Peer-to-peer information retrieval using self-organizing semantic overlay networks

Tang, Chunqiang; Xu, Zhou; Dwarkadas, Sandhya

doi:10.1145/863955.863976

Cited by 362 publications

(150 citation statements)

References 12 publications

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“…There are many studies in this issue, such as MCAN [10], M-Chord [25], Psearch [33], Prism [29] and iDISQUE [39]. MCAN using CAN as the underlying structure adopts a pivot technique, iDistance, to map objects to an N-dimensional vector space.…”

Section: Related Workmentioning

confidence: 99%

Fusion feature for LSH-based image retrieval in a cloud datacenter

Liao

Yang

et al. 2015

Multimed Tools Appl

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Since the emergence of cloud datacenters provides an enormous amount of resources easily accessible to people, it is challenging to provide an efficient search framework in such a distributed environment. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These methods are insufficient to meet requirements of content based image retrieval (CBIR) and more powerful search frameworks are needed. In this paper, we present LFFIR, a multi-feature image retrieval framework for content similar search in the distributed situation. The key idea is to effectively incorporate image retrieval based on multi-feature into the peer-to-peer (P2P) paradigm. LFFIR fuses the multiple features in order to capture the overall image characteristics. And then it constructs the distributed indexes for the fusion feature through exploiting the property of locality sensitive hashing (LSH). We implement a prototype system to evaluate the system performance with two image datasets. Comprehensive performance evaluations demonstration that our approach brings major performance and accuracy gains compared to the advanced distributed image retrieval framework.

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Section: Related Workmentioning

confidence: 99%

Fusion feature for LSH-based image retrieval in a cloud datacenter

Liao

Yang

et al. 2015

Multimed Tools Appl

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“…Query matching and rewriting is based on keywords provided by the users. GridVine [8], and pSearch [19] are based on structured P2P overlays. GridVine hashes and indexes RDF data and schemas, and pSearch represents documents as well as queries as semantic vectors.…”

Section: Related Workmentioning

confidence: 99%

Distributing and searching concept hierarchies: an adaptive DHT-based system

2010

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Concept hierarchies greatly help in the organization and reuse of information and are widely used in a variety of information systems applications. In this paper, we describe a method for efficiently storing and querying data organized into concept hierarchies and dispersed over a DHT. In our method, peers individually decide on the level of indexing according to the granularity of the incoming queries. Roll-up and drill-down operations are performed on a pernode basis in order to minimize the required bandwidth for answering queries on variable aggregation levels. We motivate our approach by applying it on a large-scale Grid system: Specifically, we apply our fully decentralized scheme that creates, queries and updates large volumes of hierarchical data on-line and replace the traditional centralized and strictly indexed information systems. Our extensive experimental results support this argument on many diverse configurations: Our system proves very efficient in skewed workloads, both over single and multiple hierarchy levels at the same time. It adapts to sudden changes in popularity and effectively stores and updates large amounts of data at very low cost.

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“…pSearch [29] rotates the dimensions and indexes multi-dimensional objects on top of CAN [22]. In [14], two high-dimensional indices are designed; SCRAP uses space-filling curves for mapping, and MURK uses kd-trees for data space partitioning.…”

Section: Related Workmentioning

confidence: 99%

Linking identical neighborly partitions for efficient high-dimensional similarity search in unstructured peer-to-peer systems

Cui

Zhao

2009

Distrib Parallel Databases

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Peer-to-Peer (P2P) computing has recently attracted a great deal of research attention. In a P2P system, a large number of nodes can potentially be pooled together to share their resources, information, and services. However, existing unstructured P2P systems lack support for content-based search over data objects which are generally represented by high-dimensional feature vectors. In this paper, we propose an efficient and effective indexing mechanism to facilitate high-dimensional similarity query in unstructured P2P systems, named Linking Identical Neighborly Partitions (LINP), which combines both space partitioning technique and routing index technique. With the aid of LINP, each peer can not only process similarity query efficiently over its local data, but also can route the query to the promising peers which may contain the desired data. In the proposed scheme, each peer summarizes its local data using the space partitioning technique, and exchanges the summarized index with its neighboring peers to construct routing indices. Furthermore, to improve the system performance with peer updates, we propose an extension of the LINP, named LINP + , where each peer can reconfigure its neighboring peers to keep relevant peers nearby. The performance of our proposed scheme is evaluated over both synthetic and real-life high-dimensional datasets, and experimental results show the superiority of our proposed scheme.

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Peer-to-peer information retrieval using self-organizing semantic overlay networks

Cited by 362 publications

References 12 publications

Fusion feature for LSH-based image retrieval in a cloud datacenter

Fusion feature for LSH-based image retrieval in a cloud datacenter

Distributing and searching concept hierarchies: an adaptive DHT-based system

Linking identical neighborly partitions for efficient high-dimensional similarity search in unstructured peer-to-peer systems

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