The encrypted execution of database queries promises powerful security protections, however users are currently unlikely to benefit without significant expertise. In this demonstration, we illustrate a simple workflow enabling users to design secure executions of their queries. The Data Storm system demonstrated simplifies both the design and execution of encrypted execution plans, and represents progress toward the challenge of developing a general planner for encrypted query execution.

OpenII (openintegration.org) is a collaborative effort to create a suite of open-source tools for information integration (II). The project is leveraging the latest developments in II research to create a platform on which integration tools can be built and further research conducted. In addition to a scalable, extensible platform, OpenII includes industrial-strength components developed by MITRE, Google, UC-Irvine, and UC-Berkeley that interoperate through a common repository in order to solve II problems. Components of the toolkit have been successfully applied to several large-scale US government II challenges.

Large, dynamic, and ad-hoc organizations must frequently initiate data integration and sharing efforts with insufficient awareness of how organizational data sources are related. Decision makers need to reason about data model interactions much as they do about data instance interactions in OLAP: at multiple levels of granularity. We demonstrate an integrated environment for exploring schema similarity across multiple resolutions. Users visualize and interact with clusters of related schemas using a tool named Affinity. Within any cluster, users may drill-down to examine the extent and content of schema overlap. Further drill down enables users to explore fine-grained element-level correspondences between between two selected schemas.

This demonstration presents Galaxy, a schema manager that facilitates easy and correct data sharing among autonomous but related, evolving data sources. Galaxy reduces heterogeneity by helping database developers identify, reuse, customize, and advertise related schema components. The central idea is that as schemata are customized, Galaxy maintains a derivation graph, and exploits it for data exchange, discovery, and multi-database query over the "galaxy" of related data sources. Using a set of schemata from the biomedical domain, we demonstrate how Galaxy facilitates schema and data sharing.

Whereas strategies for discovering content on the surface web are commonplace, similar strategies for the private web are nonexistent. In this paper we first establish a formal framework for advertising the existence of private web resources that subsumes many existing summarization strategies based on succinct statistical summaries (which we call digests). We then investigate the tradeoff between the data owners' desires to minimize disclosure and the searchers' desires to minimize query error, demonstrating that our techniques are superior to k-anonymity. Finally, we show that our techniques for summarization do, in fact, make it possible to discover private web data resources.

Cars, aircraft, mobile cell phones, ships, tanks, and mobile robots all have the common property that they are moving objects. A kinematic representation can be used to describe the location of these objects as a function of time. For example, a moving point can be represented by the function p ( t ) = x → 0 + ( t - t 0 ) v → , where x → 0 is the start location, t 0 is the start time, and v → is its velocity vector. Instead of storing the location of the object at a given time in a database, the coefficients of the function are stored. When an object's behavior changes enough so that the function describing its location is no longer accurate, the function coefficients for the object are updated. Because the location of each object is represented as a function of time, spatial query results can change even when no transactions update the database. We present efficient algorithms to maintain k -nearest neighbor, and spatial join queries in this domain as time advances and updates occur. We assume no previous knowledge of what the updates will be before they occur. We experimentally compare these new algorithms with more straight forward adaptations of previous work to support updates. Experiments are conducted using synthetic uniformly distributed data, and real aircraft flight data. The primary metric of comparison is the number of I/O disk accesses needed to maintain the query results and the supporting data structures.

Shared scientific data, such as neuroimagery, offers great benefits to science. However, data owners must exercise custodial responsibilities which can conflict with the unhindered sharing of their data. Given simple choices of sharing widely or not at all, the result will frequently be no sharing. We hypothesize that neuroimagery sharing will be enhanced if data owners are provided with well-defined intermediate levels of data visibility. In this paper, we describe a broadly applicable data sharing model, Structured Sharing Communities (SSC), in which data becomes incrementally visible to communities structured as a complete partial-order; the associated properties of Privacy and Fairness regulate access to shared data. Within SSC, a customized policy space is defined capturing the sharing relationships among specific collaborators.

