Youtopia is a platform for collaborative management and integration of relational data. At the heart of Youtopia is an update exchange abstraction: changes to the data propagate through the system to satisfy user-specified mappings. We present a novel change propagation model that combines a deterministic chase with human intervention. The process is fundamentally cooperative and gives users significant control over how mappings are repaired. An additional advantage of our model is that mapping cycles can be permitted without compromising correctness. We investigate potential harmful interference between updates in our model; we introduce two appropriate notions of serializability that avoid such interference if enforced. The first is very general and related to classical final-state serializability; the second is more restrictive but highly practical and related to conflict-serializability. We present an algorithm to enforce the latter notion. Our algorithm is an optimistic one, and as such may sometimes require updates to be aborted. We develop techniques for reducing the number of aborts and we test these experimentally.
There is a growing body of work which adapts classical data integration ideas to the community setting, including substantial theoretical work @cite_15 @cite_11 @cite_19 . Systems like Orchestra @cite_28 , Piazza @cite_26 , Hyperion @cite_0 and the system introduced in @cite_8 focus on maintaining data utility despite significant disagreement. However, none of these enable best-effort cooperation to its fullest extent. They all come with some centralized logical component that is an extensibility bottleneck; usually this is either a global schema or an acyclicity restriction on the mappings, or both. In addition, they do not provide facilities for users to manage the metadata collaboratively.
@cite_26 @cite_8 @cite_28 @cite_0 @cite_19 @cite_15 @cite_11
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Data integration systems offer a uniform interface to a set of data sources. Despite recent progress, setting up and maintaining a data integration application still requires significant upfront effort of creating a mediated schema and semantic mappings from the data sources to the mediated schema. Many application contexts involving multiple data sources (e.g., the web, personal information management, enterprise intranets) do not require full integration in order to provide useful services, motivating a pay-as-you-go approach to integration. With that approach, a system starts with very few (or inaccurate) semantic mappings and these mappings are improved over time as deemed necessary. This paper describes the first completely self-configuring data integration system. The goal of our work is to investigate how advanced of a starting point we can provide a pay-as-you-go system. Our system is based on the new concept of a probabilistic mediated schema that is automatically created from the data sources. We automatically create probabilistic schema mappings between the sources and the mediated schema. We describe experiments in multiple domains, including 50-800 data sources, and show that our system is able to produce high-quality answers with no human intervention. Modern Internet communities need to integrate and query structured information. Employing current information integration infrastructure, data integration is still a very costly effort, since source registration is performed by a central authority which becomes a bottleneck. We propose the community-based integration paradigm which pushes the source registration task to the independent community members. This creates new challenges caused by each community member's lack of a global overview on how her data interacts with the application queries of the community and the data from other sources. How can the source owner maximize the visibility of her data to existing applications, while minimizing the clean-up and reformatting cost associated with publishing? Does her data contradict (or could it contradict in the future) the data of other sources? We introduce RIDE, a visual registration tool that extends schema mapping interfaces like that of MS Biz Talk Server and IBM's Clio with a suggestion component that guides the source owner in the autonomous registration, assisting her in answering these questions. RIDE's implementation features efficient procedures for deciding various levels of self-reliance of a GLAV-style source registration for contributing answers to an application query and checking potential and definite inconsistency across sources. The problem of integrating information from conflicting sources comes up in many current applications, such as cooperative information systems, heterogeneous databases, and multiagent systems. We model this by the operation of merging first-order theories. We propose a formal semantics for this operation and show that it has desirable properties, including abiding by majority rule in case of conflict and syntax independence. We apply our semantics to the special case when the theories to be merged represent relational databases under integrity constraints. We then present a way of merging databases that have different or conflicting schemas caused by problems such as synonyms, homonyms or type conflicts mentioned in the schema integration literature. The integration of the classical Web (of documents) with the emerging Web of Data is a challenging vision. In this paper we focus on an integration approach during searching which aims at enriching the responses of non-semantic search systems (e.g. professional search systems, web search engines) with semantic information, i.e. Linked Open Data (LOD), and exploiting the outcome for providing an overview of the search space and allowing the users (apart from restricting it) to explore the related LOD. We use named entities (e.g. persons, locations, etc.) as the "glue" for automatically connecting search hits with LOD. We consider a scenario where this entity-based integration is performed at query time with no human effort, and no a-priori indexing, which is beneficial in terms of configurability and freshness. To realize this scenario one has to tackle various challenges. One spiny issue is that the number of identified entities can be high, the same is true for the semantic information about these entities that can be fetched from the available LOD (i.e. their properties and associations with other entities). To this end, in this paper we propose a Link Analysis-based method which is used for (a) ranking (and thus selecting to show) the more important semantic information related to the search results, (b) deriving and showing top-K semantic graphs. In the sequel, we report the results of a survey regarding the marine domain with promising results, and comparative results that illustrate the effectiveness of the proposed (Page Rank-based) ranking scheme. Finally, we report experimental results regarding efficiency showing that the proposed functionality can be offered even at query time.