One of the distinctive features of Information Retrieval systems comparing to Database Management systems, is that they offer better compression for posting lists, resulting in better I O performance and thus faster query evaluation. In this paper, we introduce database representations of the index that reduce the size (and thus the disk I Os) of the posting lists. This is not achieved by redesigning the DBMS, but by exploiting the non 1NF features that existing Object-Relational DBM systems (ORDBMS) already offer. Specifically, four different database representations are described and detailed experimental results for one million pages are reported. Three of these representations are one order of magnitude more space efficient and faster (in query evaluation) than the plain relational representation.
This paper summarizes the salient aspects of the SIGMOD 2005 panel on "Databases and Information Retrieval: Rethinking the Great Divide". The goal of the panel was to discuss whether we should rethink data management systems architectures to truly merge Database (DB) and Information Retrieval (IR) technologies. The panel had very high attendance and generated lively discussions. Databases (DB) and information retrieval (IR) have evolved as separate fields. However, modern applications such as customer support, health care, and digital libraries require capabilities for both data and text management. In such settings, traditional DB queries, in SQL or XQuery, are not flexible enough to handle applicationspecific scoring and ranking. IR systems, on the other hand, lack efficient support for handling structured parts of the data and metadata, and do not give the application developer adequate control over the ranking function. This paper analyzes the requirements of advanced text- and data-rich applications for an integrated platform. The core functionality must be manageable, and the API should be easy to program against. A particularly important issue that we highlight is how to reconcile flexibility in scoring and ranking models with optimizability, in order to accommodate a wide variety of target applications efficiently. We discuss whether such a system needs to be designed from scratch, or can be incrementally built on top of existing architectures. The results of our analyses are cast into a series of challenges to the DB and IR communities.