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.
18207157
Clustering Structured Web Sources: A Schema-Based, Model-Differentiation Approach
The Web has been rapidly “deepened” with the prevalence of databases online On this “deep Web,” numerous sources are structured, providing schema-rich data Their schemas define the object domain and its query capabilities This paper proposes clustering sources by their query schemas, which is critical for enabling both source selection and query mediation, by organizing sources of with similar query capabilities In abstraction, this problem is essentially clustering categorical data (by viewing each query schema as a transaction) Our approach hypothesizes that “homogeneous sources” are characterized by the same hidden generative models for their schemas To find clusters governed by such statistical distributions, we propose a novel objective function, model-differentiation, which employs principled hypothesis testing to maximize statistical heterogeneity among clusters Our evaluation shows that, on clustering the Web query schemas, the model-differentiation function outperforms existing ones with the hierarchical agglomerative clustering algorithm.
10906069
Optimal multimodal fusion for multimedia data analysis
Considerable research has been devoted to utilizing multimodal features for better understanding multimedia data. However, two core research issues have not yet been adequately addressed. First, given a set of features extracted from multiple media sources (e.g., extracted from the visual, audio, and caption track of videos), how do we determine the best modalities? Second, once a set of modalities has been identified, how do we best fuse them to map to semantics? In this paper, we propose a two-step approach. The first step finds <i>statistically independent modalities</i> from raw features. In the second step, we use <i>super-kernel fusion</i> to determine the optimal combination of individual modalities. We carefully analyze the tradeoffs between three design factors that affect fusion performance: <i>modality independence</i>, <i>curse of dimensionality</i>, and <i>fusion-model complexity</i>. Through analytical and empirical studies, we demonstrate that our two-step approach, which achieves a careful balance of the three design factors, can improve class-prediction accuracy over traditional techniques.
2761350
Statistical schema matching across web query interfaces
Schema matching is a critical problem for integrating heterogeneous information sources. Traditionally, the problem of matching multiple schemas has essentially relied on finding pairwise-attribute correspondence. This paper proposes a different approach, motivated by integrating large numbers of data sources on the Internet. On this "deep Web," we observe two distinguishing characteristics that offer a new view for considering schema matching: First, as the Web scales, there are ample sources that provide structured information in the same domains (e.g., books and automobiles). Second, while sources proliferate, their aggregate schema vocabulary tends to converge at a relatively small size. Motivated by these observations, we propose a new paradigm, statistical schema matching: Unlike traditional approaches using pairwise-attribute correspondence, we take a holistic approach to match all input schemas by finding an underlying generative schema model. We propose a general statistical framework MGS for such hidden model discovery, which consists of hypothesis modeling, generation, and selection. Further, we specialize the general framework to develop Algorithm MGSsd, targeting at synonym discovery, a canonical problem of schema matching, by designing and discovering a model that specifically captures synonym attributes. We demonstrate our approach over hundreds of real Web sources in four domains and the results show good accuracy.
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.
11826830
Minimal probing: supporting expensive predicates for top-k queries
This paper addresses the problem of evaluating ranked top-k queries with expensive predicates. As major DBMSs now all support expensive user-defined predicates for Boolean queries, we believe such support for ranked queries will be even more important: First ranked queries often need to model user-specific concepts of preference, relevance, or similarity, which call for dynamic user-defined functions. Second, middleware systems must incorporate external predicates for integrating autonomous sources typically accessible only by per-object queries. Third, fuzzy joins are inherently expensive, as they are essentially user-defined operations that dynamically associate multiple relations. These predicates, being dynamically defined or externally accessed, cannot rely on index mechanisms to provide zero-time sorted output, and must instead require per-object probe to evaluate. The current standard sort-merge framework for ranked queries cannot efficiently handle such predicates because it must completely probe all objects, before sorting and merging them to produce top-k answers. To minimize expensive probes, we thus develop the formal principle of "necessary probes," which determines if a probe is absolutely required. We then propose Algorithm MPro which, by implementing the principle, is provably optimal with minimal probe cost. Further, we show that MPro can scale well and can be easily parallelized. Our experiments using both a real-estate benchmark database and synthetic datasets show that MPro enables significant probe reduction, which can be orders of magnitude faster than the standard scheme using complete probing.
1146148
A Bayes Net Toolkit for Student Modeling in Intelligent Tutoring Systems
This paper describes an effort to model a student's changing knowledge state during skill acquisition. Dynamic Bayes Nets (DBNs) provide a powerful way to represent and reason about uncertainty in time series data, and are therefore well-suited to model student knowledge. Many general-purpose Bayes net packages have been implemented and distributed; however, constructing DBNs often involves complicated coding effort. To address this problem, we introduce a tool called BNT-SM. BNT-SM inputs a data set and a compact XML specification of a Bayes net model hypothesized by a researcher to describe causal relationships among student knowledge and observed behavior. BNT-SM generates and executes the code to train and test the model using the Bayes Net Toolbox [1]. Compared to the BNT code it outputs, BNT-SM reduces the number of lines of code required to use a DBN by a factor of 5. In addition to supporting more flexible models, we illustrate how to use BNT-SM to simulate Knowledge Tracing (KT) [2], an established technique for student modeling. The trained DBN does a better job of modeling and predicting student performance than the original KT code (Area Under Curve = 0.610 > 0.568), due to differences in how it estimates parameters.
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.
14390424
A Temporal Assertion Extension to Verilog
Many circuit designs need to follow some temporal rules. However, it is hard to express and verify them in the past. Therefore, a temporal assertion extension to Verilog, called Temporal Wizard, is proposed in this paper. It provides several Verilog system tasks for the users to write assertions in testbench directly. Two new concepts, tag and thread, are also introduced so that data can be associated with temporal assertions and provide more functionalities than previous temporal assertion checkers.
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.
45899286
Computer Supported Cooperative Work Oriented Architecture