A non-equijoin of relations R and S is a band join if the join predicate requires values in the join attribute of R to fall within a speci ed band about the values in the join attribute of S. We propose a new algorithm, termed a partitioned band join, for evaluating band joins. We present a comparison between the partitioned band join algorithm and the classical sort-merge join algorithm (optimized for band joins) using both an analytical model and an implementation on top of the WiSS storage system. The results show that the partitioned band join algorithm outperforms sortmerge unless memory is scarce and the operands of the join are of equal size. We also describe a parallel implementation of the partitioned band join on the Gamma database machine, and present data from speedup and scaleup experiments demonstrating that the partitioned band join is efficiently parallelizable.

Object-Relational database management systems allow knowledgeable users to de ne new data types, as well as new methods (operators) for the types. This exibility produces an attendant complexity, which must be handled in new ways for an Object-Relational database management system to be e cient. In this paper we study techniques for optimizing queries that contain time-consuming methods. The focus of traditional query optimizers has been on the choice of join methods and orders; selections have been handled by \pushdown " rules. These rules apply selections in an arbitrary order before as many joins as possible, using the assumption that selection takes no time. However, users of Object-Relational systems can embed complex methods in selections. Thus selections may take signi cant amounts of time, and the query optimization model must be enhanced. In this paper, we carefully de ne a query cost framework that incorporates both selectivity and cost estimates for selections. We develop an algorithm called Predicate Migration, and prove that it produces optimal plans for queries with expensive methods. We then describe our implementation of Predicate Migration in the commercial Object-Relational database management system Illustra, and discuss practical issues that a ect our earlier assumptions. We compare Predicate Migration to a variety of simpler optimization techniques, and demonstrate that Predicate Migration is the best general solution to date. The alternative techniques we presentmaybe useful for constrained workloads.

. Object-Relational and Object-Oriented DBMSs allow users to invoke time-consuming ("expensive") methods in their queries. When queries containing these expensive methods are run on data with duplicate values, time is wasted redundantly computing methods on the same value. This problem has been studied in the context of programming languages, where "memoization" is the standard solution. In the database literature, sorting has been proposed to deal with this problem. We compare these approachesalong with a third solution, a variant of unary hybrid hashing which we call Hybrid Cache. We demonstrate that Hybrid Cache always dominates memoization, and significantly outperforms sorting in many instances. This provides new insights into the tradeoff between hashing and sorting for unary operations. Additionally, our Hybrid Cache algorithm includes some new optimizations for unary hybrid hashing, which can be used for other applications such as grouping and duplicate elimination. We conclude...

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Wireless sensor networks are an emerging area of research interest with a number of compelling potential applications. By architecting sensor networks as virtual databases, we can provide a well-understood nonprocedural programming interface suitable to data management, allowing the community to realize sensornet applications rapidly. We argue here that in order to achieve an energy-efficient and useful implementation, query processing operators should be implemented within the sensor network, and that approximate query results will play a key role. We observe that innetwork implementations of database operators require novel data-centric routing mechanisms, as well as a reconsideration of traditional network and database interface layering.

In large data recording and warehousing environments, it is often advantageous to provide fast, approximate answers to queries. The goal is to provide an estimated response in orders of magnitude less time than the time to compute an exact answer, by avoiding or minimizing the number of accesses to the base data. This paper presents the Approximate QUery Answering (AQUA) System, for fast, highlyaccurate approximate answers to queries. Aqua provides approximate answers using small, precomputed synopses (samples, counts, etc.) of the underlying base data. An important feature of Aqua is that it provides accuracy guarantees without any a priori assumptions on either the data distribution, the order in which the base data is loaded, or the layout of the data on the disks. Currently, the system provides fast approximate answers for queries with selects, aggregates, group bys and/or joins (especially, the multi-way foreign key joins that are popular in OLAP). We present several ne...

This paper reviews recent literature on techniques for obtaining random samples from databases. We begin with a discussion of why one would want to include sampling facilities in database management systems. We then review basic sampling techniques used in constructing DBMS sampling algorithms, e.g., acceptance/rejection and reservoir sampling. A discussion of sampling from various data structures follows: B + trees, hash files, spatial data structures (including R-trees and quadtrees)). Algorithms for sampling from simple relational queries, e.g., single relational operators such as selection, intersection, union, set difference, projection, and join are then described. We then describe sampling for estimation of aggregates (e.g., the size of query results). Here we discuss both clustered sampling, and sequential sampling approaches. Decision theoretic approaches to sampling for query optimization are reviewed. DRAFT of March 22, 1994. 1 Introduction In this paper we sur...

Viswanath Poosala z In large data recording and warehousing environments, it is often advantageous to provide fast, approximate answers to queries, whenever possible. The goal is to provide an estimated response in orders of magnitude less time than the time to compute an exact answer, by avoiding or minimizing the number of accesses to the base data. This white paper describes the Approximate QUery Answering (AQUA) Project underway in the Information Sciences Research Center at Bell Labs. We present a framework for an approximate query engine that observes new data as it arrives and maintains small synopsis data structures on that data. These data structures are used to provide fast, approximate answers to a broad class of queries. We describe metrics for evaluating approximate query answers. We also present new synopsis data structures, and new techniques for approximate query answers. We report on the goals and status of the Aqua project, and plans for future work.

Abstract. The goal of the CONTROL project at Berkeley is to develop systems for interactive analysis of large data sets. We focus on systems that provide users with iteratively refining answers to requests and online control of processing, thereby tightening the loop in the data analysis process. This paper presents the database-centric subproject of CONTROL: a complete online query processing facility, implemented in a commercial Object-Relational DBMS from Informix. We describe the algorithms at the core of the system, and detail the end-to-end issues required to bring the algorithms together and deliver a complete system.

The goal of the ADMS project is to create a framework for caching materialized views, access paths, and experience obtained during query execution. The rationale behind this project is to amortize database access cost over an extended time period and adapt execution strategies based on experience. ADMS demonstrates the versatility of the views and their role in performance, data warehousing, management and control of data distribution and replication.