study a collection of heterogeneous XML databases maintain- ing similar and related information, exchanging data via a peer to peer overlay network. In this setting, a mediated global schema is unrealistic. Yet, users applications wish to query the d atabases via one peer using its schema. We have recently developed Hep- ToX, a P2P Heterogeneous XML database system. A key idea is that whenever a peer enters the system, it establishes an acquain- tance with a small number of peer databases, possibly with dif- ferent schema. The peer administrator provides correspondences between the local schema and the acquaintance schema using an informal and intuitive notation of arrows and boxes. We develop a novel algorithm that infers a set of precise mapping rules between the schemas from these visual annotations. We pin down a seman- tics of query translation given such mapping rules, and present a novel query translation algorithm for a simple but expressive frag- ment of XQuery, that employs the mapping rules in either direction. We show the translation algorithm is correct. Finally, we demon- strate the utility and scalability of our ideas and algorith ms with a detailed set of experiments on top of the Emulab, a large scale P2P network emulation testbed.
P2P systems with non-conventional lookups Popular P2P networks, e.g. Kazaa, Gnutella, advertise simple lookup queries on file names. The idea of building full-fledged P2P DBMS is being considered in many works. Internet-scale database queries and functionalities @cite_19 as well as approximate range queries in P2P @cite_18 and XPath queries in small communities of peers @cite_21 have been extensively dealt with. All these works do not deal with reconciling schema heterogeneity. @cite_21 relies on a DHT-based network to address simple XPath queries, while @cite_30 realizes IR-style queries in an efficient P2P relational database.
@cite_30 @cite_19 @cite_18 @cite_21
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We present the design and evaluation of PeerDB, a peer-to-peer (P2P) distributed data sharing system. PeerDB distinguishes itself from existing P2P systems in several ways. First, it is a full-fledge data management system that supports fine-grain content-based searching. Second, it facilitates sharing of data without shared schema. Third, it combines the power of mobile agents into P2P systems to perform operations at peers' sites. Fourth, PeerDB network is self-configurable, i.e., a node can dynamically optimize the set of peers that it can communicate directly with based on some optimization criterion. By keeping peers that provide most information or services in close proximity (i.e., direct communication), the network bandwidth can be better utilized and system performance can be optimized. We implemented and evaluated PeerDB on a cluster of 32 Pentium II PCs. Our experimental results show that PeerDB can effectively exploit P2P technologies for distributed data sharing. In this paper, we address the problem of designing a scalable, accurate query processor for peer-to-peer filesharing and similar distributed keyword search systems. Using a globally-distributed monitoring infrastructure, we perform an extensive study of the Gnutella filesharing network, characterizing its topology, data and query workloads. We observe that Gnutella's query processing approach performs well for popular content, but quite poorly for rare items with few replicas. We then consider an alternate approach based on Distributed Hash Tables (DHTs). We describe our implementation of PIERSearch, a DHT-based system, and propose a hybrid system where Gnutella is used to locate popular items, and PIERSearch for handling rare items. We develop an analytical model of the two approaches, and use it in concert with our Gnutella traces to study the trade-off between query recall and system overhead of the hybrid system. We evaluate a variety of localized schemes for identifying items that are rare and worth handling via the DHT. Lastly, we show in a live deployment on fifty nodes on two continents that it nicely complements Gnutella in its ability to handle rare items. We present an architecture for a data sharing peer-to-peer system where the data is shared in the form of database relations. In general, peer-to-peer systems try to locate exactmatch data objects to simple user queries. Since peer-to-peer users generally tend to submit broad queries in order to find data of their interest, we develop a P2P data sharing architecture for computing approximate answers for the complex queries by finding data ranges that are similar to the user query. Thus this paper represents the first step towards solving the general range lookup problem over P2P systems instead of exact lookup operations. 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.