Aurora: a new model and architecture for data stream management

This paper describes the basic processing model and architecture of Aurora, a new system to manage data streams for monitoring applications. Monitoring applications differ substantially from conventional business data processing. The fact that a software system must process and react to continual inputs from many sources (e.g., sensors) rather than from human operators requires one to rethink the fundamental architecture of a DBMS for this application area. In this paper, we present Aurora, a new DBMS currently under construction at Brandeis University, Brown University, and M.I.T. We first provide an overview of the basic Aurora model and architecture and then describe in detail a stream-oriented set of operators.Received: 12 September 2002, Accepted: 26 March 2003, Published online: 21 July 2003Edited by Y. Ioannidis     

Performance considerations for an operating system transactionmanager

Results of a previous comparison study (A. Kumar and M. Stonebraker, 1987) between a conventional transaction manager and an operating system (OS) transaction manager indicated that the OS transaction manager incurs a severe performance penalty and appears to be feasible only in special circumstances. Three approaches for enhancing the performance of an OS transaction manager are considered. The first strategy is to improve performance by reducing the cost of lock acquisition and by compressing the log. The second strategy explores the possibility of still further improvements from additional semantics to be built into an OS transaction system. The last strategy is to use a modified index structure that makes update operations less expensive to perform. The results show that the OS will have to implement essentially all of the specialized tactics for transaction management that are currently used by a database management system (DBMS) in order to match DBMS performance     

Future trends in database systems

The author discusses the likely evolution of commercial data managers over the next several years. Topics to be covered include the following: why SQL (structured query language) has become a universal standard; who can benefit from SQL standardization; why the current SQL standard has no chance of lasting; why all database systems can be distributed soon; what new technologies are likely to be commercialized; and why vendor independence may be achievable     

Mariposa: a wide-area distributed database system

The requirements of wide-area distributed database systems differ dramatically from those of local-area network systems. In a wide-area network (WAN) configuration, individual sites usually report to different system administrators, have different access and charging algorithms, install site-specific data type extensions, and have different constraints on servicing remote requests. Typical of the last point are production transaction environments, which are fully engaged during normal business hours, and cannot take on additional load. Finally, there may be many sites participating in a WAN distributed DBMS. In this world, a single program performing global query optimization using a cost-based optimizer will not work well. Cost-based optimization does not respond well to site-specific type extension, access constraints, charging algorithms, and time-of-day constraints. Furthermore, traditional cost-based distributed optimizers do not scale well to a large number of possible processing sites. Since traditional distributed DBMSs have all used cost-based optimizers, they are not appropriate in a WAN environment, and a new architecture is required. We have proposed and implemented an economic paradigm as the solution to these issues in a new distributed DBMS called Mariposa. In this paper, we present the architecture and implementation of Mariposa and discuss early feedback on its operating characteristics. Edited by Henry F. Korth and Amith Sheth. Received November 1994 / Revised June 1995 / Accepted September 14, 1995     

Formulating and solving sequential decision analysis models withcontinuous variables

This paper presents a new decision analysis approach for modeling decision problems with continuous decision and/or random variables, and applies the approach to a research and development (R&D) planning problem. The approach allows for compact, natural formulation for classes of decision problems that are less appropriately addressed with standard discrete-variable decision analysis methods. Thus it provides a useful alternative analysis approach for problems that are often addressed in practice using simulation risk analysis methods. An illustrative application is presented to energy system R&D planning. The continuous-variable version of this model more directly represents the structure of the decision than a discrete approximation, and the resulting model can be efficiently solved using standard nonlinear optimization methods     

Chabot: retrieval from a relational database of images

Selecting from a large, expanding collection of images requires carefully chosen search criteria. We present an approach that integrates a relational database retrieval system with a color analysis technique. The Chabot project was initiated at our university to study storage and retrieval of a vast collection of digitized images. These images are from the State of California Department of Water Resources. The goal was to integrate a relational database retrieval system with content analysis techniques that would give our querying system a better method for handling images. Our simple color analysis method, if used in conjunction with other search criteria, improves our ability to retrieve images efficiently. The best result is obtained when text-based search criteria are combined with content-based criteria and when a coarse granularity is used for content analysis     

Retrospective on Aurora

This experience paper summarizes the key lessons we learned throughout the design and implementation of the Aurora stream-processing engine. For the past 2 years, we have built five stream-based applications using Aurora. We first describe in detail these applications and their implementation in Aurora. We then reflect on the design of Aurora based on this experience. Finally, we discuss our initial ideas on a follow-on project, called Borealis, whose goal is to eliminate the limitations of Aurora as well as to address new key challenges and applications in the stream-processing domain.Received: 21 October 2003, Accepted: 16 April 2004, Published online: 14 September 2004Edited by: J. Gehrke and J. Hellerstein.     

The implementation of POSTGRES

The design and implementation decisions made for the three-dimensional data manager POSTGRES are discussed. Attention is restricted to the DBMS backend functions. The POSTGRES data model and query language, the rules system, the storage system, the POSTGRES implementation and the current status and performance are discussed     

The integration of rule systems and database systems

The integration of rule systems into database management systems is explored. Research activities in this area over the past decade are surveyed. The focus is on prototype systems that have been completely specified and the implementation issues encountered. A research agenda which should be addressed by the research community over the next few years is presented