425294
A Decomposition-Based Probabilistic Framework for Estimating the Selectivity of XML Twig Queries
In this paper we present a novel approach for estimating the selectivity of XML twig queries. Such a technique is useful for answering approximate queries as well as for determining an optimal query plan for complex queries based on said estimates. Our approach relies on a summary structure that contains the occurrence statistics of small twigs. We rely on a novel probabilistic approach for decomposing larger twig queries into smaller ones. We then show how it can be used to estimate the selectivity of the larger query in conjunction with the summary information. We present and evaluate different strategies for decomposition and compare this work against a state-of-the-art selectivity estimation approach on synthetic and real datasets. The experimental results show that our proposed approach is very effective in estimating the selectivity of XML twig queries.
9741529
Mining frequent itemsets in distributed and dynamic databases
Traditional methods for frequent itemset mining typically assume that data is centralized and static. Such methods impose excessive communication overhead when data is distributed, and they waste computational resources when data is dynamic. We present what we believe to be the first unified approach that overcomes these assumptions. Our approach makes use of parallel and incremental techniques to generate frequent itemsets in the presence of data updates without examining the entire database, and imposes minimal communication overhead when mining distributed databases. Further, our approach is able to generate both local and global frequent itemsets. This ability permits our approach to identify high-contrast frequent itemsets, which allows one to examine how the data is skewed over different sites.
46393116
Learning Approximate MRFs from Large Transaction Data
In this paper we consider the problem of learning approximate Markov Random Fields (MRFs) from large transaction data. We rely on frequent itemsets to learn MRFs on the data. Since learning exact large MRFs is generally intractable, we resort to learning approximate MRFs. Our proposed modeling approach first employs graph partitioning to cluster variables into balanced disjoint partitions, and then augments important interactions across partitions to capture interdependencies across them. A novel treewidth based augmentation scheme is proposed to boost performance. We learn an exact local MRF for each partition and then combine all the local MRFs together to derive a global model of the data. A greedy approximate inference scheme is developed on this global model. We demonstrate the use of the learned MRFs on the selectivity estimation problem. Empirical evaluation on real datasets demonstrates the advantage of our approach over extant solutions.
14600917
Sharp Disjunctive Decomposition for Language Emptiness Checking
We propose a Sharp disjunctive decomposition approach for language emptiness checking which is specifically targeted at Large or Difficult problems. Based on the SCC (Strongly-Connected Component) quotient graph of the property automaton, our method partitions the entire state space so that each state subspace accepts a subset of the language, the union of which is exactly the language accepted by the original system. The decomposition is sharp in that this allows BDD operations on the concrete model to be restricted to small sub-spaces, and also in the sense that unfair and unreachable parts of the submodules and automaton can be pruned away. We also propose sharp guided search algorithms for the traversal of the state subspaces, with its guidance the approximate distance to the fair SCCs. We give experimental data which show that our algorithm outperforms previously published algorithms, especially for harder problems.
325991
Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas
The simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) problem is not only to solve the SLAM problem in dynamic environments but also to detect and track these dynamic objects. In this paper, we derive the Bayesian formula of the SLAM with DATMO problem, which provides a solid basis for understanding and solving this problem. In addition, we provide a practical algorithm for performing DATMO from a moving platform equipped with range sensors. The probabilistic approach to solve the whole problem has been implemented with the Navlab11 vehicle. More than 100 miles of experiments in crowded urban areas indicated that SLAM with DATMO is indeed feasible.
5531222
Divide and Compose: SCC Refinement for Language Emptiness
We propose a refinement approach to symbolic SCC analysis, which performs large parts of the computation on abstracted systems, and on small subsets of the state space. For language-emptiness checking, it quickly discards uninteresting parts of the state space; for the remaining states, it adapts the model checking computation to the strength of the SCCs at hand. 
 
We present a general framework for SCC refinement, which uses a compositional approach to generate and refine overapproximations. We show that our algorithm significantly outperforms the one of Emerson and Lei.
214819
Scalable search-based image annotation of personal images
With the prevalence of digital cameras, more and more people have considerable digital images on their personal devices. As a result, there are increasing needs to effectively search these personal images. Automatic image annotation may serve the goal, for the annotated keywords could facilitate the search processes. Although many image annotation methods have been proposed in recent years, their effectiveness on arbitrary personal images is constrained by their limited scalability, i.e. limited lexicon of small-scale training set. To be scalable, we propose a search-based image annotation (SBIA) algorithm that is analogous to Web page search. First, content-based image retrieval (CBIR) technology is used to retrieve a set of visually similar images from a large-scale Web image set. Then, a text-based keyword search (TBKS) technique is used to obtain a ranked list of candidate annotations for each retrieved image. Finally, a fusion algorithm is used to combine the ranked lists into the final annotation list. The application of both efficient search technologies and Web-scale image set guarantees the scalability of the proposed algorithm. Experimental results on U. Washington dataset show not only the effectiveness and efficiency of the proposed algorithm but also the advantage of image retrieval using annotation results over that using visual features.
14558760
A Constrained Clustering Approach to Duplicate Detection Among Relational Data
This paper proposes an approach to detect duplicates among relational data. Traditional methods for record linkage or duplicate detection work on a set of records which have no explicit relations with each other. These records can be formatted into a single database table for processing. However, there are situations that records from different sources can not be flattened into one table and records within one source have certain (semantic) relations between them. The duplicate detection issue of these relational data records/instances can be dealt with by formatting them into several tables and applying traditional methods to each table. However, as the relations among the original data records are ignored, this approach generates poor or inconsistent results. This paper analyzes the characteristics of relational data and proposes a particular clustering approach to perform duplicate detection. This approach incorporates constraint rules derived from the characteristics of relational data and therefore yields better and more consistent results, which are revealed by our experiments.