We propose a new system that is able to handle the entire Personal Dataspace of a user. A Personal Dataspace includes all data pertaining to a user on all his disks and on remote servers such as network drives, email and web servers. This data is represented by a heterogeneous mix of files, emails, bookmarks, music, pictures, calendar data, personal information streams and so on. State-of-the-art tools such as desktop search engines and desktop operating systems (including the upcoming Vista) are not enough as they neither solve the problem of physical personal information independence (where is my data) nor format and data model independence (how is it stored and which application do I have to use in order to access that data). Our work builds on the visions presented in [DSKB05], which calls for a single system to manage the personal information jungle, and [FHM05], which advocates dataspaces as a new abstraction for information management. In contrast to [FHM05] this paper presents a concrete implementation of a Personal DataSpace Management System (PDSMS) termed iMeMex: integrated memex. We discuss the core architecture of iMeMex and services offered by our system. As we will show, a PDSMS can be seen as a system that occupies the middleground between a search engine, a database management system, and a traditional information integration system. A PDSMS has to bridge these separate worlds and requires: (1) no full control on data, i.e., data may be accessed bypassing the interfaces of a PDSMS, (2) simple keyword search on all data available in a dataspace without performing any semantic data integration, (3) rich querying able to mix structural, attribute, and content predicates, (4) pay-as-you-go integration capabilities, (5) the ability to define arbitrary logical views on all data, (6) durability and consistency guarantees to avoid loss of data assigned to a dataspace, and (7) update capabilities. iMeMex is the first implementation of a PDSMS we are aware of. This paper presents the architecture of iMeMex and reports on the current state of the iMeMex research project at ETH Zurich. ∗This work is partially supported by the Swiss National Science Foundation (SNF) under contract 200021112115.

A Personal Dataspace includes all data pertaining to a user on all his local disks and on remote servers such as network drives, email and web servers. This data is represented by a heterogeneous mix of files, emails, bookmarks, music, pictures, calendar, personal information streams and so on. We demonstrate a new breed of system that is able to handle the entire Personal Dataspace of a user. Our system, named iMeMex (integrated memex), is a first implementation of a Personal DataSpace Management System (PDSMS). Visions for this type of systems have been proposed recently [13, 10, 12, 17]. We showcase how iMeMex allows dataspace navigation across data source/file boundaries, how iMeMex offers rich contextual information on query results and how our system returns best-effort results.

Dataspace management has been recently identified as a new agenda for information management [17, 22] and information integration [23]. In sharp contrast to standard information integration architectures, a dataspace management system is a data-coexistence approach: it does not require any investments in semantic integration before querying services on the data are provided. Rather, a dataspace can be gradually enhanced over time by defining relationships among the data. Defining those integration semantics gradually is termed pay-as-you-go information integration [17], as time and effort (pay) are needed over time (go) to provide integration semantics. The benefits are better query results (gain). This paper is the first to explore pay-as-you-go information integration in dataspaces. We provide a technique for declarative pay-as-you-go information integration named iTrails. The core idea of our approach is to declaratively add lightweight 'hints' (trails) to a search engine thus allowing gradual enrichment of loosely integrated data sources. Our experiments confirm that iTrails can be efficiently implemented introducing only little overhead during query execution. At the same time iTrails strongly improves the quality of query results. Furthermore, we present rewriting and pruning techniques that allow us to scale iTrails to tens of thousands of trail definitions with minimal growth in the rewritten query size.