Interesting Papers from CIDR 2009

CIDR 2009 looks like it was an interesting conference, there were a lot of very interesting papers covering a whole range of data management and retrieval issues. The full list of papers can be browsed online, or downloaded as a zip file. There’s plenty of good stuff in there ranging from the energy costs of data management, forms of query analysis and computation on “big data”, and discussions on managing inconsistency in distributed systems.
Below I’ve pulled out a few of the papers that particularly caught my eye. You can find some other picks and summary on the Data Beta blog: part 1, and part 2.
Requirements for Science Databases and SciDB from Michael Stonebraker et al, presents the results of a requirement analysis covering the data management needs of scientific researchers in a number of different fields. Interestingly it seems that for none of the fields covered, which includes astronomy, oceanography, biologic, genomics and chemistry, is a relational structure a good fit for the underlying data models used in the data capture or analysis. In most cases an array based system is most suitable, while for biology, chemistry and genomics in particular a graph database would be best; semantic web folk take note. The paper goes on to discuss the design of SciDB which will be an open source array-based database suitable for use in a range of disciplines.
The Case for RodentStore, an Adaptive, Declarative Storage System, Cudre-Mauroux et al, introduces RodentStore an adaptive storage system that can be used at the heart of a number of different data management solutions. The system provides a declarative storage algebra that allows a logical schema to be mapped to a specific physical disk layout. This is interesting as it allows greater experimentation within the storage engine, allowing exploration of how different layouts may be used to optimise performance for specific applications and datasets. The system supports a range of different structures, including multi-dimensional data, and the authors note that the system can be used to manage RDF data.
Principles for Inconsistency, proposes some approaches for cleanly managing inconsistency in distributed applications, providing some useful additional context and implementation experience for those wrapping their heads around the notion of eventual consistency. I’m not sure that’d I’d follow all of these principles, mainly due to the implementation and/or storage overheads, but there’s a lot of good common sense here.
Harnessing the Deep Web: Present and Future, Madhavan et al, describes some recent work at Google to explore how to begin surfacing “Deep Web” information and data into search indexes. The Deep Web is defined by them as pages that are currently hidden behind search forms and that are not currently accessible to crawlers through other means. The work essentially involved discovering web forms, analysing existing pages from the same site in order to find candidate values to fill in fields in those forms, then automatically submitting the forms and indexing the results. The authors describe how this approach can be used to help answer factual queries, and is already in production on Google. This probably explains the factual answers that are appearing on search results pages. The approach is clearly in-line with Google’s mission to do as much as possible with statistical analysis of document corpora as possible, there’s very little synergy with other efforts going on elsewhere, e.g. linked data. There is reference to how understanding the semantics of forms, in particular the valid range of values for a field (e.g. a zip code) and co-dependencies between fields, could improve the results, but the authors also note that they’ve achieved a high level of accuracy in automated approaches to identifying common fields such as zip code, etc. A proposed further avenue for research is exploration of whether the contents of an underlying relational database can be reconsistuted through automated form submission and scraping of structured data from the resulting pages. Personally I think there are easier ways to achieve greater data publishing on the web! The authors reference some work on a search engine specifically for data surfaced in this way, called Web Tables which I’ve not looked at yet.
DBMSs Should Talk Back Too, Yannis Ioannidis and Alkis Simitsis, describes some work to explore how database query results and queries themselves can be turned into human-readable text (i.e. the reverse of a typical natural-language query system), arguing that this provides a good foundation for building more accessible data access mechanisms, as well as allowing easier summarisation of what a query is going to do, in order to validate it against the users expectations. The conversion of queries to text was less interesting to me than the exploration of how to walk a logical datamodel to generate text. I’ve very briefly explored summarising data in FOAF files, in order to generate an audible report using a text-to-speech engine, and so it was interesting to me to see that the authors were using a graph based representation of the data model to drive their engine. Class and relation labelling, with textual templates, are a key part of the system, and it seems much of this would work well against RDF datasets.
SocialScope: Enabling Information Discovery on Social Content Sites, Amer-Yahia et al, is a broad paper that introduces SocialScope a logical architecture for managing, analysing and presentation information derived from social content graphs. The paper introduces a logical algebra for describing operations on the social graph, e.g. producing recommendations based on analysis of a users social network; introduces a categorisation for types of content present in the social graph and means for managing it; and also discusses some ways to present results of searches against the content graph (e.g. for travel recommendations) using different facets and explanations of how recommendations are derived.