randomizing the schema data into many trials and aggregating their ranked results by taking majority voting. As a principled basis, we provide analytic justification of the robustness of the ensemble approach. Empirically, our experiments show that the “ensemblization” indeed significantly boosts the matching accuracy, over automatically extracted and thus noisy schema data. By employing the DCM framework with the ensemble approach, we thus complete an automatic process of matchings Web query interfaces.
5658776
Metadata for digital libraries: architecture and design rationale
In a distributed, heterogeneous, proxy-based digital library, autonomous services and collections are accessed indirectly via proxies. To facilitate metadata compatibility and interoperability in such a digital library, we have designed a metadata architecture that includes four basic component classes: attribute model proxies, attribute model translators, metadata facilities for search proxies, and metadata repositories. Attribute model proxies elevate both attribute sets and the attributes they define to first-class objects. They also allow relationships among attributes to be captured. Attribute model translators map attributes and attribute values from one attribute model to another (where posMetadata facilities for search proxies provide structured descriptions both of the collections to which the search proxies provide access and of the search capabilities of the proxies. Finally, metadata repositories accumulate selected metadata from local instances of the other three component classes in order to facilitate global metadata queries and local metadata caching. In this paper, we outline further the roles of these component classes, discuss our design rationale, and analyze related work. Keywords: Metadata architecture, interoperability, attribute model, attribute model translation, metadata repository, InfoBus, proxy architecture, heterogeneity, digital libraries, CORBA.
2091518
Evaluating the cost of Boolean query mapping
Non-uniform query languages make searching over heterogeneous information sources difcult. Our approach is to allow a user to compose Boolean queries in one rich front-end language. For each user query and target source, we transform the user query into a subsuming query that can be supported by the source but that may return extra documents. The results are then processed by a lter query to yield the correct nal results. This post-ltering approach may involve signicant cost because the documents that the users will not see may have to be retrieved and ltered. There are generally two ways to implement post-ltering: batch post-ltering and incremental post-ltering. In this paper we evaluate the costs of both methods for different search features such as proximity operators. The experimental results show that in many cases incremental post-ltering cost may be acceptable, while the batch post-ltering cost may sometimes be extremely large.
18951865
Heterogeneous learner for Web page classification
Classification of an interesting class of Web pages has been an interesting problem. Typical machine learning algorithms for this problem require two classes of data for training: positive and negative training examples. However in application to Web page classification, gathering an unbiased sample of negative examples appears to be difficult. We propose a heterogeneous learning framework for classifying Web pages, which (1) eliminates the need for negative training data, and (2) increases classification accuracy by using two heterogeneous learners. Our framework uses two heterogeneous learners-a decision list and a linear separator which complement each other-to eliminate the need for negative training data in the training phase and to increase the accuracy in the testing phase. Our results show that our heterogeneous framework achieves high accuracy without requiring negative training data; it enhances the accuracy of linear separators by reducing the errors on "low-margin data". That is, it classifies more accurately while requiring less human efforts in training.
1459502
EntityRank: Searching Entities Directly and Holistically
As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. While entities appear in many pages, current engines only find each page individually. Toward searching directly and holistically for finding information of finer granularity, we study the problem of entity search, a significant departure from traditional document retrieval. We focus on the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We evaluate our online prototype over a 2TB Web corpus, and show that EntityRank performs effectively.
6980873
Making holistic schema matching robust: an ensemble approach
The Web has been rapidly "deepened" by myriad searchable databases online, where data are hidden behind query interfaces. As an essential task toward integrating these massive "deep Web" sources, large scale schema matching (i.e., discovering semantic correspondences of attributes across many query interfaces) has been actively studied recently. In particular, many works have emerged to address this problem by "holistically" matching many schemas at the same time and thus pursuing "mining" approaches in nature. However, while holistic schema matching has built its promise upon the large quantity of input schemas, it also suffers the robustness problem caused by noisy data quality. Such noises often inevitably arise in the automatic extraction of schema data, which is mandatory in large scale integration. For holistic matching to be viable, it is thus essential to make it robust against noisy schemas. To tackle this challenge, we propose a data-ensemble framework with sampling and voting techniques, which is inspired by bagging predictors. Specifically, our approach creates an ensemble of matchers, by randomizing input schema data into many independently downsampled trials, executing the same matcher on each trial and then aggregating their ranked results by taking majority voting. As a principled basis, we provide analytic justification of the effectiveness of this data-ensemble framework. Further, empirically, our experiments on real Web data show that the "ensemblization" indeed significantly boosts the matching accuracy under noisy schema input, and thus maintains the desired robustness of a holistic matcher.
