Content-Based Overlay Networks for XML Peers Based on Multi-level Bloom Filters

Abstract

Abstract. Peer-to-peer systems are gaining popularity as a means to effectively share huge, massively distributed data collections. In this paper, we consider XML peers, that is, peers that store XML documents. We show how an extension of traditional Bloom filters, called multi-level Bloom filters, can be used to route path queries in such a system. In addition, we propose building content-based overlay networks by linking together peers with similar content. The similarity of the content (i.e., the local documents) of two peers is defined based on the similarity of their filters. Our experimental results show that overlay networks built based on filter similarity are very effective in retrieving a large number of relevant documents, since peers with similar content tend to be clustered together.

A Deduplication-Enabled P2P Protocol for VM Image Distribution

“…Bloom filters can be used for approximate matching of two sets of image blocks. More specifically, a bitwise logical AND operation of two Bloom filters can be performed to quickly estimate similarity among the contents they represent [7]- [9]. The dot product of two bit vectors can be used for a similarity measure of two VM images being represented by their corresponding filters.…”

A semantic-based P2P resource organization model R-Chord

“…(2) Query Routing -including semantic-based content search approach in P2P networks (Shen et al, 2004), and the structure index over XML documents by using multi-level Breadth and Depth Bloom filters (Koloniari et al, 2003). (3) Query Reformulation -including semantic and algorithmic issues of mapping data in P2P systems (Hellerstein, 2004;Kementsietsidis et al, 2003), algorithms for query reformulation and data integration between peers (Lenzerini, 2004;Tatarinov and Halevy, 2004), and the Piazza system for mediating between data sources on the Semantic Web (Halevy et al, 2003).…”

Cardinality estimation and dynamic length adaptation for Bloom filters

“…For example, Bender et al [13] but also Koloniari et al [24] merge the Bloom filter summaries of peers with bitwise-AND, to detect the peers with the smaller and larger overlap. However, these works are based on the restricting assumption that all Bloom filters contain approximately the same number of elements.…”