Querying large numbers of data sources is gaining importance due to increasing numbers of independent data providers. One of the key challenges is executing queries on all relevant information sources in a scalable fashion and retrieving fresh results. The key to scalability is to send queries only to the relevant servers and avoid wasting resources on data sources which will not provide any results. Thus, a catalog service, which would determine the relevant data sources given a query, is an essential component in efficiently processing queries in a distributed environment. This paper proposes a catalog framework which is distributed across the data sources themselves and does not require any central infrastructure. As new data sources become available, they automatically become part of the catalog service infrastructure, which allows scalability to large numbers of nodes. Furthermore, we propose techniques for workload adaptability. Using simulation and real-world data we show that our approach is valid and can scale to thousands of data sources.

Querying XML documents typically involves both tree-based navigation and pattern matching similar to that used in structured information retrieval domains. In this paper, we show that for good performance, a native XML query processing system should support query plans that mix these two processing paradigms. We describe our prototype native XML system, and report on experiments demonstrating that even for simple queries, there are a number of options for how to combine tree-based navigation and structural joins based on information retrieval-style inverted lists, and that these options can have widely varying performance. We present ways of transparently using both techniques in a single system, and provide a cost model for identifying efficient combinations of the techniques. Our preliminary experimental results prove the viability of our approach.

This paper presents Oracle Database Replay, a novel approach to testing changes to the relational database management system component of an information system (software upgrades, hardware changes etc). Database Replay makes it possible to subject a test system to a real production system workload, which helps identify all potential problems before implementing the planned changes on the production system. Any interesting workload period of a production database system can be captured with minimal overhead. The captured workload can be used to drive a test system while maintaining the concurrency and load characteristics of the real production workload. Therefore, the test results using database replay can provide very high assurance in determining the impact of changes to a production system before applying these changes. This paper presents the architecture of Database Replay as well as experimental results that demonstrate its usefulness as testing methodology.

Abstract. While current search engines seem to easily handle the size of the data available on the Internet, they cannot provide fresh results. The most up-to-date data always resides on the data sources. Efficiently interconnecting data providers, however, is not an easy problem. Peer-to-peer computing is the latest technology to address this problem. However, efficient query processing in peer-to-peer networks remains an open research area. In this paper, we present a performance study of a system that facilitates efficient searches of large numbers of independent data providers on the Internet. In our scenario, each data provider becomes an autonomous node in a large peer-to-peer system. Using small indices on each node, we can efficiently direct queries submitted on any node to the relevant sources. Experiments with a large peer-to-peer network demonstrate the feasibility of our approach.

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