46726656
OBL: a formal deduction method for object-oriented systems
Object Behavior Logic (OBL) is proposed in order to specify object oriented system behavior. An inference engine is introduced for verifying the consistency of the systems based on the specification in OBL. OBL uses the paradigms of state machine to catch the dynamic aspect of object oriented systems in formal specification, plus the features of object oriented concepts such as inheritance, instantiation, and so forth, which separate the unique object oriented techniques from the function oriented techniques.
13199279
Rule-mitigated collaboration framework
Computer supported cooperative work (CSCW) research is the discipline that explores how to utilize computing and networking technologies to facilitate cooperation and collaboration among people. A variety of research has been conducted on CSCW architecture. However, how to ensure CSCW system effectiveness and efficiency on supporting collaboration remains a challenge. In this paper we present a rule mitigated framework for CSCW applications. This research contributes to the state of the art by presenting a robust and comprehensive architecture to support distributed collaboration.
11683736
The GETA Sandals: A Footprint Location Tracking System
This paper presents the design, implementation, and evaluation of a footprint-based indoor location system on traditional Japanese GETA sandals. Our footprint location system can significantly reduce the amount of infrastructure required in the deployed environment. In its simplest form, a user simply has to put on the GETA sandals to track his/her locations without any setup or calibration efforts. This makes our footprint method easy for everywhere deployment. The footprint location system is based on the dead-reckoning method. It works by measuring and tracking the displacement vectors along a trial of footprints (each displacement vector is formed by drawing a line between each pair of footprints). The position of a user can be calculated by summing up the current and all previous displacement vectors. Additional benefits of the footprint based method are that it does not have problems found in existing indoor location systems, such as obstacles, multi-path effects, signal noises, signal interferences, and dead spots. However, the footprint based method has a problem of accumulative error over distance traveled. To address this issue, it is combined with a light RFID infrastructure to correct its positioning error over some long distance traveled.
15818010
Automatic Partitioner for Behavior Level Distributed Logic Simulation
As the complexity of circuit design increases, verification through simulation has become a bottleneck of the IC design process. Distributed parallel simulation is one way to solving the problem. In order to distribute the simulation workload to multiple processors, the design must be carefully partitioned first. While most previous work focus on gate level partitioning, our work extends a previously implemented Verilog gate-level partitioner to support RTL and behavior level partitioning. Techniques to partition special constructs specific to these levels, such as global access, function calls and memory access, are described in this paper. The experimental results show that our techniques are capable of finding partitions which can accelerate simulation.
2264058
A Unified Model for Probabilistic Principal Surfaces
Principal curves and surfaces are nonlinear generalizations of principal components and subspaces, respectively. They can provide insightful summary of high-dimensional data not typically attainable by classical linear methods. Solutions to several problems, such as proof of existence and convergence, faced by the original principal curve formulation have been proposed in the past few years. Nevertheless, these solutions are not generally extensible to principal surfaces, the mere computation of which presents a formidable obstacle. Consequently, relatively few studies of principal surfaces are available. We previously (2000) proposed the probabilistic principal surface (PPS) to address a number of issues associated with current principal surface algorithms. PPS uses a manifold oriented covariance noise model, based on the generative topographical mapping (GTM), which can be viewed as a parametric formulation of Kohonen's self-organizing map. Building on the PPS, we introduce a unified covariance model that implements PPS (0 1) by varying the clamping parameter /spl alpha/. Then, we comprehensively evaluate the empirical performance of PPS, GTM, and the manifold-aligned GTM on three popular benchmark data sets. It is shown in two different comparisons that the PPS outperforms the GTM under identical parameter settings. Convergence of the PPS is found to be identical to that of the GTM and the computational overhead incurred by the PPS decreases to 40 percent or less for more complex manifolds. These results show that the generalized PPS provides a flexible and effective way of obtaining principal surfaces.