MOVE: A Large Scale Keyword-Based Content Filtering and Dissemination System

Rao, Weixiong; Chen, Lei; Hui, Pan; Tarkoma, Sasu

doi:10.1109/icdcs.2012.32

Cited by 15 publications

(12 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the previous works [11,12,10,13] were focusing on keywords-based content filtering and dissemination (intuitively treated as a reverse application of keyword searches) on large distributed systems such as P2P networks and NoSql Cloud platforms. Significantly differing from such works, in this paper, we target on mobile phones to save energy for keyword searches.…”

Section: Related Workmentioning

confidence: 99%

Energy-aware keyword search on mobile phones

Rao

Zhao

Lagerspetz

et al. 2012

Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing

Self Cite

View full text Add to dashboard Cite

With the explosive growth of communication technologies, modern mobile phones become more powerful than ever. Unfortunately, the battery lifetime of mobile phones is still limited, and energy awareness is a priority of designing applications on mobile phones. To demonstrate the energy awareness on mobile phones, we propose a new approach for full-text keyword searches to retrieve content matching input keywords. The proposed approach generalizes the two solutions that answer the keyword searches either (i) locally by mobile phones themselves or (ii) remotely by power servers when the keywords are offloaded to such servers. Instead, for low energy, we propose to split the keywords in a search into two subsets, such that one subset is processed by the mobile phone and another by the remote server. Our preliminary experiment results indicate the hybrid approach can save the most energy for the keyword searches than the local and remote approaches. In our example tests, energy of the hybrid approach saves 75% and 47% of energy when compared to local execution and complete offloading approaches, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Energy-aware keyword search on mobile phones

Rao

Zhao

Lagerspetz

et al. 2012

Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…One the earliest pioneers in this area -Pazzani (1999) -proposed an intelligent agent that attempted to predict user web page preference by using a naive Bayesian classifier. With the arrival of the Big Data era, Rao (2012) has presented research on how to apply keyword-based content filtering method in distributed system.…”

Section: Studies On Recommender Systemmentioning

confidence: 99%

The Application of Recommender Systems to Data-Driven Digital Memory

Luo¹,

Nunes²

2019

Proceedings of the 18th International Conference on WWW/Internet 2019

View full text Add to dashboard Cite

Data-driven digital memory applications lack predefined navigation paths and strict hierarchical structures. They are based on large collections of memory items that can become overwhelming to users. Recommender systems can improve user experience through the proposal of personalized relevant items. However, very little academic literature has been dedicated to discussing this type of filtering of digital memory resources and the provision of customized contents to active users. In this paper, an architecture of a hybrid enhanced recommender (HER) system, which integrates collaborative filtering and content based filtering techniques and resolves most of the weaknesses of the individual approaches. This architecture also proposes an ontology to build semantic user profiles and represent memory items to mitigate the lack of semantics of traditional content-based method. Through combining those techniques, this architecture has the potential to cope with data sparsity problems, avoid overspecialization issues and partially resolve cold start problems.

show abstract

“…To enable each subscription to be covered ln n times with a small number of connections, Feverfew nodes run a gossip-based hybrid sampling service to explore the subscription similarities and select the nodes with which they share the most subscriptions as neighbors. As shown in Figure 1, more than 70% of topics have size not greater than 16 for 10 5 users on the social networks of Facebook 2) and Douban 3) . To reach ln-covered, the average node degrees of these small topics are fewer than four.…”

Section: Introductionmentioning

confidence: 98%

“…Each message should be delivered to its corresponding subscribers with low latency and traffic cost when the topics and nodes expand to Internet-scale. Recently, cloud computing environments and distributed computing environments with high performance [1] have provided great opportunities to meet the requirements of complex computing and high speed communication [2][3][4]. Amazon SNS 1) , as an example, provides a real-time topic-based pub/sub service that finds users interested in different events.…”

Section: Introductionmentioning

confidence: 99%

Feverfew: a scalable coverage-based hybrid overlay for Internet-scale pub/sub networks

Wang

Sun

2014

Sci. China Inf. Sci.

View full text Add to dashboard Cite

The publish/subscribe (pub/sub) paradigm is a popular communication model for data dissemination in large-scale distributed networks. However, scalability comes with a contradiction between the delivery latency and the memory cost. On one hand, constructing a separate overly per topic guarantees real-time dissemination, while the number of node degrees rapidly increases with the number of subscriptions. On the other hand, maintaining a bounded number of connections per node guarantees small memory cost, while each message has to traverse a large number of uninterested nodes before reaching the subscribers. In this paper, we propose Feverfew, a coverage-based hybrid overlay that disseminates messages to all subscribers without uninterested nodes involved in, and increases the average number of node connections slowly with an increase in the number of subscribers and nodes. The major novelty of Feverfew lies in its heuristic coverage mechanism implemented by combining a gossip-based sampling protocol with a probabilistic searching protocol. Based on the practical workload, our experimental results show that Feverfew significantly outperforms existing coverage-based overlay and DHT-based overlay in various dynamic network environments.

show abstract

MOVE: A Large Scale Keyword-Based Content Filtering and Dissemination System

Cited by 15 publications

References 19 publications

Energy-aware keyword search on mobile phones

Energy-aware keyword search on mobile phones

The Application of Recommender Systems to Data-Driven Digital Memory

Feverfew: a scalable coverage-based hybrid overlay for Internet-scale pub/sub networks

Contact Info

Product

Resources

About