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.
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.
32378250
Using Distributed Objects to Build the Stanford Digital Library Infobus
For digital libraries to thrive, the providers of information processing services must be able to evolve their systems autonomously. However, as the complexity of their offerings increases, software tools more sophisticated than existing Web facilities are needed. Distributed object technology may be the answer. The availability of high-volume, increasingly sophisticated information is making the need for metadata facilities more urgent. Traditional, library-based approaches break down when used in an advanced digital library. More modular mechanisms are needed, and the CORBA system is one approach. Digital libraries are affected at a deep technical level by the widely differing user traditions of Web users and library patrons. The challenge and opportunity of digital libraries will be the synthesis of these traditions. The authors set out to create a technical infrastructure to support the construction of digital libraries. In their view, a digital library comprises widely distributed resources that can be maintained autonomously by different organizations and will not require adherence to uniform interfaces.
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.
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.
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.
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
17374517
Model Checking with Induction
We have proposed inductive model checking (IMC) to alleviate the state explosion problem in model checking. IMC divides the whole verification tasks into several sub-tasks, and applies the three inductive steps (basis, hypothesis, and induction) to verify a system with model checking. In this paper, we continue our research by determining those temporal formulas, namely, persistent temporal formulas, which are verifiable with IMC. One important characteristic of a persistent temporal formula is that it contains a periodic temporal formula that must be held persistently. From the verification results of the periodic formula with different sequences of states, IMC concludes the verification of the persistent temporal formula
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.
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.