Hyperspaces dimensions movie#

Through the edit-distances to the axis labels. Optimizations, such as supernodes and expanding ring search, make the broadcast process more efficient, but the primitives are still based fundamentally on flooding. We use edit-distance as a running example without loss of generality. Our approach requires only that this measure σ obey σ(a, b) ≥ 0, σ(a, b) = σ(b, a) and σ(a, c) ≤ σ(a, b) + σ(b, c). Unstructured peer-to-peer systems provide a search 1 Closeness here is defined based on an application-supplied similarity measure, such as edit distance.

Efficiently routing a query to a set of objects whose keys are close1 but not identical to the search key is a difficult problem known as “approximate match.” Even though peer-to-peer systems were initially motivated by file-sharing, modern P2P substrates do not provide efficient primitives for approximate matching. For instance, approximately 20% of all Google queries for “Britney Spears” misspell the artist’s name. In settings where queries are based on terms provided by users, imprecision stemming from partial specifications of keywords, common variations of search terms and misspellings are unavoidable. We examine the feasibility of this approach for efficient, peer-to-peer search on inexact string keys, and show that the system provides a robust method to handle key perturbations that naturally occur in applications, such as file-sharing networks, where the query strings are provided by users.ġ INTRODUCTION Modern P2P substrates do not provide support for efficiently locating objects whose keys are not known precisely. The system works via a novel assignment of virtual coordinates to each object in a high-dimensional, synthetic space such that the proximity between two points in the coordinate space is correlated with the similarity between the strings that the points represent. In this paper, we present a scalable and efficient peerto-peer system with a new search primitive that can efficiently find the k data items with keys closest to the search key. This virtual coordinate assignment captures the relative similarities of the stringsĮxisting distributed hash tables provide efficient mechanisms for storing and retrieving a data item based on an exact key, but are unsuitable when the search key is similar, but not identical, to the key used to store the data item. For instance, for bases aaa, cbc and abd, the keys abc, abd and ddd would map to the points h2, 1, 1i, h2, 2, 0i and h3, 3, 2i, respectively. The virtual coordinate for every object is the tuple created by measuring the edit-distance to each of the axis labels. The bases (axes) for the hyperspace consist of string labels. The central insight behind ellama is to define a very high-dimensional space (a hyperspace) in which every object and node is assigned a virtual coordinate. In this paper, we present e-llama, a scalable peer-topeer system that can efficiently find the k closest data items for any search key. Overall, existing systems provide inefficient and approximate search or efficient and precise lookup, but not efficient and approximate match. Finally, systems that permit range lookups can perform a lookup within a range defined by numeric coordinates, but are difficult to adopt for use with approximate string matching.

Hyperspaces dimensions movie#

Naive approaches to layer approximate matching on top of a DHT lookup, by inserting each object under all possible key variations and performing every query in parallel with all k variants of the search key, lead to highly inefficient solutions because k is typically on the order of a few hundred even for a moderate length movie title with only two permuted characters. While these systems provide strong worst-case bounds, the lookup operation does not permit approximate matching. In contrast, structured peer-to-peer systems provide an efficient lookup primitive that can typically locate a target within log N hops. Such broadcast-based approaches are inefficient as they may take up to N hops in the worst case where N is the number of hosts, and place a super-linear aggregate load on the network. Gnutella nodes receiving the search query match it against their database of known items using a fuzzy similarity metric to yield the approximate matches. Primitive, which is based effectively on query broadcast2. of Computer Science, Cornell University, Ithaca, NY 14853 Hyperspaces for Object Clustering and Approximate Matching in Peer-to-Peer Overlays ´ Bernard Wong Ymir Vigf´usson Emin G¨un Sirer Dept.