Sharing structured data today requires standardizing upon a single schema, then mapping and cleaning all of the data. This results in a single queriable mediated data instance. However, for settings in which structured data is being collaboratively authored by a large community, e.g., in the sciences, there is often a lack of consensus about how it should be represented, what is correct, and which sources are authoritative. Moreover, such data is seldom static: it is frequently updated, cleaned, and annotated. The ORCHESTRA collaborative data sharing system develops a new architecture and consistency model for such settings, based on the needs of data sharing in the life sciences. In this paper we describe the basic architecture and implementation of the ORCHESTRA system, and summarize some of the open challenges that arise in this setting. 1

Empowering users to access databases using simple keywords can relieve the users from the steep learning curve of mastering a structured query language and understanding complex and possibly fast evolving data schemas. In this tutorial, we give an overview of the state-of-the-art techniques for supporting keyword search on structured and semi-structured data, including query result definition, ranking functions, result generation and top-k query processing, snippet generation, result clustering, query cleaning, performance optimization, and search quality evaluation. Various data models will be discussed, including relational data, XML data, graph-structured data, data streams, and workflows. We also discuss applications that are built upon

Many advanced data management operations (e.g., incremental maintenance,

The specification of schema mappings has proved to be time and resource consuming, and has been recognized as a critical bottleneck to the large scale deployment of data integration systems. In an attempt to address this issue, dataspaces have been proposed as a data management abstraction that aims to reduce the up-front cost required to setup a data integration system by gradually specifying schema mappings through interaction with end users in a pay-asyou-go fashion. As a step in this direction, we explore an approach for incrementally annotating schema mappings using feedback obtained from end users. In doing so, we do not expect users to examine mapping specifications; rather, they comment on results to queries evaluated using the mappings. Using annotations computed on the basis of user feedback, we present a method for selecting from the set of candidate mappings, those to be used for

Scientific data offers some of the most interesting challenges in data integration today. Scientific fields evolve rapidly and accumulate masses of observational and experimental data that needs to be annotated, revised, interlinked, and made available to other scientists. From the perspective of the user, this can be a major headache as the data they seek may initially be spread across many databases in need of integration. Worse, even if users are given a solution that integrates the current state of the source databases, new data sources appear with new data items of interest to the user. Here we build upon recent ideas for creating integrated views over data sources using keyword search techniques, ranked answers, and user feedback [32] to investigate how to automatically discover when a new data source has content relevant to a user’s view — in essence, performing automatic data integration for incoming data sets. The new architecture accommodates a variety of methods to discover related attributes, including label propagation algorithms from the machine learning community [2] and existing schema matchers [11]. The user may provide feedback on the suggested new results, helping the system repair any bad alignments or increase the cost of including a new source that is not useful. We evaluate our approach on actual bioinformatics schemas and data, using state-of-the-art schema matchers as components. We also discuss how our architecture can be adapted to more traditional settings with a mediated schema.

In many scenarios, such as emergency response or ad hoc collaboration, it is critical to reduce the overhead in integrating data. Here, the goal is often to rapidly integrate “enough ” data to answer a specific question. Ideally, one could perform the entire process interactively under one unified interface: defining extractors and wrappers for sources, creating a mediated schema, and adding schema mappings — while seeing how these impact the integrated view of the data, and refining the design accordingly. We propose a novel smart copy and paste (SCP) model and architecture for seamlessly combining the design-time and run-time aspects of data integration, and we describe an initial prototype, the CopyCat system. In CopyCat, the user does not need special tools for the different stages of integration: instead, the system watches as the user copies data from applications (including the Web browser) and pastes them into CopyCat’s spreadsheet-like workspace. CopyCat generalizes these actions and presents proposed auto-completions, each with an explanation in the form of provenance. The user provides feedback on these suggestions — through either direct interactions or further copy-and-paste operations — and the system learns from this feedback. This paper provides an overview of our prototype system, and identifies key research challenges in achieving SCP in its full generality. 1.

User feedback is gaining momentum as a means of addressing the difficulties underlying information integration tasks. It can be used to assist users in building information integration systems and to improve the quality of existing systems, e.g., in dataspaces. Existing proposals in the area are confined to specific integration sub-problems considering a specific kind of feedback sought, in most cases, from a single user. We argue in this paper that, in order to maximize the benefits that can be drawn from user feedback, it should be considered and managed as a first class citizen. Accordingly, we present generic operations that underpin the management of feedback within information integration systems, and that are applicable to feedback of different kinds, potentially supplied by multiple users with different expectations. We present preliminary solutions that can be adopted for realizing such operations, and sketch a research agenda for the information integration community.

An important means of allowing non-expert end-users to pose ad hoc queries — whether over single databases or data integration

Knowledge bases of entities and relations (either constructed manually or automatically) are behind many real world search engines, including those at Yahoo!, Microsoft 1, and Google. Those knowledge bases can be viewed as graphs with nodes representing entities and edges representing (primary) relationships, and various studies have been conducted on how to leverage them to answer entity seeking queries. Meanwhile, in a complementary direction, analyses over the query logs have enabled researchers to identify entity pairs that are statistically correlated. Such entity relationships are then presented to search users through the “related searches ” feature in modern search engines. However, entity relationships thus discovered can often be “puzzling ” to the users because why the entities are connected is often indescribable. In this paper, we propose a novel problem called entity relationship explanation, which seeks to explain why a pair of entities are connected, and solve this challenging problem by integrating the above two complementary approaches, i.e., we leverage the knowledge base to “explain ” the connections discovered between entity pairs. More specifically, we present REX, a system that takes a pair of entities in a given knowledge base as input and efficiently identifies a ranked list of relationship explanations. We formally define relationship explanations and analyze their desirable properties. Furthermore, we design and implement algorithms to efficiently enumerate and rank all relationship explanations based on multiple measures of “interestingness. ” We perform extensive experiments over real web-scale data gathered from DBpedia and a commercial search engine, demonstrating the efficiency and scalability of REX. We also perform user studies to corroborate the effectiveness of explanations generated by REX. 1.

Sharing structured data today requires agreeing on a standard schema, then mapping and cleaning all of the data to achieve a single queriable mediated instance. However, for settings in which structured data is collaboratively authored by a large community, such as in the sciences, there is seldom consensus about how the data should be represented, what is correct, and which sources are authoritative. Moreover, such data is dynamic: it is frequently updated, cleaned, and annotated. The ORCHESTRA collaborative data sharing system develops a new architecture and consistency model for such settings, based on the needs of data sharing in the life sciences. A key aspect of ORCHESTRA’s design is that the provenance of data is recorded at every step. In this paper we describe ORCHESTRA’s provenance model and architecture, emphasizing its integral use of provenance in enforcing trust policies and translating updates efficiently. 1