iMeMex: From Search to Information Integration and Back

In this paper, we report on lessons learned while building iMeMex, the first incarnation of a Personal Dataspace Management System (PDSMS). In contrast to traditional keyword search engines, users may not only search their collections with iMeMex but also semantically integrate them over time. As a consequence, the system may improve on precision and recall of queries in a pay-as-you-go fashion. We discuss the impact of several conceptual and architectural decisions made in iMeMex and outline open research challenges in combining search and information integration in order to build personal dataspace management systems. 1 Bringing Information Integration to Search in Personal Information Personal Information Management (PIM) is the process performed daily by users to collect, store, organize, and access their collections of digital objects [3]. Personal information is composed of a heterogeneous data mix, including files, folders, emails, office documents, contacts, images, music, calendar items, videos, among others. In addition to heterogeneity, an added dimension regarding personal information is that it is also distributed, being stored in a wide variety of data sources such as file systems, network shares, external drives, iPods, cell phones, email servers, and on the Web. There have been two extreme solutions to offer a unified query service over a set of heterogeneous and distributed data sources. At one extreme, traditional search engines allow users to pose simple keyword queries over unstructured sources. XML-IR search engines go one step further and allow users to also pose path queries over semi-structured sources. The semantics of those keyword or path queries are not always precise, meaning that the quality of query answers is evaluated in terms of precision and recall. As a consequence, search engines have difficulties in dealing with queries on structured data, such as grasping the meaning of complex folder hierarchies or finding relationships among documents based on their modification times, authors, or lineage. At the other extreme, an information-integration system allows users to pose complex structural queries over semantically integrated structured or even semi-structured data sources. These systems offer precise query semantics, defined in terms of data models and schemas. However, defining schemas, and mappings among different schemas, is known to be a manual and costly process, which requires the involvement of specialized professionals. As a consequence, information-integration systems are inapplicable to situations in which the number of schemas is too large or users are not specialized, such as in personal information spaces. In the iMeMex project at ETH Zurich, we have explored a new hybrid data management architecture inbetween the two extremes of search engines and traditional information-integration systems. We term a system Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering