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Author

Mao Ye

Other affiliations: Hewlett-Packard, Nanjing University
Bio: Mao Ye is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Wireless sensor network & Social network. The author has an hindex of 19, co-authored 33 publications receiving 4624 citations. Previous affiliations of Mao Ye include Hewlett-Packard & Nanjing University.

Papers
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Proceedings ArticleDOI
01 Mar 2010
TL;DR: The sufficient set-based (SSB) algorithm for inter-cluster query processing of probabilistic Top-k queries in cluster-based wireless sensor networks is developed and Experimental results show that the proposed algorithm reduces data transmissions significantly.
Abstract: In this paper, we propose the notion of sufficient set for distributed processing of probabilistic Top-k queries in cluster-based wireless sensor networks. Through the derivation of sufficient boundary, we show that data items ranked lower than sufficient boundary are not required for answering the probabilistic top-k queries, thus are subject to local pruning. Accordingly, we develop the sufficient set-based (SSB) algorithm for inter-cluster query processing. Experimental results show that the proposed algorithm reduces data transmissions significantly.

30 citations

Proceedings ArticleDOI
16 Apr 2012
TL;DR: In this paper, the authors studied the group purchasing behavior of daily deals in Groupon and LivingSocial and formulated a predictive dynamic model of collective attention for group buying behavior using large data sets from both groups.
Abstract: We present a study of the group purchasing behavior of daily deals in Groupon and LivingSocial and formulate a predictive dynamic model of collective attention for group buying behavior. Using large data sets from both Groupon and LivingSocial we show how the model is able to predict the success of group deals as a function of time.We find that Groupon deals are easier to predict accurately earlier in the deal lifecycle than LivingSocial deals due to the total number of deal purchases saturating quicker. One possible explanation for this is that the incentive to socially propagate a deal is based on an individual threshold in LivingSocial, whereas in Groupon it is based on a collective threshold which is reached very early. Furthermore, the personal benefit of propagating a deal is greater in LivingSocial.

28 citations

Proceedings ArticleDOI
10 Sep 2007
TL;DR: A family of Peer-exchange Routing Optimization Protocols (PROP) to reconstruct the overlay of a self-organizing peer-to-peer system and shows that these two protocols greatly reduce the average latency of the overlay and achieve a location-aware topology with low overhead.
Abstract: A self-organizing peer-to-peer system is built upon an application level overlay, whose topology is independent of underlying physical network. A well-routed message path in such systems may result in a long delay and excessive traffic due to the mismatch between logical and physical networks. In order to solve this problem, we present a family of Peer-exchange Routing Optimization Protocols (PROP) to reconstruct the overlay. It includes two policies: PROP- G for generic condition and PROP-0 for optimized one. Both theoretical analysis and simulation experiments show that these two protocols greatly reduce the average latency of the overlay and achieve a location-aware topology with low overhead. Their overall performance can be further improved if combined with other recent approaches. Specifically, PROP-G can be easily applied to both structured and unstructured systems without the loss of their primary characteristics, such as efficient routing and anonymity. PROP- O, on the other hand, is more efficient, especially in a heterogeneous environment where nodes have different processing capabilities.

26 citations

Journal ArticleDOI
TL;DR: The notion of sufficient set and necessary set for distributed processing of probabilistic top-k queries in cluster-based wireless sensor networks and an adaptive algorithm that dynamically switches among the three proposed algorithms to minimize the transmission cost are introduced.
Abstract: In this paper, we introduce the notion of sufficient set and necessary set for distributed processing of probabilistic top-k queries in cluster-based wireless sensor networks. These two concepts have very nice properties that can facilitate localized data pruning in clusters. Accordingly, we develop a suite of algorithms, namely, sufficient set-based (SSB), necessary set-based (NSB), and boundary-based (BB), for intercluster query processing with bounded rounds of communications. Moreover, in responding to dynamic changes of data distribution in the network, we develop an adaptive algorithm that dynamically switches among the three proposed algorithms to minimize the transmission cost. We show the applicability of sufficient set and necessary set to wireless sensor networks with both two-tier hierarchical and tree-structured network topologies. Experimental results show that the proposed algorithms reduce data transmissions significantly and incur only small constant rounds of data communications. The experimental results also demonstrate the superiority of the adaptive algorithm, which achieves a near-optimal performance under various conditions.

24 citations

Book ChapterDOI
13 May 2014
TL;DR: This work proposed a trajectory recommendation framework and developed three recommendation methods, namely, Activity-Based Recommendation (ABR), GPS-Based recommendation (GBR) and Hybrid Recommendation, which turned out the hybrid solution displays the best performance.
Abstract: The wide use of GPS sensors in smart phones encourages people to record their personal trajectories and share them with others in the Internet. A recommendation service is needed to help people process the large quantity of trajectories and select potentially interesting ones. The GPS trace data is a new format of information and few works focus on building user preference profiles on it. In this work we proposed a trajectory recommendation framework and developed three recommendation methods, namely, Activity-Based Recommendation (ABR), GPS-Based Recommendation (GBR) and Hybrid Recommendation. The ABR recommends trajectories purely relying on activity tags. For GBR, we proposed a generative model to construct user profiles based on GPS traces. The Hybrid recommendation combines the ABR and GBR. We finally conducted extensive experiments to evaluate these proposed solutions and it turned out the hybrid solution displays the best performance.

20 citations


Cited by
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Journal ArticleDOI

6,278 citations

01 Nov 2008

2,686 citations

Journal ArticleDOI
TL;DR: The concept of urban computing is introduced, discussing its general framework and key challenges from the perspective of computer sciences, and the typical technologies that are needed in urban computing are summarized into four folds.
Abstract: Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

1,290 citations

Journal ArticleDOI
TL;DR: A new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks, which is called DEEC, is proposed and evaluated, which achieves longer lifetime and more effective messages than current important clustering protocols in heterogeneous environments.

1,131 citations

Journal ArticleDOI
TL;DR: This paper synthesises existing clustering algorithms news's and highlights the challenges in clustering.
Abstract: A wireless sensor network (WSN) consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms in WSNs and highlights the challenges in clustering.

1,097 citations