study a collection of heterogeneous XML databases maintain- ing similar and related information, exchanging data via a peer to peer overlay network. In this setting, a mediated global schema is unrealistic. Yet, users applications wish to query the d atabases via one peer using its schema. We have recently developed Hep- ToX, a P2P Heterogeneous XML database system. A key idea is that whenever a peer enters the system, it establishes an acquain- tance with a small number of peer databases, possibly with dif- ferent schema. The peer administrator provides correspondences between the local schema and the acquaintance schema using an informal and intuitive notation of arrows and boxes. We develop a novel algorithm that infers a set of precise mapping rules between the schemas from these visual annotations. We pin down a seman- tics of query translation given such mapping rules, and present a novel query translation algorithm for a simple but expressive frag- ment of XQuery, that employs the mapping rules in either direction. We show the translation algorithm is correct. Finally, we demon- strate the utility and scalability of our ideas and algorith ms with a detailed set of experiments on top of the Emulab, a large scale P2P network emulation testbed.
Schema-matching systems Automated techniques for schema matching (e.g. CUPID @cite_1 , @cite_17 @cite_13 ) are able to output elementary schema-level associations by exploiting linguistic features, context-dependent type matching, similarity functions etc. These associations could constitute the input of our rule inference algorithm if the user does not provide the arrows.
@cite_13 @cite_1 @cite_17
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Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past solutions, showing that a rich range of techniques is available. We then propose a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches. Some of our innovations are the integrated use of linguistic and structural matching, context-dependent matching of shared types, and a bias toward leaf structure where much of the schema content resides. After describing our algorithm, we present experimental results that compare Cupid to two other schema matching systems. The purely manual specification of semantic correspondences between schemas is almost infeasible for very large schemas or when many different schemas have to be matched. Hence, solving such large-scale match tasks asks for automatic or semiautomatic schema matching approaches. Large-scale matching needs especially to be supported for XML schemas and different kinds of ontologies due to their increasing use and size, e.g., in e-business and web and life science applications. Unfortunately, correctly and efficiently matching large schemas and ontologies are very challenging, and most previous match systems have only addressed small match tasks. We provide an overview about recently proposed approaches to achieve high match quality or and high efficiency for large-scale matching. In addition to describing some recent matchers utilizing instance and usage data, we cover approaches on early pruning of the search space, divide and conquer strategies, parallel matching, tuning matcher combinations, the reuse of previous match results, and holistic schema matching. We also provide a brief comparison of selected match tools. Despite significant progress of deep learning in recent years, state-of-the-art semantic matching methods still rely on legacy features such as SIFT or HoG. We argue that the strong invariance properties that are key to the success of recent deep architectures on the classification task make them unfit for dense correspondence tasks, unless a large amount of supervision is used. In this work, we propose a deep network, termed AnchorNet, that produces image representations that are well-suited for semantic matching. It relies on a set of filters whose response is geometrically consistent across different object instances, even in the presence of strong intra-class, scale, or viewpoint variations. Trained only with weak image-level labels, the final representation successfully captures information about the object structure and improves results of state-of-the-art semantic matching methods such as the deformable spatial pyramid or the proposal flow methods. We show positive results on the cross-instance matching task where different instances of the same object category are matched as well as on a new cross-category semantic matching task aligning pairs of instances each from a different object class. Despite significant progress of deep learning in recent years, state-of-the-art semantic matching methods still rely on legacy features such as SIFT or HoG. We argue that the strong invariance properties that are key to the success of recent deep architectures on the classification task make them unfit for dense correspondence tasks, unless a large amount of supervision is used. In this work, we propose a deep network, termed AnchorNet, that produces image representations that are well-suited for semantic matching. It relies on a set of filters whose response is geometrically consistent across different object instances, even in the presence of strong intra-class, scale, or viewpoint variations. Trained only with weak image-level labels, the final representation successfully captures information about the object structure and improves results of state-of-the-art semantic matching methods such as the Deformable Spatial Pyramid or the Proposal Flow methods. We show positive results on the cross-instance matching task where different instances of the same object category are matched as well as on a new cross-category semantic matching task aligning pairs of instances each from a different object class.