Much of the manipulation of large data sets today involves significant computations, often performed in certain stylized ways. However, traditional database query languages do not provide support for such computations, requiring them frst to fetch data from the database and then manipulate it outside. Where such manipulations are data intensive, it has been shown in many contexts that performing the manipulation within the database itself is more efficient. It can also relieve the application programmer of the burden of specifying the algorithms for these manipulations.

The MauveDB paper studies two frequently used computational operations -- regression and interpolation. It provides a mechansim to specify each within the database, with the result being a "view" that the application programmer can use. The expected performance improvements are demonstrated experimentally. There is much still to be done, in terms of better access mathods for such selected new operators, better integration with the standard query optimization techniques, identification of additional such operations, and so on. What this paper does is open the gates to a new area of research, into which I hope others will follow. For doing so, this paper merits both attention and credit.

We, in the database community, tend to have boolean logic deeply ingrained in the way we think. A consequence is that once a result set has been computed, we do not give it much further thought, at best ordering tuples in the result. Yet, from a user perspective, result presentation is crucial, and not just in terms of "eye-candy". This paper does a very nice job of identifying one specific problem in this broad space, and addressing it well.

The problem studied is one of attribute selection -- when screen real estate is insufficient to show all attributes at the same time, which ones should be shown? This is a well-posed question, and the paper suggests some reasonable choices that a system could automatically make.

The problem specification is narrow, and the solution may not be optimum. Nevertheless, this paper deserves attention because it establishes a beachhead in an important but poorly studied area -- that of results presentation. It shows how one can do quality work in this context. Hopefully many others will follow this lead.

There are many systems proposed for XML query evaluation, and even more for text queries, that have quite ad hoc definitions and empiricially specified behaviors. In contrast, the bedrock for relational database systems has been a very well-specified algebra that has provided a valuable intellectual basis and a useful framework for query optimization. This paper represents a strong attempt at establishing an algebraic basis for querying text in XML.

Whether the proposed algebra will suffice, it is too early to tell. I myself (along with my co-authors) had proposed the TIX algebra [citation 1 in the bibliography of this paper] some years ago to address precisely this need. The current paper significantly extends that proposal, and is thus more likely to capture enough of the nuances of queries over text data.

It has been a while since anyone had anything really fresh to say about concurrency control -- a fundamental piece of database technology was widely believed to be "solved". Yet, serious issues remain. Most distributed systems do not operate in transactional mode because the overheads are too high to maintain serializability. With mobile systems that could operate in disconnected mode, it is not even possible.

This refreshing paper introduces the notion of "freshness", and a corresponding notion of relaxed currency for a system in which the user is aware of multiple versions, establishing a firm analytic foundation for a very real practical problem. I expect to see real systems using these ideas in the near future.

Relative customer preferences for feature combinations have long been represented in multi-dimensional space. In fact, the whole area of multi-dimensional scaling arose from this application.

Skyline queries over multi-dimensional data sets have, justifiably, become a topic of intense study in recent years. The set of skyline points present a scale-free choice of data points worthy of further consideration in many contexts.

This paper, in a very clever way, ties these two ideas together. Three new classes of skyline queries are defined, to help firms delineate market opportunities based on customer preferences and competitive products. Efficient computation for such queries is achieved through a novel data structure.

Sometimes users issue "bad" transactions, and these have to be rolled back after they have been committed. This creates a problem of cascading roll-backs. This paper suggests an efficient way to roll-back as little as possible while removing the effects of bad transactions.

The paper is written in the context of a traditional transaction-oriented (database) system. But I think the ideas in the paper are applicable more broadly. For example, consider a system that is gather information from new sources on the internet and performing some analyses. What happens when one of these news sources retracts a story? Can we efficiently edintify the dependent analyses and redo them? The same applies to biomedical science, and the retraction of a data set because of discovery of scientific fraud.

Cost-based query optimization is central to database systems, but is rarely used outside of this context. In this paper, the authors consider tex-centric data retrieval and integration tasks, and propose a cost model for such tasks. Using this cost model, it is possible to estimate the cost of alternative query plans, and thereby make an informed decision between them.

Even if it turns out that the cost models are inadequate, this paper still has made a significant contribution in bringing classic query optimization ideas to bear on a new and important problem domain. To the extent that the cost models turn out to be good approximations of reality, this paper is even more impactful.