The authors describe the development of a four-dimensional atlas and reference system that includes both macroscopic and microscopic information on structure and function of the human brain in persons between the ages of 18 and 90 years. Given the presumed large but previously unquantified degree of structural and functional variance among normal persons in the human population, the basis for this atlas and reference system is probabilistic. Through the efforts of the International Consortium for Brain Mapping (ICBM), 7,000 subjects will be included in the initial phase of database and atlas development. For each subject, detailed demographic, clinical, behavioral, and imaging information is being collected. In addition, 5,800 subjects will contribute DNA for the purpose of determining genotype- phenotype-behavioral correlations. The process of developing the strategies, algorithms, data collection methods, validation approaches, database structures, and distribution of results is described in this report. Examples of applications of the approach are described for the normal brain in both adults and children as well as in patients with schizophrenia. This project should provide new insights into the relationship between microscopic and macroscopic structure and function in the human brain and should have important implications in basic neuroscience, clinical diagnostics, and cerebral disorders.

The benefits of distributed systems and shared database resources are widely recognized, but they often cannot be exploited by users who must protect their data by using label-based access controls. In particular, users of label-based data need to read and write data at different security levels within a single database transaction, which is not currently possible without violating multilevel security constraints. The paper presents a formal model of multilevel transactions which provide this capability. We define four ACIS (atomicity, consistency, isolation, and security) correctness properties of multilevel transactions. While atomicity, consistency and isolation are mutually achievable in standard single-site and distributed transactions, we show that the security requirements of multilevel transactions conflict with some of these goals. This forces trade-offs to be made among the ACIS correctness properties, and we define appropriate partial correctness properties. Due to such trade-offs, an important problem is to design multilevel transaction execution protocols which achieve the greatest possible degree of correctness. These protocols must provide a variety of approaches to making trade-offs according to the differing priorities of various users. We present three transaction execution protocols which achieve a high degree of correctness. These protocols exemplify the correctness trade-offs proven in the paper, and offer realistic implementation options.

There have been many R&D efforts in recent years to develop computer environments that directly enhance group problem-solving productivity. An example of such efforts is the computer-based Virtual Teaming Technology (VTT) currently being developed at the Knowledge-based Engineering Systems Research Laboratory (KESRL). The core software environment for KESRL's VTT is called SWIFT (System Workbench for Integrating and Facilitating Teams) and is based on the knowledge processing technology (KPT) previously developed at KESRL to support various engineering design tasks. SWIFT is a flexible environment which enables the rapid development of collaborative applications, linking workers in distributed environments, of many domains. This paper presents the overall system architecture of SWIFT, and introduces the current and future R&D efforts at KESRL in realizing SWIFT for significant industrial applications.

The addition of stringent security specifications to the list of requirements for an application poses many new problems in DBMS design and implementation, as well as database design, use, and maintenance. Tight security requirements, such as those that result in silent masking of witholding of true information from a user or the introduction of false information into query answers, also raise fundamental questions about the meaning of the database and the semantics of accompanying query languages. In this paper, we propose a belief-based semantics for secure databases, which provides a semantics for databases that can “lie” about the state of the world, or about their knowledge about the state of the world, in order to preserve security. This kind of semantics can be used as a helpful retrofit for the proposals for a “multilevel secure” database model (a particularly stringent form of security), and may be useful for less restrictive security policies as well. We also propose a family of query languages for multilevel secure relational database applications, and base the semantics of those languages on our semantics for secure databases. Our query languages are free of the semantic problems associated with use of ordinary SQL in a multilevel secure context, and should be easy for users to understand and employ.

A query processing method that produces approximate answers to queries posed in standard relational algebra is presented. This method is monotone in the sense that the accuracy of the approximate result improves with the amount of time spent producing the result. This strategy makes it possible to trade the time to produce the result for the accuracy of the result. An approximate relational model that characterizes approximate relations and a partial order for comparing them is developed. Relational operators which operate on and return approximate relations are defined.>