a description of some of the community issues and initiatives that surfaced. We describe the group’s consensus view of new focus areas for research, including database engine architectures, declarative programming languages, interplay of structured data and free text, cloud data services, and mobile and virtual worlds. We also report on discussions of the database community’s growth and processes that may be of interest to other research areas facing similar challenges.

Over the past 20 years, small groups of database researchers have periodically gathered to assess the state of the field and propose directions for future research. Reports of the meetings

1, 3–7

served to foster debate within the database research community, explain research directions to external organizations, and help focus community efforts on timely challenges.

The theme of the Claremont meeting was that database research and the data-management industry are at a turning point, with unusually rich opportunities for technical advances, intellectual achievement, entrepreneurship, and benefits for science and society. Given the large number of opportunities, it is important for the database research community to address issues that maximize relevance within the field, across computing, and in external fields as well.

The sense of change that emerged in the meeting was a function of several factors:

iLLustration by gLuekit

Excitement over “big data.” In recent years, the number of communities working with large volumes of data has grown considerably to include not only traditional enterprise applications and Web search but also e-science efforts (in astronomy, biology, earth science, and more), digital entertainment, natu-ral-language processing, and social-network analysis. While the user base for traditional database management systems (DBMSs) is growing quickly, there is also a groundswell of effort to design new custom data-management solutions from simpler components. The ubiquity of big data is expanding the base of users and developers of data-management technologies and will undoubtedly shake up the database research field.

Data analysis as profit center. In tradi-

tional enterprise settings, the barriers between IT departments and business units are coming down, and there are many examples of companies where data is indeed the business itself. As a consequence, data capture, integration, and analysis are no longer viewed as a business cost but as the keys to efficiency and profit. The value of software to support data analytics has been growing as a result. In 2007, corporate acquisitions of business-intelligence vendors alone totaled $15 billion, ² and

crawls of deep-Web sites. There is also an explosion of text-focused semistructured data in the public domain in the form of blogs, Web 2.0 communities, and instant messaging. New incentive structures and Web sites have emerged for publishing and curating structured data in a shared fashion as well. Text-centric approaches to managing the data are easy to use but ignore latent structure in the data that might add significant value. The race is on to develop techniques that extract useful

 

that is only the “front end” of the data-analytics tool chain. Market pressure for better analytics also brings new users to the technology with new demands. Statistically sophisticated analysts are being hired in a growing number of industries, with increasing interest in running their formulae on the raw data. At the same time, a growing number of nontechnical decision makers want to “get their hands on the numbers” as well in simple and intuitive ways.

Ubiquity of structured and unstructured data. There is an explosion of structured data on the Web and on enterprise intranets. This data is from a variety of sources beyond traditional databases, including large-scale efforts to extract structured information from text, software logs and sensors, and

data from mostly noisy text and structured corpora, enable deeper exploration into individual data sets, and connect data sets together to wring out as much value as possible.

Expanded developer demands. Programmer adoption of relational DBMSs and query languages has grown significantly in recent years, accelerated by the maturation of open source systems (such as MySQL and Postgr-eSQL) and the growing popularity of object-relational mapping packages (such as Ruby on Rails). However, the expanded user base brings new expectations for programmability and usability from a larger, broader, less-special-ized community of programmers.

Some of them are unhappy or unwilling to “drop into” SQL, viewing DBMSs